Miracle-dz/UniFAR-FTUs huggingface dataset:
Palm-print Recognition based on CNN against Rotation and Noise

A palm-print recognition method based on Convolutional Neural Network (CNN) is proposed, which can improve the recognition eﬀectiveness in rotation and noise condition. First, the training database with rotations and noises is created based on the original database considering the possible eﬀects in palm-print image acquisition and the big samples set requirement of the CNN method. Second, the experimental CNN model construction approach is proposed, in which the number of hidden layers and convolution kernels, batchsize and epochs are determined by experiments. At last, the test palm-print image is recognized using the softmax method. The experimental results based on Institute of AutomationChinese Academy of Sciences (CASIA) palmprint database show that the proposed method is more eﬀective than the Gabor, Local Binary Pattern (LBP), Multi-block Local Binary Patterns (MLBP) and Vanilla Neural Net (VNN) methods, and has robustness and stability for the images with diﬀerent noises and rotation degrees.
Phylogenetic Relationships among Turbellarian Orders Inferred from 18 S rDNA Sequences

The turbellarian flatworm is a key group to understand the origin and the early evolution of triploblastic, bilaterally symmetrical animals, but phylogenetic relationships among turbellarian orders have been a subject of debates for decades, especially on the position of the acoel turbellarians. Some workers have considered the acoel representing the most primitive turbellarian order but others have regarded them as regressive. We determined almost the entire lengths of the nucleotide sequences of 18S ribosomal RNA gene (rDNA) in 17 species from 9 turbellarian orders (the Acoela, Catenulida, Macrostomida, Lecithoepitheliata, Rhabdocoela, Prolecithophora, Proseriata, Tricladida, and Polycladida). After adding the sequences of a cestode, two trematodes and some diploblastic animals obtained from databases, we reconstructed phylogenetic trees using the neighbor-joining, maximum-likelihood and maximum-parsimony methods. All trees significantly indicated that the Acoela is the earliest divergent group among the turbellarian orders. The trees also suggested that the Tricladida evolved in the separate lineage from that of a cluster of the Catenulida, Macrostomida, Lecithoepitheliata, Rhabdocoela, Polycladida, Trematoda and Cestoda after the divergence of the Acoela.
Using the Intel STM for Protected Execution

We describe our work to demonstrate an enhanced SMI transfer monitor (STM) to provide protected execution services on the x86 platform. An STM is a hypervisor that executes in x86 system management mode (SMM) and functions as a peer to the hypervisor or operating system. The STM constrains the SMI handler, by hosting the handler in a virtual machine (VM). Otherwise, the SMI handler holds unconstrained access to the platform, which could undermine the assurance provided by D-RTM or TXT. Our STM enhancements create a protected execution capability by extending the STM to support additional VMs (PE/VM). These enhancements utilize the existing capabilities of the x86 processor and, thus, require no additional hardware. We modified an existing hypervisor integrity measurement engine to function in a PE/VM. The related discussion explains how the module can be loaded from a guest virtual machine and how page tables are used to restrict the access that the measurement engine is allowed to memory.
Hardware-software complex for acoustic monitoring of meteorological fields in the atmospheric boundary layer

In the present paper, a hardware-software complex (HSC) intended for monitoring of lower layers of the atmosphere and subsequent forecasting of meteorological situations in a limited territory is described. The measuring network comprises remote and local meteorological measuring instruments distributed over the limited territory. The remote means of sounding are acoustic radars (sodars) and temperature profilometers providing measurements of atmospheric parameters in the atmospheric boundary layer. The local instruments for measuring meteorological parameters in the near-ground layer of the atmosphere are ultrasonic meteorological stations. The key feature of the hardware-software complex is the application of multirotor aeromobile platforms (MAP) to the verification of data of remote meteorological measurements (RMP) in the atmospheric boundary layer to improve data quality.
The impact of Fadama III on the poverty status of food crop farmers in Yobe State , Nigeria

This study examined the impact of National Fadama III Development Project on poverty status of participating food crop farmers in Yobe state, Nigeria. 200 respondents consisting of 100 Fadama III food crop farmers and 100 non-Fadama III were selected using Multi-stage random sampling technique. Data gathered were analyzed using descriptive statistics such as with mean, frequency distributions and inferentially with head count ratios on poverty indices and paired t-test. The result showed that poverty incidence was 0.517 for Fadama III farmers and 0.685 for non-Fadama III farmers. The poverty gap was 0.425 for Fadama III farmers and 0. 516 for nonFadama III farmers. The paired t-test indices showed that national Fadama III Programme impacted positively and significantly on farmer participant’s income and farm size at 5.0% level of significance. The study recommended making more land available to participating farmers and increased funding to venture into new areas of investments like product processing and packaging aimed at reduction of Fadama farming household poverty level.
Total hip uncemented arthroplasty : assessment of revision etiologies in patients with minimum 10-year follow-up *

* From Serviço de Ortopedia e Traumatologia, Hospital de Clínicas da Universidade Federal do Paraná, Brazil. 1. Assistant Surgeon, Group of Hip and Knee Surgery, Santa Casa de Porto Alegre, Rio Grande do Sul, Brazil; Post-Graduated from the Group of Hip and Knee Surgery from Hospital de Clínicas da UFPr, Brazil. 2. Full member, SBOT; Post-Graduated from the Group of Hip and Knee Surgery from Hospital de Clínicas da UFPr, Brazil. 3. MSc in Orthopedics; Head from Group of Hip and Knee Surgery from Hospital de Clínicas da UFPr, Brazil. * Trabalho realizado no Serviço de Ortopedia e Traumatologia do Hospital de Clínicas da Universidade Federal do Paraná. 1. Médico Assistente do Grupo de Quadril e Joelho da Santa Casa de Porto Alegre; Pós-graduado do Grupo de Cirurgia do Quadril e Joelho do Hospital de Clínicas da UFPr. 2. Membro Titular da SBOT; Pós-graduado do Grupo de Cirurgia do Quadril e Joelho do Hospital de Clínicas da UFPr. 3. Mestre em Ortopedia; Chefe do Grupo de Cirurgia de Quadril e Joelho do Hospital de Clínicas da UFPr. Endereço para correspondência (Correspondence to): Rua Santo Antônio, 733, ap. 304 – 90220-011 – Porto Alegre, RS. Recebido em (Received in) 8/4/03. Aprovado para publicação em (Approved in) 5/8/03. Copyright RBO2003 ARTIGO ORIGINAL / ORIGINAL ARTICLE
An Algorithm and Architecture for HEVC Video Compression

The video coding technique as modified HEVC coding based on saliency features is proposed tries to reduce attentiongrabbing artifacts and provides high quality in areas where viewer’s attention is drawn. The process allows visual saliency to increase quality in ROI parts and reduce in low-quality parts of the frame. This approach allows achieving the same subjective quality as contending state of the art methods at a low bit rate. The eye tracking datasets such as DIEM and SFU can be utilized to test the saliency algorithm for video and evaluate video quality assessment. The model provides better efficiency in terms of AUC, NSS and average PSNR.
Presence and Sources of Contrarian and Momentum Profits in Bangladeshi Stock Market Returns

Early studies have shown that stock returns are unpredictable. Literature in the late 1980s and 1990s has confirmed the existence of various forms of return regularities in stock returns. Two most well-known return regularities are contrarian and momentum profits. This paper, using weekly data for the period 2002 through 2013, investigates the presence of both contrarian and momentum profits and their sources in the Bangladeshi stock market. The paper follows the methodology of Lo and MacKinlay (1990) to form portfolios with a weighted relative strength scheme. Then methodology of Jegadeesh and Titman (1995) is used to decompose the contrarian/momentum profits into three elements: compensation for cross-sectional risk, lead-lag effect in time series with respect to the common factor, and time pattern of stock returns. Results give the evidence of significant contrarian profits for the holding period of one through eight weeks during the whole period. During the sub-period 2002-2008, there are also contrarian profits for one through eight week holding period strategies. However, contrarian profits only occur for holding period of eight weeks during 2008-2013. Time series pattern is found to be the main source of contrarian profits. This suggests that idiosyncratic (firm-specific) information is the main contributor of contrarian profits. Interestingly, the impact of idiosyncratic information on such profits has gradually decreased since 2008. Returns of zero weight portfolios are robust to market risk factors such as market return, small minus big firm returns (SMB) and widely used sentiment factors like Arms index. JEL Code: G12, G14
A method for the quality assessment of automated news

This paper focus on a research about the quality assessment of automated news, which was based on computational linguistics methodology (Belz and Reiter 2006, 2009)[1][2]. The approach was here complementary with the use of metrics (quantitative indicators) and of evaluations based on human judgements (qualitative indicators, Clerwall 2014[3], Krahmer and van der Kaa 2014 [15]). Automated metrics aim to analyse the language correction, while human based judgements evaluations aim to evaluate the quality of the contents. A sample of twenty articles generated in the economic and financial field were first submitted to several automated metrics, independent of the language and based on the comparison between two strings, which implied to get a corpus composed of source texts, written by humans, and target texts, written by softwares. The three texts that got the best results were then submitted to a panel of 80 human judges. None of them knew the real aim of this experience. Human judges were chosen among native speakers experts in writing.If this panel have quoted the qualities of precision and objectivity of the texts, they did not estimate that the three articles submitted to their appreciation were pleasant to read and well written. In two cases, they did not recognize the non-human nature of the generated articles.
www.econstor.eu Mode of Entry and Expropriation

We develop a politico-economic model to analyze the relationship between mode of entry into a new market and institutional quality of the host country. A foreign investor can either purchase a domestic firm, what we consider as FDI, or form a joint venture, in which the control right over the firm rests with the domestic entrepreneur. In an autocratic regime, the ruling elite uses its political power to implement expropriatory policies. In an integrated firm the risk of expropriation targets the foreign investor whereas in a joint venture the domestic agent bears this risk. We determine the equilibrium level of the probability of expropriation and show that the ruling elite, by choosing it, discriminates in favor of the foreign investor. This has implications for the form of invested capital, and thus for the organizational structure of active firms in the host country. JEL classification: F21, L22, P48
Predicting the Popularity of Reddit Posts General Machine Learning

Accurately predicting the attention a Reddit submission receives elucidates the relative significance of inherent and engineered post features, thereby granting us a greater understanding of popularity on the site as a whole. While predicting the popularity of articles in news domains is well charted, the task of predicting the popularity of Reddit submissions is not as significantly explored. Furthermore, any attempts to do so do not utilize natural language processing techniques and features such as the use of existing, well-developed word embeddings. In this paper, the task of predicting Reddit submission popularity given a post is tackled. Sentiment classification of a post’s text is obtained and utilized in addition to the GloVe embeddings of its component words. Linear, K-Nearest-Neighbors, and Random Forest Regression are utilized and assessed. Code: github.com/ashuaibi7/RedditPopularityPrediction
14: Research and Development of Advanced Radar Data Quality Control and Assimilation for Nowcasting and Forecasting Severe Storms

Advance the state of the art in radar data quality control (QC) and assimilation for nowcasting and forecasting severe storms and other high-impact hazardous weather by attacking outstanding problems and addressing basic science and applied research issues in radar data QC and assimilation that are important and yet very challenging for nowcasting and forecasting high-impact hazardous weather not only over US homeland but also in US Navy’s battle spaces worldwide, especially over oceans and in remote areas where conventional observations are very limited.
AUTONOMOUS GREEDY ROUTING IN WIRELESS SENSOR NETWORKS

Routing is challenging issue in WSN: Cryptography and key management schemes seem good, but they are too expensive in WSN. Prevention-based and detection based are the two approaches that are used in MANET. In prevention-based approaches a centralized key management is required, These applications require a good Quality of Service (QoS) from sensor networks, such as, minimum percentage of sensor coverage in the required area, continuous service during required time slot with minimum (or limited) resources (like sensor energy and channel bandwidth) and minimum outside intervention. The whole network may be affected if the infrastructure is destroyed. So this approach is used to prevent misbehavior, but not detect malicious based routes Detection based approaches are used to detect selfish node along with route that helps to identify malicious misbehavior route. Detection based approaches are based on trust in MANETs. Hence this approach is used to calculate the trust value in trust management schemes. The proposed scheme differentiates, routes, data packets and control packets, and also excludes the other causes that results in dropping packets, such as unreliable wireless connections and buffer overflows. The proposed scheme in a MANET routing protocol, evaluation of the AODV (Adhoc on demand on distance vector) and Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with the NS2 simulator.
Fertilization and Subsequent Development of Bovine Embryos Following In-Vitro Fertilization in Commercially-Prepared Medium

This study was conducted to compare the capability and efficiency of two different media of IVF in prompting subsequent development and quality embryo. Matured oocytes in-vitro were fertilized using frozen semen and co-incubated in two different media (BODM and FHK medium) and followed the subsequent development stage up to in-vitro culture with simple modifications. Results showed that oocytes fertilized in BODM medium appeared to have 60 % induced pronuclear formation during 12 h and 100 % during 18 h, whereas oocytes achieved 58.54 % in cleavage formation at 2-cell during 72 to 96 h post culture and a blastocyst formation of 6.66%. Moreover, for the embryo quality assessment through the total cell count, analysis showed a mean value of 189.00 per embryo. Consequently, upon the usage of FHK medium for fertilization, oocytes prompted 80 % during 12 h and 60% during 18h of pronuclear formation. In addition, 60.46 % of the total samples had exhibited cleavage formation of 2-cell stage during 72-96 h post culture and 28.37 % for the blastocyst formation rate. Moreover, embryo quality assessment showed that total cell count had a mean value of 182.22 per embryo. The study concluded that both media are efficient in embryo production in-vitro, however FHK medium is considered to be more efficient in the production of blastocyst in bovine species.
Population-based analyses with argyle

In this vignette we introduce argyle utilities for analyzing genotypes from natural populations. We make use of a dataset containing genotypes for 28 wild-caught mice, representing the three subspecies of Mus musculus, from the Mouse Diversity Array (Yang et al. 2011). The MDA is a high-density Affymetrix array; for sake of demonstration we use only the 14306 markers on chromosome 19. This vignette also provides an overview of utilities for interacting with genotypes stored in the PLINK (Purcell et al. 2007) binary format. PLINK is widely used software with many utilities for management of genetic datasets and association studies. Although PLINK accepts many input formats, they are all converted to a space-efficient binary intermediate prior to analysis. The PLINK binary fileset consists of three parts distinguished by their suffix:
Cooperative Navigation and Path Planning using a Jumping Robot and Ground Rover for E ffi cient Exploration

To increase the scientific return of planetary missions in future, cooperation among different types of exploration vehicles like orbiter, lander and rover will be required. Cooperative approach seems to be more effective than using a single rover as each rover adds a different perspective of the environment being explored. This paper presents ongoing works on cooperative Mars exploration using a conventional rover and a jumping robot. A jumping robot provides an additional aerial perspective much closer to the surface as compared to orbiter. Added to that, a jumping robot has greater mobility than a conventional one. The data obtained from these two perspectives are then used for localization, mapping and path planning. In this paper simulation results on visual odometry for jumping robot and cooperative path planning are presented.
Efficient SVM based Object Classification and Detection

Over the last few years, the amount of image and video data present over the internet, and in the personal collections has been increasing rapidly. Therefore, the need for organizing and searching these vast collections of data efficiently has also increased. This has led to the research in the areas of content based retrieval and recognition of scenes/objects in visual data. There has been lot of research in these areas over the last few years and are still not yet at a deployable stage for real-world usage. For these technologies to be deployable, these solutions should not only be accurate, but also efficient and scalable. For all these related problems of visual recognition, there are two major phases, namely feature extraction stage and learning stage. The feature extraction stage deals with building a representation for the image/video data and the learning stage deals with learning how a function which can distinguish the classes. In this thesis, we focus on building efficient methods for visual content recognition and detection in images and videos. We mainly propose new ideas for the learning stage. For this purpose we start from using the state-of-the-art techniques and then show how our proposed ideas influence the computational time and performance. Firstly, we show the utility of state of the art image representations and classification methods for the purpose of large scale semantic video retrieval. We demonstrate this on TRECVID 2008 and TRECVID 2009 datasets containing videos for the retrieval of various scenes, objects and actions. We use Support Vector Machines(SVMs) as classifiers, which have been the popular choice for classification tasks in many fields. They have become popular mainly because of their good generalization capability. For obtaining non-linear decision boundaries, SVMs use a kernel function. This kernel function helps in finding a linear classifier in some high dimensional feature space, without actually computing the higher dimensional vectors. In many situations, we need to use computationally expensive non-linear functions as kernels. On the other hand, Linear kernel is computationally inexpensive, however it gives poorer results in most of the cases. Another contribution of this thesis is a method for improving the performance of computationally inexpensive classifiers like linear SVM. For this purpose, we explore the utility of sub-categories, which are the sub groupings present in the feature space of each semantic class of data. We model these subcategories by using Structural SVM framework. Also, we analyze how the choice of the groupings effect the results and present a method to learn the optimal groupings. We investigate our methods on various synthetic two dimensional datasets and real world datasets namely, VOC 2007 and TRECVID 2008.
Adaptive GNSS / INS Integration Based on Supervised Machine Learning Approach

Since the usage of Unmanned Aerial Vehicle (UAV) for civil applications is increasing, the localization accuracy in urban becomes an important issue for safety. However, the GNSS localization solution suffers a large error by the multipath effect. Since the multipath effect is unable to be completely solved but to mitigate, the multi-sensor integrated localization method is a common method to reduce this error. This study develops an adaptive Kalman filter adjusting the weighting between GNSS and INS measurements for different circumstance, further to improve the integration performance. The adaptation is based on supervised machine learning model classification, predicting the GNSS conditions with measurement features. The principle component analysis (PCA) is employed to aid selecting major features and labeling data for machine learning. Then, the supervised machine learning model is trained base on the decision tree and random forest (RF) learning algorithm with real operation data covering most situations. To reduce the missclassification error, the fuzzy logic algorithm is designed to avoid the classification result with rapid change. Besides, the process noise covariance is determined with Allan variance analysis. The localization performance of the proposed adaptive Kalman filter is compared with conventional Kalman filter and onboard localization solution provided by commercial fly controller. The results prove that the presented adaptive Kalman filter using random forest with fuzzy logic can achieve better GNSS condition classification outperforming other algorithms. The fuzzy logic in the proposed algorithm can mitigate jumping error causing by miss-classification. For urban areas, the overall localization result improves about 50% comparing with the onboard solutions. The maximum localization error can be reduced from 43.2 to 14.7 meters. The result verifies that the proposed adaptive Kalman filter can mitigate the localization error from multipath effect as well as achieving more accurate localization solution for UAV in urban areas.
Ground Vehicle Driving Aids: Assessing Driver Workload and Performance in Degraded Visual Environments

To enable indirect-driving maneuverability and threat detection in degraded visual environments (DVE), TARDEC’s ground DVE program is developing and testing a range of sensors and driver aid display systems. The current paper presents the first in a series of three simulator studies. It examined driver performance with two of the candidate driving aids, the Lane/Road Departure Warning System (LRDWS) and Optic Flow Enhancer (OFE), in three levels of degraded visual environment. Results indicated that the LRDWS best supported drivers in degraded visual environments, eliciting the lowest workload ratings and highest system usability ratings and facilitating faster driving in severe DVE conditions, with no apparent speed-accuracy tradeoff.