Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness and challenges of recruitment process outsourcing (RPO) in the Indian Hotel sector

The present study tries of evaluate the effectiveness and challenges faced by adopting RPO practice in the India Hotel sector. The research objectives are driven from the purpose of the research which deals with distinct issues related to RPO and various perspectives of utilizing RPO. These objectives guided in identifying factors influencing the rationale for outsourcing the recruitment processes. The need for RPO has gained significance with the impact of organizational structure, stringent project deadlines, insufficient time for internal HR department for recruiting personnel, inefficiencies in integrating all stages of recruitment and expansion of geographies. The study is developed on building a theoretical framework which is prepared through analysing previous literature about outsourcing, and their utilization and efficiency in organizations. Mixed method approach has been followed to analyse with better validity and exploration of the context related to the effectiveness of RPO in the Indian hotel sector. The key findings indicated that the culture was positive influence on the usage of RPO in the organizations, in terms of adopting RPO during expansion of geographies and globalization. Also, the organizational structure was the major challenge in adopting these practices along with monitoring the outsourcing activities, lack of communication and inefficient HR practices.
Crowd-Labeling Fashion Reviews with Quality Control

We present a new methodology for high-quality labeling in the fashion domain with crowd workers instead of experts. We focus on the Aspect-Based Sentiment Analysis task. Our methods filter out inaccurate input from crowd workers but we preserve differ- ent worker labeling to capture the inherent high variability of the opinions. We demonstrate the quality of labeled data based on Facebook’s FastText framework as a baseline.
Transfer Learning for British Sign Language Modelling

Automatic speech recognition and spoken dialogue systems have made great advances through the use of deep machine learning methods. This is partly due to greater computing power but also through the large amount of data available in common languages, such as English. Conversely, research in minority languages, including sign languages, is hampered by the severe lack of data. This has led to work on transfer learning methods, whereby a model developed for one language is reused as the starting point for a model on a second language, which is less resourced. In this paper, we examine two transfer learning techniques of fine-tuning and layer substitution for language modelling of British Sign Language. Our results show improvement in perplexity when using transfer learning with standard stacked LSTM models, trained initially using a large corpus for standard English from the Penn Treebank corpus.
What Should Entity Linking link?

Some decades have passed since the concept of “named entity” was used for the first time. Since then, new lines of research have emerged in this environment, such as linking the (named) entity mentions in a text collection with their corresponding knowledge-base entries. However, this introduces problems with respect to a consensus on the definition of the concept of “entity” in the literature. This paper aims to highlight the importance of formalizing the concept of “entity” and the benefits it would bring to the Entity Linking community, in particular relating to the construction of gold standards for evaluation purposes.
Symptoms detection in eye retina image

Diabetic retinopathy and age related macular degeneration are among the most common eye retina diseases, which cause partial or complete blindness. The purpose of this study is to design and implement software for automatic detection of symptoms from eye fundus images. The detection algorithm is based on segmentation methods and follow up analysis of segmented areas. Detection of retina objects such as optic disc, macula and blood vessels is important prior symptoms detection as they can adversely affect the results of the analysis. Total 259 images of four databases were analyzed and algorithm reaches more than 90 % average success rate. The software might be useful in combination with appropriate hardware and optic devices, and can find a practical application in global population screening.
Noise Modeling and Estimation of Remotely-Sensed Images

A noise model is defined for remotely sensed images, and the noise statistics are estimated by using the means and variances from small (4x4 or 8x8) image blocks. Since most images contain many small but homogeneous areas, a scatter plot of variance vs (mean)' reveals characteristics of the noise. The Hough transform is then applied to the scatter plot to detect a straight line through the maj or cluster of data points. This defines the image noise statistics. Images from SAR, Landsat TM, and passive microwave Sensors are used for illustration.
WordNet-based similarity metrics for adjectives

Le and Fokkens (2015) recently showed that taxonomy-based approaches are more reliable than corpus-based approaches in estimating human similarity ratings. On the other hand, distributional models provide much better coverage. The lack of an established similarity metric for adjectives in WordNet is a case in point. I present initial work to establish such a metric, and propose ways to move forward by looking at extensions to WordNet. I show that the shortest path distance between derivationally related forms provides a reliable estimate of adjective similarity. Furthermore, I find that a hybrid method combining this measure with vector-based similarity estimations gives us the best of both worlds: more reliable similarity estimations than vectors alone, but with the same coverage as corpus-based methods.
Water, stream morphology and landscape: complex habitat determinants for the fire salamander Salamandra salamandra

Understanding the relationship between environmental features and species distribution is a key step for successful habitat conservation. In semiaquatic amphibians, the features of both breeding sites and the surrounding upland habitat can play important roles. We evaluated the relative role of (1) stream morphology, (2) biotic features of water, and (3) composition of landscape surrounding wetlands, for the distribution of the fire salamander, Salamandra salamandra. We determined the presence of larvae in 132 localities, and we used an information-theoretic approach to build species distribution models. We then used variance partitioning to evaluate the relative importance of environmental variables. A model including both stream and landscape features explained a large proportion of variation. Larvae were associated to heterogeneous and shallow streams, with scarce periphyton, rich macrobenthos communities characteristic of oligotrophic water, and surrounded by woodlands. Variance partitioning showed that stream morphology was the parameter with the largest independent effect, but most of variation was explained by the combined effect of multiple variables, suggesting a strong interplay among biotic and abiotic features in determining species distribution. The complementation between multiple elements, such as wetlands and landscape features, can be the key of a correct understanding of distribution of semi-aquatic amphibians.
The Heme Transporter HtsABC of Group A Streptococcus Contributes to Virulence and Innate Immune Evasion in Murine Skin Infections

Group A Streptococcus (GAS) requires iron for growth, and heme is an important source of iron for GAS. Streptococcus heme transporter A (HtsA) is the lipoprotein component of the GAS heme-specific ABC transporter (HtsABC). The objective of this study is to examine the contribution of HtsABC to virulence and host interaction of hypervirulent M1T1 GAS using an isogenic htsA deletion mutant (ΔhtsA). The htsA deletion exhibited a significantly increased survival rate, reduced skin lesion size, and reduced systemic GAS dissemination in comparison to the wild type strain. The htsA deletion also decreased the GAS adhesion rate to Hep-2 cells, the survival in human blood and rat neutrophils, and increased the production of cytokine IL-1β, IL-6, and TNF-α levels in air pouch exudate of a mouse model of subcutaneous infection. Complementation of ΔhtsA restored the wild type phenotype. These findings support that the htsA gene is required for GAS virulence and that the htsA deletion augments host innate immune responses.
Optimising a Nitric oxide sensing technique for hypoxic tumor cell cultures

A Nitric oxide (NO) electrochemical sensor for hypoxic, tumor cell culture applications is developed. Qualitative analysis of the electrode has been studied by cyclic voltammetry. NO amperometric measurement methods have been extensively analyzed. Square wave voltammetry method is found to be one of the optimal methods for the measurement of NO in cell cultures. A hypoxic tumor cell culture measurement (MCF-7 cell type) has also been conducted to observe the NO response to hypoxia.
An Efficient Truncated Nuclear Norm Constrained Matrix Completion For Image Inpainting

The matrix completion problem has found many applications in image and graphics fields. Developing fast and exact algorithms still remains challenging. The truncated nuclear norm (TNN), taking advantage of priori target rank information, is known as a better surrogate function to the rank constraint than the traditional nuclear norm. However, the TNN penalized algorithms always converge slowly due to the two-step scheme for avoiding directly updating the noncovex functions. In this paper, we propose a computationally efficient algorithm, Momentum Adaptive and Rank Revealing (MARR), for the TNN regularized matrix completion problem. The distinct advantages are to (1) reduce iterations by introducing non-monotonic constraints, and (2) decrease computational burden by controlling the size of matrix. Moreover, the descent property and convergence of the search are proven. Experiments on image inpainting, including images and range data, show that the proposed algorithm achieves very competitive results visually and numerically compared to several state-of-the-art approaches, while providing substantial reduction of iterations and runtime, thereby more applicable in real-world problems.
DNA barcoding of microgastrine parasitoid wasps (Hymenoptera: Braconidae) using high‐throughput methods more than doubles the number of species known for Australia

The Microgastrinae are a hugely diverse subfamily of endoparasitoid wasps of lepidopteran caterpillars. They are important in agriculture as biological control agents and play a significant ecological role in the regulation of caterpillar populations. Whilst the group has been the focus of intensive rearing and DNA barcoding studies in the Northern Hemisphere, the Australian fauna has received little attention. In total, 99 species have been described from or have been introduced into Australia, but the real species diversity for the region is clearly much larger than this. In this study, museum ethanol samples and recent field collections were mined for hundreds of specimens of microgastrine wasps, which were then barcoded for the COI region, ITS2 ribosomal spacer and the wingless nuclear genes, using a pooled sequencing approach on an Illumina Miseq system. Full COI sequences were obtained for 525 individuals which, when combined with 162 publicly available sequences, represented 417 haplotypes, and a total of 236 species were delimited using a consensus approach. By more than doubling the number of known microgastrine wasp species in Australia, our study highlights the value of DNA barcoding in the context of employing high‐throughput sequencing methods of bulk ethanol museum collections for biodiversity assessment.
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering

Recently, Visual Question Answering (VQA) has emerged as one of the most significant tasks in multimodal learning as it requires understanding both visual and textual modalities. Existing methods mainly rely on extracting image and question features to learn their joint feature embedding via multimodal fusion or attention mechanism. Some recent studies utilize external VQA-independent models to detect candidate entities or attributes in images, which serve as semantic knowledge complementary to the VQA task. However, these candidate entities or attributes might be unrelated to the VQA task and have limited semantic capacities. To better utilize semantic knowledge in images, we propose a novel framework to learn visual relation facts for VQA. Specifically, we build up a Relation-VQA (R-VQA) dataset based on the Visual Genome dataset via a semantic similarity module, in which each data consists of an image, a corresponding question, a correct answer and a supporting relation fact. A well-defined relation detector is then adopted to predict visual question-related relation facts. We further propose a multi-step attention model composed of visual attention and semantic attention sequentially to extract related visual knowledge and semantic knowledge. We conduct comprehensive experiments on the two benchmark datasets, demonstrating that our model achieves state-of-the-art performance and verifying the benefit of considering visual relation facts.
A novel hierarchical regression approach for human facial age estimation based on deep forest

Human facial age estimation has received continuous attention in security control, age-targeted advertising, biometrics, etc. In this paper, a novel deep forest approach (named fcXGBoost, i.e., multi-features fusion cascade XGBoost) is proposed for the regression task of human facial age estimation. The fcXGBoost approach consists of Cascade XGBoost with a cascade structure for representation learning, and multi-feature fusion for the enhancement of its representational learning ability with fusion of multiple features (including Active Appearance Models (AAM), Local Binary Patterns (LBP) and Gabor Wavelets (GW)). In addition, a novel hierarchical regression approach is proposed to further improve the estimation performance, which consists of a rough regression stage and a detailed regression stage. Furthermore, a flexible overlapping of age ranges in the detailed age estimation is proposed to eliminate the influence of error in rough regression stage. The proposed approach is evaluated on the benchmark and public facial image dataset of MORPH Album 2 by comparison to the state-of-the-art results.
Text recognition on PAN card using template matching method

Object recognition in computer vision refers to detecting a particular object in a video or an image. Computer vision being a branch of Machine Learning helps us to achieve a goal by making the machine automate the entire process through multiple algorithms and paradigms. PAN (Permanent Account Number) is a 10 character alpha-numeric code which is issued by Government of India to each of its citizen, especially those who are tax payee. The purpose of this research is to identify the PAN code and extract them so as to automate the process of retyping it manually. We achieve the above through text recognition process of template matching under Normalized Cross Correlation (NCC) method.
Cancer stem cells of hepatocellular carcinoma

Hepatocellular carcinoma is a malignant tumor arising from hepatocytes. The hepatocellular carcinoma is dictated by a subset of cells with stem cell-like features. These cells are apoptosis-resistant and have particular biomarkers, which serve as seeds in different stages of tumorigenesis including initiation, progression, metastasis, and relapse of hepatocellular carcinoma. Signaling pathways of cancer stem cells are novel targets for the radical intervention of hepatocellular carcinoma.
Spontaneous mutations in CYC8 and MIG1 suppress the short chronological lifespan of budding yeast lacking SNF1/AMPK

Chronologically aging yeast cells are prone to adaptive regrowth, whereby mutants with a survival advantage spontaneously appear and re-enter the cell cycle in stationary phase cultures. Adaptive regrowth is especially noticeable with short-lived strains, including those defective for SNF1, the homolog of mammalian AMP-activated protein kinase (AMPK). SNF1 becomes active in response to multiple environmental stresses that occur in chronologically aging cells, including glucose depletion and oxidative stress. SNF1 is also required for the extension of chronological lifespan (CLS) by caloric restriction (CR) as defined as limiting glucose at the time of culture inoculation. To identify specific downstream SNF1 targets responsible for CLS extension during CR, we screened for adaptive regrowth mutants that restore chronological longevity to a short-lived snf1∆ parental strain. Whole genome sequencing of the adapted mutants revealed missense mutations in TPR motifs 9 and 10 of the transcriptional co-repressor Cyc8 that specifically mediate repression through the transcriptional repressor Mig1. Another mutation occurred in MIG1 itself, thus implicating the activation of Mig1-repressed genes as a key function of SNF1 in maintaining CLS. Consistent with this conclusion, the cyc8 TPR mutations partially restored growth on alternative carbon sources and significantly extended CLS compared to the snf1∆ parent. Furthermore, cyc8 TPR mutations reactivated multiple Mig1-repressed genes, including the transcription factor gene CAT8, which is responsible for activating genes of the glyoxylate and gluconeogenesis pathways. Deleting CAT8 completely blocked CLS extension by the cyc8 TPR mutations on CLS, identifying these pathways as key Snf1-regulated CLS determinants.
An intelligent scheduling for Network Traffic Management System in congestion Control using GA

NETWORK traffic management can prevent a network from severe congestion and degradation in throughput delay Performance. Traffic congestion control is one of the effective approaches to manage the network traffic. Traffic management involves the design of a set of mechanisms which ensure that the network bandwidth and computational resources are efficiently utilized while meeting the various Quality of Service (QoS) guarantees given to sources as part of a traffic contract.The general problem of network traffic management involves all the available traffic classes. In existing intelligent congestion control use the Intel Rate Controller for the instantaneous queue size alone to effectively throttle the source sending rate with max-min fairness while does not considered the non linearity of the traffic control systems and external attacks. In this dissertation we concentrate our efforts on intelligent optimization techniques, such as genetic algorithms (GAs), intrusion detection system (IDS). In GAs facilitate an efficient non-linear function optimization paradigm and avoid congestion in traffic management. The intrusion detection system (IDS) is can detect the attacks. These evaluation result shows our new techniques can achieve better performances than the existing schemes.
End-Stage Renal Disease (ESRD): Physical Activity and Elucidation of Its Effects on Biochemical and Haematological Parameters in Haemodialysis Patients

End-stage renal disease (ESRD) is a progressive disorder for which there is no prospect of recovery, and for which patients receive regular haemodialysis. In ESRD the ability of the kidneys to excrete metabolic waste products and to regulate the composition of extracellular fluid is compromised. Symptoms of ESRD also include cardiovascular dysfunction, anaemia, malnutrition, muscle wasting, muscle weakness, glucose intolerance and reduced bone density. Patients with ESRD are likely to be challenged by participation in exercise whilst undergoing a HD regimen. Nevertheless there is a case for prescribing exercise in patients who struggle to become active. Some benefits of exercise In patients receiving haemodialysis, the benefits of physical exercise, which include: 1) Reduction of toxins and fluid overload in the circulatory system, permitting improved tolerance to the HD regimen and 2) Increased heart rate, which improves cardiovascular performance, 3) a reduction in comorbidities and an increase in well-being, and 4) the facilitation of positive coping strategies. An understanding how exercise can be of clinical advantage in ESRD patients is unique and challenging given the variety of nutritional and clinical conditions with which they present. The haematological and biochemical parameters which should be monitored in these patients (including blood potassium, urea, calcium and hemoglobin levels) need to be prioritized, and this prioritization depends in turn upon general patient well-being. More research is also required in the understanding of physiology and importance of good nutrition in this population. The aim of this work is to provide some background of the key parameters in HD patients and to identify literature on biochemical parameters which are affected during physical activity in HD patients. The results help to emphasize that closer attention should be paid to monitoring HD efficacy and Kt/V, a measure of dialysis adequacy.
THE ROLE OF PHONETICS AND PHONETIC DATABASES IN JAPANESE SPEECH TECHNOLOGY

This paper summarizes the complex allophonic variations that need to be considered in developing Japanese speech technology applications, with special emphasis on natural speech synthesis, and describes a large-scale phonetic database that provides IPA transcriptions with accent codes and including hundreds of thousands of allophones. The common notion that the kana syllabary correctly represents the Japanese phoneme stock is only partially true. A truly phonemic orthography requires unambiguous grapheme-tophoneme mappings. Yet speech technology developers often use kana pronunciation dictionaries because of easy availability and low cost. Even more challenging is to accurately generate the allophonic variants and accent patterns for each phoneme and lexeme, which has been done in the project reported in this paper.
Susceptibility of elite cultivars of pepper Capsicum fruitisens to the root-knot nematodes , Meloidogyne incognita Race 2 , in parts of Southwestern Nigeria

Two season field trials were conducted in the early and late planting seasons of 2008 to test the vulnerability of thirty-two elite cultivars of pepper collected from Southwestern Nigeria to Meloidogyne incognita attack. Infested root systems of field grown twelve week-old Celosia argentea L. cv. TLV-13 was ploughed 0.20m depth into the experimental plot to serve as primary source of inoculums. Initial population of the predominant nematodes species prior to experimentation was determined from composited 200cm3 soil sample. The design was a randomized completed block with three replications. Resistance to M. incognita was observed in six of the tested lines while eight cultivars were highly susceptible to the nematodes with significantly high number of eggs, soil and root nematode populations as well as highest values in root-knot rating. Plant biomass was also significantly low at 5% level of probability in susceptible cultivars. Other cultivars displayed varying degrees of virulence to the nematodes. None of the tested cultivars was immune to the pest attack. Line AI07-8 recorded the highest number of seeds. The number of fruits and corresponding weights were significantly higher in eight of the tested cultivars. However, AI07-3 had the highest significant yield value suggestive of its genetic desirability in breeding program.