Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison and evaluation of registration methods of intra-oral radiographs images

This paper presents comparisons and evaluations of registration methods of intra-oralradiograph images. Several automatic and manual algorithms were examined. Three similarityfunctions for automatic registration are described and evaluated. In addition, the results of twomanual registration tests are compared for both 3 and 10 control points marked interactively by theoperator.
Cross domain text categorization using K-means,Expectation-Maximization algorithm

: Text Analysis is important, emerging, research area, because lots of textual content resources developing rapidly through the net and virtual world. In the textual content records evaluation textual content categorization is one among the important techniques. Traditional text categorization methods are not capable of handle well with studying across specific domains. Cross-area type is more challenging hassle than single area class . In this mission cross area text categorization is implemented using EM(expectation maximization) algorithm.
Reduction of ensemble of classifiers with a rule sets analysis

The article shortly discusses the aim of classification task and its application to different domains of life. The idea of ensemble of classifiers is presented and some aspects of grouping methods are discussed. The paper points to the need of ensemble classifier pruning and presents a new approach for ensemble reduction. The proposed method is dedicated to committees of decision trees and bases on transformation of a tree set into a rule set and the new, suited to the pruning method, the weighted voting algorithm is also presented. There are also described experiments showing properties and effectiveness of the proposed method. Finally, directions of further research are mentioned.
Effect of Schizochytrium sp., on the Growth, Fatty Acid Composition, Digestive Enzyme and Serum Biochemical Composition of Postlarval Litopenaeus vannamei

In this study, shrimp feed was substituted with a dried alga, Schizochytrium sp. , at five levels (0, 10, 20, 40, and 60% of compound feed) to yield protein/energy (P/E) ratios of 22.72, 19.75, 16.71, 13.72 and 10.75 mg protein/KJ, respectively. The effects of this substitution on the specific growth rate (SGR), survival rate, fatty acid composition, digestive enzyme activities and biochemical composition of the sera in postlarval white shrimp (Litopenaeus vannamei) were evaluated. Four replicates, each consisting of 5,000 postlarval shrimp in a single cage, were performed at each substitution level. The postlarval shrimp were randomly distributed to 20 cages, and the duration of the trial was 50 d. The results showed that the best performance was obtained with a diet containing 40% dried Schizochytrium. The survival rate of the 40% trial group was 24% higher than that of the shrimp fed a control diet (p < 0.05). The final body weight gain and SGR peaked at a substitution level of 40% dried Schizochytrium (p < 0.05). The feed conversion ratios (FCRs) of the 20% and 40% trial groups were significantly lower than that of the control group (p < 0.05). The muscle protein and ash contents of the shrimp fed diets containing 20% and 40% dried algae were higher than those of the shrimp fed the control diet (p < 0.05). Although the level of dried Schizochytrium was associated with a decrease in the protease specific activity, an increase in the lipase activity was observed. The serum biochemical composition of the shrimp was significantly affected by the level of dried Schizochytrium in the diet (p < 0.05). In summary, when the feed content of dried Schizochytrium ranged from 20 to 40%, an optimal P/E ratio of 13.72-16.71 mg protein/KJ was achieved. Thus, such supplementation could increase the amount of highly unsaturated fatty acids (HUFAs) and decrease the required level of protein in the production of high-quality feed.
Three New Species of the Genus Autocharis Swinhoe, 1854 (Lepidoptera: Crambidae, Odontiinae) from the Southern Arabian Peninsula

New taxonomic results on the genus Autocharis Swinhoe, 1854 (Lepeidoptera: Crambidae, Odontiinae) are presented on the basis of a sample of 11 specimens collected by the author in south-western Oman between 2017 - 2019. The attribution of the material to the genus Autocharis Swinhoe, 1854 and the exclusion of other genera close to Autocharis Swinhoe, 1854 was done on the basis of differential features in the forewing venation. Examination of the wing pattern and the genitalia resulted in the attribution of one part of the sample (10 specimens) to the Autocharis seyrigalis (Marion & Viette, 1956) species complex and of the other part (1 specimen) to the Autocharis fessalis (Swinhoe, 1886) complex. Morphpmetric analysis of the sample and comparison with type material of the respective species groups revealed significant differences between the specimens of the sample and the representants of the two species groups. These differences result in the description of three new species: Autocharis trisignalis Seizmair, sp.nov. Autocharis renalis Seizmair, sp.nov. belonging to the Autocharis seyrigalis (Marion & Viette, 1956) complex and Autocharis arrondalis Seizmair, sp.nov. belonging to the Autocharis fessalis (Swinhoe, 1886) complex. Furthermore, the presence of the Autocharis seyrigalis (Marion & Viette, 1956) complex is reported as new to the entomofauna of the Arabian Peninsula. Up to now, records of the Autocharis seyrigalis (Marion & Viette, 1956) complex have been restricted to South Africa and the Malagasy region.
Design and implementation of a stealthy OpenWPM web scraper

OpenWPM [EN16] is a scraper framework designed to take privacy measurements on the web, for example to investigate online tracking. Jonker et al. [JKV19] identified that websites can detect web scrapers, and they found websites that behave differently to web bots than to regular visitors, e.g. by omitting advertisements or videos. Currently, it is not known in how far OpenWPM measurements are affected by web bot detection. We analyze the stability of OpenWPM’s detectability to be a web bot, using the fingerprint surface of Jonker et al. [JKV19] and by applying JavaScript template attacks [SLG19]. Further, existing countermeasures such as user agent spoofing extensions and FP-Block are investigated for their effectiveness to reduce OpenWPM’s detectability. Focusing on JavaScript spoofing, we derive the most advanced method to overwrite a revealing JavaScript object in order to minimize the risk of web bot detection. This results in a stealthy version of OpenWPM such that it’s fingerprint is much less distinguishable from a regular web browser. Further, it is shown common browser extensions spoofing JavaScript attributes are not robust against access inside (dynamically created) iframes. With the stealthy version, the reliability of previous OpenWPM studies can be analyzed. Further, future studies can benefit from more accurate results.
Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling

Localizing thoracic diseases on chest X-ray plays a critical role in clinical practices such as diagnosis and treatment planning. However, current deep learning based approaches often require strong supervision, e.g. annotated bounding boxes, for training such systems, which is infeasible to harvest in large-scale. We present Probabilistic Class Activation Map (PCAM) pooling, a novel global pooling operation for lesion localization with only image-level supervision. PCAM pooling explicitly leverages the excellent localization ability of CAM during training in a probabilistic fashion. Experiments on the ChestX-ray14 dataset show a ResNet-34 model trained with PCAM pooling outperforms state-of-the-art baselines on both the classification task and the localization task. Visual examination on the probability maps generated by PCAM pooling shows clear and sharp boundaries around lesion regions compared to the localization heatmaps generated by CAM. PCAM pooling is open sourced at this https URL.
Community Based Tourism (CBT) as a Model of Tourism and Self-Reliance Development of Coastal Villages in Banyuwangi

Banyuwangi is one of the regencies in East Java that has potential of coastal resources to develop. Community involvement in the community-based tourism process is marked by the presence of several community organizations dealing with tourism issues, such as Pokdarwis, Pokmas, and Pokmaswas, which are almost found throughout the coastal areas of Banyuwangi, which shows that the enthusiasm and involvement of the community in managing tourist destinations in the coastal areas of Banyuwangi. It is influenced by the unique characteristics of the coastal village community that makes the variants of objectives. It also influences the understanding of the efforts undertaken, including its involvement in engagement, as well as in other processes, as a description of the conditions that occur in coordination patterns, governance systems, participation patterns and forms of independence. CBT has been carried out showing that efforts to develop a community-based tourism sector and the development of the independence.
A Review Study On Various Algorithms Of Machine Learning

: Machine Learning is a phenomenon that helps the processor or system to learn from past experiences, train, and practice, without being programmed directly. Rather than writing the whole code, what you do is feed data into the generic algorithm and the algorithm/machine generates the logic, centered on the data supplied. This study has been undertaken to find a brief introduction to the different types of learning you may experience in the machine learning area. Each type of learning provides many algorithms which are suitable for carrying out many processes. The major advantage of using machine learning is that if an algorithm knows what to do with input, it will automatically do its job and generate the outcomes.
Healthcare: Alzheimer’s Disease Stage Diagnosis, Applying 3D Convolution Neural Networks to MRI Brain Scans

Alzheimer’s disease is an incurable, progressive neurological brain disorder and is the 6th leading cause of death in the USA [1]. Magnetic resonance imaging (MRI) is a technique used to diagnose Alzheimer’s disease in patients. In this paper we apply a number of varying neural network architectures, auto encoding and machine learning techniques to predict the condition of patients based on MRI scans. We produce multiple models with comparable performance to previous researches in the area based on ADNI dataset.
Robust Planning for Partially Informed Agents

A common approach to planning with partial information is replanning : the agent computes a plan based on some assumptions it makes about unknown information and replans if those assumptions are refuted during execution. To date, most planners with incomplete information have been designed to provide guarantees on completeness and soundness for the generated plan. Switching focus to performance, we present a new robustness measure for plans, which quantiﬁes the plan’s ability to avoid failure. Given a plan and an agent’s belief, robustness counts the number of the world states in the belief from which the plan will achieve the goal without the need to replan. We then discuss the trade-off between robustness and plan cost and offer a solver that is guaranteed to produce plans that comply with a robustness level speciﬁed by the user. By evaluating our approach on a set of standard benchmarks, we demonstrate how it enhances the performance of a partially informed agent.
Comparative Development of Five Sympatric Coastal Fundulid Species From the Northern Gulf of Mexico

There are five coastal fundulid species in the northern Gulf of Mexico (nGOM) including Fundulus jenkinsi, which is recognized within all states from Louisiana through Florida and federally as a Species of Concern. This particular fundulid was listed because of a perceived linkage to salt marsh habitat, only scant data available on its ecology, and salt marsh habitat being altered throughout its range. Little is known about the life history of this coastal killifish, but a detailed diagnostic key of the early life stages of select members of Fundulidae can provide the foundation needed to accurately identify this rare species in need of conservation. Larval and postlarval stages of select members of the family Fundulidae along the east coast of the U.S. have been described and compared among species. We illustrated various stages of coastal Fundulus spp. and Adinia xenica in the northern GOM and quantified morphometric and meristic characters. Total length, head length, body depth, caudal peduncle width, snout length, eye diameter, anal and dorsal fin counts, and diagnostic pigment patterns were compared among species. The first branch of our dichotomous key utilized the number of branchiostegal rays to separate young fundulids (< 15 mm TL) into two groups that can then be identified by selected pigment patterns, morphometrics, and meristics.
A Step Forward Progress in Physics and Cosmology with an Alternative Approach to General Relativity

A property of spacetime which is the ability for more space to come into existence was first discovered by Albert Einstein with his General Theory of Relativity (GTR). This theory had predicted some observations like gravitational waves, black holes, orbital movements etc. Another property of spacetime is the fact that empty space can possess its own energy. As more space comes into existence, more of this energy of space appears, this form of energy regarded as dark energy would cause the universe to expand and accelerate at a fast rate. GTR had predicted some cosmological observations we see today but its still unable to account for the nature of dark energy, this has resulted into theorists insisting that there could be something wrong with GTR and a new theory could include a field that creates this cosmic acceleration.
ECOLOGICAL ASPECTS OF METAZOAN PARASITES OF Astyanax altiparanae GARUTTI & BRITSKI, 2000 (CHARACIDAE) OF THE UPPER PARANÁ RIVER FLOODPLAIN, BRAZIL*

One hundred and ninety specimens of Astyanax altiparanae Garutti & Britski, 2000 were collected in the upper Paraná River floodplain, Brazil, between February 2000 and November 2001, with the aim to identify the parasite fauna and to observe the ecological aspects of metazoan parasites of A. altiparanae. A total of 3908 parasite specimens of 23 species were collected. The metacercaria Ascocotyle (Ascocotyle) tenuicollis Price, 1935 was the most abundant and the acanthocephalan Quadrigyrus sp. was the most prevalent. According to degree of importance, all species were classified as satellites. The community of metazoan parasites of A. altiparanae showed a typical overdispersion pattern. The diversity mean was H=0.1492 ± 0.2406. Ascocotyle (A.) tenuicollis had a significant correlation between standard length and parasitism abundance and prevalence. The monogenean Amphitecium sp. had a significant correlation only between parasitism prevalence and host length classes. No significant differences in abundance and prevalence of parasitism, by sex, were observed.
Image Distortion Classiﬁcation With Deep CNN Final Report

One of the largest trend of experiments related to image distortion is called Image Quality Assessment (IQA). It refers to the process of measuring the weighted combination of all of the visually significant attributes of an image, and particularly the distortions that affect it. Almost all related methods tend to compute a score for the distortion, reflecting the perceptual quality of digital images in a manner that is consistent with human subjective opinions.
Comparison Of Qualitative Research Methods

Purposeful sampling is an important strategy in a qualitative study. In this type of sampling, participants will be selected by the researcher based on the research questions and research purpose [3]. The author will introduce different qualitative research methodologies and compare them with each other. The author will analyze the differences and similarities of these methods and indicate their advantages. Additionally, the author will introduce the knowledge of data collection and data analysis processes, as well as provide insights on how to analyze findings in a research. By understanding the key characters of the qualitative research methods, the researcher will be able to identify when to apply the qualitative methods and how to choose according to the content of a research.
Time Inconsistency and Endogenous Borrowing Constraints

This paper studies the welfare of time-inconsistent, partially sophisticated agents living under two different regimes, one with complete, unfettered credit markets (CM) and the other with endogenous borrowing constraints (EBC) where the borrowing limits are set to make agents indifferent between defaulting and paying back their unsecured loans. The CM regime cannot deliver the first best because partially sophisticated agents would undo plans laid out by previous selves and borrow too much. Somewhat counterintuitively, in some cases, the EBC regime may deliver higher welfare than the CM regime. These results speak to the academic debate surrounding the creation and functioning of the CFPB (Consumer Financial Protection Bureau) in the U.S. and its implementation of the ability-to-repay rule on lenders after the 2007-8 crisis. Such institutions generate commitment publicly and may help time inconsistent agents economize on the costs of private commitment provision.
Incremental Semi-Supervised Learning for Multi-Genre Speech Recognition

In this work, we explore a data scheduling strategy for semi-supervised learning (SSL) for acoustic modeling in automatic speech recognition. The conventional approach uses a seed model trained with supervised data to automatically recognize the entire set of unlabeled (auxiliary) data to generate new labels for subsequent acoustic model training. In this paper, we propose an approach in which the unlabelled set is divided into multiple equal-sized subsets. These subsets are processed in an incremental fashion: for each iteration a new subset is added to the data used for SSL, starting from only one subset in the first iteration. The acoustic model from the previous iteration becomes the seed model for the next one. This scheduling strategy is compared to the approach employing all unlabeled data in one-shot for training. Experiments using lattice-free maximum mutual information based acoustic model training on Fisher English gives 80% word error recovery rate. On the multi-genre evaluation sets on Lithuanian and Bulgarian relative improvements of up to 17.2% in word error rate are observed.
Predicting Student Performance: A Solution for the KDD Cup 2010 Challenge

This paper describes our solution for the KDD Cup 2010 Challenge. The solution is based on a solution framework designed by us. And the framework is a general data-mining procedure that is able to handle asymmetric training and test sets as well as non-atomic and variable-length attributes. The paper will ﬁrst present the solution framework
Waste Object Detection and Classiﬁcation

We try to categorize the different pieces of the waste in an collaged image into categories : glass, plastic, paper, trash, metal, cardboard. We have tried Hybrid Transfer Learning for Classiﬁcation and Faster R-CNN to get region proposals for object detection. First, we deﬁne our waste classiﬁcation problem and present current research and solutions to similar object detection problems. We then give an outline of our proposed architecture for creating collages using GANs and model for approaching our speciﬁc task object detection, which uses a ﬁne-tuned Faster R-CNN. We will also describe the nature and generation of our dataset, as well as the results we achieved from our experiments. Lastly, we outline our next steps in terms of optimizing and improving upon our solution.
Image Level Forgery Identiﬁcation and Pixel Level Forgery Localization via a Convolutional Neural Network

Image forgery detection is important for data credibility. However, progress in generic detection technique development is slow due to the varieties in forgery types and complexity in forgery locations. Here, we report on a light-weight ( 5k parameters) VGG-like convolutional neural network architecture that allows for image forgery detection with an accuracy of 91% and AUC of 85% on test data. The modiﬁed network is adapted and coupled with a local anomaly detection network for forgery area localization and achieved an accuracy of 92% and AUC of 80% on test data. Our result demonstrates a relatively light-weight detection networks that is easy to train and adopt for image level forgery identiﬁcation and pixel level localized area detection.
SHORT COMMUNICATION IN VITRO MICROPROPAGATION OF GLADIOLUS GRANDIFLORA (VAR. SNOW PRINCESS) FLOWER FROM CORMEL EXPLANT

Protocol for micropropagation of Gladiolus grandiflora, an economically important horticultural crop has been developed using aseptically grown cormel slices as explants. Better proliferation and multiplication of calli were obtained on Murashige and Skoog (MS) basal medium supplemented with 2.0 mgl-1 NAA and 1.0 mgl-1 BAP. Regenerable calli were formed from the basal region of in vitro cultured cormel slices in approximately three weeks after explants were placed on callus induction medium. For most prolific shoot induction one gram (fresh weight) of callus was placed on shoot induction medium containing MS basal salt and 0.5 mgl-1 BAP. Shoot formation occurred one month after inoculation of callus on shoot induction medium. The standardized regeneration protocol will help to impart desirable traits in Gladiolus cultivar ‘Snow Princess’ for further transformation and genetic modification.
Effects of Hearing Loss and Fast-Acting Compression on Amplitude Modulation Perception and Speech Intelligibility

Objective: The purpose was to investigate the effects of hearing-loss and fast-acting compression on speech intelligibility and two measures of temporal modulation sensitivity. Design: Twelve adults with normal hearing (NH) and 16 adults with mild to moderately severe sensorineural hearing loss were tested. Amplitude modulation detection and modulation-depth discrimination (MDD) thresholds with sinusoidal carriers of 1 or 5 kHz and modulators in the range from 8 to 256 Hz were used as measures of temporal modulation sensitivity. Speech intelligibility was assessed by obtaining speech reception thresholds in stationary and fluctuating background noise. All thresholds were obtained with and without compression (using a fixed compression ratio of 2:1). Results: For modulation detection, the thresholds were similar or lower for the group with hearing loss than for the group with NH. In contrast, the MDD thresholds were higher for the group with hearing loss than for the group with NH. Fast-acting compression increased the modulation detection thresholds, while no effect of compression on the MDD thresholds was observed. The speech reception thresholds obtained in stationary noise were slightly increased in the compression condition relative to the linear processing condition, whereas no difference in the speech reception thresholds obtained in fluctuating noise was observed. For the group with NH, individual differences in the MDD thresholds could account for 72% of the variability in the speech reception thresholds obtained in stationary noise, whereas the correlation was insignificant for the hearing-loss group. Conclusions: Fast-acting compression can restore modulation detection thresholds for listeners with hearing loss to the values observed for listeners with NH. Despite this normalization of the modulation detection thresholds, compression does not seem to provide a benefit for speech intelligibility. Furthermore, fast-acting compression may not be able to restore MDD thresholds to the values observed for listeners with NH, suggesting that the two measures of amplitude modulation sensitivity represent different aspects of temporal processing. For listeners with NH, the ability to discriminate modulation depth was highly correlated with speech intelligibility in stationary noise.