Miracle-dz/UniFAR-FTUs huggingface dataset:
Atherosclerotic Plaque Component Segmentation in Combined Carotid MRI and CTA Data Incorporating Class Label Uncertainty

Atherosclerotic plaque composition can indicate plaque vulnerability. We segment atherosclerotic plaque components from the carotid artery on a combination of in vivo MRI and CT-angiography (CTA) data using supervised voxelwise classification. In contrast to previous studies the ground truth for training is directly obtained from 3D registration with histology for fibrous and lipid-rich necrotic tissue, and with CT for calcification. This registration does, however, not provide accurate voxelwise correspondence. We therefore evaluate three approaches that incorporate uncertainty in the ground truth used for training: I) soft labels are created by Gaussian blurring of the original binary histology segmentations to reduce weights at the boundaries between components, and are weighted by the estimated registration accuracy of the histology and in vivo imaging data (measured by overlap), II) samples are weighted by the local contour distance of the lumen and outer wall between histology and in vivo data, and III) 10% of each class is rejected by Gaussian outlier rejection. Classification was evaluated on the relative volumes (% of tissue type in the vessel wall) for calcified, fibrous and lipid-rich necrotic tissue, using linear discriminant (LDC) and support vector machine (SVM) classification. In addition, the combination of MRI and CTA data was compared to using only one imaging modality. Best results were obtained by LDC and outlier rejection: the volume error per vessel was 0.91. 0% for calcification, 12.77. 6% for fibrous and 12.18. 1% for necrotic tissue, with Spearman rank correlation coefficients of 0.91 (calcification), 0.80 (fibrous) and 0.81 (necrotic). While segmentation using only MRI features yielded low accuracy for calcification, and segmentation using only CTA features yielded low accuracy for necrotic tissue, the combination of features from MRI and CTA gave good results for all studied components.
Investigating Mammalian Visual System with methods of informational theory

We examine a simple model of mammalian visual system. This structure is simulated by means of several hundred Hodgkin-Huxley neurons. We investigate signal processing properties of the model. Some methods taken from informational theory are applied to the analysis of Primary Visual Cortex' dynamics. Discussion of efficiency of such methods in two dimensional movement detection is presented in some detail.
Deep learning approach to describing and classifying fungi microscopic images

Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identiﬁcation of the species due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identiﬁcation process up to 10 days. Such a delay in the implementation of targeted therapy may be grave in consequence as the mortality rate for immunosuppressed patients is high. In this paper, we apply a machine learning approach based on deep neural networks and bag-of-words to classify microscopic images of various fungi species. Our approach makes the last stage of biochemical identiﬁcation redundant, shortening the identiﬁcation process by 2-3 days, and reducing the cost of the diagnosis.
Frequency of CMV- Infection among Hemodialysis Patients in Tikrit City

It has been well documented that hemodialysis patients have impaired immune response, which may result in higher prevalence rates of viral infections, including CMV. Infections in these patients may be due to primary infection or, more commonly, by reactivation of latent virus or re-infection with exogenous virus, which may be introduced by blood transfusion. The aim of this study was to evaluate the frequency of CMV-IgG and IgM antibodies among hemodialysis patients. The current research included 116 hemodialysis patients (69 male and 47 female) who attended to Tikrit Teaching Hospital for the period from beginning of October/2013 to the end of April/2014. CMV-IgG was found in 102 out of 116 (87.9%), while CMVIgM was detected in 10 out of 116 (8.6%) of hemodialysis patients. CMV-IgG was found in a higher rate in females than that in males (91.5% and 85.5% respectively), while CMV-IgM was detected in a higher rate in males than that in females (10.1% and 6.4% respectively). However, there was nonsignificant difference (p> 0.05) between sex and seropositivity. Also, nonsignificant relation was found between age and seropositivity for both CMV-IgG and CMV-IgM antibodies (p> 0.05). Since patients receiving hemodialysis treatment are immunocompromised and can be at risk of primary infection or reactivation of latent infection of CMV, its necessary to identify these patients with anti-CMV IgG and IgM specific serological tests for appropriate management.
Modeling of grasps in judo competition contests

In judo, the two contestants get close to each other, perform their grasps, move on the mat and attack. The approaches and kumi-kata (grip technique) give way to specific behaviors between the two contestants. The briefness and the capacity of simultaneous actions, the multiple interactions and the result at stake result in a complex system that is most of the time clarified by the coach to the contestant. A computerized observation may help to highlight some actions associated to success from that complex system. The winners of 35 contests were analyzed (9 beginners, 16 intermediate levels, and 10 experts) through their behaviors (approaches and kumi-kata) and positions. The results enable experts to assume lengths (15.8±9.5s) and amount (9±6) of the combat phases (lapse of time between beginning and stop, announced by the referee). Beginners approach each other with much care and precision, mainly holding their opponents with both hands for the projection. The percentage of total time spent by beginners and experts grabbing the opponent's judogi with both hands, attack and throw was 86% and 24%, respectively, indicating a much lower time spent between grip contact and technique application in experts compared to beginners.
Subaltern Agency in the Cultural Industries: Palestinian Creative Labor in the Israeli Series Fauda

This article advances the theorization of creative labor in cultural production in conflict zones. It argues that exploring minority creative workers’ behavior in media production in conflict zones helps to reveal patterns of othering of minorities and the coping strategies they develop to deal with being caught in the dominant narrative facilitated by the cultural industry. This venture is framed within postcolonial theorization, which contributes to revealing the unique impact of the interconnection between economic and symbolic factors on the behavior of creative labor. It also allows us to join other scholars in de-Westernizing creative labor studies and challenging the thesis of the silenced subaltern. To that end, we explore the meaning of “circumscribed agents” and “subaltern agency” through an analysis of ethnographic observations conducted during the production of the Israeli television series, Fauda. Examining the patterns of behavior of Palestinian–Israeli creative workers on the set of the series reveals three strategies of agency claiming—namely, authentication, self-orientalization, and mimicking.
Provable anonymity

This paper provides a formal framework for the analysis of information hiding properties of anonymous communication protocols in terms of epistemic logic.The key ingredient is our notion of observational equivalence, which is based on the cryptographic structure of messages and relations between otherwise random looking messages. Two runs are considered observationally equivalent if a spy cannot discover any meaningful distinction between them.We illustrate our approach by proving sender anonymity and unlinkability for two anonymizing protocols, Onion Routing and Crowds. Moreover, we consider a version of Onion Routing in which we inject a subtle error and show how our framework is capable of capturing this flaw.
A learning approach to improving sentence-level MT evaluation

The problem of evaluating machine translation (MT) systems is more challenging than it may first appear, as diverse translations can often be considered equally correct. The task is even more difficult when practical circumstances require that evaluation be done automatically over short texts, for instance, during incremental system development and error analysis. While several automatic metrics, such as BLEU, have been proposed and adopted for largescale MT system discrimination, they all fail to achieve satisfactory levels of correlation with human judgments at the sentence level. Here, a new class of metrics based on machine learning is introduced. A novel method involving classifying translations as machine or humanproduced rather than directly predicting numerical human judgments eliminates the need for labor-intensive user studies as a source of training data. The resulting metric, based on support vector machines, is shown to significantly improve upon current automatic metrics, increasing correlation with human judgments at the sentence level halfway toward that achieved by an independent human evaluator.
Evolutionary Feature Evaluation for Online Reinforcement Learning

Most successful examples of Reinforcement Learning (RL) report the use of carefully designed features, that is, a representation of the problem state that facilitates effective learning. The best features cannot always be known in advance, creating the need to evaluate more features than will ultimately be chosen. This paper presents Temporal Difference Feature Evaluation (TDFE), a novel approach to the problem of feature evaluation in an online RL agent. TDFE combines value function learning by temporal difference methods with an evolutionary algorithm that searches the space of feature subsets, and outputs franking over all individual features. TDFE dynamically adjusts its ranking, avoids the sample complexity multiplier of many population-based approaches, and works with arbitrary feature representations. Online learning experiments are performed in the game of Connect Four, establishing (i) that the choice of features is critical, (ii) that TDFE can evaluate and rank all the available features online, and (iii) that the ranking can be used effectively as the basis of dynamic online feature selection.
Improved personalized suggestions on websites using machine learning

Automated web personalization is a desired feature both for a website visitor and owner. The visitor is released from the burden of selecting settings or use search tools to adapt the website for their needs and find the right content. The owner benefits from having the visitors find content on the website they otherwise would have overlooked. In this project an existing model for the program Red Pine was improved on. The program Red Pine is a solution which can provide web personalization to website visitors by suggesting content based on the available information on the visitor. To predict which offer a visitor should be suggested the program uses a model with a classification algorithm. The model is trained with previous instances of made suggestions and the recorded responses from the visitors. The model was improved through selecting features, evaluating algorithm performance, and tuning algorithm parameters. Eight different algorithms from Azure Machine Learning Studio were used for the experiments. This resulted in finding an improved combination of features, algorithm parameters and algorithm for the model. New features describing the current session were added to the model and a correlation based filtering method for feature selection was used to discover relevant features. The identified features from the filtering was used in a greedy search to find an improved feature set. The default parameter values of the algorithm were tuned and the final combination of features, algorithm parameters and algorithm was cross evaluated. It is concluded that session data can be used as a replacement or complement for visitor data, when visitor data is unavailable, to get personalization with equal performance. Two of the algorithms that obtained highest performance scores had a linear decision boundary.
Extraction of Airways From CT (EXACT'09)

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate 15 different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of 20 chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.
Agent based infrastructure for real-time applications

In this paper we propose a new infrastructure for real-time applications. As a preliminary, we describe basic characteristics of the most popular real-time services like VoIP, videoconferencing, live media streaming, and network multiplayer games. We focus on the end-to-end latency, bandwidth and efficient transmission methods. Next, we present our project concepts, infrastructure model, details of implementation and our testing environment which was designed for testing many aspects of real-time services. The system combines mechanisms for ensuring best possible connection quality (QoS), load balance of servers in infrastructure and gives control over the packet routing decisions. Additionally, provided security mechanisms make it a good choice even in the environment where a high security level is required. The system is based on the Peer-to-Peer (P2P) model and data between users is routed over an overlay network, consisting of all participating peers as network nodes. This overlay can by used for application level multicast or live media stream. In the logging process each user is assigned to a specific node (based on his geographic location and nodes load). Because nodes are participating in data transmission, we have control over the data flow route. It is possible to specify the desired route, so, regardless of the external routing protocol, we can avoid paths that are susceptible to eavesdropping. Another feature of the presented system is usage of agents. Each agent acts within the single node. Its main task is to constantly control the quality of transmission. It analyzes such parameters like link bandwidth use, number of lost packets, time interval between each packet etc. The information collected by the agents from all nodes allows to build a dynamic routing table. Every node uses the Dijkstra's algorithm to find the best at the moment route to all other nodes. The routes are constantly modified as a consequence of changes found by agents or updates sent by other nodes. In VoD services agents also analyze popularity of streamed media, which helps build intelligent video cache. To ensure greater security and high reliability of the system, we have provided a reputation mechanism. It is used during bringing up to date the information about possible routes and their quality, given by other nodes. Owing to this solution nodes and routes which are more reliable get higher priority.
Simpliﬁed Planner Selection

There exists no planning algorithm that outperforms all others. Therefore, it is important to know which algorithm works well on a task. A recently published approach uses either image or graph convolutional neural networks to solve this problem and achieves top performance. Especially the transformation from the task to an image ignores a lot of information. Thus, we would like to know what the network is learning and if this is reasonable. As this is currently not possible, we take one step back. We identify a small set of simple graph features and show that elementary and interpretable machine learning techniques can use those features to outperform the neural network based approach. Furthermore, we evaluate the importance of those features and verify that the performance of our approach is robust to changes in the training and test data.
Documents Clustering techniques

Documents Clustering is a technique in which relationships between sets of documents are being automatically discovered and documents are divided into groups of similar specimens. The groups that are created during the process of clustering should be specified by the high degree of similarity between the elements that belong to the same group and low degree of similarity between the elements that belong to different groups. Such way of organizing documents allows the user to review content quickly and makes it easier to retrieve particularly interesting information. The following article describes the most popular documents clustering techniques and issues associated with it, like: text documents representation and similarity measure of documents. Additionally, the author is going to introduce his own concept of new effective method of documents clustering based on Ant System.
SURVEYING WHITE GRUBS (SCARABAEIDAE) IN THE SWAZILAND SUGARCANE INDUSTRY: THE 2006-2012 SURVEYS

In Swaziland, damage by scarab beetle grubs/larvae to sugarcane roots is of concern and occasionally necessitates replanting. Annual white grub surveys are carried out by the industry to detect fields with excessive infestations. Survey records from 2006 to 2012 showed that most larvae were recovered from fields on Tambankulu Sugar Estate, specifically sections 1 and 2, and fewer on the Umbuluzi Sugar Estate. Over this survey period, 11 618 larvae were recovered from 29 096 pits, representing an average area-wide density of 0.4 larvae/pit. The highest mean larval infestation was recorded in 2009 of 0.62 larvae from 30x30x30 cm sized pits dug beneath sugarcane stools. The maximum number of larvae found in a single pit was 7 individuals, and it was believed that these localities were where replant was required. From the specimens recovered the field teams identified a large proportion of the specimens as Asthenopholis minor (Melolonthinae). For example, in 2010 there were 94.4% of this species. In the same year, Heteronychus licas (Dynastinae) comprised 2.7% of larval recoveries. Past area-wide surveys showed that, during the 1960s, H. licas was the most common species encountered in this sugar industry, and then during the 1980s, A. minor was recorded as the predominant species. The other two taxa usually recorded during these surveys are Anomala spp. and Adoretus spp. that typically represented a substantially lower proportion of 0.9% and 1.6%, respectively, during the 2010 survey, while a group of unknown scarab taxa routinely recorded each year comprised 0.43%. Low density and relatively small body size of the last mentioned three groups probably preclude them from causing appreciable damage. The subject of the identity of white grub larvae in sugarcane fields is being studied at present. To this end, this paper gives a simple identification key that can be used in the field to identify third instar larvae of A. minor and H. licas. Additional research is recommended to confirm the identity of all four species recorded during these surveys and the unknown taxa using integrative (morphological and molecular) taxonomic techniques.
Promoting Social Cohesion: The Development of E-Learning Management System Materials through Life Based Learning for Sociology of Religion Course

The development of information and communication technology (ICT) encourages learning transformation to maximize it. Some transformations are through the development of E-Learning Management System (E-LMS). This paper explained the development of E-LMS material which is integrated with Life Based Learning in sociology of religion course. It integrated with life-based learning model through the ADDIE method which consists of Analysis, Design, Development, Implementation and Evaluation. The material was developed by a student group in the form of videos and thematic learning modules based local wisdom. The results of validation and evaluation showed the development can improve the effectiveness and interaction of learning through asynchronous learning in E-LMS. The developed material can improve students understanding of the religious practices and local wisdom of the community. Through life-based learning, students can construct knowledge and make it happen in the learning media produced. In the other hands, the media product that has been produced and validated can be one of the products can be applied in the process of sociology of religion
Time-Varying Word Clouds

We visualize time-varying text information with physically based simulation. Word-clouds are a popular means of visualizing the frequency of different concepts in a text document, but there is little work using text that has a time component, for instance, news feeds, twitter, or abstracts of papers published in a given journal or conference by year. We use physically simulated words that grow and shrink with time as an interactive web based visualization tool. We choose to use an existing 2D simulation framework Matter.js to develop the interface, with carefully designed forces to ensure a robust animation. We perform an informal user study to understand the ability of users to understand information presented in this dynamic way.