Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Modification of Ureteral Reimplantation (Combined Technique) in Pediatric Patients: A Preliminary Case Series*

Background: In this video, we present a new open ureteral reimplantation approach (combined technique) with preliminary results from 32 renal units. Material and methods: Written informed consent was obtained from the parents of the patients. We used a suprapubic Pfannenstiel incision to reach the bladder. After preparation of the ureters with the guidance of the vesicoureteral reflux surgery principle, they were moved from the bladder to the extravesical area. A submucosal tunnel was created above and below the old hiatus with reference to the old hiatus site. The required submucosal tunnel length is adjusted to be 2/3 above the old hiatus and 1/3 below the old hiatus. The ureters were carried down through the submucosal tunnel using a right-angle clamp and fixed to the bladder with 5/0 polyglactin sutures, step by step, respectively. Results: A total of 22 patients (9 females/13 males) with a median age of 7 (min: 2and max: 15) years were operated on using the combined technique. There were 16 cases with vesicoureteral reflux and 6 cases with unilateral obstructive megaureter. The success rate was found to be 100% for vesicoureteral reflux and 83.3% for primer obstructed megaureter. When we focus on the number of ureters, the overall success rate was found to be 97%. Conclusion: The vertical angulation or kinking of the ureter at the entrance to the bladder can be prevented in this modification. New orifice localization is close to the normal position. Short tunnel length is out of the question in this modification. We think that with potential surgical advantages, a novel combined technique can be used in ureteral reimplantation.
An evaluation of inter-organisational identity theft knowledge sharing practice in the UK retail sector

Knowledge is an essential source of competitive advantage in modern society and is particularly important in the current on-line environment due to increased business interactions throughout the world. Knowledge sharing initiatives taken by organisations to improve technicalities to tackle cyber threat have been extensively investigated. A particular focus of this study was on the security professionals sharing their learning experience in order to help address and mitigate identity theft. Multiple case studies were employed to interpret the triangulated data collected. ShoppingCo, PaymentCo, TeleCo, and NetworkingCo participated in this investigation. Semi structured interviews were scheduled and conducted in conjunction to company reports, personal communication, presentation slides and related materials was gathered to ensure trustworthiness and authenticity. Pattern matching analysis was employed to draw conclusions by evaluating 30 transcripts and 11 internal documents. The major theoretical contribution of this study was the proposal of a conceptual framework that adapts for private sector organisations knowledge sharing elements in the security profession. Lack of knowledge of the manager’s role is addressed. Current knowledge sharing and corporate communication practices are synthesised. Formal and informal communication, social forums and networking events are evaluated. Thus, improving the current understanding of identity theft. This empirical study contributes to an improved understanding of inter-organisational knowledge sharing practice within three retailers and an official networking forum. Because of this evaluation, an extended framework is proposed and components synthesised into a new framework. Recommendations are drawn based on an evaluation of what is working and what does not seem to be providing benefits with regard to knowledge that address and mitigate identity theft. The framework suggested that the key to improved knowledge sharing was to persuade a range of security officials working for different private sector organisations to share their knowledge of identity theft prevention.
Effect of particle size and concentration on the chemical, physical and functional properties of rice-cornsilk composite flour paste

Abstract This study aimed to investigate the effect of cornsilk particle size and concentration on the properties of rice flour-cornsilk composite. Ground cornsilk (CS) was classified into sizes ranging from smaller than 45 to 300 µm and was added to rice flour (RF) at different concentrations (5 to 20% by weight). The RF-CS composite samples were analyzed for chemical, physical, and functional properties. The RF-CS composite flour samples had higher protein and fiber contents and showed higher water-binding capacity than the RF sample but lower swelling power at 90 °C. Lower solubility was observed when CS particles smaller than 125 µm were added. Adding large CS particles (>150 µm) caused the average pasting temperature of RF-CS to increase but reduced peak viscosity, breakdown, and setback significantly at all addition levels. The addition of smaller-sized CS particles (<125 µm) reduced peak viscosity significantly from the 10% addition level onwards but tended to increase the breakdown and setback viscosity. RF-CS composite paste had decreasing gel strength with increasing CS addition level. Lower gel syneresis after several freeze-thaw cycles was noted when adding CS. A more significant reduction in flour gel syneresis was observed with the addition of CS smaller than 75 µm. Cornsilk particle is a potential flour additive to increase protein and fiber content. Due to the ability to reduce syneresis, cornsilk particles could be used to produce a high amylose flour gel that can withstand freeze-thaw cycles. GRAPHICAL ABSTRACT
Examining WHO’s Crisis Communication in Issue Arenas during COVID-19: A Socio-semantic Network Analysis

ABSTRACT COVID-19 has posed several challenges to public health regulatory bodies and experts in dealing with crisis communication. The World Health Organization’s role in dealing with crisis communication came under scrutiny and opened a debate on Twitter regarding COVID-19 information. This study investigates the delay in the dissemination of information on COVID-19 by the World Health Organization (WHO) and its effects on communication about the pandemic through the lens of issue arenas and communication network theories. It examines the actors, topics, and sentiments that emerged in Twitter because of the communication as well as the crisis response strategies adopted by the WHO. The findings reveal five main actors – organizations, media, independent users, political actors, and others; the dominant emotions in the arena such as anticipation, anger, fear, and trust. Additionally, the WHO employs minimization, and corrective action strategies supported by bolstering. By introducing a new context to the socio-semantic network in the virtual space, this study sheds light on strategic communication while focusing on health communication, public relations, and crisis communication. These insights and findings can help organizations to moderate and manage issues during crises.
Winter severity affects occupancy of spring‐ and summer‐breeding anurans across the eastern United States

Climate change is an increasingly important driver of biodiversity loss. The ectothermic nature of amphibians may make them particularly sensitive to changes in temperature and precipitation regimes, adding to declines from other threats. While active season environmental conditions can influence growth and survival, effects of variation in winter conditions on population dynamics are less well‐studied. Given that extreme winter temperatures can influence amphibian survival and fitness, we expected that increased winter severity—as measured by variability in winter temperatures and snow cover—would be associated with decreased occupancy, and that populations that experience more severe winters would have the largest sensitivities and show the greatest declines.
Process-as-a-Service: FaaSt Stateful Computing with Optimized Data Planes

Fine-grained and ephemeral functions power many new applications that benefit from elastic scaling and pay-as-you-use billing model with minimal infrastructure management overhead. To achieve these properties, Function-as-a-Service (FaaS) platforms disaggregate compute and state and, consequently, introduce non-trivial costs due to data locality loss, complex control plane interactions, and expensive communication to access state. We revisit the foundations of FaaS and propose a new cloud abstraction, cloud process , that re-tains all the benefits of FaaS while significantly reducing the overheads that result from the disaggregation. We show how established operating system abstractions can be adapted to provide powerful granular computing on dynamically pro-visioned cloud resources while building our Process as a Service (PraaS) platform. PraaS improves current FaaS by offering data locality, fast invocations, and efficient communication. PraaS delivers invocations up to 32 times faster and reduces communication overhead by up to 99%.
Learning Complex Natural Language Inferences with Relational Neural Models

Large language models based on neural networks have recently achieved remarkable results across a wide range of NLP tasks. However, they still struggle with deeper understanding of semantics and reasoning about entities and their relationships. To address these shortcomings, in this thesis we examine alternative deep machine learning architectures with the intent of testing their logical reasoning and systematic generalization capabilities. We propose and implement several deep learning models, mainly from the deep relational learning category, and evaluate the proposed models on a textual benchmark selected from the NLU domain. In the empirical part, we manage to demonstrate the substantial performance gap between the standard NLU models that work with unstructured text data and more advanced models, mainly graph and recurrent neural networks, that allow to process more structured inputs. Finally, we propose suitable relational model biases to address the particular forms of relational reasoning in the selected benchmark and manage to achieve results comparable to state-of-the-art.
Review article: Design and evaluation of weather index insurance for multi-hazard resilience and food insecurity

Abstract. Ensuring food security against climate risks has been a growing challenge recently. Weather index insurance has been pointed out as a tool for increasing the financial resilience of food production. However, the multi-hazard insurance design needs to be better understood. This paper aims to review weather index insurance design for food security resilience, including the methodology for calculating natural hazards' indices, vulnerability assessment, and risk pricing. We searched for relevant research papers in the Scopus database using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol. Initially, 364 peer-reviewed papers from 1 January 2010 to 19 February 2022 were screened for bibliometric analysis. Then, the 26 most relevant papers from the last 5 years were systematically analyzed. Our results demonstrate that despite a significant research effort on index insurance, most papers focused on food production. However, research considering other aspects of food security, such as transportation, storage, and distribution, is lacking. Most research focuses on droughts. Other hazards, such as extreme temperature variation, excessive rainfall, and wildfires, were poorly covered. Most studies considered only single-hazard risk, and the multi-hazard risk studies assumed independence between hazards, neglecting the synergy hypothesis between hazards. Lastly, we proposed a conceptual framework that illustrates design paths for a generalized weather index insurance design and evaluation. Solutions for addressing multi-hazard problems are considered. An illustrative example demonstrates the importance of testing the multi-hazard risk hypothesis for weather-based index insurance design for soybean production in Brazil.
DNN Acceleration

—The Internet-of-Things requires end-nodes with ultra-low-power always-on capability for a long battery lifetime, as well as high performance, energy efﬁciency, and extreme ﬂexibility to deal with complex and fast-evolving near-sensor analytics algorithms (NSAAs). We present Vega, an IoT end-node SoC capable of scaling from a 1.7 µ W fully retentive cognitive sleep mode up to 32.2 GOPS (@ 49.4 mW) peak performance on NSAAs, including mobile DNN inference, exploiting 1.6 MB of state-retentive SRAM, and 4 MB of non-volatile MRAM. To meet the performance and ﬂexibility requirements of NSAAs, the SoC features 10 RISC-V cores: one core for SoC and IO management and a 9-cores cluster supporting multi-precision SIMD integer and ﬂoating-point computation. Vega achieves SoA-leading efﬁciency of 615 GOPS/W on 8-bit INT computation (boosted to 1.3TOPS/W for 8-bit DNN inference with hardware acceleration). On ﬂoating-point (FP) compuation, it achieves SoA-leading efﬁciency of 79 and 129 GFLOPS/W on 32-and 16-bit FP, respectively. Two programmable machine-learning (ML) accelerators boost energy efﬁciency in cognitive sleep and active states, respectively.
Bidirectional Context-Sensitive DGA Detection Using DistilBERT

Malware use domain generation algorithms (DGAs) to generate pseudo-random domain names to evade supervision. In order to defend against DGA traffic, security researchers have to discover and comprehend the al-gorithm by reverse engineering malware samples and register these domains in a DNS blacklist. Even though, this list has to be frequently updated, it is readily circumvented by malware authors. An alternative approach is to detect DGA domains using deep learning techniques to classify domains. Recent work in DGA detection have leveraged deep learning architectures such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to classify domains. However, these classifiers perform inconsistently. Specifically wordlist-based DGA families have been a struggle for these architectures. We propose a novel model based on a distilled version of Bidirectional Encoder Representation from Transformers (Distil-BERT) to detect DGA domains. The word embeddings are pre-trained on a large unrelated corpus to learn contextual embeddings for words bidirectionally. Afterwards, the pre-trained parameters enable for short training durations on DGA domains, while the language knowledge stored in the representation grants high performance with a small training dataset. We show that our model outperforms existing techniques on DGA classification, while simultaneously we need less time to train our model. Experiments in this paper are run on open datasets and the models’ source code is provided to reproduce the results.
Facial Time Lapse Video A Generative Approach to Creating Facial Time Lapse Videos

blend the key-frames into the generated sequence to increase identity fidelity. (6) Combine the resulting sequence of images into a video. Compared to the naive method, which produces a video by interpolating the pixel intensities of the key-frames, our method produces more natural and realistic facial time lapse videos. However, the naive method performed better in identity loss because the neural network computing the identity loss has minimal sensitivity to “ghosts” artifacts from the previous or next key-frame in the time lapse.
Evaluation of different solvents for extraction of total phenolic compounds in flour of Byrsonima coccolobifolia Kunth. fruit

The present work aimed to evaluate different solvents for the extraction of total phenolic compounds from murici-bravo fruit flours. Flours from the husk, pulp and pyrene with seeds were produced. The extracting solvents were: water, 100, 80 and 70% ethanol and 100, 80 and 70% methanol. To determine the amount of total phenolics, Folin-Ciocalteau reagent was used. The total phenolic contents for husk flour were 19.19 mg GAE 100 g -1 for 70% ethanol solution, followed by 18.91 80% ethanol solution and 18.72 mg GAE 100 g -1 for 100% methanol; the farinaceous extracts of the pulp showed better yields in 100% ethanol 19.08 and water 19.04 mg EAG 100 g -1 followed by 80% ethanol and 70% methanol solutions equal to 18.80 and 18.70 mg GAE 100 g -1 , and for the farinaceous extracts of pyrene and seeds with 17.54 for 80% methanolic solution and 17.10 mg EAG 100 g -1 for water, followed by 16.70 for 70% ethanolic solution and 16.56 mg GAE 100 g -1 for 100% methanol. The solvents applied in this study showed satisfactory results in the extraction of phenolic compounds. However, the use of less aggressive solvents such as water and ethanol proved to be excellent options for use, since methanol presents toxicity in the environment. Para determinar la cantidad de fenoles totales se utilizó el reactivo de Folin-Ciocalteau . Los contenidos fenólicos totales para la harina de cáscara fueron 19,19 mg EAG 100 g -1 para solución de etanol al 70%, seguido de 18,91 solución de etanol al 80% y 18,72 mg EAG 100 g -1 para metanol al 100%; los extractos farináceos de pulpa presentaron mejores rendimientos en etanol 100% 19,08 y agua 19,04 mg EAG 100 g -1 seguido de soluciones en etanol 80% y metanol 70% igual a 18,80 y 18,70 mg EAG 100 g -1 , y para los extractos farináceos de pireno y semillas con 17,54 para solución metanólica al 80% y 17,10 mg EAG 100 g -1 para agua, seguido de 16,70 para solución etanólica al 70% y 16,56 mg EAG 100 g -1 para metanol al 100%. Los solventes aplicados en este estudio mostraron resultados satisfactorios en la extracción de compuestos fenólicos. Sin embargo, el uso de solventes menos agresivos como el agua y el etanol resultaron ser excelentes opciones de uso, ya que el metanol presenta toxicidad en el medio ambiente.
Institutional Knowledge at Singapore Management University Time-aware conversion prediction

The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that di ﬀ erent products take di ﬀ erent time periods for conversion. The “conversion” here refers to actu-ally a more general set of pre-de ﬁ ned actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product’s actual conversion period and the application’s target conversion period has been the subtle culprit compromising many existing recommendation algorithms. The challenging question: what products should be recommended for a given time period to maximize conversion—is what has motivated us in this paper to propose a rank-based time-aware conversion prediction model (rTCP), which considers both recommendation relevance and conversion time. We adopt lifetime models in survival analysis to model the conversion time and personalize the temporal prediction by incorporating context information such as user preference. A novel mixture lifetime model is proposed to further accom-modate the complexity of conversion intervals. Experimental results on two real-world data sets illustrate the high goodness of ﬁ t of our proposed model rTCP and demonstrate its e ﬀ ectiveness in time-aware conversion rate prediction for advertising and product recommendation.
Regional Guidelines for the Application of Ridge to Reef (R2R) Spatial Prioritization and Planning Procedures to Identify and Select Priority Coastal Areas and Sites for the Conservation and Sustainable Use of Ecosystem Goods and Services

The R2R fine-scale spatially explicit decision support framework for conservation actions is adapted, applied and scaled up from previously developed tool for quantifying the effect of nutrient enriched groundwater and sediment stream runoff on coral reefs in Fiji and Hawaiʻi (Delevaux et al. 2018). The R2R modelling framework now provides for the identification of priority land areas for land-use incl. forest conservation that can maximize downstream benefits – i.e. maps of reef areas at risk, and maps of priority land areas for conservation.
Classiﬁcation Methods for t ∗ Search Undergraduate Research Report

A summary of the multivariate analysis (MVA) methods used for classiﬁcation of events in the search for t ∗ ¯ t ∗ pair production in the CMS experiment in presented. Signal events are the pair production of excited top quarks t ∗ (sometimes denoted T ) which decay into the lepton + photon + jets ﬁnal states: t ∗ ¯ t ∗ → t ¯ tγg → l + γ +jets. Several MVA methods are investigated here: an Adaboost boosted decision tree (BDTA) with the TMVA package, a BDT with the XGBoost package, and ﬁve neural networks (NN) with the Keras package. The discriminant distributions and receiver operating characteristic (ROC) curves are computed for each method. The upper limits on the signal strength (signal cross section) at 95% conﬁdence level are also computed for the BDTA (TMVA) and NN (Keras) models.
Lyrics-Based Music Band and Genre Topic Similarity Analysis

Based on hundreds of thousands of song lyrics from thousands of bands, Word2Vec models have been trained to quantitatively identify similarities between band texts and terms. Using prominent examples, this demonstrates for the cases studied, that music bands can be assigned to a similarity network solely on the basis of their song lyrics, which also corresponds to their musical style. Furthermore, using exemplary words, it is demonstrated that semantic term networks vary strongly from genre to genre. In addition, the semantic similarity matrices were studied using network analysis methods. As it turned out, term and band text networks differ significantly. While the former resemble random networks, the latter partly exhibit powerlaw behavior. Both also exhibit threshold-dependent regimes.
Real-Time Teacher Assistance in Technologically-Augmented Smart Classrooms

—The role of the teacher in the classroom environment is of crucial importance for the effectiveness of the learning process. However, recent studies on the technological enhancement of education have shown that teacher’s activities are not adequately supported, as the focus remains rather on the student’s side. This article discusses a learner-centric approach towards supporting instructors in improving the teaching and learning processes in ambient educational environments. The proposed solution introduces an intelligent multi-agent infrastructure that monitors unobtrusively the students’ activities and identiﬁes potential learning weaknesses and pitfalls that need to be addressed at an individual or classroom level. Such real-time insights enable the instructor to intervene providing help and adapt the teaching process according to the needs of the class. For that to be achieved, several applications have been developed: (i) a real-time classroom activity visualizer, (ii) a behavioral reasoner that aims to identify common behaviors by analyzing classroom activities, (iii) a statistics records manager targeting to showcase students’ progress and performance at both an individual and classroom level, and ﬁnally (iv) a series of mini-tools that enhance typical procedures that can be found in conventional classrooms, such as the classroom attendance record, the schedule manager, etc. Following the system’s description, ﬁndings of a preliminary expert-based evaluation are presented and some concluding remarks regarding the deployment of the system in real-life environments are formulated. Finally, potential future extensions of the system are proposed.