Miracle-dz/UniFAR-FTUs huggingface dataset:
Liver Ultrasound Image Enhancement Using 3-Level Discrete Wavelet Transforms Technique

— Medical picture processing is again because the prognosis regarding ailments by means of the use of the medical doctors or radiologists. Noise is delivered into pursuance together with the scientific pics in shape in accordance with a variety over factors within clinical imaging. Noise corrupts the medical images after the exorcism regarding the images degrades. This degradation consists regarding depth upstairs edges, structural details, blurring boundaries etc. To diagnosticate mettle ailments edge and essential points quotation are useless significant. Medical image denoising function assist the physicians between conformity together with diagnose the diseases. Medical images include MRI, CT scan, X-ray images, ultrasound photographs etc. In it consignment regarding change we conducted out bilateral filtering due to the fact scientific photo denoising. Its approach and implementation are reachable but the overall performance on bilateral filter depends upstairs its parameter. Therefore for acquiring the close gratifying stop result parameter have in imitation of lie estimated. We bear applied bilateral filtering about clinical images that are noised through additive haar Gaussian model including unique values concerning variances. It is a nonlinear and partial approach and tons preserves the features whilst smoothing the images. It eliminates the additive fair Gaussian muddle efficaciously then again its overall performance is bad among putting off lime then peppercorn noise.
Instruction folding in Java processor

Traditionally, the performance of a stack machine was limited by the true data dependency. A performance enhancement mechanism-Stack Operations Folding-was used in Sun Microelectronics' picoJava design and it can reduce up to 60% of all stack operations. In this paper, we use the Java bytecode language as the target machine language, and study its instruction folding on a proposed machine model. Three folding strategies: 2-foldable, 3-foldable and 4-foldable, were simulated and evaluated. Statistical data show that our third folding strategy eliminates 73% of all stack operations, and each strategy has an overall program speedup of 1.19, 1.25 and 1.26, respectively, as compared to a traditional stack machine. Moreover, a Java machine model suitable for instruction folding, together with its pipeline stages, are presented. It seems to have the best cost/performance effectiveness of a Java stack machine if six bytes decoder width and the second folding strategy-the three-foldable strategy-are adopted.
Real-time seismic-image interpretation via deconvolutional neural network

Seismic interpretation is now serving as a fundamental tool for depicting subsurface geology and assisting activities in various domains, such as environmental engineering and petroleum exploration. In the past decades, a number of computer-aided tools have been developed for speeding the interpretation process and improving the interpretation accuracy. However, most of the existing interpretation techniques are designed for interpreting a certain seismic feature (e.g., faults and salt domes) in a seismic section or volume at a time; correspondingly, the rest features would be ignored. Full-feature interpretation becomes feasible with the aid of multiple classification techniques. When implemented into the seismic domain, however, the major drawback is the low efficiency particularly for a large dataset, since the classification need to be repeated at every seismic sample. To resolve such limitation, this study proposes implementing the deconvolutional neural network (DCNN) for the purpose of real-time seismic interpretation, so that all the important features in a seismic image can be identified and interpreted both accurately and simultaneously. The performance of the new DCNN tool is verified through application of segmenting the F3 seismic dataset into nine major features, including salt domes, strong reflections, steep dips, etc. Good match is observed between the results and the original seismic signals, indicating not only the capability of the proposed DCNN network in seismic image analysis but also its great potentials for realtime seismic feature interpretation of an entire volume.
Analysis of fungal air pollution using different samplers

__________________________________________________________________________ Purpose: The aim of this study was the analysis of fungal air pollution in different rooms using different the air samplers. Material and methods: Air for mycological studies was collected from various hospital rooms. To monitor fungal air pollution were used samplers: SAS SUPER 100 (pbi-international), MAS 100 (Merck) and AIR IDEAL (BioMerieux). Results: We found differences in CFU/L values in depending on hospital room and the air sampler type. The CFU/L values of air samples taken by the AIR IDEAL ranged from 350 to 850 and (724.2 ± 159.9), and the CFU/L values of air samples taken by the sampler SAS SUPER 100 ranged from 160 to 800 (455.3 ± 250.73). The CFU/L values of air samples taken by the MAS 100 sampler varied from 50 to 1340 (302.5±56.6) From the air samples of both samplers was incubated 6 genera/species of fungi. Candia albicans fungi species and genus Penicillium species were most frequently isolated from SAS Super 100 sampler, and Penicillium species from the AIR IDEAL sampler. From the air samples of MAS 100 was isolated 11 types/species of fungi and in air samples of SAS Super 100 was isolated 7 types/species. Conclusions: Significant differences of CFU/L values in the tested rooms were found in depending on the used sampler. Mycological analysis of the obtained cultures from air samples suggests that there is not same isolation of fungi using the different samplers.
PowerAqua: Supporting users in querying and exploring the Semantic Web

With the continued growth of online semantic information, the processes of searching and managing this massive scale and heterogeneous content have become increasingly challenging. In this work, we present PowerAqua, an ontologybased Question Answering system that is able to answer queries by locating and integrating information, which can be distributed across heterogeneous semantic resources. We provide a complete overview of the system including: the research challenges that it addresses, its architecture, the evaluations that have been conducted to test it, and an in-depth discussion showing how PowerAqua effectively supports users in querying and exploring Semantic Web content.
Direct and intuitive input device for 3-D shape deformation

Shumin Zhai Department of Industrial Engineering University of Toronto Toronto, Ontario, M5S 1A4, Canada +1-416-978-3776 zhai@ ie.toronto. ed William Buxton Paul Milgram University of Toronto Department c)f & Xerox PARC Industrial Engineering Toronto, Ontario, M5S 1A4, University of Toronto Canada Toronto, Ontario, M5S 1A4, +1-416-978-1961 Canada buxton@dgp.toronto, edu +1-416-978-3776 milgram @ie. toronto. edu
The Evolution of Distributed Dataset Synchronization Solutions in NDN

Distributed dataset synchronization, or Sync in short, plays the role of a transport service in the Named Data Networking (NDN) architecture. In this paper, we conduct a systematic examination of NDN Sync protocol designs, identify common design patterns, reveal the insights behind different design approaches, and collect lessons learned over the years. We show that (i) each Sync protocol can be characterized by its design decisions on three basic components – dataset namespace representation, namespace encoding for sharing, and change notification mechanism, and (ii) two or three types of choices have been observed for each design component. Through analysis and experimental evaluation, we reveal how different design choices influence the latency, reliability, overhead, and security of dataset synchronization. In addition, we discuss the relationship between transport and application naming, the implications of namespace encoding for Sync group scalability, and support for Interest multicast.
Semantic Reasoning in Zero Example Video Event Retrieval

Searching in digital video data for high-level events, such as a parade or a car accident, is challenging when the query is textual and lacks visual example images or videos. Current research in deep neural networks is highly beneﬁcial for the retrieval of high-level events using visual examples, but without examples it is still hard to 1) determine which concepts are useful to pre-train ( Vocabulary challenge ); 2) which pre-trained concept detectors are relevant for a certain unseen high-level event ( Concept Selection challenge ). In our paper, we present our Semantic Event Retrieval System which 1) shows the importance of high-level concepts in a vocabulary for the retrieval of complex and generic high-level events and 2) uses a novel concept selection method ( i-w2v ) based on semantic embeddings. Our experiments on the international TRECVID Multimedia Event Detection benchmark show that a diverse vocabulary including high-level concepts improves performance on the retrieval of high-level events in videos and that our novel method outperforms a knowledge-based concept selection method.
Webtracking under the New Data Protection Law: Design Potentials at the Intersection of Jurisprudence and HCI

The GDPR regulates at present the handling with personal data fundamentally new and thereby opens new leeway. At the same time, it creates great uncertainty among those affected. One example of this is web trackers that help designers to improve the utility and usability of their websites on the basis of, in part, extensive (personal) data collection, or enable operators to finance them. Against this background, in this article we first show the practical relevance of web tracking by collecting the web trackers of the 100 most popular pages of each of the 28 EU member states. Building on this, we show which data these trackers collect and analyze their legal bases. Finally, we discuss possible design and architectural consequences for fulfilling the legally outlined requirements, taking into account the user's perspective.
Dynamic routing and spectrum allocation in elastic optical networks with mixed line rates

We focus on the dynamic Routing and Spectrum Allocation (RSA) problem in EONs with mixed line rates. To solve the dynamic RSA problem efficiently, we decompose the problem into routing and spectrum allocation sub-problems. For the routing sub-problem, we propose an efficient multi-constrained routing algorithm, Sorted Feasible Paths Searching (SFPS), to find the shortest feasible paths for the dynamic traffic demands. For the spectrum allocation sub-problem, we propose a spectrum allocation strategy named Adaptive Segmentation (AS) to allocate spectrum for the non-commensurate traffic demands of EONs with mixed line rates. Simulation results prove that the proposed dynamic RSA algorithm is time-efficient and perform better than existing dynamic RSA algorithms in terms of bandwidth blocking probability and spectrum fragmentation ratio.
IoT in Education: Implementation Scenarios through the Lens of Data Privacy Law

The Internet of Things (IoT) is today one of the most rapidly developing digital technologies and has managed to establish a solid presence in the academic realm. This article highlights the promising implications of IoT in Education including efficient school management, real-time data collection and analysis, resource management and global interconnectedness. Implementation scenarios are many and incorporate billions of devices that collect huge amounts of data that can be exploited for a wide range of educational purposes. In the post Covid19 era, the IoT as the so-called 3rd revolution of Information technology appeared to be able to support distance learning as well as to protect students in terms of virus spreading, guaranteeing a safe learning environment in their coming back to the classrooms. At the same time, all implementations scenarios raise concerns related to data privacy rights and data security. This article highlights the need for a legal context that protects privacy as well as fundamental rights and freedoms. Since the Internet of Things encompasses new technologies, such as Artificial Intelligence and Blockchain, the study of its regulatory framework is of primary interest. In addition, serious legal and ethical concerns arise which demand a relative critical approach. Therefore, it is pursued the proposal of fruitful guidelines which would lead to a powerful, productive and human-centric educational environment.
Shifter : Containers for HPC

Container-based computed is rapidly changing the way software is developed, tested, and deployed. This paper builds on previously presented work on a prototype framework for running containers on HPC platforms. We will present a detailed overview of the design and implementation of Shifter, which in partnership with Cray has extended on the early prototype concepts and is now in production at NERSC. Shifter enables end users to execute containers using images constructed from various methods including the popular Docker-based ecosystem. We will discuss some of the improvements over the initial prototype including an improved image manager, integration with SLURM, integration with the burst buffer, and user controllable volume mounts. In addition, we will discuss lessons learned, performance results, and real-world use cases of Shifter in action. We will also discuss the potential role of containers in scientific and technical computing including how they complement the scientific process. We will conclude with a discussion about the future directions of Shifter. Keywords-Docker; User Defined Images; Shifter; containers; HPC systems
Patterns of spatial distribution of Parodia maassii (Heese) A. Berger (Cactaceae) in a semi-desert of the subtropical Andes, the prepuna

El proposito del estudio fue examinar la relacion entre el patron espacial de Parodia maassii, una cactacea globular, y los estratos lenosos dominantes de la localidad donde se realizo el estudio. Nuestros datos muestran un acentuado patron agregado de la especie en parches de 1-11 m, lo que sugiere que la facilitacion (sindrome de la planta nodriza) podria estar operando en la zona e influyendo en las etapas tempranas del establecimiento de P. maassii. No obstante, evidenciamos atraccion de los individuos de la especie hacia arbustos a escalas pequenas (<2 m) y repulsion a escalas intermedias (9-13 m). Una posible interpretacion apuntaria a que la especie necesita la sombra no muy intensa de arbustos de tamano intermedio (< 1 m) para establecerse, pero que una vez que lo logra, prefiere los espacios abiertos, donde posiblemente crece mejor. Se necesitaran estudios de campo experimentales que demuestren esta aseveracion.
The Dispersion of Nearest-Neighbor Decoding for Additive Non-Gaussian Channels

We study the second-order asymptotics of information transmission using random Gaussian codebooks and nearest neighbor decoding over a power-limited stationary memoryless additive non-Gaussian noise channel. We show that the dispersion term depends on the non-Gaussian noise only through its second and fourth moments, thus complementing the capacity result (Lapidoth, 1996), which depends only on the second moment. Furthermore, we characterize the second-order asymptotics of point-to-point codes over <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-sender interference networks with non-Gaussian additive noise. Specifically, we assume that each user’s codebook is Gaussian and that NN decoding is employed, i.e., that interference from the <inline-formula> <tex-math notation="LaTeX">$K-1$ </tex-math></inline-formula> unintended users (Gaussian interfering signals) is treated as noise at each decoder. We show that while the first-order term in the asymptotic expansion of the maximum number of messages depends on the power of the interfering codewords only through their sum, this does not hold for the second-order term.
Fractional Masks Based On Generalized Fractional Differential Operator for Image Denoising

This paper introduces an image denoising algorithm based on generalized Srivastava-Owa fractional differential operator for removing Gaussian noise in digital images. The structures of n n× fractional masks are constructed by this algorithm. Experiments show that, the capability of the denoising algorithm by fractional differential-based approach appears efficient to smooth the Gaussian noisy images for different noisy levels. The denoising performance is measured by using peak signal to noise ratio (PSNR) for the denoising images. The results showed an improved performance (higher PSNR values) when compared with standard Gaussian smoothing filter. Keywords—Fractional calculus, fractional differential operator, fractional mask, fractional filter.
Project Teachers , Incentives , and Student Performance

What kinds of policies can governments adopt to attract, retain, motivate and develop qualified teachers in elementary and secondary schools? The project will pursue two avenues to address this question. The first component will study the effect of recent reforms on the teaching profession in four countries. Some of these reforms involve increases in compensation linked to specific teacher behaviors that are believed to affect educational quality and/or equity. In some countries, reforms have linked teacher pay increases to student outcomes. In these cases, we plan to use econometric methods to identify the effect of the reforms ex post on teacher quality and, where possible, on student outcomes. The second component is designed to understand teaching careers and the compensation structures used in a particular country in order to help the government develop new and more effective incentive systems to attract, retain, develop and motivate qualified teachers. We expect that some of the reforms will have an experimental design, including the random assignment of groups of schools or school districts to participate in the incentive innovation. We plan to collect and analyze data after adequate time has passed to observe an effect of the reforms on teacher quality and/or student outcomes.
ePerceptive—Energy Reactive Embedded Intelligence for Baeryless Sensors

For long, we have studied tiny energy harvesters to liberate sensors from batteries. With remarkable progress in embedded deep learning, we are now re-imagining these sensors as intelligent compute nodes. Naturally, we are approaching a crossroad where sensor intelligence is meeting energy autonomy enabling maintenancefree swarm intelligence and unleashing a plethora of applications ranging from precision agriculture to ubiquitous asset tracking to infrastructure monitoring. One of the critical challenges, however, is to adapt intelligence delity in response to available energy to maximise the overall system availability. To this end, we present the design and implementation of ePerceptive: a novel framework for best-eort embedded intelligence, i.e., inference delity varies in proportion to the instantaneous energy supplied. ePerceptive operates on two core principles. First, it enables training a single deep neural network (DNN) to operate on multiple input resolutions without compromising accuracy or incurring memory overhead. Second, it modies a DNN architecture by injecting multiple exits to guarantee valid, albeit lower-delity inferences in the event of energy interruption. The combination of these techniques oers a smooth adaptation between inference latency and recognition accuracy while matching the computational load to the available power budget. We report the manifestation of ePerceptive in designing batteryless cameras and microphones built with TI MSP430 MCU and o-the-shelf RF and solar energy harvesters. Our evaluation of these batteryless sensors with multiple vision and acoustic workloads suggest that the dynamic adaptation of ePerceptive can increase the inference throughput by up to 80% compared to a static baseline while ensuring a maximum accuracy drop of less than 6%. ∗This work was done when these authors were aliated with the Pervasive Systems Department at Nokia Bell Labs Cambridge. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specic permission and/or a fee. Request permissions from permissions@acm.org. SenSys ’20, November 16–19, 2020, Virtual Event, Japan © 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-7590-0/20/11. $15.00 https://doi.org/10.1145/3384419.3430782 CCS CONCEPTS • Software and its engineering! Embedded software; •Computing methodologies ! Neural networks; • Hardware ! Analysis and design of emerging devices and systems.
Engineering Applications of Artificial Intelligence

adequate monitoring to prevent dynamic failures from excessively affecting components’ health status, increase the level of safety, and reduce operative costs. Health and Usage Monitoring Systems have been developed to monitor helicopters during their lifetime in the last few decades. Recent works demonstrated that despite analyzing physical components’ behavior over time, tracking the regimes performed during each flight contributes to estimating the aircraft’s health and usage status, paving the way for designing accurate prognostics algorithms. However, today, most regime recognition systems rely on data recorded during certification flights. It follows that the training regimes differ from the ones proposed in the prediction phase
Expert Systems With Applications

Color quantization is a common image processing operation with various applications in computer graphics, image processing, and computer vision. Color quantization is essentially a large-scale combinatorial optimization problem. Many clustering algorithms, both of hierarchical and partitional types, have been applied to this problem since the 1980s. In general, hierarchical color quantization algorithms are faster, whereas partitional ones produce better results provided that they are initialized properly. In this paper, we propose a novel partitional color quantization algorithm based on a binary splitting formulation of MacQueen’s online k-means algorithm. Unlike MacQueen’s original algorithm, the proposed algorithm is both deterministic and free of initialization. Experiments on a diverse set of public test images demonstrate that the proposed algorithm is significantly faster than two popular batch k-means algorithms while yielding nearly identical results. In other words, unlike previously proposed k-means variants, our algorithm addresses both the initialization and acceleration issues of k-means without sacrificing the simplicity of the algorithm. The presented algorithm may be of independent interest as a general-purpose clustering algorithm.