Miracle-dz/UniFAR-FTUs huggingface dataset:
Whole Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection.

BACKGROUND Most hospitals use traditional infection prevention (IP) methods for outbreak detection. We developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes, respectively. METHODS We performed WGS surveillance of healthcare-associated bacterial pathogens from November 2016 to November 2018. EHR ML was used to identify the transmission routes for WGS-detected outbreaks, which were investigated by an IP expert. Potential infections prevented were estimated and compared to traditional IP practice during the same period. RESULTS Of 3,165 isolates, there were 2,752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates were identified by WGS; clusters ranged from 2-14 patients. At least one transmission route was detected for 65.7% of clusters. During the same time, traditional IP investigation prompted WGS for 15 suspected outbreaks involving 133 patients, for which transmission events were identified for 5 (3.8%). If EDS-HAT had been running in real-time, 25-63 transmissions could have been prevented. EDS-HAT was found to be cost-saving and more effective than traditional IP practice, with overall savings of $192,408 - $692,532. CONCLUSION EDS-HAT detected multiple outbreaks not identified using traditional IP methods, correctly identified the transmission routes for most outbreaks, and would save the hospital substantial costs. Traditional IP practice misidentified outbreaks for which transmission did not occur. WGS surveillance combined with EHR ML has the potential to save costs and enhance patient safety.
Improved Zero Text Watermarking Algorithm against Meaning Preserving Attacks

— Internet is largely composed of textual contents and a huge volume of digital contents gets floated over the Internet daily. The ease of information sharing and re-production has made it difficult to preserve author’s copyright. Digital watermarking came up as a solution for copyright protection of plain text problem after 1993. In this paper, we propose a zero text watermarking algorithm based on occurrence frequency of non-vowel ASCII characters and words for copyright protection of plain text. The embedding algorithm makes use of frequency non-vowel ASCII characters and words to generate a specialized author key. The extraction algorithm uses this key to extract watermark, hence identify the original copyright owner. Experimental results illustrate the effectiveness of the proposed algorithm on text encountering meaning preserving attacks performed by five independent attackers.
Prolog to Electronic Textiles : A Platform for Pervasive Computing An introduction to the paper

In our era of converging technologies, it is significant that the second Industrial Revolution should meet the first on common ground: textiles. Modern data processing is, after all, an offshoot of technology first introduced in the 18th century to automate the production of woven textiles. The punched-card system developed by J.-M. Jacquard in 1804 to regulate the weaving of patterned fabrics was later adapted by the English inventor C. Babbage for his proposed analytical engine, the precursor of all binary-based digital computers. Now researchers are delving into a new technology that literally weaves a computing platform into wearable fabrics, creating electronic textiles, or e-textiles, that offer the wearer ready access to information anytime, anywhere, through the medium of an unobtrusive interface. This new field of research draws together an interdisciplinary cadre of specialists in information technology, computing and communications, microtechnology, textiles, and application domains. Their objective is to develop an economical process for the manufacture of large-area, flexible, conformable information systems that can be used in such diverse areas as medicine, sports, space research, firefighting, and law enforcement. In this paper, the authors explore the synergies accruing from wedding the capabilities of textiles with those of computing. In addition to the twin dimensions of functionality and aesthetics, clothing can now be woven to accommodate a third dimension: intelligence. Clothing, the authors point out, meets the user’s requirements for interactivity, connectivity, individuality, and adaptability. It is also probably the most universal of human–computer interfaces: it is familiar; it can be tailored to individual sizes, tastes, and budgets; and it is adaptable to varying climatic conditions. Textiles also provide the ultimate flexibility in systems design by offering a broad range of fibers, yarns, fabrics, and manufacturing techniques. Fabrics also have the spatial capacity needed for hosting the large numbers of sensors and processors demanded for covering broad terrains, such as a battlefield. The size of clothing further enables designers the latitude to build in redundant systems to ensure fault tolerance.
Simultaneous Triple Primary Neoplasms in the Gastrointestinal Tract: A Rare Case Report

Case Presentation: Here, we report a rare case of a 78-year-old woman diagnosed with simultaneous triple primary neoplasms in the ascending colon, the transverse colon and the appendix. The patient presented with lower abdominal pain in the right upper quadrant of 2 months duration. The serum carcinoembryonic antigen (CEA) level (185.83 ug/L) was significantly higher than the normal range (0-5 ug/L). An abdominal computed tomography (CT) scan with intravenous contrast scan showed multiple soft tissue masses in the hepatic flexure of the ascending colon and transverse colon, and a 2.8 × 2.8 cm soft tissue mass in the right ileocecal appendix. She underwent a laparoscopic radical extended right hemicolectomy. The patient has been seen in follow-up every 3 months, and there has been no recurrence after 16-months. We explore the etiology and discuss the treatment and prognosis of MPNs.
ScholarWorks@UMass Amherst

Common approaches to multi-label classiﬁcation learn independent classiﬁers for each category, and employ ranking or thresholding schemes for classiﬁcation. Because they do not exploit dependencies between labels, such techniques are only well-suited to prob-lems in which categories are independent. However, in many domains labels are highly interdependent. This paper explores multi-label conditional random ﬁeld (CRF) classiﬁcation models that directly parameterize label co-occurrences in multi-label classiﬁcation. Experiments show that the models outperform their single-label counterparts on standard text corpora. Even when multi-labels are sparse, the models improve subset classiﬁcation error by as much as 40%.
Origin of Uranium Mineralization at Coles Hill Virginia (USA) and its Natural Attenuation within an Oxidizing Rock-Soil-Ground Water System

Development of a scientific basis for management of uranium bearing wastes and contaminants requires information from natural geologic systems. The following study of the Coles Hill uranium deposit and associated weathered zone constrains processes leading to the natural attenuation of uranium within an oxidizing, fluid rich environment typical of the eastern US. At the Coles Hill deposit fracture hosted, primary U(IV) bearing mineral assemblages formed during hydrothermal activity associated with Mesozoic faulting. The most abundant ore assemblage consists of coffinite and apatite, but uraninite-zeolite and uraninite-calcite assemblages are also present. Within the shallow bedrock there is a uranium redox transition where alteration of U(IV) minerals has produced secondary uranium minerals. Geochemical data suggests that the volume of rock containing this U(IV)/U(VI) transition is acting as a closed system with respect to uranium mass transport during oxidation. The dominant mechanism of uranium fixation within the oxidizing zone is the precipitation of Ba-U(VI) phosphates (metaautunite group). Speciation and mineral stability calculations indicate that ground waters from the Coles Hill weathered zone are saturated with respect to Ba-meta-autunite and that this mineral is capable of buffering dissolved uranium concentrations to values lower than 20 parts per billion. U(VI) phosphates of the meta-autunite group are not stable in the vadose zone (soil pH ~ 4.5) at the Coles Hill site. In this zone uranium is associated with (Ba, Ca, Sr) aluminum phosphate of the crandallite group as well as with phosphate sorbed to iron oxy-hydroxide mineral coatings. Uranium leached from the vadose zone is reprecipitated as new meta-autunite minerals below the water table due to higher pH conditions of ~6.0 and relatively high activity ratios of dissolved phosphate to carbonate (e.g. log [H2PO4/HCO3] > -3). It is estimated that the U(VI) phosphates responsible for the natural attenuation of uranium at this site persist within the weathering zone for hundreds of thousands of years. Thus, the Coles Hill deposit represents an excellent natural laboratory for the study of uranium attenuation with potential applications for the design and implementation of cost effective remediation and containment strategies, such as soil amendments techniques and in-situ reactive barriers technologies. Funding for Research Byron N. Cooper Geoscience Fellowship (Virginia Tech, Department of Geological Sciences) Virginia Museum of Natural History Graduate Fellowship Virginia Department of Health Research Grant Virginia Division of Mineral Resources Society of Economic Geologists Research Fellowship
Semantic Segmentation Using Modified U-Net Architecture for Crack Detection

SEMANTIC SEGMENTATION USING MODIFIED U-NET ARCHITECTURE FOR CRACK DETECTION MICHAEL SUN 2020 The visual inspection of a concrete crack is essential to maintaining its good condition during the service life of the bridge. The visual inspection has been done manually by inspectors, but unfortunately, the results are subjective. On the other hand, automated visual inspection approaches are faster and less subjective. Concrete crack is an important deficiency type that is assessed by inspectors. Recently, various Convolutional Neural Networks (CNNs) have become a prominent strategy to spot concrete cracks mechanically. The CNNs outperforms the traditional image processing approaches in accuracy for the high-level recognition task. Of them, U-Net, a CNN based semantic segmentation method, has been one of the most popular in the deep learning because of its excellent performance in open-source crack classification. Although the results of the trained U-Net look good for some dataset, the model still requires further improvement for the set of hard examples of concrete crack that contains the stain, waterspot, and small width crack. In this paper, we address the challenging problem of accurately detecting a thin concrete crack. We designed a U-Net like structure that has a contracting path and an expansive path to overcome this challenge and compared it to current models, including original U-Net and pyramid pooling module network. The proposed architecture utilizes multiple feature maps in a down-sampling path to obtain a higher pixel-level
Value Creation Logics: A UK Case Study

Value creation is the core purpose of organisations, and the Value Creation Logics (VCLs) describe how organisations create value for their customers through the provision of goods or services. VCLs can help organisations understand and model their business processes in order to fully utilise their resources and achieve optimal performances. Most organisations rely heavily on IS/IT for their value creation. Alignment between IS/IT and business strategies as well as with the VCLs thus plays a key role in the benefits realisation of IT investment. This paper investigates the empirical realisation of different VCLs, and the role of IS/IT therein, through a case study of a UK law firm. The findings show that the different types of value creation logics co-exist and that problems occur when the organisational structure does not support the various needs of the different logics. In order to support the logics, a Viable System Model-inspired organisational design is suggested. This is intended to drive the IS/IT strategy in order to support concurrently different value creation logics.
Large Exploration for HW / SW partitioning of Multirate and Aperiodic Real-Time Systems

This paper addresses the domain of fine and coarse grain HW / SW codesign for Real-Time System On-Chip. We propose a new method for the real-time scheduling and the HW / SW partitioning of multi-rate or aperiodic tasks. The large design space exploration is based on parallelism/delay trade-off curves.
Bone Marrow Derived Pluripotent Stem Cells in Ischemic Heart Disease: Bridging the Gap Between Basic Research and Clinical Applications

Ahmed Abdel-Latif1, Ewa Zuba-Surma2 and Mariusz Z. Ratajczak3 1Gill Heart Institute and Division of Cardiovascular Medicine, University of Kentucky, and Lexington VA Medical Center, Lexington, KY 2Department of Medical Biotechnology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Cracow, 3Stem Cell Biology Institute, James Graham Brown Cancer Center, University of Louisville, Louisville, KY, 1,3USA 2Poland
Adaptive Personalization of Web Search

An often stated problem in the state-of-the-art web search is its lack of user adaptation, as all users are presented with the same search results for a given query string. A user submitting an ambiguous query such as ”java” with a strong interest in traveling might appreciate finding pages related to the Indonesian island Java. However, if the same user searched for programming tutorials a few minutes ago, the situation would be completely different, and call for programming-related results. Furthermore suppose our sample user searches for ”java hashmap”. Again imposing her interest into traveling might this time have the contrary effect and even harm the result quality. Thus the effectiveness of a personalization of web search shows high variance in performance depending on the query, the user and the search context. To this end, carefully choosing the right personalization strategy in a contextsensitive manner is critical for an improvement of search results. In this thesis, we present a general framework that dynamically adapts the query-result ranking to the different information needs in order to improve the search experience for the individual user. We distinguish three different search goals, namely whether the user re-searches known information, delves deeper into a topic she is generally interested in, or satisfies an ad-hoc information need. We take an implicit relevance feedback approach that makes use of the user’s web interactions, however, vary what constitutes the examples of relevant and irrelevant information according to the user’s search mode. We show that incorporating user behavior data can significantly improve the ordering of top results in a real web search setting.
Social Network Collaborative Filtering

This paper reports on a preliminary empirical study comparing methods for collaborative filtering (CF) using explicit data on consumers’ social networks. To our knowledge it is the first study to carefully evaluate the potential of explicit, publicly represented social networks for making product recommendations. Understanding social-network CF is important because traditional CF over a large consumer base is tremendously expensive computationally. An often-ignored aspect of CF is the selection of the set of users from which to make recommendations. Social theory tells us that social relationships are likely to connect similar people. If this similarity is in line with the recommendation task, they may provide a small, dense set of “recommenders” for CF. We examine a unique dataset from Amazon.com that contains a social network of consumer-selected friends. We examine two ways to incorporate social-network information into CF: using the social network to restrict the set of recommenders selected, and (further) using proximity in the social network to modify the traditional CF calculation. The results show that that CF with socialnetwork members selected as recommenders can be remarkably superior as compared to collaborative filtering with the recommenders not socially connected. Once the social network is selected, social network proximity does not seem to improve recommendations.
Managing non-functional requirements in agile software development

Non‐functional requirements define qualities of the software system that ensure effectiveness while embedding any constraints and restrictions on the design. A challenge rises with agile implementation in handling non‐function requirements in regulated environments. Thus, a practitioner's perceptions of agile method tailoring are described in relation to inter‐team boundaries and non‐functional requirements. The research comprises 18 practitioner interviews from two multinational agile software development companies. Interviews were recorded, transcribed, and analysed using an approach informed by grounded theory and information flow models were used to compare and contrast interactions of processes. It was discovered that one of the case study companies managed non‐functional requirements as artefacts in their agile methodology, while the other company reverts to conventional plan‐based software development practices of documentation, timeline estimations, and safety critical requirements. This research creates a detailed comparison of these contrasting approaches. The main contribution of this study is a set of proposed recommendations to deal with non‐functional requirements in a regulated environment using agile techniques. The introduction of two new artefacts, Documentation Work Item and Safety Critical Work Item, is recommended and it is accompanied with an illustrative example, to transform the handling of documentation and safety critical requirements in a more agile way.
The prospectivity of the Vøring and Møre basins on the Norwegian Sea continental margin

The focus of exploration activity on the Norwegian Sea Continental Shelf has shifted westwards from the shallow water areas over the Trondelag Platform and Halten Terrace to the deep water areas over the Cretaceous Voring and More basins. The basins, especially the Voring Basin, have been involved in a complex tectonic development since the main extensional phase in the late Middle Jurassic to Early Cretaceous, including Cenomanian–Campanian and Maastrichtian–Eocene extension and thermal uplift and intermittent phases of compression/transpression. While the Jurassic is the prospective interval in the Trondelag Platform and Halten Terrace, the potential in the Voring and More basins is seen mainly in the Cretaceous and Paleocene – the Jurassic is too deeply buried. The two first deep water exploration wells in these basins, 6707/10-1 and 6305/5-1, have already proven two effective plays, with excellent reservoir sands in the Upper Cretaceous and lower Paleocene charged with thermogenic gas in structural traps in rotated fault blocks and compressional domes. Palaeogeographical reconstructions indicate that these reservoir rocks may be widespread within the basin areas. No good oil-prone source rock at a suitable depth of burial is so far proven, but the palaeogeographical reconstructions linked to surrounding well data and regional analyses indicate a chance for the existence of Upper Cretaceous oil source intervals. The thermal history of the basins is as complex as their tectonic history, and is further complicated by the possible effects of the Paleocene–Eocene magmatic activity, making any basin modelling results at the present stage unreliable.
1 2 N ov 2 01 8 Solving Weakly-Convex-Weakly-Concave Saddle-Point Problems as Successive Strongly Monotone Variational Inequalities ∗

In this paper, we consider first-order algorithms for solving a class of non-convex non-concave min-max saddle-point problems, whose objective function is weakly convex (resp. weakly concave) in terms of the variable of minimization (resp. maximization). It has many important applications in machine learning, statistics, and operations research. One such example that attracts tremendous attention recently in machine learning is training Generative Adversarial Networks. We propose an algorithmic framework motivated by the inexact proximal point method, which solves the weakly monotone variational inequality corresponding to the original min-max problem by approximately solving a sequence of strongly monotone variational inequalities constructed by adding a strongly monotone mapping to the original gradient mapping. In this sequence, each strongly monotone variational inequality is defined with a proximal center that is updated using the approximate solution of the previous variational inequality. Our algorithm generates a sequence of solution that provably converges to a nearly stationary solution of the original min-max problem. The proposed framework is flexible because various subroutines can be employed for solving the strongly monotone variational inequalities. The overall computational complexities of our methods are established when the employed subroutines are subgradient method, stochastic subgradient method, gradient descent method and Nesterov’s accelerated method and variance reduction methods for a Lipschitz continuous operator. To the best of our knowledge, this is the first work that establishes the non-asymptotic convergence to a nearly stationary point of a non-convex non-concave min-max problem.
Bulletin of Electrical Engineering and Informatics

Dinda Lestarini, Taufiqurrahman Rusdy, Silfi Iriyani, Sarifah Putri Raflesia Database and Big Data Laboratory, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia Department of Computerized Accounting, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia Cyber Defence Center, Defence Strategic Installation Agency, Ministry of Defence of the Republic of Indonesian, Jakarta, Indonesia Center for Transdisciplinary and Sustainability Sciences, Institut Pertanian Bogor University, Bogor, Indonesia
Stuff I''''ve Seen: A System for personal information retrieval

A novel beet harvesting machine is disclosed having a rectangular frame with a front yoke and longitudinal and transverse support beams and being pulled by a tractor at the yoke for harvesting and delivering beets into a truck. The beet harvesting machine of the present invention utilizes hydraulic motors for powering the flailing of the dug beets rearwardly into the machine, the conveying of the flailed beets further rearwardly in the machine, the delivering of the beets laterally and the elevating of the beets upwardly and into the truck. The reservoir for the hydraulic fluid is integral with and disposed in the center of one of the longitudinal and transverse beams. A pump is provided for delivering the fluid from the reservoir and into the hydraulic motors of the present invention. A cooler for dissipating heat in the fluid is further provided.