Miracle-dz/UniFAR-FTUs huggingface dataset:
Consensus for Non-volatile Main Memory

Traditionally, computer storage has been separated into a hierarchy based on response time, volatility, and cost of media. This tiering is undergoing a significant upheaval as a new breed of memory technologies, termed Storage Class Memories (SCM), now make it feasible to replace several tiers of the hierarchy with a single, cost-effective, uniform type of memory/storage. To make large-scale SCM deployments practical, however, memory system designers will first need to solve the problem of how to guard against unavoidable storage wear-out and failures—problems traditionally absent from "main memory" and handled by software at leisurely timescales in the domain of storage. In this paper, we propose a novel approach to providing fault tolerance in SCM-based main memory. Our key insight is to treat memory as a distributed storage system and rely on data replication and a consensus protocol to keep the replicas consistent. Separate memory instances store replicated copies of the data, and we use a programmable network interconnect to provide fast consensus between the memory instances. Our initial experiments using software memory controller emulation demonstrate reasonable overhead over local memory reads and show great promise as scalable main memory.
Multi Purpose Security System in Automobile Industry

Development is closely related to the technology. Technology is one of the reason to make human being life luxury. By combining Internet of Things (IoT) to this emerging technology makes much easier to process the data where it is necessary. The main goal of the paper is to explain a Multi Purpose Security system in automobile industry which is used to track or monitor a vehicle location and post that data into a app called ThingSpeak by using Internet. Multi Purpose Security system in automobile industry provides the results of some more important parameters like theft protection to the vehicle, accident detection of the vehicle and provides security to the passengers especially to the women by pressing the panic switch inside the vehicle if they are in dangerous situation. Proposed system can be used in many fields where the security is needed and the system consists of microcontroller, magnetic sensor, panic switch, GPS and GSM.
Ecg Based Identification Under Different Physical Status

Relatively stable feature of Electrocardiogram (ECG) waveform in ECG based identification authentication is an important issue to be discovered in biometric. It can benefit the biomaterial which is weak and easy to be affected when it is collected under different physical status. From the paper, it was found that P waveform was stable when several ECG waveforms were overlapped under Matthew Effect rule, therefore P waveform period could be more dependent than other part of ECG waveform in identity. And this result was used in the following experiments. The experiments in the paper were designed to study whether ECG based identification was affected by the status of ECG signal collecting. The status contained four status pairs, that was, emotion status (calm, high pressure), eating (starve, satition), sleeping (full, lack) and health (healthy, tired/cold). From the result, Negative emotion and lacking sleeping reduced TPR slightly (about 2–3%), but there was no effect on both eating and health pair status. However, if we used ECG data mixed with all four statuses, the overall True Positive Rate (TPR) of identification reached around 85%.
Introduction of the Surgical Site Fire Risk Assessment at The Lister Hospital, part of HCA Healthcare UK

Introduction A surgical site fire within the operating theatres is a continuous risk in modern surgery. When such an event does happen, clinical personnel can feel stunned, because such events are rare, and often happen rapidly. At The Lister Hospital, part of HCA Healthcare UK, a Fire Risk Assessment Tool and Protocol was developed to reduce such risks. Method Reviewing the scholarly literature and case studies relating to surgical site fires provides information and recommendations to highlight and improve such dangerous incidents from occurring in the future. Conclusion From the review of literature, two extensively documented cases of surgical site fire concluded in the identification of the elements of the fire triangle that create risk. From the recommendations a Fire Risk Assessment Tool and Protocol were developed and implemented at The Lister Hospital, including an addition to the WHO five Steps to Safer Surgery Checklist.
Rapid Reconstruction of a Three-Dimensional Mesh Model Based on Oblique Images in the Internet of Things

One of the main targets of the Internet of Things (IoT) is the construction of smart cities, and many industries based on the IoT serve popular applications in a smart city. However, 3-D reconstruction constitutes a major difficulty in the construction of a smart city. In recent years, oblique photography technology has been widely applied in the rapid 3-D modeling and other aspects of smart cities. However, in the automatic construction of a 3-D mesh model for oblique photogrammetry, complex building geometries make it very difficult to construct a triangular mesh model. Therefore, a network construction method is needed that can not only effectively construct a 3-D mesh model but also address the results of auto-modeling for an oblique image. The representative network construction method is a huge triangulation network in which the constructed surface of the object does not satisfy the manifold features and it is inconvenient to optimize and edit the model, yielding a low network construction efficiency. To solve these problems, a new method for constructing a high-quality manifold mesh model is proposed in this paper. First, an adaptive octree division algorithm is used to divide the point cloud data into sub domains that cover each other. Then, a mesh reconstruction is performed in each sub domain, and an efficient mesh construction algorithm based on relabeling the vertices of the directed graph is proposed to construct the manifold mesh. Finally, a triangular facet orientation method is used to homogenize the normal vectors of the mesh. The experimental results proof that the proposed method greatly improves the mesh reconstruction, effectively reflects the model details, and possesses a strong anti-noise ability. Also, it has a good robustness and is particularly suitable for the 3-D reconstruction of large scenes and complex surfaces.
Smart Sensor Box for Alivemaps Based on NB-IOT

In this paper we introduce the concept of pollution city maps. Real time maps that can be used to evaluate pollution in a smart city regarding air pollution, noise pollution, natural light quality and UV index, hot areas and buildings security. These maps are build based on the information transmitted to a central web server by smart boxes scattered around the city based on NB-lOT connection. The proposed system uses a low amount of data and can be expanded by using signal processing algorithms for automatically detecting accidents and crime.
Interleaved Online Task Planning, Simulation, Task Allocation and Motion Control for Flexible Human-Robot Cooperation

Modern manufacturing paradigms introduce the need for robots able to naturally cooperate with humans in an unstructured and dynamic environment. In this article we extend FlexHRC, an architecture for flexible and collaborative manufacturing robots, with an online perception-simulation-planning framework that allows the robot to assess the status of the workspace, keeping track at all times of the stage at which the cooperative manufacturing process is, to identify its next action, to simulate it to check its feasibility and, as a consequence, to dynamically allocate tasks to itself or the human operator. We have tested the FlexHRC with a dual-arm manipulator cooperating with a person to assemble a table with one tabletop and four legs.
Highly Sensitive Ammonia Sensors Based on Ag-Decorated WO3 Nanorods

In this paper, WO<sub>3</sub> nanorods are successfully synthesized via a one-step hydrothermal method, and then, silver nanoparticles are deposited on the WO<sub>3</sub> surface by <italic>in situ</italic> photoreduction method to obtain Ag/WO<sub>3</sub> gas sensitive materials. WO<sub>3</sub> and Ag/WO<sub>3</sub> are characterized by scanning electron microscope techniques, transmission electron microscope, and X-ray diffraction. The experimental results indicated that Ag nanoparticles have an important effect on the gas response of WO<sub>3</sub>. The Ag/WO <sub>3</sub> sensor greatly improves the selectivity and response compared to the traditional WO<sub>3</sub> sensor; the response is 300 for 100 ppm ammonia when the Ag/WO<sub>3</sub> sensor operates at the optimum operating temperature. The Ag/WO<sub>3</sub> sensor can respond to 50 ppb ammonia.
Empirical Evidence of Heuristic and Cost Based Query Optimizations in Relational Databases

A SQL query may be expressed in different forms with varying execution costs; hence, it is essential to choose the best when performance is considered. There are many factors that affect the cost and performance of a query. For instance, a query on a relation without proper indexes can cause full relation scans, which in turn causes either performance or locking problems. Query asking for a relation held by another query can cause dead locks and an improperly formed query can also lead to greater complexity. In this context, the paper presents an empirical analysis of the heuristic and cost-based query optimization techniques. The execution cost of a number of queries was calculated and compared with execution plans obtained after optimization. In particular, we have experimentally checked the impact of query rewriting; the ordering of SQL joins and Select statements and replacement of joins with semi-joins. The experiments have yielded a clear insight into the effect of the mentioned optimizations.
Image Cartoon-Texture Decomposition Using Isotropic Patch Recurrence

Aiming at separating the cartoon and texture layers from an image, cartoon-texture decomposition approaches resort to image priors to model cartoon and texture respectively. In recent years, patch recurrence has emerged as a powerful prior for image recovery. However, the existing strategies of using patch recurrence are ineffective to cartoon-texture decomposition, as both cartoon contours and texture patterns exhibit strong patch recurrence in images. To address this issue, we introduce the isotropy prior of patch recurrence, that the spatial configuration of similar patches in texture exhibits the isotropic structure which is different from that in cartoon, to model the texture component. Based on the isotropic patch recurrence, we construct a nonlocal sparsification system which can effectively distinguish well-patterned features from contour edges. Incorporating the constructed nonlocal system into morphology component analysis, we develop an effective method to both noiseless and noisy cartoon-texture decomposition. The experimental results have demonstrated the superior performance of the proposed method to the existing ones, as well as the effectiveness of the isotropic patch recurrence prior.
Detection and Prevention of HIV AIDS Using Big Data Tool

Big Data is an accumulation of data sets which are abundant and intricate in character. They comprise both structured and unstructured data that evolve abundant, so speedy they are not convenient by classical relational database systems or current analytical tools. Big Data Analytics is not linearly able to expand. It is a predefined schema. Big data analysis is the demanding one because it contains huge number of records. In today's world, the massive instruction about health care is to be prepared in order to recognize, diagnose, identify and prevent the various diseases. It is projected to develop a centralized patient monitoring system using big data. Health care is the conservation or advancement of health along the avoidance, interpretation and medical care of disorder, bad health, abuse, and other substantial and spiritual deterioration in mortal. One of the best areas where big data can be used to form an advance medical care. Medical care has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general. Average human lifespan is increasing along world population, which poses new challenges to today's treatment delivery methods. A disease is a particular abnormal condition that affects part or all of an organism not caused by external force and that consists of a disorder of a structure or function, usually serving as an evolutionary disadvantage. The study of disease is called pathology, which includes the study of cause. This paper mainly focuses on the prediction of disease like HIV/AIDS using R programming.
ANALYSIS VS SYNTHESIS - AN INVESTIGATION OF (CO)SPARSE SIGNAL MODELS ON GRAPHS

In this work, we present a theoretical study of signals with sparse representations in the vertex domain of a graph, which is primarily motivated by the discrepancy arising from respectively adopting a synthesis and analysis view of the graph Laplacian matrix. Sparsity on graphs and, in particular, the characterization of the subspaces of signals which are sparse with respect to the connectivity of the graph, as induced by analysis with a suitable graph operator, remains in general an opaque concept which we aim to elucidate. By leveraging the theory of cosparsity, we present a novel (co)sparse graph Laplacian-based signal model and characterize the underlying (structured) (co)sparsity, smoothness and localization of its solution subspaces on undirected graphs, while providing more refined statements for special cases such as circulant graphs. Ultimately, we substantiate fundamental discrepancies between the cosparse analysis and sparse synthesis models in this structured setting, by demonstrating that the former constitutes a special, constrained instance of the latter.
Study of Clustering Algorithms for Library Management System

Library is a place where large amount of data are generated and stored. These data must be transformed into information and knowledge which then could be used by researchers and users. The librarians might need to understand how to transform, analyze, and present data in order to facilitate knowledge creation. The knowledge, data mining extracts can be expressed as Concepts, Rule, and Pattern, Constraints, and Visualization etc. As we progress into a more integrated part of the business processes, the process of transfer of information has become more complicated. Today, one of the biggest challenges that libraries face is the explosive growth of library data and to use this data to improve the quality of managerial decisions. Data mining is a field of intersection of computer Science and statistics used to discover patterns in the information bank. Data mining uses methods of extraction, combination, and analysis of data to create new information by revealing trends, patterns, and relationships. The patterns observed from data mining will be helpful in managing the library and understanding the reader's mindset towards reading and frequency of visits and choices of books could be analyzed.
Fast Retrieving Approach for 3D Models Based on Semantic Tree

To solute the problem in 3D model retrieval based on text and content, the application of semantic tree is put forward. The method constructs 3D model semantic tree based on WordNet, under PSB(Princeton Shape Benchmark) ontology library, to proposes a novel approach of constructing semantic tree. It can simplify the process of expansion of semantic tree and adopts amount of information to compute the model which has higher returning similarity. It provides a retrieving method and can optimize the retrieval results according to the feedback mechanism based on shape feature, simultaneously. The experiments show our approach can improve the efficiency of 3D model retrieving effectively.
MicroRNA-668 represses MTP18 to preserve mitochondrial dynamics in ischemic acute kidney injury

The pathogenesis of ischemic diseases remains unclear. Here we demonstrate the induction of microRNA-668 (miR-668) in ischemic acute kidney injury (AKI) in human patients, mice, and renal tubular cells. The induction was HIF-1 dependent, as HIF-1 deficiency in cells and kidney proximal tubules attenuated miR-668 expression. We further identified a functional HIF-1 binding site in the miR-668 gene promoter. Anti–miR-668 increased apoptosis in renal tubular cells and enhanced ischemic AKI in mice, whereas miR-668 mimic was protective. Mechanistically, anti–miR-668 induced mitochondrial fragmentation, whereas miR-668 blocked mitochondrial fragmentation during hypoxia. We analyzed miR-668 target genes through immunoprecipitation of microRNA-induced silencing complexes followed by RNA deep sequencing and identified 124 protein-coding genes as likely targets of miR-668. Among these genes, only mitochondrial protein 18 kDa (MTP18) has been implicated in mitochondrial dynamics. In renal cells and mouse kidneys, miR-668 mimic suppressed MTP18, whereas anti–miR-668 increased MTP18 expression. Luciferase microRNA target reporter assay further verified MTP18 as a direct target of miR-668. In renal tubular cells, knockdown of MTP18 suppressed mitochondrial fragmentation and apoptosis. Together, the results suggest that miR-668 is induced via HIF-1 in ischemic AKI and that, upon induction, miR-668 represses MTP18 to preserve mitochondrial dynamics for renal tubular cell survival and kidney protection.
Reply to: Vaccination for hepatitis B virus in an Australian pre-biologic population with rheumatoid arthritis.

Sirs, We appreciate Dr Ihdayhid’s (1) comments on our paper “Vaccinations in adults with rheumatoid arthritis (RA) in an era of new disease-modifying anti-rheumatic drugs” by Meroni et al. (2). Dr Ihdayhid reported an audit on Hepatitis B virus (HBV) vaccination rates in patients with rheumatoid arthritis (RA) commencing biologics in 3 public tertiary hospitals in Western Australia between January 2011 and January 2016. RA patients were included only if bDMARD naïve and with no evidence of prior infection, carrier status or prior vaccination. The authors reported a very low full HBV screening (25% of the included patients) and more importantly an incomplete screening in more than half of the patients, further supporting the suggestion that HBV screening for vaccination is not carried out in the real life in Australia. Although the audit is retrospective and on a relatively small number of patients, it is almost in line with the data discussed in the review (2). We agree on the possible causes of such a low frequency of HBV screening in addition to the lack of physicians’ education and compliance. We do believe that HBV screening and eventually vaccination in RA patients before starting a bDMARD therapy is a hot issue which requires more attention by the rheumatology scientific societies in order to widely spread the message and improve the adherence of rheumatologists to the recommendations from both the EULAR and the ACR (3, 4).
Power Pattern Matching Through the Hybrid Bayesian Compressive Sensing

In this work, the synthesis problem of sparse linear arrays complying with user defined power masks is addressed by means of a Compressive Sensing $(CS)$-based methodology. A novel hybrid Bayesian $CS$ (BCS) approach integrating a mask-constrained synthesis within a $BCS$ solver is proposed, enabling an effective and efficient tool for the design of sparse arrays, starting from arbitrary user-defined requirements. A simple example validating the proposed approach is reported and discussed.
The MemProtMD database: a resource for membrane-embedded protein structures and their lipid interactions

Abstract Integral membrane proteins fulfil important roles in many crucial biological processes, including cell signalling, molecular transport and bioenergetic processes. Advancements in experimental techniques are revealing high resolution structures for an increasing number of membrane proteins. Yet, these structures are rarely resolved in complex with membrane lipids. In 2015, the MemProtMD pipeline was developed to allow the automated lipid bilayer assembly around new membrane protein structures, released from the Protein Data Bank (PDB). To make these data available to the scientific community, a web database (http://memprotmd.bioch.ox.ac.uk) has been developed. Simulations and the results of subsequent analysis can be viewed using a web browser, including interactive 3D visualizations of the assembled bilayer and 2D visualizations of lipid contact data and membrane protein topology. In addition, ensemble analyses are performed to detail conserved lipid interaction information across proteins, families and for the entire database of 3506 PDB entries. Proteins may be searched using keywords, PDB or Uniprot identifier, or browsed using classification systems, such as Pfam, Gene Ontology annotation, mpstruc or the Transporter Classification Database. All files required to run further molecular simulations of proteins in the database are provided.
A Systematic Map for Improving Teaching and Learning in Undergraduate Operating Systems Courses

Operating Systems (OS) is an important area of knowledge included in virtually all undergraduate computing curricula and in some engineering curricula as well. Teaching and learning an OS undergraduate course have always been a challenge. Several different approaches have been used for OS teaching and learning. Nevertheless, it is not easy for a teacher to choose one of them. No guidelines are available on how to choose one of them to match the specific objectives of each OS course. The objective of this paper is to analyze the approaches that have been used to improve OS teaching and learning by applying a systematic map. In particular, we consider the following dimensions: learning objectives, assessment, empirical study, methodology, and mode (face-to-face, online, or blended). The systematic map devised in this paper is focused on the time span from 1995 to 2017 and considered six of the major publications on the Computer Science Education. We considered three journals (the Journal of Engineering Education, the IEEE TRANSACTIONS ON EDUCATION, and the International Journal of Engineering Education) and three conferences (the ACM Technical Symposium on Computer Science Education—SIGCSE, the Conference on Computing Education Research—ITiCSE, and the International Conference on Computing Education Research—Koli). A total of 55 papers were included in the study after performing a search based on the inclusion/exclusion criteria. Nine approaches to improve OS teaching and learning were identified and analyzed. Furthermore, the implications for OS instructors and for research in this field are discussed.
False Detection Filtering Method for Magnetic Sensor-Based Vehicle Detection Systems

Magnetometer-based vehicle detection systems offer many advantages compared to other technologies. These systems can effectively detect vehicle presence, but vehicles with high metallic content passing in the neighboring lane can cause false detections. In this work, a new method is proposed for the filtering of these false detections. Filtering is based on different rules constructed using various data types extracted from the signals. All data types were tested separately to find the parameters with most influence, and later these parameters were combined in more complex rules to achieve more accurate results. The optimization of the parameter values was realized using genetic algorithms. The obtained results show that using properly constructed rules, 97% of false detections can be filtered with losing only nearly 0.3 % of good detections, and even a single parameter can be sufficient to reliably filter the false detections.