Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Characterization of DWT and CS Compression Algorithms for Multi-Resolution ECG data in WBAN

— Wearable nodes are powered by battery and need to be always functional for continuous remote monitoring of patients which demands that node life time has to be prolonged to the maximum extent. In this direction, Discrete Wavelet Transform (DWT) and Compressive sensing (CS) based energy efficient compression algorithms have been developed and tested for single lead 11-bit and 24-bit resolution ECG data. High resolution (24-bit) test data have been acquired from normal subjects using TI ADS1293 EVM module. Comparative study of both algorithms has been carried out by applying MIT-BIH Arrhythmia data base and acquired signals for healthy subjects. It was found that as block size varies the CS algorithm reconstruction quality diminishes for higher resolution ECG data.
Named Entity Extraction From the Colloquial Setting of Twitter

—This paper suggests a study of Named Entity Recognition (NER) as it applies to Twitter and strategies that can be used to make NER systems more successful in colloquial settings such as Twitter. Named Entities are named nouns which fall into the catagories following: People, Locations, and Organizations. The strategies explored are useing a text normalizer to shape the text into a format that NER programs can recognize and cross checking classiﬁers to increase the precision of NER tools.
Opleiding Informatica Anomaly detection in real-world networks

The overwhelming amount of data that is nowadays available, leads to an increased demand for techniques that automatically identify abnormal or anomalous behavior. In this paper, we consider this problem in the context of networks and investigate how network-based anomaly detection techniques can be used for the purpose of law enforcement and criminal investigations in the field of electronic discovery. This problem is firstly investigated on static networks. Examples of anomalies in static networks are network intruders in physical networks or spammers spreading unwanted advertisements in online social networks. While existing methods typically identify anomalies from a local perspective, we propose a novel method that identifies nodes from a global perspective. The proposed community-aware CADA algorithm outperforms previous methods on both synthetic and real-world data and scales remarkably well to larger networks. Second, we approach the problem in networks that change over time. We compare two existing methods that indicate to what extent two graphs are similar to each other. Results on real-world data show that the techniques successfully indicate points in time when something significant happened in the network. In general, these network-driven techniques can uncover critical information about events in the data that was otherwise not discovered.
An Approach to Facilitate Business System by Multiple Barcode Detection using Faster RCNN

Barcoding system is a cheap and reliable way of tagging the products. The barcode detection process is needed for an inventory system to detect the barcodes of the products and for the billing system of the products. Nowadays, laser scanners are used to detect single barcode in super shops, but they are costly. If multiple barcodes could be detected from an image, it may help everyone to save some more time than scanning them separately. In this paper, a model that has been proposed to develop which will work better for detecting and decoding multiple barcodes simultaneously. In this work, the Faster RCNN model is used for the detection of multiple barcodes. The detection process is also done using the Pyzbar library separately. But Faster RCNN gives us better output to detect barcodes from an image than this library does. With the help of TensorFlow API, we worked on our dataset for the detection process of barcodes using transfer learning method. The decoding process is done on the Arte-Lab dataset with the help of the Zbar library. Though the detection rate using Faster RCNN is a little bit slower, but it gives better accuracy. The detection and decoding accuracy throughout a model can facilitate a business system for faster transaction.
Pertussis sensitization as an animal model for the abnormal bronchial sensitivity of asthma.

For half a century anaphylaxis has served us as the animal model for bronchial asthma, and it has served us very well, generating many substantial contributions to the understanding of the pathogenesis of the disease and to its treatment. We have just heard Patterson describe extensions of this model which offer promise for still further developments, but as a matter of fact anaphylaxis has been such a successful model that for years we have been inclined to ignore facets of asthma that it does not explain. The purpose of my presentation is to list some features of asthma which I think a model should explain, to call your attention to a different animal model which could help explain these features and to discuss briefly how well the model and the disease correspond. We are all painfully aware of the difficulties in the terminology of chronic obstructive lung diseases, so for the purpose of the present discussion I will use the following operational definition of asthma. ' Bronchial asthma is characterized by diffuse airway obstruction which is virtually completely reversible, either spontaneously or with treatment. It is often familial and is associated with eosinophilia in the blood or sputum. It is further characterized by a remarkable bronchial sensitivity to various kinds of stimulation, notably including cholinergic drugs and histamine. Asthma has often been considered to be a syndrome like hypertension produced by many agents, but we can also consider it a disease like diabetes which has a common definable, though complicated, metabolic abnormality. My argument starts with the premise that "asthma is asthma is asthma," and that the variety of etiologic features we recognize in our patients has a single final common pathway of pathogenesis. The most important of those features are allergic reactions, respiratory infections, air born irritants, emotional disturbances and familial influences, and a major reason for considering them provocative factors of a disease with a common underlying pathogenesis is that individual patients present all varieties of permutations and combinations of them. A unifying hypothesis of asthma should account for all of these factors and also explain the eosinophilia, the
Modeling Of Microscale Solid Rocket Composite Propellant

The heterogeneous propellants consisting of Ammonium Perchlorate (AP) particles imbedded in a Hydroxyl Terminated Polybutadience (HTPB) are widely used in solid rocket industries. In this study, the effect of AP particles diameter and the random distribution with the HTPB on the burning of such propellant is presented. A mathematical model which describes the unsteady burning of a heterogeneous propellant by simultaneously solving the combustion fields in the gas phase and the thermal field in the solid phase with appropriate jump condition across the gas/solid interface is developed. The gas-phase kinetics is represented by a two-step reaction mechanism for the primary premixed flame and the primary diffusion flame between the decomposition products of the HTPB and the oxidizer AP. The propagation of the unsteady non-planer regression surface is described, using the Essentially-Non-Oscillatory (ENO) scheme with the aid of the level set strategy. The results show that the large AP particle diameter has a great effect on the combustion surface deformation and on the burning rate as well. Moreover, the effect of various parameters on the surface propagation speed, flame structure, and the burning surface geometry is obtained. [Abdelkarim Morsy Hegab, Hani HussainSait, Ahmad Hussain, Ashraf Balabel, Mahdi Almutawa, Fathi Djouider. Modeling Of Microscale Solid Rocket Composite Propellant. Life Sci J 2013; 10(4): 523-531]. (ISSN:10978135). http://www.lifescienceite. com. 68
International Journal of Instruction

This study aims to explore the relationships between cognitive flexibility (CF), perceived quality of faculty life (PQFL), learning approaches (LA) and academic achievement (AA). This correlational comparison study was conducted with 1573 undergraduates at Ankara University. The data collection tools were “Cognitive Flexibility Scale (CFS)”, “Quality of Faculty Life Scale (QFLS)” and “Approaches to Learning Questionnaire (ALQ)”. Grade point average (GPA) was used as a measurement of AA. The three subscales of QFLS; satisfaction from faculty (SF), faculty members (SFM), and school climate and student relationships (SSCSR) were found positively correlated with deep approach to learning (DAL), CF and AA, and negatively correlated with surface approach to learning (SAL). DAL was also found positively correlated with CF and AA, but negatively correlated with SAL. CF and AA were positively correlated with all variables, except SAL. Although CF showed a positive correlation with AA, it assumed a negative explanatory role for AA when it was included in the model as a mediating variable. The regression estimates in the path analysis model revealed that DAL, SAL and SF were positive explanatory variables for AA, whereas SSCSR was a negative explanatory variable for AA and SAL was a negative explanatory variable for CF.
Speech and Attitude Predicates and their Subjects

In this paper, we examine the distributional differences of a relatively narrow class of “proposition embedding” predicates, those that fit into the sentential complement schema in (1), excluding predicates that only take expletive subjects. In particular, we are concerned with answering why a predicate like claim is acceptable with an animate subject like critic but not with an inanimate like notepad, as exemplified in (2).
Investing in Stock IPOs with Sentiment Analysis from Twitter optimized by Genetic Algorithms

— This research looks at the predictive capability of social networks, e.g. Twitter, focusing on the financial community and the stock market. The goal of this study is to predict the stock price of companies that have offered IPOs. This is performed through the analysis of Twitter messages and the differences between the sentiment analyses of 8 algorithms applied to study Twitter sentences regarding the financial market. The above referred algorithms differ in the way tweets are selected, the community is chosen and events are detected through Twitter. In order to optimize results a genetic algorithm solution is considered with the purpose of determining the weights of the applied indicators to each sentiment algorithm. The proposed approach, lead to returns of 59% during the first negotiation days, and of 72% a week after.
Uso de la estimulación de la médula espinal en el tratamiento del dolor neuropático pies

Objective: In cases of complex regional pain syndrome where conservative treatment is unsuccessful in controlling neuropathic foot pain spinal cord stimulation may be considered. To our knowledge there have been no such cases reported in the foot & ankle literature. The aim of the study was to establish useful information that may supplement our understanding of this complex multifactorial problem and help to inform future management of similar cases. Methods: A pilot observational case series study was undertaken to investigate the use of spinal cord stimulation in the management of neuropathic foot pain using five cases with complex regional pain syndrome (type I). Results: Reduced pain following spinal cord stimulation was reported. The interval between diagnosis and commencement of spinal cord stimulation was variable between cases and maybe responsible for differing levels and timing of pain relief experienced. Conclusion: Careful preoperative diagnosis, robust patient selection and close postoperative monitoring are vital for a successful outcome. The small sample size and potential for bias, limit the generalizability to a larger population. A larger study is therefore indicated to expand upon preliminary findings.
Towards De-Anonymization of Google Play Search Rank Fraud

Search rank fraud, the fraudulent promotion of products hosted on peer-review sites, is driven by expert workers recruited online, often from crowdsourcing sites. In this paper we introduce the fraud de-anonymization problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in peer-review sites. We collect and study data from crowdsourced search rank fraud jobs, and survey the capabilities and behaviors of 58 search rank fraud workers recruited from 6 crowdsourcing sites. We collect a gold standard dataset of Google Play user accounts attributed to 23 crowdsourced workers and analyze their fraudulent behaviors in the wild. We propose Dolos, a fraud de-anonymization system that leverages traits and behaviors we extract from our studies, to attribute detected fraud to crowdsourcing site workers, thus to real identities and bank accounts. We introduce MCDense, a min-cut dense component detection algorithm to uncover groups of user accounts controlled by different workers, and use stylometry and supervised learning to attribute them to crowdsourcing site profiles. Dolos correctly identified the owners of 95 percent of fraud worker-controlled communities, and uncovered fraud workers who promoted as many as 97.5 percent of fraud apps we collected from Google Play. When evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6 months, Dolos identified 1,056 apps with suspicious reviewer groups. We report orthogonal evidence of their fraud, including fraud duplicates and fraud re-posts. Dolos significantly outperformed adapted dense subgraph detection and loopy belief propagation competitors, on two new coverage scores that measure the quality of detected community partitions.
What can we learn by computing 13 C a chemical shifts for X-ray protein models ?

# 2009 International Union of Crystallography Printed in Singapore – all rights reserved The room-temperature X-ray structures of ubiquitin (PDB code 1ubq) and of the RNA-binding domain of nonstructural protein 1 of influenza A virus (PDB code 1ail) solved at 1.8 and 1.9 Å resolution, respectively, were used to investigate whether a set of conformations rather than a single X-ray structure provides better agreement with both the X-ray data and the observed C chemical shifts in solution. For this purpose, a set of new conformations for each of these proteins was generated by fitting them to the experimental X-ray data deposited in the PDB. For each of the generated structures, which show R and Rfree factors similar to those of the deposited X-ray structure, the C chemical shifts of all residues in the sequence were computed at the DFT level of theory. The sets of conformations were then evaluated by their ability to reproduce the observed C chemical shifts by using the conformational average root-mean-square-deviation (car.m.s.d.). For ubiquitin, the computed set of conformations is a better representation of the observed C chemical shifts in terms of the ca-r.m.s.d. than a single X-ray-derived structure. However, for the RNA-binding domain of nonstructural protein 1 of influenza A virus, consideration of an ensemble of conformations does not improve the agreement with the observed C chemical shifts. Whether an ensemble of conformations rather than any single structure is a more accurate representation of a protein structure in the crystal as well as of the observed C chemical shifts is determined by the dispersion of coordinates, in terms of the all-atom r.m.s.d. among the generated models; these generated models satisfy the experimental X-ray data with accuracy as good as the PDB structure. Therefore, generation of an ensemble is a necessary step to determine whether or not a single structure is sufficient for an accurate representation of both experimental X-ray data and observed C chemical shifts in solution. Received 3 August 2008 Accepted 31 March 2009
Cloud-based Implementation and Validation of a Predictive Fire Risk Indication Model

The high representation of wooden houses in Norwegian cities combined with periods of dry and cold climate during the winter time often results in a high risk of severe fires. This makes it important for public authorities and fire departments to have an accurate estimate of the current fire risk in order to take proper precautions. We report on the implementation of a predictive mathematical model based on first order principles which exploits cloud-provided measurements from weather stations and weather forecasts from the Norwegian Meteorological Institute to predict the current and future fire risk at a given geographical location. We have experimentally validated the model during the winter 2018-2019 at selected geographical locations, and by considering weather data from the time of several historical fires. Our results show that our cloud and web-based implementation is both time and storage efficient, and capable of being able to accurately predict the fire risk measured in terms of the estimated time to flashover. The paper demonstrates that our methodology in the near future may become a valuable risk predicting tool for Norwegian fire brigades.
Firefighter Indoor Navigation using Distributed SLAM (FINDS)

In December of 1999, a fire broke out in a cold storage warehouse in Worcester, MA. A combination of poor building construction and faulty floor plans led to two firefighters becoming lost, and subsequently running out of air. Additional teams were sent in to rescue the two firefighters. By the end, six firefighters lost their lives. The need was established to supply the Incident Commander with the current location of each firefighter in order to both communicate directions to firefighters and direct rescue teams to the location of a downed firefighter.
Cache Oblivious Algorithms

This project presents cache-optimal algorithms for Large Integer Multiplication, Floyd All Pair Shortest Path, and Longest Common Sequence. These algorithms are cache oblivious: no variables dependent on hardware parameters, such as cache size and cache-line length, need to be turned to achieve optimality. For a cache with size Z and cache-line length L where Z = (cid:10)( L 2 ) the number of cache misses for two large integers (of lengths m and n respectively) multiplication is Q ( m; n ) = (cid:2)( mL + nL + m + n L
Structural Optimization of a Landing Gear

This dissertation consists on the process of optimizing the landing gear of an existing Unmanned Aerial Vehicle (UAV). For this purpose, a composite optimization was performed with the main objective of reducing the weight of the structure while maintaining its structural integrity. The studies were carried out using a finite element structural analysis software coupled with an appropriate optimization algorithm. Regarding the Finite Element Analysis, the structure was subjected to a load during the most critical flight phase, the landing. In this phase, the landing gear supports the weight corresponding to the rest of the aircraft structure. In order to write this thesis, a bibliographic research was conducted to better understand the behaviour of composite materials. Also, a research about the optimization method called Direct MultiSearch (DMS) was made. Secondly, it was necessary to do an initial model of the Landing Gear (LG) with the following steps: choosing the adequate mesh and element types; modelling the initial state of the material composites around the carcass of the LG; modelling the drop and shock tests that correspond to the landing phase; to divide posteriorly, the optimization process into smaller problems with each problem gradually increasing the design space. Finally, a comparative analysis was done between the final results and the starting point: the primary goal, which was the reduction of the weight of the structure, was achieved. Additionally, it was verified that the structural integrity of the LG was maintained.
Untriviality of Trivial Packages

Nowadays, developing software would be unthinkable without the use of third-party packages. Although such code reuse helps to achieve rapid continuous delivery of software to end-users, blindly reusing code has its pitfalls. Prior work investigated the rationale for using packages of small size, known as trivial packages, that implement simple functionality. This prior work showed that, although these trivial packages are simple, they are popular and prevalent in the \npm ecosystem. This popularity and prevalence of trivial packages piqued our interest in questioning; first, the `triviality' of these packages and, second, the impact of using these packages on the quality of the client software applications. To better understand the `triviality' of trivial packages and their impact, in this thesis we report on two large scale empirical studies. In both studies, we mine a large set of JavaScript applications that use trivial \npm packages. In the first study, we evaluate the `triviality' of these packages from two complementary points of view: based on application usage and ecosystem usage. Our result shows that trivial packages are being used in important JavaScript files, by the means of their `centrality', in software applications. Additionally, by analyzing all external package API calls in these JavaScript files, we find that a high percentage of these API calls are attributed to trivial packages. Therefore, these packages play a significant role in JavaScript files. Furthermore, in the package dependency network, we observe that 16.8% packages are trivial and in some cases removing a trivial package can break approximately 30% of the packages in the ecosystem. In the second study, we started by understanding the circumstances which incorporate trivial packages in software applications. We analyze and classify commits that introduce trivial packages into software applications. We notice that developers resort to trivial packages while performing a wild range of development tasks that are mostly related to `Building' and `Refactoring'. We empirically evaluate the bugginess of the files and applications that use trivial packages. Our result shows that JavaScript files and applications that use trivial packages tend to have a higher percentage of bug-fixing commits than files and applications that do not depend on trivial packages. Overall, the findings of our thesis indicate that although smaller in size and complexity, trivial packages are highly depended on packages. These packages may be trivial by the means of size, their utility in software applications suggests that their role is not so trivial.
View Relevancy for Model-based Pose Estimation in Single Photos

Given a photo of an object, and a 3D representation of that object, the pose estimation problem is determining the object’s transformation (rotation and translation). We present a method for model-based pose estimation of rigid objects in a single casual photo. Having a 3D CG model of the subject object in the photo allows reducing the 3D-2D matching problem to a series of conventional image to image matching operations based on image-space descriptors. However, treating all views of the CG model alike, independent of the given model and input photo, often fails. We argue that not all views contribute equally to the task and thus develop a view relevancy measure. First, for each CG view and a calibrated training photo of the same model, our method computes a series of measurements – a feature vector that captures characteristic properties of the match. We learn a model that predicts the reliability and relevancy of each view based on these feature vectors. Then, given a new query photo, and a new CG model, the pose estimation based on matches to each generated CG view is evaluated via the learned model to select the best pose estimate. To evaluate our method we have assembled two challenging data sets of 3D CG models of cars and building combined with uncontrolled web images. We demonstrate empirical results showing that our pose estimation improves existing methods.