Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Associated with Treatment Success among Pulmonary Tuberculosis and HIV Co-infected Patients in Oyo State, South West-Nigeria

Background The co-existence of Tuberculosis (TB) and Human immunodeficiency Virus (HIV) is known to increase morbidity and mortality in patients. The determinants of treatment success in TBHIV co-infection are not yet well studied. Such information can help optimise treatment and reduce morbidity and mortality. Objective To determine factors associated with antitubercular treatment success among TB I HIV coinfected patients. Methods A cross sectional study was carried out in fifty three DOT clinics and treatment centres using tuberculosis patient's records from January 2009 to December 2010 in Oyo state, Nigeria. The study population consisted of 7905 tuberculosis patients. Information on variables of interest were obtained with the use of data extraction forms. Chi-square and logistic regression were used to test the relationship between TB I HIV coinfection and socio-demographic variables, clinical characteristics and treatment success. Results Prevalence ofTB I HIV co-infection was found to be 14.2%. Patients with TB-HIV co-infection were younger and more likely to be females. There were statistically significant association between treatment success and gender, marital status and patient point of care. After adjusting for other variables, it was found that patients receiving treatment in private facilities were independently less likely to be successfully treated compared with those receiving care in Public facilities. Female patients were also independently more likely to have better treatment outcome than male. Conclusion In addition to patients' point of care, gender of the patients can adversely impact on their treatment success. Efforts from the government to strengthening the private public mix, health education and media awareness on adherence to treatment to improve treatment success should be intensified in the country. Keywords: Tube rc ulosis; Human immunodeficiency virus, Directly observe therapy short-course; Treatment success, Coinfection.
VirtualStack: Green High Performance Network Protocol Processing Leveraging FPGAs

In times of cloud services and IoT, network protocol processing is a big part of the CPU utilization today. Foong et al. proposed the rule of thumb for TCP, that a single-core CPU needs about 1 Hz clock frequency to produce 1 bit/s worth of TCP data packets. Unfortunately, CPU speed has stagnated around 5 GHz in recent years resulting in a upper limit of 5 GBit/s throughput with single-threaded network processing. Further, CPUs featuring such high clock rates (e.g., Intel Core i7-8086K) have rated TDP around 95 W, resulting in very high power consumption for high throughput situations. Meanwhile, industry offers some hardware acceleration for TCP as part of their server network cards, to relief the server CPUs and increase the energy efficiency. However this is just a small support as state and management still needs the CPU of the host system. In this paper, we present an approach based on field pro- grammable gate arrays (FPGA) to not only free up CPU cycles but provide a scaleable and energy efficient concept to fully utilize high-speed network interfaces, while maintaining the flexibility of software solutions. For our evaluation, we utilized the NetFPGA Sume, proofing to achieve the linerate of connected SFP+ ports while power consumption stays below 6 W. By leveraging network protocol virtualization, the hardware acceleration approach is not only deployable but stays flexible enough to adapt new networking paradigms quickly.
Morphological active contour without edge-based model for real-time and non-rigid uterine fibroid tracking in HIFU treatment

High-intensity focused ultrasound (HIFU) therapy represents an image-guided and non-invasive surgical approach to treat uterine fibroid. During the HIFU operation, it is challenging to obtain the real-time and accurate lesion contour automatically in ultrasound (US) video. The current intraoperative image processing is completed manually or semi-automatic. In this Letter, the authors propose a morphological active contour without an edge-based model to obtain accurate real-time and non-rigid US lesion contour. Firstly, a targeted image pre-processing procedure is applied to reduce the influence of inadequate image quality. Then, an improved morphological contour detection method with a customised morphological kernel is harnessed to solve the low signal-to-noise ratio of HIFU US images and obtain an accurate non-rigid lesion contour. A more reasonable lesion tracking procedure is proposed to improve tracking accuracy especially in the case of large displacement and incomplete lesion area. The entire framework is accelerated by the GPU to achieve a high frame rate. Finally, a non-rigid, real-time and accurate lesion contouring for intraoperative US video is provided to the doctor. The proposed procedure could reach a speed of more than 30 frames per second in general computer and a Dice similarity coefficient of 90.67% and Intersection over Union of 90.14%.
Face Validation Based Anomaly Detection Using Variational Autoencoder

In unconstrained facial images, large visual variations concerning pose, scale, the presence of occlusions, expressions and lighting usually cause difficulties in discriminating faces from the background accurately. As a result, some non-face regions are recognized as faces (false positive) and that influences the effectiveness of face detection algorithms which is characterized by low false positive (FP) rate, high detection rate and high speed of processing. In order to reduce these non-face regions, they are considered as anomalies and then try to detect them. In this paper, we propose an anomaly detection method using reconstruction error from variational autoencoder (VAE), which is a generative machine learning model. We train VAE to learn reconstructing faces that are close to its original input faces using FDDB dataset, then the difference between the original input face and the reconstructed output is measured to obtain the reconstruction error which can be used as an anomaly score. Consequently, the regions resulting from faces detection algorithm with high reconstruction error are defined as anomalies or false positives.
Aná́lise do Mecanismo de Incentivo Prefeitura em Locais do Foursquare-Swarm

Este trabalho apresenta um estudo sobre o mecanismo de incentivo prefei- tura utilizado no Foursquare-Swarm. Prefeito e ́ o usua ́rio que mais realizou check- ins1 nos u ́ltimos trinta dias em um determinado local. Atrave ́s de um monitoramento automa ́tico temporal, estudamos como ocorrem as alternaˆncias e disputa de prefeitos nos locais. Foram coletados dados de centenas de locais em Curitiba (Brasil) e Chi- cago (Estados Unidos). Identificamos, por exemplo, que grandes redes americanas de alimentac ̧a ̃o podem influenciar mais significativamente a disputa por prefeitura em Curitiba. Este estudo fornece um melhor entendimento do mecanismo de incen- tivo prefeitura, ajudando a determinar locais em que o mesmo e ́ vantajoso. Isso pode auxiliar no aprimoramento do engajamento de usua ́rios em sistemas da web social.
Detecting Malicious Behavior in OpenWrt with QEMU Tracing

Porter, Jeremy. M.S.C.S. , Department of Computer Science and Engineering, Wright State University, 2019. Detecting Malicious Behavior in OpenWrt with QEMU Tracing. In recent years embedded devices have become more ubiquitous than ever before and are expected to continue this trend. Embedded devices typically have a singular or more focused purpose, a smaller footprint, and often interact with the physical world. Some examples include routers, wearable heart rate monitors, and thermometers. These devices are excellent at providing real time data or completing a specific task quickly, but they lack many features that make security issues more obvious. Generally, Embedded devices are not easily secured. Malware or rootkits in the firmware of an embedded system are difficult to detect because embedded devices do not have the usual human interfaces such as a keyboard, video, or a mouse. Traditional rootkits typically come in three variants: binary, library, and kernel. Binary rootkits aim to replace a binary file in the operating system such as ls (list files) or cd (change directory). Library rootkits replace system libraries with malicious code that can intercept system calls and provide incomplete or false information as it is relayed between user and kernel spaces. Kernel rootkits hook directly into the kernel and provide false or incomplete information to system calls. Kernel rootkits are often loadable kernel modules (LKM) that can be installed at run time. Typically, countermeasures and detection methods require specific security hardware tools or scanning the system in a traditional way with some interactive inputs/outputs provided to an end user or security researcher. These methods don’t work well with embedded devices that lack additional security hardware and a keyboard, video, or mouse to display or interact. A more tailored and focused approach is required for embedded devices. This thesis takes a step toward building a framework for embedded device security auditing. The first component of this framework is a malicious router, the second component is QEMU used to trace the execution of the malicious router. An example
ALLSAT compressed with wildcards: From CNF's to orthogonal DNF's by imposing the clauses one by one

We present a novel technique for converting a Boolean CNF into an orthogonal DNF, aka exclusive sum of products. Our method (which will be pitted against a hardwired command from Mathematica) zooms in on the models of the CNF by imposing its clauses one by one. Clausal Imposition invites parallelization, and wildcards beyond the common don't-care symbol compress the output. The method is most efficient for few but large clauses. Generalizing clauses one can in fact impose superclauses. By definition, superclauses are obtained from clauses by substituting each positive litereal by an arbitrary conjunction of positive literals.
Two Stream Auto-encoder Decoder Network for Kidney and Tumor Segmentation

In this competition, we apply two-streams auto-encoder de-coder structure for learning kidney and kidneys tumor segmentation. To do so, ﬁrst, we extract axial layers of the tissues along with their segmentation mask from the 3D volume. These axial layers are then clipped using Hanford distance between +512 to -512 to eliminate non-object of interest. These axial layers are then normalized to form the 2D grayscale images. For each of these normalized images, we generate kidney and kidney tumor masks to train two-stream deep networks. The two-streams deep model learns kidney and tumor masks separately and they generate ﬁnal mask by concatenating the generated masks. We utilize BCDU-net (extended version of U-Net model) as a deep auto-encoder decoder model for segmentation. We utilize 70% of the Kits19 as the training set and the rest of data as the validation set. Experimental results demonstrate that the proposed structure achieves state-of-the-art performance in the segmentation of kidney and tumor region.
Loading… loading… The influence of download time on information search

When browsing online, there is considerable variation in the amount of time that one has to wait for content to appear once the link to that content has been activated (i.e., clicked). In two experiments we examined how ‘download time’–a potential barrier to information access–influences search behaviour. In both experiments, participants completed a video-watching task in which they were presented with a screen containing six clickable icons, each of which represented a unique video. When participants clicked an icon, a video would begin to load and then play. The participants’ task was to gain as much information from the videos as possible for a later memory test. Critically, however, the ‘download time’ (i.e., the time between the click on the icon and the video beginning to play) of the available videos in a given search session varied. In Experiment 1, these download times were 0 (instant), 2, or 30 seconds, and in Experiment 2, they were 5, 15, or 30 seconds. In general, we found that participants terminated and avoided videos with longer download times than videos with shorter download times. Interestingly, this effect was attenuated when the experienced download times were more similar to each other (Experiment 2) than when they were more different from each other (Experiment 1).
Computational Shedding in Stream Computing

Stream Computing, a generic on-line data processing paradigm has emerged as the preferred approach in the processing of continuous data streams. Data streams suffer from a bursty characteristic where the data rate of the stream can spike temporarily up to orders of magnitude greater than normal expected levels. As such, producing timely application results is difficult as queues fill. The classic response to these temporary scenarios is to shed input data. However, Load Shedding (LS) impacts negatively on application output accuracy as relevant data is discarded before it is processed. Further, LS rates tend to be proportional to the input rate of the stream, as such high data rates can lead to high data loss during overload events. For many classes of applications, this can have a particularly negative impact on the quality of the output result, given that data is simply not processed before it is shed. This thesis presents a new approach, Computational Shedding (CS), to the problem of maintaining application result accuracy while attempting to forgo input data loss during transient busty data events. Rather shedding input data within the stream, we propose to adapt the application and shed tasks or subtasks of the executing application temporarily, to reduce message process costs in the stream. As such, this mechanism provides for an opportunity to temporarily increase processing capacity thereby forgoing the need for deliberate data loss during a bursty data event. We have evaluated this approach against traditional LS techniques in a number of ways, such as in terms of output application accuracy and application processing duration. In experimentation, we have found in applicable applications, subtasks can be discarded for a time. Remaining subtasks can continue to produce a valid imprecise result. CS was compared to LS alternatives which simply do not process any discarded data. We show that through the results of our evaluation, CS leads to more timely and accurate results when compared to LS alternatives.
Language/Dialect Recognition based on Unsupervised Deep Learning

: Over assume a significant job in our everyday life. Feelings are the normal physiological reaction of the human body which can be perceived by the outward appearance. In the proposed framework research has been done in the field of Human PC Cooperation (HCI). The whole task is separated into three noteworthy advances for example Pre preparing, highlight extraction and characterization. In the primary stage discourse recognition has been finished utilizing Vokaturi calculation. The framework identifies and crops the lip district for further orders, and afterward the highlights are removed into vectorized structure. Removed highlights are contrasted and prepared database utilizing Strategic Relapse.
The Effect of the ADASYN Method on Widespread Metrics of Machine Learning Efficiency

The article presents the results of experimental work comparing the performance metrics of machine learning algorithms on imbalanced text corpora using the method of synthetic data generation ADASYN and without it. The work was carried out on an imbalanced corpus, consisting of 5,211 news texts, formed by cluster sampling for one year. The corpus annotation is produced according to the indicators of the tonality of texts by categories: neutral, positive, and negative, with a significant predominance of articles of neutral tone. There are many widely used methods to overcome the problem of data imbalance. Often, when working with imbalanced data, the resulting accuracy provides acceptable results, but other performance indicators are low. Such contradictory results usually occur when it comes to in-depth analysis of the text in the study of social or medical phenomena. This paper shows how performance metrics of the same machine learning algorithms change when using the ADASYN method while analyzing an imbalanced text corpus using the K-nearest neighbors’ method and Naive Bayes. The study considers the issue of the application of the method and its results in solving the problem of text classification. Comparative characteristics of the machine algorithms operation before and after the application of ADASYN provide a researcher with a better understanding of which machine learning performance metrics are more suitable when working with imbalanced data. As a result, authors present the observations and conclusions about the features of the method and put forward a number of proposals for further research in this area to compare the results obtained with the effects of the application of another method.
Basic concepts in the theoretical and practical field of strategic cost management

Strategic cost management as a type of activity is in the process of its estab-lishment and manifests itself, first of all, in an abundance of models as local processes, such as the development of "global" strategies. An analysis of enterprises activity in various activity areas proves that, despite the abundance of publications on strategic management, it is rare that an enterprise can not demonstrate the organizational structure of the strategic management. On the contrary, the number of unprofitable enterprises demonstrates that there are serious problems in the organization of the cost management system. The enterprise management becomes more complicated under the conditions of a dy-namic complex and uncertain market environment, and this in turn leads to qualitative changes in structure and methods of management. A cost management at the enterprise sets such tasks as identifying the role of cost management as a factor in increas-ing economic performance, determining costs for the main stages of the product's economic cycle, management functions at the enterprise, calculating costs for operating and geographical segments, production units of the enterprise, calculating the necessary costs per unit of output (works, services), preparation of an information base that allows estimating costs in the selection and adoption of business solutions.
Secure Cooperative Transmission in Rateless-Coded Environment Using TAS and Artificial Noise

In this paper, we analyse a secure transmission scheme using rateless codes (RC) in a delay constrained system. By using RC, secrecy is achieved if the legitimate receiver can accumulate required number of packets before an eavesdropper does. To achieve this, we employ transmit antenna selection (TAS) at the base station to improve the main channel and, artificial noise (AN) is generated by the use of a cooperative jammer to degrade the eavesdropper's channel. Quality of violation probability (QVP) which consists of both the delay violation probability and the intercept probability is used as a performance metric. A closed-form expression for QVP is derived. We show that using TAS for reducing QVP is effective only at lower signal to noise ratio (SNR). We also observe that on increasing number of antennas at the source (<inline-formula><tex-math notation="LaTeX">$N_{A}$</tex-math></inline-formula>) while using TAS along with AN, QVP decreases for delay sensitive services, whereas, in case of delay tolerant applications, increasing <inline-formula><tex-math notation="LaTeX">$N_{A}$</tex-math></inline-formula> is effective at low SNR.
Wideband power amplifier based on Wilkinson power divider for s-band satellite communications

This paper presents design and simulation of wideband power amplifier based on multi-section Wilkinson power divider. Class-A topology and ATF-511P8 transistor have been used. Advanced Design System (ADS) software used to simulate the designed power amplifier. The simulation results show an input return loss (S11) 10 dB over the entire bandwidth, and an output power around 28dBm at the Centre frequency of 3GHz. The designed amplifier is stable over the entire bandwidth (K>1). Inter-modulation distortion is -65.187dBc which is less than -50dBc. The designed amplifier can be used for the microwave applications which include weather radar, satellite communication, wireless networking, mobile, and TV.
Investigating Bayesian Optimization for rail network optimization

ABSTRACT Optimizing the operation of rail networks using simulations is an on-going task where heuristic methods such as Genetic Algorithms have been applied. However, these simulations are often expensive to compute and consequently, because the optimization methods require many (typically >104) repeat simulations, the computational cost of optimization is dominated by them. This paper examines Bayesian Optimization and benchmarks it against the Genetic Algorithm method. By applying both methods to test-tasks seeking to maximize passenger satisfaction by optimum resource allocation, it is experimentally determined that a Bayesian Optimization implementation finds ‘good’ solutions in an order of magnitude fewer simulations than a Genetic Algorithm. Similar improvement for real-world problems will allow the predictive power of detailed simulation models to be used for a wider range of network optimization tasks. To the best of the authors’ knowledge, this paper documents the first application of Bayesian Optimization within the field of rail network optimization.
Mechanical Recanalization of Cerebral Artery Embolic Occlusion Using a Self-Expanding Stent: Experimental Analysis in Canine Model

Purpose: To evaluate the feasibility of a self-expanding stent for acute embolic occlusion, and recanalization mechanism by histologic examination. Materials and Methods: Five mongrel dogs were used as study subjects. Each vertebral artery was occluded, and a self-expanding stent was used for recanalization. We evaluated the technical success rate for the placement of the stent to the tar-geted vessel, the recanalization rate, and residual stenosis. We obtained two specimens of the stented vertebral arteries for histologic evaluation. Results: One dog died of an unknown cause during the induction of anesthesia. In two dogs, only one side of the vertebral artery was used, whereas both vertebral arteries were used in the remaining dogs. A total of six vertebral arteries were successfully occluded. The technical success rate for stenting without complication was 66.7%. The immediate recanalization rate after stenting was 100%. The residual stenosis was 35.6 ± 18.6%. On microscopic examination, the stent concentrically displaced the clot and the clot was captured between the stent mesh and arterial wall. Conclusion: Self-expanding stents were effective in revascularizing the cerebrovascular embolic occlusion. The self-expanding stent seemed to achieve recanalization by pushing the clot to the arterial wall and capturing the clot between the stent mesh and arterial wall.