Miracle-dz/UniFAR-FTUs huggingface dataset:
Routing for Crowd Management in Smart Cities: A Deep Reinforcement Learning Perspective

The concept of smart city has been flourishing based on the prosperous development of various advanced technologies: mobile edge computing (MEC), ultra-dense networking, and software defined networking. However, it becomes increasingly complicated to design routing strategies to meet the stringent and ever changing network requirements due to the dynamic distribution of the crowd in different sectors of smart cities. To alleviate the network congestion and balance the network load for supporting smart city services with dramatic disparities, we design a deep-reinforcement- learning-based smart routing algorithm to make the distributed computing and communication infrastructure thoroughly viable while simultaneously satisfying the latency constraints of service requests from the crowd. Besides the proposed algorithm, extensive numerical results are also presented to validate its efficacy.
Study the Topology Effect on a G3-PLC based AMI Network

Smart grid is the concept for next-generation power grid that integrates information and communication technology into power grid. Advanced metering infrastructure (AMI) is an important aspect of smart grid, which requires establishment of a two-way communication system between consumers and utility. In this paper, the effect of network topology on the performance of a PLC-based AMI system is investigated. For this purpose, a narrow-band power line communication simulator in accordance with the G3-PLC standard is proposed that implemented using the OMNeT++ network simulator. It is shown that the topology of the AMI network has significant impact on the collision rate of the data concentrator switch.
A Second-order Finite Difference Method for Option Pricing Under Jump-diffusion Models

We develop a finite difference method to solve partial integro-differential equations which describe the behavior of option prices under jump-diffusion models. With localization to a bounded domain of the spatial variable, these equations are discretized on uniform grid points over a finite domain of time and spatial variables. The proposed method is based on three time levels and leads to linear systems with tridiagonal matrices. In this paper the stability of the proposed method and the second-order convergence rate with respect to a discrete $\ell^{2}$-norm are proved. Numerical results obtained with European put options under the Merton and Kou models show the behaviors of the stability and the second-order convergence rate.
Optimal Selection of DC Microgrid Architecture

In this paper, an approach to decide DC microgrid architecture is presented to obtain the optimal voltage level for the DC microgrid system. An important factor in designing a DC micro grid is its voltage level. Factors like cost of power electronic converters, cables, and protection scheme depends on the voltage level of the system. Losses occurring in the DC distribution, such as in the cables, switching losses etc. are also directly and indirectly governed by the system voltage level. Therefore, optimal voltage level has to be determined for the given loads in the microgrid based on economic factors as well as system performance. A techno-economic evaluation is presented for a given system, which includes system performance evaluation through efficiency and voltage drops calculation and economic evaluation from Present Worth Concept (PWC).
Comparison between random frame error and SINR-based transmission power control in DS-CDMA cellular system from the viewpoint of TCP performance

Many reports have investigated TCP performance over wireless links, where a high and time-invariant frame error rate is assumed for the wireless link characteristic. However, the frame error rate is temporally and geographically changeable by fading and interference in cellular systems. On the other hand, SINR-based transmission power control, which is employed for randomization of the frame errors in DS-CDMA cellular systems, cannot always work properly depending on the control parameters or the channel characteristics. In this paper, we investigate TCP performance over wireless links in a DS-CDMA cellular system by computer simulation. From the simulation results, it has been found that the assumption of random frame error is valid only for a limited part of the TCP performance even in the system with an SINR-based transmission power control scheme.
PUE or GPUE: A Carbon-Aware Metric for Data Centers

Greening Information and Communication Tech-nology (ICT) sector is extensively undertaken globally. Metrics were announced to control ICT impacts. For example, Power Usage Effectiveness (PUE) metric was developed to report the effectiveness of consumed power in the data center. Consequently, researchers and developers produced several prototypes and implementations on green data centers and they claimed the accuracy of their solutions with wrongly understood metrics. In this paper, we review PUE metric and its green extension (GPUE), in addition to a review of green determinants on data centers. Then, we demonstrated the application of PUE and GPUE in a modern and recent green data center (the Parasol data center). Our demonstration showed the impact of Parasol data center, and reveal insights on how to improve the carbon footprints of the data center.
Serotype-independent pneumococcal experimental vaccines that induce cellular as well as humoral immunity

For prevention of Streptococcus pneumoniae (pneumococcus) infections in infancy, protein-conjugated capsular polysaccharide vaccines provide serotype-specific, antibody-mediated immunity but do not cover all of the 90+ capsule serotypes. Therefore, microbiologists have sought protective noncapsular antigens common to all strains. Alternatively, we investigated killed cells of a noncapsulated strain, which expose many such common antigens. Given to mice intranasally, this vaccine elicits antibody-independent, CD4+ T lymphocyte-dependent accelerated clearance of pneumococci of various serotypes from the nasopharynx mediated by the cytokine IL-17A. Such immunity may reproduce the natural resistance that develops in infants before capsular antibodies arise. Given by injection, the killed cell vaccine induces bifunctional immunity: plasma antibodies protective against fatal pneumonia challenge, as well as IL-17A–mediated nasopharyngeal clearance. Human testing of this inexpensive candidate vaccine by intramuscular injection is planned. Bacterial cellular vaccines are complex—a challenge for reproducibility. However, when several known protective antigens were deleted, the killed pneumococcal vaccine was still protective. This antigenic redundancy may prevent vaccine escape variants by recombinational loss, which is frequent in pneumococcus. Biochemically defined immunogens with bifunctional activity have also been devised. These immunogens are three-component conjugates in which cell wall teichoic acid (a common antigen capable of T cell activation) is coupled to a genetic fusion of two common pneumococcal proteins: a protective surface antigen and a derivative of pneumolysin, which provides TLR4 agonist activity and induces antitoxic immunity. Such constructs induce accelerated clearance when given intranasally and induce both immune mechanisms when injected. The defined composition permits analysis of structure-function activity.
Stacked Autoencoders Based Deep Learning Approach for Automatic Epileptic Seizure Detection

Epilepsy is one of the major chronic nervous disorders, which affects the lives of millions of patients per annum globally, because of occurrence of sudden death or major injuries occurred during walk, driving or working in hazardous work environment. Its prognosis through modern technologies is the need of the day, which is attaining worldwide attention in research community with the use of latest technologies like internet of things, machine learning and cloud computing. This paper presents a model of automatic epileptic seizure detection model using Stacked Autoencoders based deep learning approach, which is an advanced form of machine leaning, employed for effectively handling the problem of big data with reduced complexity and processing time and to make this process more real time compatible with least delays. This model processes the sensed EEG signals by breaking it into short duration segments. Then, these EEG segments are fed to Stacked Autoencoders for its classification into different epileptic seizure stages like normal, preictal and ictal. The performance of this model has been compared with other existing models consisting of higher order spectral analysis based feature extraction and classification using traditional machine learning algorithms like Bayes Net, Naïve Bayes, Multilayer Perceptron, Radial basis function neural networks and C4.5 decision tree classifier. The analysis of performance through simulation results reveal that Stacked Autoencoders based deep learning approach is an efficient model for real time automatic epileptic seizures detection at early stage with classification accuracy 88.8%, sensitivity 89.44%, specificity 93.77% values and least value of processing time, which is approximately 23 times lesser than that of models utilizing traditional higher order statistics feature extraction and machine learning based classification approaches.
A Topic-Agnostic Approach for Identifying Fake News Pages

Fake news and misinformation have been increasingly used to manipulate popular opinion and influence political processes. To better understand fake news, how they are propagated, and how to counter their effect, it is necessary to first identify them. Recently, approaches have been proposed to automatically classify articles as fake based on their content. An important challenge for these approaches comes from the dynamic nature of news: as new political events are covered, topics and discourse constantly change and thus, a classifier trained using content from articles published at a given time is likely to become ineffective in the future. To address this challenge, we propose a topic-agnostic (TAG) classification strategy that uses linguistic and web-markup features to identify fake news pages. We report experimental results using multiple data sets which show that our approach attains high accuracy in the identification of fake news, even as topics evolve over time.
An Algorithm for Computing Efficiently in Cloud Based Data Centers

Cloud computing has shifted the paradigm when it comes to handling our data. We are no more restricted to our standalone systems and can now access our data on the go. All the big companies like Google, Amazon et al have shifted their business on the cloud and are now offering their customers a new lease of life. However, with this ease come several challenges which have drawn the attention of people be it cryptographers, scientists or from business fraternity round the globe. Two main issues that one can relate to cloud computing is security and energy consumption. The aim of this paper is to address the energy concern in cloud data center. It would not be an aberration to say that cloud computing is at odds with the interests of the company when it comes to the financial aspect. If we go by stats, nearly 5% of the world’s total energy is being consumed by these data centers as their energy requirements are huge owing to services like PaaS, Iaas, SaaS.In this paper, an algorithm is proposed for minimizing the energy consumption of a typical data center catering to the need of cloud users. By modeling the incoming workload of a data center based on this approach, it can be proved that considerable savings in energy can be achieved
Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features

Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using uncurated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available. To that effect, we propose a new unsupervised approach which leverages self-supervision and clustering to capture complementary statistics from large-scale data. We validate our approach on 96 million images from YFCC100M, achieving state-of-the-art results among unsupervised methods on standard benchmarks, which confirms the potential of unsupervised learning when only uncurated data are available. We also show that pre-training a supervised VGG-16 with our method achieves 74.6% top-1 accuracy on the validation set of ImageNet classification, which is an improvement of +0.7% over the same network trained from scratch.
Percutaneous tumor curettage and interstitial delivery of samarium-153 coupled with kyphoplasty for treatment of vertebral metastases.

OBJECT The object of this study was to investigate the use of a minimally invasive technique for treating metastatic tumors of the vertebral body, aimed at relieving pain, preventing further tumor growth, and minimizing the adverse effects of systemic use of samarium-153 ((153)Sm). METHODS The procedure is performed in the same fashion as a kyphoplasty, using a unilateral extrapedicular approach under local anesthesia/mild general sedation, with the patient in the lateral decubitus position. The tumor is accessed as in a standard kyphoplasty. The side is chosen according to the location of the metastasis. Prior to inflation of the balloon the tumor is debulked by percutaneous curettage. Balloon inflation is carried out as per standard kyphoplasty in an attempt to create a larger space and reduce a possible kyphotic deformity. Three mCi of (153)Sm-EDTMP (ethylenediaminetetramethylenephosphonic acid) is then mixed with bone cement (polymethylmethacrylate) and injected into the void created by the balloon tamp. RESULTS Twenty-four procedures were performed in 19 patients. There was reliable and reproducible delivery of the radiolabeled (153)Sm-EDTMP to the metastatic site, without spillage. The procedure was safe. There were no procedure-related complications. There was no hematological toxicity with the low doses of (153)Sm used. Pain improved in all patients. The long-term results related to tumor control continue to be investigated. CONCLUSIONS Combined percutaneous debulking of confined vertebral metastases and administration of local (153)Sm is feasible and safe. Furthermore, this technique leads to immediate relief of cancer-related pain and may help prevent or slow down the progression of vertebral metastatic tumors.
THE DISTINCT EFFECTS OF FK506 ON THE ACTIVATION, PROLIFERATION, AND DIFFERENTIATION OF HUMAN B LYMPHOCYTES

We examined the effect of FK506 on the activation, proliferation and differentiation of human B lymphocytes in vitro. FK506 inhibited the proliferative response of resting B cells induced by Staphylococcus aureua Cowan strain I (SAC) and phorbol myristate acetate (PMA) in a dose-dependent manner. Inhibition of cell proliferation by FK506 was caused by a selective block of GO to G1 phase transition leading to cell arrest. In addition, the proliferative response of in vivo–activated B cells and lymphokine-driven B cell proliferation were also found to be sensitive to FK506. Interestingly, FK506 did not affect the expression of activation antigens such as CD23, IL-2 receptor (CD25), and transferrin receptor (CD71). Finally, FK506 had little effect on B cell antibody generation in a T cell-independent system. Conversely, FK506 suppressed neither proliferation nor immunoglobulin secretion in a human B lymphoblastoid cell line. These results indicate that FK506 has discrete effects on the different stages of the B cell maturation.
Comparative Analysis of Multi-label Classification Algorithms

Multi-label classification has generated enthusiasm in many fields over the last few years. It allows the classifications of dataset where each instance can be associated with one or more label. It has successfully ended up being superiorstrategy as compared to Single labelclassification. In this paper, we provide an overview of multi-label classification approaches. We also discussed the various tools thatutilizes MLC approaches. Lastly, we have presented an experimental study to compare different algorithms of multi-label classification. After applying and studying the accuracies of various multilabel classification techniques, we have found that performance of Random Forest is better than the rest of the other compared multilabelclassification algorithms with 96% accuracy.
Implementing Participative Management

This article tells the story of an attempt to transform the management of Extrusion Company from a traditional to a participative form. It is based on a longitudinal study of this endeavor. The chief executive officer initiated the attempt in a way that created commitment but that led later to disappointment and frustration and, still later, to more relaxed expectations. After more than 2 years, however, the outcome of this transformation endeavor remains undecided. The article analyzes what happened and suggests the term instrumental change management to signify the insufficient attention that was paid to the change process, which seems in turn to account for the lack of change momentum. It further suggests that this term indicates the importance of a leadership dimension as well as a management dimension if organizational transformation is to occur.
Total duration of apnea and hypopnea events and average desaturation show significant variation in patients with a similar apnea–hypopnea index

Obstructive sleep apnea (OSA) is commonly diagnosed based on the apnea-hypopnea index (AHI). Presently, novel indices were introduced for sleep apnea severity: total duration of sleep apnea and hypopnea events (TAHD%) and a combined index including duration and severity of the events (TAHD% × average desaturation). Two hundred and sixty-seven subjects were divided based on their AHI into four categories (normal, mild, moderate, severe OSA). In the most severe cases TAHD% exceeded 70% of the recorded time. This is important as excessive TAHD% may increase mortality and cerebro-vascular complications. Moreover, simultaneous increase in duration and frequency of apnea and hypopnea events leads to a paradoxical situation where AHI cannot increase along severity of the disease. Importantly, the combined index including duration and severity of the events showed significant variation between patients with similar apnea-hypopnea indices. To conclude, the present results suggest that the novel parameters could give supplementary information to AHI when diagnosing the severity of OSA.
A Low-Power CMOS Implementation of Programmable CNN ’ s with Embedded

In this brief, an analog CMOS implementation of a Cellular Neural Network (CNN) is presented, which is based on a combination of MOS transistors operating in different modes: weak and stronginversion and MOS transistors operated in the lateral bipolar mode. This combination has enabled a VLSI implementation of a simplified version of the original CNN model with the main characteristics of low-power consumption, programmability, and embedded photosensors to process images directly projected on the chip. An 8 8-cell CNN chip prototype is reported with experimental results for different image processing tasks. A density of 10.7 cells/mm in a 1.2m CMOS technology and a power consumption of tens of microwatts per cell are obtained.
Voltage Balancing Control of Three-Level Active NPC Converter Using SHE-PWM

This paper presents a neutral point voltage control strategy for the three-level active neutral point clamped (ANPC) converter using selective harmonic elimination pulsewidth modulation (SHE-PWM). The control strategy introduces a small change in the switching angles, which varies the duty cycle of the zero-level switching state, controls the neutral point current and, therefore, the neutral point voltage. The decision to vary a given switching angle takes into account the polarity of the output currents and the voltage across the lower dc-link capacitor. The effectiveness of the proposed strategy is verified through extensive simulation studies and experimentally validated using a low power laboratory prototype.
Channel and Doppler Estimation In Underwater Communication

Underwater channels being highly unpredictable and rapidly time varying, are recognized as one of the most difficult communication media to deal with. Compared to mobile radio channels, underwater channels suffer severe multipath propagation and significant Doppler shift. In order to improve communication quality, the time varying channels should be estimated at regular intervals. The sparsity of multipath channel motivates to exploit compressive sensing methods for channel estimation. The proposed work considers the simulation of OFDM communication system with QAM modulation and channel estimation is performed using both conventional and compressive sensing methods. The work explores the possibilities of using VirTEX software package for a realistic channel modelling.
A 32‐bp deletion (2991del32) in the cystic fibrosis gene associated with CFTR mRNA reduction

Cystic fibrosis, a common recessive disorder of exocrine glands, is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. We describe the identification of a 32‐bp deletion within the coding region of CFTR that involves the nucleotides 2991–3022 in exon 15 (2991del32). This unusual frameshift mutation was confirmed in three unrelated German families, accounting for a frequency of 0.3% in 1,028 CF chromosomes. AH identified patients are compound heterozygotes for 2991del32 and for the most frequent cystic fibrosis mutation, ΔF508. The evaluation of clinical data revealed typical symptoms of cystic fibrosis, including pancreatic insufficiency, in all three index cases. To characterize further the mutation in the CFTR transcript, we analysed RNA from lymphocytes by reverse transcription and PCR amplification. 2991del32 transcripts were detectable neither in the RNA sample from a patient compound heterozygous ΔF508/2991del32 nor in the parental sample heterozygous wild‐type/2991del32. These data indicate that the 32‐bp deletion causes a pancreas insufficient cystic fibrosis phenotype by a severe reduction of CFTR mRNA. © 1994 Wiley‐Liss, Inc.
Coach Training as a Strategyfor Promoting Youth Social Development

Organized youth sports afford a unique opportunity for promoting positive youth development, but little is known about why these settings can be beneficial for youth. The purposes of this article are (a) to discuss the instrumental role coaches play in determining the developmental yield of sport participation for youth and (b) to examine the efficacy of coach training programs for enhancing youth development in light of an expanded model of coaching effects on youth. This model features an elaborated internalization mechanism involving cognitive and motivational pathways. Emerging support for this model is reviewed and future directions for coach training researchers and practitioners are highlighted.
Event information extraction from Indonesian tweets using conditional random field

Information extraction is a process to find structured text from unstructured or semi-structured text. This research has an objective to build an information extraction system specialized for Events in Indonesian tweets. The system consists of two main parts. First part filters relevant tweet from irrelevant tweet. This part is only using a rule based approach with additional bag of words feature and gets the best accuracy of 86%. The second part is doing the extraction process. From our experiments, we get the best combination for extractor module by using multi token tokenization method, all feature set and 1st Order Conditional Random Field. This combination result in average accuracy of 74% per token.