Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Robust, Transferable Sentence Representations for Text Classification

Despite deep recurrent neural networks (RNNs) demonstrate strong performance in text classification, training RNN models are often expensive and requires an extensive collection of annotated data which may not be available. To overcome the data limitation issue, existing approaches leverage either pre-trained word embedding or sentence representation to lift the burden of training RNNs from scratch. In this paper, we show that jointly learning sentence representations from multiple text classification tasks and combining them with pre-trained word-level and sentence level encoders result in robust sentence representations that are useful for transfer learning. Extensive experiments and analyses using a wide range of transfer and linguistic tasks endorse the effectiveness of our approach.
A Robotic System for Localization of Passive UHF-RFID Tagged Objects on Shelves

This paper faces the problem of a robot that patrols a warehouse and localizes objects on shelves using RFID technology. A two-step localization system has been developed. First, the robot localizes itself by means of a Kalman-based algorithm that fuses robot odometry with the information coming from the phase of the signals of few reference RFID tags deployed along the shelves. Then, the objects on the shelves are localized using an algorithm that matches the phase of the signals from tagged objects collected along specific paths (suitably devised to decouple the estimation problem of the different tag coordinates) with a parametric electromagnetic model. A numerical analysis has been reported to show that the estimation error in the tag coordinates remains in the order of a few centimeters under several operating conditions of the system (e.g., for different values of the standard deviation of the measurement noise or under several parameter perturbations). Experimental tests in real scenarios assess the effectiveness of the system: the average position estimation error of the objects is about 10 cm in the case of cluttered metallic shelves but decreases up to a few centimeters in the case of stacked cartons.
A review Report on WiMAX Vulnerabilities, Security Threats and their Solutions

Now a days with the increasing popularity of WiMAX It has under gone various changes in its technology and also, now available new versions. In every version there may be different severe security serious security intimidation and their solution have been made to check the security issues. This review paper is discussing about the different problems related to security discussed about various security problems in WimMAX technology and their solution available for it.
Investigation of Nrf2, AhR and ATF4 Activation in Toxicogenomic Databases

Toxicological responses to chemical insult are largely regulated by transcriptionally activated pathways that may be independent, correlated and partially or fully overlapping. Investigating the dynamics of the interactions between stress responsive transcription factors from toxicogenomic data and defining the signature of each of them is an additional step toward a system level understanding of perturbation driven mechanisms. To this end, we investigated the segregation of the genes belonging to the three following transcriptionally regulated pathways: the AhR pathway, the Nrf2 pathway and the ATF4 pathway. Toxicogenomic datasets from three projects (carcinoGENOMICS, Predict-IV and TG-GATEs) obtained in various experimental conditions (in human and rat in vitro liver and kidney models and rat in vivo, with bolus administration and with repeated doses) were combined and consolidated where overlaps between datasets existed. A bioinformatic analysis was performed to refine pathways' signatures and to create chemical activation capacity scores to classify chemicals by their potency and selectivity of activation of each pathway. With some refinement such an approach may improve chemical safety classification and allow biological read across on a pathway level.
Elasecutor: Elastic Executor Scheduling in Data Analytics Systems

Modern data analytics systems use long-running executors to run an application’s entire DAG. Executors exhibit salient time-varying resource requirements. Yet, existing schedulers simply reserve resources for executors statically, and use the peak resource demand to guide executor placement. This leads to low utilization and poor application performance. We present Elasecutor, a novel executor scheduler for data analytics systems. Elasecutor dynamically allocates and explicitly sizes resources to executors over time according to the predicted time/varying resource demands. Rather than placing executors using their peak demand, Elasecutor strategically assigns them to machines based on a concept called dominant remaining resource to minimize resource fragmentation. Elasecutor further adaptively reprovisions resources in order to tolerate inaccurate demand prediction and reschedules tasks to deal with inadequate reprovisioning resources on one machine. Testbed evaluation on a 35-node cluster with our Spark-based prototype implementation shows that Elasecutor reduces makespan by more than 36% on average, and improves cluster utilization by up to 55% compared to existing work.
Evaluation of Dynamic-Adjusting Threshold-Voltage Scheme for Low-Power FinFET Circuits

In this paper, we report on the efforts of applying the dynamic-adjusting threshold-voltage scheme (DATS) to the design of an independent-gate (IG)-mode fin-type field-effect transistor (FinFET) circuit. DATS makes use of the intrinsic advantage of the IG-mode FinFET to adjust the threshold of the front gate with the back-gate bias adaptively according to the operating frequency. Consequently, the power consumption, especially leakage power of the circuit, would be reduced effectively without circuit performance deterioration. By further comparing the circuits where the DATS scheme is employed with the circuits that do not adopt the scheme, the power optimization levels with different path delays in different operating frequency ranges are discussed in detail. A design instance of DATS based on digitally controlled phase-locked loop is implemented with an ASAP 7-nm FinFET model. Simulation results illustrate that by employing DATS the power reduction could be up to 30% in the best case.
Reactive Power Provision from Distributed Energy Resources in Market Environment

Conventionally, generators with a considerable generation capacity have been responsible for providing ancillary services, e.g. reactive power support and spinning reserve. However, with recent highly penetrated distributed energy resources (DERs) systems and considering the benefit that DERs can bring to the system, it seems reasonable to optimally utilize them as a distributed reactive power service provider. Therefore, this paper proposes a settlement for DERs which participate in the reactive power market. To do so, an algorithm is proposed which determines the maximum DERs reactive power transmission capability from distribution networks into transmission systems. Also, a reactive power procurement model is proposed here to consider DERs participation in the reactive market. To justify the effectiveness and robustness of the proposed method, the 33-bus distribution and CIGRE 32-bus test systems are utilized.
Emergence and Within-Host Genetic Evolution of Methicillin-Resistant Staphylococcus aureus Resistant to Linezolid in a Cystic Fibrosis Patient

Methicillin-resistant Staphylococcus aureus (MRSA) infection has increased in recent years among cystic fibrosis (CF) patients. Linezolid (LZD) is one of the antistaphylococcal antibiotics widely used in this context. ABSTRACT Methicillin-resistant Staphylococcus aureus (MRSA) infection has increased in recent years among cystic fibrosis (CF) patients. Linezolid (LZD) is one of the antistaphylococcal antibiotics widely used in this context. Although LZD resistance is rare, it has been described as often associated with long-term treatments. Thirteen MRSA strains isolated over 5 years from one CF patient were studied for LZD resistance emergence and subjected to whole-genome sequencing (WGS). Resistance emerged after three 15-day LZD therapeutic regimens over 4 months. It was associated with the mutation of G to T at position 2576 (G2576T) in all 5 rrl copies, along with a very high MIC (>256 mg/liter) and a strong increase in the generation time. Resistant strains isolated during the ensuing LZD therapeutic regimens and until 13 months after LZD stopped harbored only 3 or 4 mutated rrl copies, associated with lower MICs (8 to 32 mg/liter) and low to moderate generation time increases. Despite these differences, whole-genome sequencing allowed us to determine that all isolates, including the susceptible one isolated before LZD treatment, belonged to the same lineage. In conclusion, LZD resistance can emerge rapidly in CF patients and persist without linezolid selective pressure in colonizing MRSA strains belonging to the same lineage.
Multidimensional Multiplexing in Multicore Fibre for Hybrid Optical Backhaul provision: The XCORE Approach

In this paper a critical overview of the required technologies to open up multi-core fibre as a valid solution leveraging optical access networks infrastructure already deployed on the field for hybrid radio-optical backhaul is reported. Optical transmission using multi-core fibre (MCF) has been already proposed as an interesting solution for optical access network, providing a lower installation cost and higher transmission capacity. In this work, we propose and evaluate experimentally the joint use of SSMF and MCF for and hybrid backhaul solution supporting single-carrier (SC) and multi-carrier OFDM connectivity for cellular service and radio transport provision, respectively. The two modulations schemes are supported as a multidimension modulation employing space-dimension multiplexing at the same wavelength over different cores of the MCF, in particular, we compare the performance of single-carrier (SC) and multi-carrier OFDM signals. SC-QAM signals over 2 GHz bandwidth can provide up to 14.22 Gb/s using 256QAM modulation, while OFDM signals with 512 subcarriers over the same bandwidth provide up to 14.36 Gb/s per channel. The optical performance is evaluated for an optical link comprising 10 km on standard single-mode fibre (SSMF) and 150 m multi-core fibre (MCF) for the final connectivity stage considering different received optical power levels. Experimental results confirm that both OFDM and SC-QAM signals meet error-free BER thresholds for modulation orders up to 256QAM, although OFDM requires higher received power levels compared to SC-QAM implementation.
Evaluation of MOS social support in low-income caregivers of African American children with poorly controlled asthma

Abstract Objective: The purpose of this study was to examine the factor structure of the Medical Outcomes Study Social Support Survey (MOS-SSS) in a sample of low-income, urban caregivers of African American children with poorly controlled asthma. Although the MOS-SSS is a commonly used measure of social support, its psychometric properties have not been studied in this population. Methods: Confirmatory factor analysis was conducted to determine the most appropriate factor structure for the MOS-SSS in caregivers of African American children with frequent Emergency Department visits for uncontrolled asthma. The following models were tested and compared using established fit statistics: an 18-item second-order four factor model, an 18-item four factor model, a bifactor model and an 18-item one factor model with nested models. Results: Participating caregivers were single (75.6%) and female (97%). An 18-item one factor version of the scale had the best fit statistics compared to the other models tested: (142) = 308.319, p > 0.001; Root mean square error of approximation (RMSEA) = 0.077; CFI (Comparative Fit Index) = 0.990; and Tucker-Lewis Index (TLI) = 0.988. Construct validity was supported by a statistically significant negative relationship between our final MOS-SSS model and caregiver depressive symptoms (= −0.374, p < 0.001). Conclusions: The 18-item one factor MOS-SSS may be appropriate for use in research and clinical practice with caregivers of African American children with poorly controlled asthma. It appears promising as a mechanism to advance understanding of relationships between social support and asthma outcomes in this vulnerable population.
Hybrid Power-Line/Wireless Communication Systems For Indoor Applications

The high attenuation which increases as frequency increases in power line communication systems (PLC) makes achieving and sustaining high data rates over such channels a challenging task. In most communication systems, relays are normally deployed to overcome the effect of signal attenuation and to achieve reliable transmissions between users. Amplify-and-forward (AF) and decode-and-forward (DF) relays are often used. In contrast to the existing work on this topic which advocate utilizing relays either between PLC links or between wireless links, the authors of this paper focus on using AF relaying for in-home PLC and wireless systems where one PLC user wants to share data with one wireless user which has no access to the PLC network. We refer to this system as the Hybrid approach. A mathematical method is developed for this network to formulate the capacity of the system by exploiting the statistical properties of both PLC and wireless channels. Monte Carlo simulations are used throughout this paper to validate the analytical results. The impact of several system parameters is investigated in this paper. For the sake of comparison, we also provide the performance analysis and results for a PLC only network. The results showed that the performance of the hybrid system enhances as we increase the relay gain and deteriorates as the end-to-end distance increases. It is also found that the PLC only scheme may outperform the hybrid one if the relay gain is relatively small; however, the user mobility offered by the hybrid system remains the main advantage over the PLC only system.
HEVC Intra Frame Coding Based on Convolutional Neural Network

The HEVC standard offers high performance with a lower bitrate for intra frame coding, but still requires many bits. An alternative intra frame coding framework based on convolutional neural network (CNN) is proposed in this paper. Two CNN models, simplified CNN (S-CNN) and complicated CNN (C-CNN), were designed and trained to improve the coding performance of HEVC. In intra frame coding, the trained CNN predicts the residual for reconstructed blocks to enhance visual quality. Due to the high computational complexity of CNN in HEVC encoding, we further explore the tradeoff between computational complexity and coding performance. For S-CNN, an early termination mechanism is proposed to further reduce the HEVC encoding complexity. With regard to C-CNN, a GPU-based heterogeneous architecture is proposed to accelerate CNN processing. Experimental results show that the proposed method with S-CNN achieves bitrate savings of 3.1% with a 37% increase in time cost, and 2.8% in bitrate savings with only a 23% increase in time cost when applying the early termination algorithm. In the case of C-CNN, the execution time of CNN can reach increases in speed of 8.8–17.8 $\times$ and a bitrate savings of up to 5.1% on average.
Diffusion of exit sites on the endoplasmic reticulum – a random walk on a shivering backbone

Major parts of the endoplasmic reticulum (ER) in eukaryotic cells are organized as a dynamic network of membrane tubules connected by three-way junctions. On this network, self-assembled membrane domains, called ER exit sites (ERES), provide platforms at which nascent cargo proteins are packaged into vesicular carriers for subsequent transport along the secretory pathway. While ERES appear stationary and spatially confined on long time scales, we show here via single-particle tracking that they exhibit a microtubule-dependent anomalous diffusion behavior on short and intermediate time scales. By quantifying key parameters of their random walk, we show that the subdiffusive motion of ERES is distinct from that of ER junctions, i.e. ERES are not tied to junctions but rather are mobile on ER tubules. We complement and corroborate our experimental findings with model simulations that also indicate that ERES are not actively moved by microtubules. Altogether, our study shows that ERES perform a random walk on the shivering ER backbone, indirectly powered by microtubular activity. Similar phenomena can be expected for other domains on subcellular structures, setting a caveat for the interpretation of domain tracking data.
Iot Based Health Monitoring System Using Raspberry PI - Review

Many patients die all over the world due to lack of timely and proper help. A portable system for continuous physiological parameter monitoring is essential for elderly and ill patients who are not in the hospital. IOT(Internet Of Things) based patient health monitoring system will enable the doctor to view patient's health status online. Raspberry Pi board is used as the gateway or medium of sensor input devices which are attached to the patient and to send physiological parameter value of a patient to doctor computer through internet. So that necessary treatment can be given immediately by doctor remotely and doctor can be advised remotely to the patient. We are used Raspberry Pi as a gateway or medium for a serial input value of sensors. For programming, Python and C language are used. This is IOT based remote technology for medical treatment for patient.
Convex Structure-Based Nonlinear State Estimation Using Linear Kalman Filter and Developing an MPC Scheme

In this paper, a novel method for state estimation of nonlinear system is proposed using the linear Kalman Filter (KF) and Takagi-Sugeno (TS) fuzzy modeling technique to extend the estimation potentials of the classic linear KF to the nonlinear dynamics. Incorporating TS fuzzy model in linear KF provides the most accuracy in state estimation compared with Extended Kalman Filter (EKF) which uses a linear approximation of the nonlinear systems. Consequently, the estimation performance is highly improved. Moreover, an MPC-based control strategy based on the proposed estimation algorithm and TS fuzzy modeling is utilized to control the speed of a Electric Vehilcle (EV). Finally, the simulation results are provided to show the superiority of the proposed state estimation method than the conventional ones.
Text Mining and Recommender Systems for Predictive Policing

We present some results from a joint project between HP Labs, Cardiff University and Dyfed Powys Police on predictive policing. Applications of the various techniques from recommender systems and text mining to the problem of crime patterns recognition are demonstrated. Our main idea is to consider crime records for different regions and time period as a corpus of text documents with words being crime types. We apply tools from NLP and text documents classifications to analyse different regions in time and space. We evaluate performance of several measures of similarity for texts and documents clustering algorithms.
Identification of Overpricing in the Purchase of Medication by the Federal Government of Brazil, Using Text Mining and Clustering Based on Ontology

Increasing the transparency level in his actions and spending is one of the primary duties of the Brazilian Federal Government. The creation of laws that oblige full disclose of all its expenditures through transparency portals enables citizens to supervise all government entities. However, only the dissemination of these data, without a definite standard or the availability of data analysis tools, does not guarantee that the citizen is empowered to play his role. Therefore hence, the objective of this work is to identify overprice in the acquisition of products purchased by the federal government of Brazil using the unstructured data available on the Transparency Portal. The last two-years' worth of purchasing data, available in the Transparency Portal, were extracted, processed and stored. Due to his diverse nature and high volume of data, this study focused only on medicines purchased by the Ministry of Health. Ontology-based text mining and clustering techniques were applied for automatic identification and classification of products. The processing of this information was done through text mining and clustering, based on the ontology registered in another database of the Brazilian government. Because of this work, a consolidated price base per medication was created to allow the identification of distortions in prices practised, facilitating the identification of cases that merit further investigation to unravel fraud to the treasury.
Fault Estimation Sliding-Mode Observer With Digital Communication Constraints

This paper addresses the actuator fault estimation sliding-mode observer (SMO) design problem of linear continuous-time systems over digital communication channels. This problem frequently occurred in a network environment where data has to be quantized before being transmitted via digital communication channels. Traditional observers (linear Luenberger observer, Walcott–ZaK SMO) are not effective to solve this design issue since the effects of signal quantization will degrade estimation performances evidently. In this paper, a new descriptor SMO method is presented to overcome this difficult problem. It is shown that, if the quantizer density is larger than $\sqrt{2}-1$, the designed observer can compensate quantization errors completely, and the fault vector can be reconstructed despite of signal quantization. Finally, a simulation example with the F-404 aircraft engine model is proposed to demonstrate the effectiveness of the proposed robust digital observer design approach.
Ensemble Approach for Predicting Genetic Disease through Case-Control Study

The origin of many diseases can be revealed by knowing the gene regulation. Genetic disease an abnormality in individual's genome. These abnormalities can be occurred due to mutation in single base in DNA or due to the involvement in the addition or subtraction of entire chromosomes. To measure the expression levels of bulk of genes simultaneously, DNA microarrays are used. Microarray data is set of DNA sequences representing the entire set of genes of an organism, arranged in a grid pattern so that the expression of thousands of genes can be detected at the same time. In this paper we propose an approach for diagnosing genetic disease, to accomplish this goal we need to select the most informative ones that can accurately distinguish one class of genes from another. For that the genes with high significance are iteratively extracted. The goal of this paper is to develop method, which increases the classification accuracy, for identifying significant differentially expressed clusters of genes using Recursive Cluster Elimination (RCE) that is based on an ensemble clustering approach.
Persian Word Embedding Evaluation Benchmarks

Recently, there has been renewed interest in semantic word representation also called word embedding, in a wide variety of natural language processing tasks requiring sophisticated semantic and syntactic information. The quality of word embedding methods is usually evaluated based on English language benchmarks. Nevertheless, only a few studies analyze word embedding for low resource languages such as Persian. In this paper, we perform such an extensive word embedding evaluation in Persian language based on a set of lexical semantics tasks named analogy, concept categorization, and word semantic relatedness. For these evaluation tasks, we provide three benchmark data sets to show the strengths and weakness of five well-known embedding models which are trained on Wikipedia corpus. The experimental results indicates that FastText (sg) and Word2Vec(cbow) outperform other models.