Miracle-dz/UniFAR-FTUs huggingface dataset:
Methodological Issues in Using Data from Social Networking Sites

Social networking sites (SNS) have become increasingly popular in recent years. With the amount of data available on SNS, the potential exists for researchers to use these data for their research. However, like any research method, there are limitations in using data from SNS. First, as members of SNS are not representative of the population, there is the limitation in generalizing the findings to the population. Second, in SNS with a low level of activity, there is also the issue of whether the data are sufficient for analysis. Third, the validity of the postings by members of SNS should be considered, as members of SNS may not be truthful in their responses. In addition, as the environment for SNS favors a quick emotive response as opposed to a cognitive response, the review suggests that the researcher will need to be aware of possibly different behavior when members of a SNS are faced with a high involvement decision. This article concludes that while there is potential in analyzing data from SNS, researchers should be aware of the limitations in using these data.
Machine learning of real-time power systems reliability management response

In this paper we study how supervised machine learning could be applied to build simplified models of realtime (RT) reliability management response to the realization of uncertainties. The final objective is to import these models into look-ahead operation planning under uncertainties. Our response models predict in particular the real-time reliability management costs and the resulting reliability level of the system. We tested our methodology on the IEEE-RTS96 benchmark. Among the supervised learning algorithms tested, extremely randomized trees, kernel ridge regression and neural networks appear to be the best methods for this application. Furthermore, by using feature “importances” computed by tree-based ensemble methods, we were able to extract the most relevant variables to predict the response of real-time reliability management, and thus obtain a better understanding of the system properties.
Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods

Automatic measuring of speaker sincerity degree is a novel research problem in computational paralinguistics. This paper proposes covariance-based feature vectors to model speech and ensembles of support vector regressors to estimate the degree of sincerity of a speaker. The elements of each covariance vector are pairwise statistics between the short-term feature components. These features are used alone as well as in combination with the ComParE acoustic feature set. The experimental results on the development set of the Sincerity Speech Corpus using a cross-validation procedure have shown an 8.1% relative improvement in the Spearman's correlation coefficient over the baseline system.
Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors

Background Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures. Methods and Findings We used wearable sensors to detect close-range interactions (“contacts”) between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14,037 contacts were recorded overall, 94.1% of which during daytime. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders. Conclusions Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is however associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies.
Performance analysis of M-PAM signalling with Tomlinson Harashima precoding over ISI channels

An analytical method is developed to evaluate the symbol error probability of multilevel pulse amplitude modulation (M-PAM) system using Tomlinson-Harashima(1971, 1972) precoding (THP) over frequency-selective fading channels. Illustrative results obtained by using the developed analysis and simulation indicate an excellent agreement.
Myocardial Deformation in Patients with Beta‐Thalassemia Major: A Speckle Tracking Echocardiographic Study

Background: Increasing data suggest that parameters of myocardial deformation are strong indices of ventricular systolic and diastolic function. We sought to determine myocardial deformation of the left ventricle and assess relationship of deformation rates with myocardial iron load in patients with beta‐thalassemia major. Methods: The left ventricular longitudinal, circumferential, and radial myocardial deformation was determined using speckle tracking echocardiography in 42 thalassemia patients aged 24.4 ± 6.4 years. The results were compared with those of 38 age‐matched controls. The rates of longitudinal and circumferential deformation were correlated with cardiac T2* magnetic resonance findings. Results: Compared with controls, patients had significantly greater global systolic radial strain (P = 0.001), but similar global systolic longitudinal (P = 0.12) and circumferential strain (P = 0.84). On the other hand, patients had significantly lower longitudinal systolic strain rate (SR) (P = 0.019), longitudinal early diastolic SR (P = 0.036), and circumferential early diastolic SR (P = 0.04) than controls. The cardiac T2* findings correlated positively with longitudinal (r = 0.44, P = 0.004) and circumferential early diastolic SR (r = 0.37, P = 0.019), but not with the respective systolic SRs and left ventricular ejection fraction (all P > 0.05). Patients with iron overload (T2*< 20 msec), compared to those without, had significantly lower longitudinal (1.45 ± 0.33/sec vs. 1.76 ± 0.27/sec, P = 0.002) and circumferential (1.01 ± 0.31/sec vs. 1.22 ± 0.31/sec, P = 0.03) early diastolic SR. Conclusions: Patients with beta‐thalassemia major have reduced longitudinal systolic SR, longitudinal early diastolic SR, and circumferential early diastolic SR. The rates of diastolic deformation in the longitudinal and circumferential dimensions are inversely related to myocardial iron overload. (Echocardiography 2010;27:253‐259)
A Survey on Tiering and Caching in High-Performance Storage Systems

Although every individual invented storage technology made a big step towards perfection, none of them is spotless. Different data store essentials such as performance, availability, and recovery requirements have not met together in a single economically affordable medium, yet. One of the most influential factors is price. So, there has always been a trade-off between having a desired set of storage choices and the costs. To address this issue, a network of various types of storing media is used to deliver the high performance of expensive devices such as solid state drives and non-volatile memories, along with the high capacity of inexpensive ones like hard disk drives. In software, caching and tiering are long-established concepts for handling file operations and moving data automatically within such a storage network and manage data backup in low-cost media. Intelligently moving data around different devices based on the needs is the key insight for this matter. In this survey, we discuss some recent pieces of research that have been done to improve high-performance storage systems with caching and tiering techniques.
Discriminant Tchebichef based moment features for face recognition

Face representation using small number of features with highest discriminatory measure is vital in the development of a face recognition system. In the holistic based approach, features are extracted from the global appearance of a face. Fisher's Linear Discriminant Analysis (FLD) is a popular holistic based feature extractor. However, it is subject to several limitations such as small sample size problem and heavy computation due to large scatter matrix. In this paper, a new approach is introduced to remedy this problem. Firstly, discrete orthogonal Tchebichef moments are computed to summarise the information lying in a large sized facial image. Secondly, the scatter matrices are calculated on the Tchebichef moments to obtain an optimised discriminant feature using FLD. The proposed method is tested on ORL database, which consist of 40 subjects with 400 images. Highest recognition rate of 96.5% were obtained using only 29 features.
Prediction Impact of Vacuum Drying Parameters on Rice Taste Value with Neural Network Model

Process control over rice vacuum drying directly affects the taste quality of dried rice. Drying temperature, initial rice moisture content and vacuum degree are the main parameters that affect rice-drying process. This paper makes research on the change law of taste quality of brown rice versus drying parameters during vacuum drying process through establishment of neural network model between brown rice taste value and drying parameters, which will provide reference for objective evaluation on rice taste, dried rice quality and the design of vacuum dryer. The results show that, BP neural network model predicts the average relative error of 2.88%, correlation coefficient of 0.96, BP neural network can serve as a new model for description of rice taste value of vacuum drying.
Q A ] 6 A pr 2 01 6 Modules over the algebra V ir ( a , b )

Abstract: For any two complex numbers a and b, Vir(a, b) is a central extension of W(a, b) which is universal in the case (a, b) 6= (0, 1), where W(a, b) is the Lie algebra with basis {Ln, Wn | n ∈ Z} and relations [Lm, Ln] = (n − m)Lm+n, [Lm,Wn] = (a + n + bm)Wm+n, [Wm,Wn] = 0. In this paper, we construct and classify a class of non-weight modules over the algebra Vir(a, b) which are free U(CL0 ⊕ CW0)-modules of rank 1. It is proved that such modules can only exist for a = 0.
Set-cover-based critical implications selection to improvesat-based bounded model checking: extended abstract

The effectiveness of Satisfiability (SAT)-based Bounded Model Checking (BMC) critically relies on the deductive power of the BMC instance. Although implication relationships have been used to help SAT solver to make more deductions, too many such implications would result in a large number of clauses and potentially degrade the underlying SAT solver performance. In this paper, we have formulated clause selection as a set-cover problem. Secondly, we have proposed a novel greedy strategy for optimal selection of static logic clauses. This technique maximizes the number of literals that can be deduced by the SAT solver during the Boolean Constraint Propagation (BCP) operation. Our strategy improves BMC by 1.74x against the case where all extended implications were added to the BMC instance. Compared with the original BMC without any implications, up to 55.32x speedup can be achieved.
Reranking the Berkeley and Brown Parsers

The Brown and the Berkeley parsers are two state-of-the-art generative parsers. Since both parsers produce n-best lists, it is possible to apply reranking techniques to the output of both of these parsers, and to their union. We note that the standard reranker feature set distributed with the Brown parser does not do well with the Berkeley parser, and propose an extended set that does better. An ablation experiment shows that different parsers benefit from different reranker features.
Substrate Specificity of the Escherichia coli Outer Membrane Protease OmpP

ABSTRACT Escherichia coli OmpP is an F episome-encoded outer membrane protease that exhibits 71% amino acid sequence identity with OmpT. These two enzymes cleave substrate polypeptides primarily between pairs of basic amino acids. We found that, like OmpT, purified OmpP is active only in the presence of lipopolysaccharide. With optimal peptide substrates, OmpP exhibits high catalytic efficiency (k cat/Km = 3.0 × 106 M−1s−1). Analysis of the extended amino acid specificity of OmpP by substrate phage revealed that both Arg and Lys are strongly preferred at the P1 and P1′ sites of the enzyme. In addition, Thr, Arg, or Ala is preferred at P2; Leu, Ala, or Glu is preferred at P4; and Arg is preferred at P3′. Notable differences in OmpP and OmpT specificities include the greater ability of OmpP to accept Lys at the P1 or P1′, site as well as the prominence of Ser at P3 in OmpP substrates. Likewise, the OmpP P1 site could better accommodate Ser; as a result, OmpP was able to cleave a peptide substrate between Ser-Arg about 120 times more efficiently than was OmpT. Interestingly, OmpP and OmpT cleave peptides with three consecutive Arg residues at different sites, a difference in specificity that might be important in the inactivation of cationic antimicrobial peptides. Accordingly, we show that the presence of an F′ episome results in increased resistance to the antimicrobial peptide protamine both in ompT mutants and in wild-type E. coli cells.
Digital India and Women: Bridging the Digital Gender Divide

The purpose of this study is to find out whether digital technologies can reduce the existing gender inequality in Indian society. The current debate around digitalization is limited to the ones who have access and the ones who haven't. But this paper takes the debate beyond access by suggesting a three-tier framework for measuring the impact of digital technologies on women empowerment. The Digital India programme run by Government of India is taken as a case study by the authors. Rather than depending on traditional methodologies to bring gender equality, can we consider Digital India as a potential solution? This paper explores the answer to the above question from a gendered perspective with the help of primary data analysis of a few selected Digital India products and services.
Linking Entities to Wikipedia Documents

This paper addresses the challenging information extraction problem of linking named entities in text to entries in a large knowledge base such as Wikipedia. The approach, which is essentially an evolution of a system originally developed in the context of the English Entity Linking Task of the Text Analysis Conference, uses supervised learning to rank candidate knowledge base entries for each named entity, and then for classifying the top-ranked entry as the correct disambiguation or not. In this paper, I analyze the fundamental design challenges involved in the development of a learningbased entity-linking system, and provide extensive experimental results with both Portuguese and Spanish texts, for a wide range of methods and feature sets. The experiments demonstrate the effectiveness of supervised learning methods, showing that out-of-the-box algorithms and relatively simple to compute features can obtain a high accuracy in this task.
SZTE-NLP: Aspect level opinion mining exploiting syntactic cues

In this paper, we introduce our contributions to the SemEval-2014 Task 4 ‐ Aspect Based Sentiment Analysis (Pontiki et al., 2014) challenge. We participated in the aspect term polarity subtask where the goal was to classify opinions related to a given aspect into positive, negative, neutral or conflict classes. To solve this problem, we employed supervised machine learning techniques exploiting a rich feature set. Our feature templates exploited both phrase structure and dependency parses.
Composition-Dependent Quantum Magnetosonic-Wave Dispersion

Abstract Using the quantum magnetohydrodynamics (QMHD) model, linear dispersion of magnetosonic waves are studied in a quasineutral quantum electron-ion plasma in two distinct regimes of nonrelativistic and relativistic degeneracies considering also the plasma composition effect. It is shown that the paramagnetic spin effects of the degenerated electrons plays a key role in dynamics of magnetosonic waves. The linear wave-speed is found to have minimum value at some degeneracy parameter in such plasmas. This is due to delicate interplay between relativistic degeneracy and the Pauli spin-magnetization. It is also revealed that the plasma composition has significant effect on the linear dispersion in the relativistic degeneracy limit contrary to that of nonrelativistic case and Zeeman energy has significant effect in nonrelativistic degeneracy regime unlike that of relativistic one in the linear perturbation-limit. Current findings can have important applications in both inertial plasma confinement and astrophysical degenerate plasmas.
Banana Transcription Factor MaERF11 Recruits Histone Deacetylase MaHDA1 and Represses the Expression of MaACO1 and Expansins during Fruit Ripening1[OPEN]

ETHYLENE RESPONSE FACTOR11 (MaERF11) and HDA1 interact to repress the expression of ACO1 and expansins via histone deacetylation. Phytohormone ethylene controls diverse developmental and physiological processes such as fruit ripening via modulation of ethylene signaling pathway. Our previous study identified that ETHYLENE RESPONSE FACTOR11 (MaERF11), a transcription factor in the ethylene signaling pathway, negatively regulates the ripening of banana, but the mechanism for the MaERF11-mediated transcriptional regulation remains largely unknown. Here we showed that MaERF11 has intrinsic transcriptional repression activity in planta. Electrophoretic mobility shift assay and chromatin immunoprecipitation analyses demonstrated that MaERF11 binds to promoters of three ripening-related Expansin genes, MaEXP2, MaEXP7 and MaEXP8, as well as an ethylene biosynthetic gene MaACO1, via the GCC-box motif. Furthermore, expression patterns of MaACO1, MaEXP2, MaEXP7, and MaEXP8 genes are correlated with the changes of histone H3 and H4 acetylation level during fruit ripening. Moreover, we found that MaERF11 physically interacts with a histone deacetylase, MaHDA1, which has histone deacetylase activity, and the interaction significantly strengthens the MaERF11-mediated transcriptional repression of MaACO1 and Expansins. Taken together, these findings suggest that MaERF11 may recruit MaHDA1 to its target genes and repress their expression via histone deacetylation.
A Method for Expressing and Displaying the Vehicle Behavior Distribution in Maintenance Work Zones

Maintenance work zones on the road network have impacts on the normal travelling of vehicles, which increase the risk of traffic accidents. The traffic characteristic analysis in maintenance work zones is a basis for maintenance work zone related research such as layout design, traffic control and safety assessment. Due to the difficulty in vehicle microscopic behaviour data acquisition, traditional traffic characteristic analysis mainly focuses on macroscopic characteristics. With the development of data acquisition technology, it becomes much easier to obtain a large amount of microscopic behaviour data nowadays, which lays a good foundation for analysing the traffic characteristics from a new point of view. This paper puts forward a method for expressing and displaying the vehicle behaviour distribution in maintenance work zones. Using portable vehicle microscopic behaviour data acquisition devices, lots of data can be obtained. Based on this data, an endpoint detection technology is used to automatically extract the segments in behaviour data with violent fluctuations, which are segments where vehicles take behaviours such as acceleration or turning. Using the support vector machine classification method, the specific types of behaviours of the segments extracted can be identified, and together with a data combination method, a total of ten types of behaviours can be identified. Then the kernel density analysis is used to cluster different types of behaviours of all passing vehicles to show the distribution on maps. By this method, how vehicles travel through maintenance work zones, and how different vehicle behaviours distribute in maintenance work zones can be displayed intuitively on maps, which is a novel traffic characteristic and can shed light to maintenance work zone related researches such as safety assessment and design method.
A Low-Cost and Robust Maximum Likelihood Joint Estimator for the Doppler Spread and CFO Parameters Over Flat-Fading Rayleigh Channels

This paper addresses the problem of Doppler spread and carrier frequency offset (CFO) estimation under flat-fading Rayleigh channels. We develop a new low-cost and robust approximate maximum likelihood (ML) estimator for these two key parameters that builds upon an elegant two-ray approximation model of the channel’s covariance matrix. The latter is then inverted analytically thereby yielding a closed-form expression for the underlying log-likelihood function that is prone to easy evaluation by the fast Fourier transform. Computer simulations show that the new estimator is accurate over wide ranges of the Doppler spread and CFO parameters. Moreover, it outperforms many state-of-the-art techniques under the adverse conditions of short data records and/or low SNR thresholds. Most prominently, it exhibits an unprecedented robustness to the Doppler spectrum shape of the channel since it does not require its a priori knowledge.
Maximum Likelihood Decoding of Turbo Codes on the Binary Erasure Channel

In this paper we deal with Maximum Likelihood (ML) decoding of Turbo Codes on the Binary Erasure Channel. First we describe a new ML decoder. When the standard iterative decoder fails because the set of erasures includes a stopping set, with the component decoders we obtain a linear system of equations that seeks the codeword constrained by both component codes. We evaluate the complexity in terms of equivalent turbo iterations and we show that this ML decoder is implementable. We also modify the algorithm proposed in [6] for LDPC to decode Turbo Codes and we compare the two methods. We find that, in general, our method is more efficient with low memory or punctured codes. Finally, by simulation we show that m-ary Turbo Codes under ML decoding outperform the error exponent bounds for random codes down to WER=10-6, for all rates ranging from 1/3 to 7/8.