Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler

In this work, we apply anomaly detection to source code and byte-code to facilitate the development of a programming language and its compiler. We define anomaly as a code fragment that is different from typical code written in a particular programming language. Identifying such code fragments is beneficial to both language developers and end users, since anomalies may indicate potential issues with the compiler or with runtime performance. Moreover, anomalies could correspond to problems in language design. For this study, we choose Kotlin as the target programming language. We outline and discuss approaches to obtaining vector representations of source code and bytecode and to the detection of anomalies across vectorized code snippets. The paper presents a method that aims to detect two types of anomalies: syntax tree anomalies and so-called compiler-induced anomalies that arise only in the compiled bytecode. We describe several experiments that employ different combinations of vectorization and anomaly detection techniques and discuss types of detected anomalies and their usefulness for language developers. We demonstrate that the extracted anomalies and the underlying extraction technique provide additional value for language development.
Predictors of HIV Antiretroviral Treatment Failure among Patients Attending a Semi - Urban HIV Clinic– Kenya, 2012: Can Social Support Help?

ABSTRACT Background : HIV antiretroviral treatment [ART] failure increases morbidity and mortality. In Kenya, use of treatment supporter, HIV serostatus disclosure, and support groups are believed to reduce the risk of treatment failure by improving adherence. Methods : We conducted a case-control study to identify factors associated with clinical or immunologic indicators of treatment failures. Cases and controls were adult patients attending a semi-urban HIV clinic who initiated ART at least six months prior to the study. Cases were patients who had a] a decline in CD4 count [below baseline or 30% from highest value since ART initiation] or b] unimproved CD4 counts or c] a World Health Organization [WHO] stage III or IV opportunistic infection. Controls did not meet either immunologic or clinical criteria for treatment failure. Information was obtained through face-to-face interviews medical chart reviews. Results : We enrolled 52 cases and 104 controls. Twenty-eight [53%] cases and 71 [70%] controls were women, and median age was 44 years [range: 38-50 years] among cases and 43 years [range: 38-49 years] among controls. Discontinued ART for 2 weeks or more [adjusted odds ratio [AOR] 8.9, 95% confidence interval [CI] 1.4-57.4] and alcohol use [AOR 7.2, 95% CI 1.1-45.5] were found to significantly increase the risk of ART failure. Compared to men who discontinued ART for less than 2 weeks, women who discontinued ART for less than 2 weeks had reduced risk [AOR 0.4, 95% CI 0.2-0.9] while those who discontinued for two weeks or more had a greater risk [AOR 12.97, 95% CI 1.5-111.8] of developing ART failure. Social support factors were not associated with treatment failure. Conclusions : Discontinuing ART and alcohol use were identified to be modifiable factors associated with clinical or immunologic criteria of ART failure. Interventions with proven efficacy at reducing alcohol use and increasing continuous ART should be implemented.
Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection

This paper presents a novel framework for Speech Activity Detection (SAD). Inspired by the recent success of multi-task learning approaches in the speech processing domain, we propose a novel joint learning framework for SAD. We utilise generative adversarial networks to automatically learn a loss function for joint prediction of the frame-wise speech/ non-speech classifications together with the next audio segment. In order to exploit the temporal relationships within the input signal, we propose a temporal discriminator which aims to ensure that the predicted signal is temporally consistent. We evaluate the proposed framework on multiple public benchmarks, including NIST OpenSAT’ 17, AMI Meeting and HAVIC, where we demonstrate its capability to outperform state-of-the-art SAD approaches. Furthermore, our cross-database evaluations demonstrate the robustness of the proposed approach across different languages, accents, and acoustic environments.
THEORETICAL FRAMEWORKS OF REMOTE SENSING SYSTEMS BASED ON COMPRESSIVE SENSING

: As an application of Compressive Sensing (CS) in remote sensing area, the theoretical frameworks of SAR and optical imaging system based on CS are investigated. The processes of data acquisition are mathematically described. After that the sparse representation of images corresponding to the two systems are also presented. Finally, the spare recovery is employed to retrieve images. Numerical simulations validated the feasibility of such imaging systems.
Identifying Highly Influential Travellers for Spreading Disease on a Public Transport System

The recent outbreak of a novel coronavirus and its rapid spread underlines the importance of understanding human mobility. Enclosed spaces, such as public transport vehicles (e.g. buses and trains), offer a suitable environment for infections to spread widely and quickly. Investigating the movement patterns and the physical encounters of individuals on public transit systems is thus critical to understand the drivers of infectious disease outbreaks. For instance, previous work has explored the impact of recurring patterns inherent in human mobility on disease spread, but has not considered other dimensions such as the distance travelled or the number of encounters. Here, we consider multiple mobility dimensions simultaneously to uncover critical information for the design of effective intervention strategies. We use one month of citywide smart card travel data collected in Sydney, Australia to classify bus passengers along three dimensions, namely the degree of exploration, the distance travelled and the number of encounters. Additionally, wes imulate disease spread on the transport network and trace the infection paths. We investigate in detail the transmissions between the classified groups while varying the infection probability and the suspension time of pathogens. Our results show that characterizing individuals along multiple dimensions simultaneously uncovers a complex infection interplay between the different groups of passengers, that would remain hidden when considering only a single dimension. We also identify groups that are more influential than others given specific disease characteristics, which can guide containment and vaccination efforts.
Temporal Logic of Composable Distributed Components

Distributed systems are critical to reliable and scalable computing; however, they are complicated in nature and prone to bugs. To modularly manage this complexity, network middleware has been traditionally built in layered stacks of components. We present a novel approach to compositional verification of distributed stacks to verify each component based on only the specification of lower components. We present TLC (Temporal Logic of Components), a novel temporal program logic that offers intuitive inference rules for verification of both safety and liveness properties of functional implementations of distributed components. To support compositional reasoning, we define a novel transformation on the assertion language that lowers the specification of a component to be used as a subcomponent. We prove the soundness of TLC and the lowering transformation with respect to the operational semantics for stacks of distributed components. We successfully apply TLC to compose and verify a stack of fundamental distributed components.
Molecular Gas and Dust Heating in Active Galaxies: Growing Black Holes or Tidal Shocks?

We investigate if and how growing supermassive black holes (SMBH) known as active galactic nuclei (AGN) and gravitational interactions affect the warm molecular gas and dust of galaxies. Our analysis focuses on the morphologies and warm ISM properties of 630 galaxies at z < 0.1. We use grizy images from the Pan-STARRS survey to classify the galaxies into mergers, early mergers, and non-mergers. We use MIR spectroscopic measurements of emission from rotational H2 transitions, dust, and polycyclic aromatic hydrocarbon (PAH) features, and silicate emission or absorption lines at 9.7 μm to study how gravitational interactions impact the warm ISM in AGN and non-AGN hosts. We find that in AGN-hosts, the ISM is warmer, the ratios of H2 to PAHs are larger, the PAH emission-line ratios and silicate strengths have a wider range of values than in non-AGN hosts. We find some statistical differences between the H2 emission of mergers and non-mergers, but those differences are less statistically significant than those between AGN and non-AGN hosts. Our results do not establish a relation between the rate of BH growth and the warm ISM but point to highly statistically significant differences between AGN hosts and non-AGN hosts, differences that are not present with the same statistical significance between mergers and non-mergers. We speculate that the combination of triggering mechanisms, AGN orientations, and evolutionary stages that allow AGN to be classified as such in the MIR indicate that those AGN are energetically coupled on kiloparsec scales to their host galaxies’s warm ISM. Future optical and IR, spatially resolved spectroscopic studies are best suited to characterize this connection.
Factors Associated With Tobacco Dependence in People with Mental Disorders

This study’s objective was to identify socio-demographic and clinical factors associated with the degree of tobacco dependence in those with mental disorders, hospitalized in the psychiatric unit of a general hospital. An exploratory study with the participation of 96 smokers, who have mental disorders, hospitalized in the psychiatric unit of a general public hospital between August 2010 – February 2012. Individual interviews were held using the Fagerstrom Test for Nicotine Dependence (FTND). A questionnaire was administered for raising socio-demographic and clinical variables. Bivariate and multivariate analysis was undertaken. Of the 96 smokers, 53.2% had a high or very high degree of tobacco dependence. Those who had previously been hospitalized in a psychiatric unit (OR= 3.59) or who had somatic comorbidities (OR= 2.50) have greater chances of being classified as highly dependent on tobacco than do the other subjects. The variables of psychiatric hospitalization and somatic comorbidities are risk factors for a high degree of tobacco dependence. Knowing the factors involved in tobacco dependence can help in planning the care for the mentally ill.
SEF-ALDR: A Speaker Embedding Framework via Adversarial Learning based Disentangled Representation

With the pervasiveness of voice control on smart devices, speaker verification is widely used as the preferred identity authentication mechanism due to its convenience. However, the task of "in-the-wild" speaker verification is challenging, considering the speech samples may contain lots of identity-unrelated information, e.g., background noise, reverberation, emotion, etc. Previous works focus on optimizing the model to improve verification accuracy, without considering eliminating the impact from the identity-unrelated information. To solve this problem, we propose SEF-ALDR, a novel Speaker Embedding Framework via Adversarial Learning based Disentangled Representation, to further improve the performance of existing models on speaker verification. The key idea is to retrieve as much speaker identity information as possible from the original speech, i.e., minimizing the impact of identity-unrelated information on the speaker verification task. We utilize the adversarial learning method to train an eliminating encoder that produces an embedding without speaker identity representation. Hence, the speaker identity information can be well preserved in the complementary features that are not used by the eliminating encoder in the original speech. Experimental results demonstrate that the proposed framework can significantly improve the performance of the speaker verification by 20.3% and 28.7% on average over 13 tested baselines on dataset Voxceleb1 and 9 tested baselines on dataset Voxceleb2 respectively, without adjusting the structure or hyper-parameters of them. Furthermore, porting an existing model into the proposed framework is straightforward and cost-efficient, with very little effort from the model owners due to the modular design of the framework.
Sparse Concept Coded Tetrolet Transform for Unconstrained Odia Character Recognition

Feature representation in the form of spatio-spectral decomposition is one of the robust techniques adopted in automatic handwritten character recognition systems. In this regard, we propose a new image representation approach for unconstrained handwritten alphanumeric characters using sparse concept coded Tetrolets. Tetrolets, which does not use fixed dyadic square blocks for spectral decomposition like conventional wavelets, preserve the localized variations in handwritings by adopting tetrominoes those capture the shape geometry. The sparse concept coding of low entropy Tetrolet representation is found to extract the important hidden information (concept) for superior pattern discrimination. Large scale experimentation using ten databases in six different scripts (Bangla, Devanagari, Odia, English, Arabic and Telugu) has been performed. The proposed feature representation along with standard classifiers such as random forest, support vector machine (SVM), nearest neighbor and modified quadratic discriminant function (MQDF) is found to achieve state-of-the-art recognition performance in all the databases, viz. 99.40% (MNIST); 98.72% and 93.24% (IITBBS); 99.38% and 99.22% (ISI Kolkata). The proposed OCR system is shown to perform better than other sparse based techniques such as PCA, SparsePCA and SparseLDA, as well as better than existing transforms (Wavelet, Slantlet and Stockwell).
First Description of Extended-Spectrum β-Lactamases and OXA-48 Carbapenemase in Enterobacteriaceae Isolates in Brazzaville, Congo

Objective: To characterize genotypically Extended-Spetrum-Beta-Lactamase (ESBL) and OXA-48 carbapenemases producing Enterobacteriaceae especially Klebsiella, Enterobacter, Serratia (KES) and Citrobacter species in portage and infection processes at the Brazzaville hospital center. Material and Methods: The study was carried out for 7 months. Clinical samples (urine, pus, and blood cultures) were collected from inpatients and outpatients at the Brazzaville University Hospital. Strains were identified by API20E and confirmed by MALDI-TOF. Antibiotics susceptibility testing was performed on isolated strains by diffusion method on MH agar plates. ESBL and OXA-48 phenotypes were identified according to the CA-SFM synergy technique and by a decrease in inhibition diameter around the Ertapenem disk and confirmed by PCR and sequencing. MLST K. pneumoniae genotyping of OXA-48 strains was performed. Thirty-four no duplicate Enterobacteria strains were isolated from thirty-four patients, of which 12/34 (35.29%) were from outpatients and 22/34 (64.70%) from internal patients. Except for imipenem, colistin; the amikacin and fosfomycin, tested antibiotics show high resistance to the majority of the beta-lactam, as well as a resistance very frequent aminoglycosides, to sulfamides, tetracyclines and Fluoroquinolones. PCR revealed that 30/34 (88.24%) produced ESBLs, of which 2 strains harbor both ESBL and OXA-48 enzymes. blaSHV gene was the most common ESBL gene detected with 20/30 (66.67%), blaCTX-M was detected in 14 isolates (60.87%), blaTEM 15/30 (50%), blaOXA-48 2/30 (6.67%), blaCTX-M-9 1/30 (3.33%). 70% of the isolates (n=24) were isolated from urine samples. Sequencing of the amplification products revealed that the blaCTX-M1 strains were all CTX-M15; 13 variant enzymes were detected for blaSHV. Four types for TEM. Both strains OXA-48 were OXA-181 non-plasmid-borne and CTX-M-9 was CTX-M-27. These strains were resistant to gentamycin and fluoroquinolone. MLST K. pneumoniae OXA-48 showed two different standard sequences known in the ST464 and ST15 literature. Conclusion: We report here for the first time in Congo Brazzaville, the presence of β-lactamase genes including blaTEM, blaSHV, blaCTX-M and blaOXA-48 genes at disturbing frequencies within Enterobacteriaceae strains at the Brazzaville University Hospital. It proves the need to promote an infection prevention program with antibiotic regulation in hospitals in Congo Brazzaville.
Directions in Abusive Language Training Data: Garbage In, Garbage Out

Data-driven analysis and detection of abusive online content covers many different tasks, phenomena, contexts, and methodologies. This paper systematically reviews abusive language dataset creation and content in conjunction with an open website for cataloguing abusive language data. This collection of knowledge leads to a synthesis providing evidence-based recommendations for practitioners working with this complex and highly diverse data.
Mutual Fund Peer Benchmarking and Corporate Governance

The performance of mutual funds is benchmarked against their peer funds of the same style (i.e. peer benchmarking) and companies held by many peer funds become “benchmark companies” for mutual funds. This paper empirically examines how the heterogeneous degree of mutual fund peer benchmarking affects corporate governance in companies. First, benchmark companies are found to receive more attention from investors and have better corporate governance. Second, I show that mutual funds are more reluctant to sell their shares on the proxy record date and value their voting rights more in benchmark companies. Finally, I provide direct evidence of active monitoring by mutual funds in benchmark companies by examining their actual voting behavior. Incentives of an individual fund to monitor a company are affected by its peer funds of the same style, and I exploit exogenous fire sales and purchases as instruments to identify the causal effect. Overall, my findings suggest that mutual fund peer benchmarking contributes to governance in benchmark companies.
Supporting Network Reservation For Tenants In Cloud Data Centers Using Enhanced NSR

: Recently businesses move their crucial IT operations to multi-tenant cloud knowledge centres’, it is turning progressively vital to produce the network performance guarantees to individual tenants. Owing to the impact of network congestion on the performance of many common cloud applications, recent work has centered on facultative network reservation for individual tenants. Currently, Network Satisfaction Ratio (NSR) mechanism is used for network reservation in cloud data centers.NSR is defined as ratio of actual reserved bandwidth to the desired bandwidth of the tenant. Since NSR, network reservation mechanism admit only time-varying tenant requests and might fairly distribute any degradation within the NSR among the tenants and in the presence of network oversubscription the ratio is very low. In this work, a new mechanism called ENHANCED NSR is introduced where this network satisfaction mechanism admits not only time but also other network performance indicators as computation, storage and memory resource allocation. Due to this inclusion, the satisfaction ratio is increased and the tenant is reserved of its desired bandwidth even during oversubscription in cloud data centers. Hence by increasing the NSR the bandwidth is reserved for the tenant as desired.
A comparison of the 2015 Indian Academy of Pediatrics, International Obesity Task Force and World Health Organization growth references among 5–18-year-old children

Background and Rationale: The Indian Academy of Pediatrics (IAP) Growth Charts Committee has constructed revised IAP growth references for 5–18-year-old Indian children. This study was conducted with the objective of comparing these growth references with those prescribed by the International Obesity Task Force (IOTF) for Asian population and by the World Health Organization (WHO). Methods: Information about age, weight, and height was obtained from school-age children and adolescents between 5 and 18 years of age attending a health center as patients or accompanying persons. Three reference systems - the new IAP, “Asian” IOTF, and WHO-based on age- and gender-specific body mass index cutoffs were used to classify children as obese, overweight, or nonobese nonoverweight. The three reference systems were compared using weighted kappa. Results: The prevalence of overweight using new IAP, “Asian” IOTF, and WHO reference systems was 6.6%, 6.6%, and 4.0%, respectively. The prevalence of obesity using new IAP, “Asian” IOTF, and WHO reference systems was 5.0%, 5.0%, and 4.3%, respectively. The prevalence of overweight and obesity was lower when the WHO reference was used, especially for adolescents. There was an excellent agreement between new IAP and “Asian” IOTF references (weighted kappa = 0.9558, 95% confidence interval 0.9134–0.9982). Conclusions: The WHO reference may be less suitable for use in the Indian context, especially in adolescents.
Afrika Statistika ISSN 2316-090X Reliability of multi-state consecutive k-out-of-n series and parallel systems

In this paper, we focus on multi-state complex systems and their reliability. First, we provide a formula which computes the reliability of consecutive k-out-of-n: G systems. Then, we extend the used method to obtain the reliability of consecutive k-out-ofLn: G series and consecutive k-out-of-Ln: G parallel systems. In the end, we illustrate all theoretical results by numerical examples. Résumé. Dans ce papier, nous nous intéressons aux systèmes multi-états à configurations complexes et leur fiabilité. Nous commençons par établir une formule qui permet de calculer la fiabilité des systèmes k-consécutifs-sur-n : G. Ensuite, nous étendons la méthode utilisée pour obtenir la fiabilité des systèmes k-consécutifs-sur-Ln: G série et k-consécutifs-sur-Ln: G parallèle. Enfin, nous illustrons tous les résultats théoriques par des exemples numériques.
The role of non-performing loans in the transmissio n of monetary policy

Against the backdrop of a strong increase in the st ock of non-performing loans in the several European countries, this paper investigates th role of non-performing loans (NPLs) for lending rates charged for newly granted loans in the euro area. More precisely, it looks for an effect that goes beyond losses linked to that stock that are already incorporated in the banks’ capital position s. Furthermore, this paper considers the importance of funding costs as a potential link between the NPL stock and lending rates. The results indicate that a higher stock of net NPLs comes along with higher lending rates. Although the NPL stock also affects banks’ idiosyncratic funding costs, the latter do not seem to constitute the main link between the NPL stock and lending rates. The strength of the pass-through from market rat s to lending rates barely seems to be affected by the stock of NPLs.