Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequence-to-Sequence Speech Recognition for Air Traffic Control Communication

Air Traffic Control (ATC) is absolutely essential in aviation, and air traffic controllers have the highly taxing job of directing all air traffic within a specific region, preventing accidents, giving information to pilots and more. Since ATC is so important, all efforts must be made to ensure that ATC communication happens optimally and efficiently, without error. Any effort to assist the air traffic controllers or pilots in their communication is therefore warranted. An example of a system that can assist air traffic controllers and pilots is a system which detects errors in communication, such as errors in repeating instructions and callsigns, known as readback errors. What follows is an example of how an undetected readback error can lead to dangerous situations. On the 7th of March 2016, at EuroAirport Basel Mulhouse Freiburg, a serious incident took place due to a combination of factors [1]. A readback error by a pilot was not corrected by the air traffic controller, which led to two planes being on the same runway at the same time. The planes missed each other by a mere 115 meters, which is a very small distance in these kinds of situations. A system that could automatically detect this type of error, and warn pilots or controllers, could improve overall aviation safety. To build such a system, or a related system, a sufficiently fast and reliable automatic speech recognition (ASR) system is required. Currently, few ASR systems have been developed for ATC on the whole. According to Helmke et al. [3], efforts to bring ASR into the domain of ATC have been made as early as the 1990s. However, no instances of using sequenceto-sequence models for ATC have been found, which is the gap in the field that this work aims to fill.
Using Unlabelled Examples in Constraint Acquisition

Modeling a combinatorial problem is a hard and error-prone task requiring signiﬁcant expertise. Constraint acquisition attempts to automate this process by learning constraints from labelled examples, which are known solutions and (usually) non-solutions. However, no current CA system exploits unlabelled examples. The ability to do this would greatly facilitate constraint acquisition in cases where labelled examples are hard to obtain. In this short paper we explore some possibilities.
Synthetic Generation of Hourly Solar Irradiance Using a Multi-State Markov Model

Solar irradiance data is a key factor in dimensioning photovoltaic (PV) panel size and energy storage for solar powered systems. Unfortunately, long term solar irradiace data is not available for many locations, thereby affecting the accuracy and effectiveness of dimensioning efforts. To address this issue, this paper presents a model for generating accurate, synthetic traces of arbitrary length for hourly solar irradiance. The proposed model is based on an approach that combines daily correlations in weather conditions affecting the solar irradiation with fine-grained, hourly transitions in solar irradiance levels. The proposed model has been evaluated by comparing various statistical parameters of hourly solar irradiance against real data.
AdverTiming Matters: Examining User Ad Consumption for Effective Ad Allocations on Social Media

Showing ads delivers revenue for online content distributors, but ad exposure can compromise user experience and cause user fatigue and frustration. Correctly balancing ads with other content is imperative. Currently, ad allocation relies primarily on demographics and inferred user interests, which are treated as static features and can be privacy-intrusive. This paper uses person-centric and momentary context features to understand optimal ad-timing. In a quasi-experimental study on a three-month longitudinal dataset of 100K Snapchat users, we find ad timing influences ad effectiveness. We draw insights on the relationship between ad effectiveness and momentary behaviors such as duration, interactivity, and interaction diversity. We simulate ad reallocation, finding that our study-driven insights lead to greater value for the platform. This work advances our understanding of ad consumption and bears implications for designing responsible ad allocation systems, improving both user and platform outcomes. We discuss privacy-preserving components and ethical implications of our work.
Generating corner cases for crashtesting deep networks

Today, adversarial attack is almost the only kind of hard samples considered by the community to tickle deep networks. However, such additive perturbations are not realistic for many applications (e.g. remote sensing). This paper introduce a new kind of hard samples called corner cases where the entire data is generated (not just a perturbation). This allows to constraint more easily those data to be both hard but also realistic.
Preferred Housing Attributes among Elderly in Malaysia

Over the past century, the global ageing population with strong saving and pension fund has increased. In Malaysia, the property developers have targeted the emerging demand from silver-haired market by offering retirement residential. One of the challenges for them are to provide housing feature and environment that favour elderly market. This paper investigates the preferred housing attributes among Malaysian elderly by using choice-based questionnaire and conjoint analysis. Two categories of housing attributes have been investigated, namely dwelling features and environment features. The result reveals that the most preferred housing attributes among elderly in Malaysia are typed of dwelling, price, built up area and the disability convenience features. As for the environmental feature, the most preferred attributes are living convenience, followed by security, amenities in the neighbourhood, building density and public transport. Finally, this study generates preferred profile among the elderly which serve as an important reference for developers in designing and marketing their product to elderly.
Efficient Authentication Vehicular Ad-hoc Networks

Protection is one of the significant worries in vehicular Ad hoc Networks (VANETs). As of now, Azees et al. has proposed a proficient mysterious validation convention (EAAP) for VANETs. The creators guarantee that their plan can actualize restrictive protection, and that it can give obstruction against pantomime assault and fake message assault from an outside assailant. In this paper, we show that their plan neglects to stand up to these two kinds of assault just as imitation assault. By these assaults, an assailant can communicate any messages effectively. Further, the assailant can't be followed by a confided in power, which implies their plan doesn't fulfill the necessity of contingent protection. The consequences of this article unmistakably show that the plan of Azees et al. is shaky.
Learning to Classify Users in the Buyer Modalities Framework to Improve CTR

The Buyer Modalities framework divides buyers into 4 profiles, where each profile has its own specifics as to how it makes its purchasing decisions. We built an online prediction system that categorizes website visitors based on this framework. According to this categorization, a specific banner ad variant tailored to that profile was shown to the visitor, rather than a default “neutral” variant, resulting in a significantly improved CTR.
Model Identification of a Soft Robotic Neck

Soft links and actuators are nowadays emerging technologies aiming to overcome some problems in robotics such as weight, cost or human interaction. However, the nonlinear nature of their elements can make their characterization challenging and hinder the use of standard control engineering tools. In this paper, we explore different state-of-the-art identification methods for the soft neck, in order to find a reliable plant model. Even though the neck has three Degrees of freedom, in this work we only consider the planar deflection of the link as a starting point for future analysis. Given the nonlinear nature of the soft neck, we consider two identification strategies, i.e., set membership, which is a data driven, nonlinear and nonparametric identification strategy, and Recursive Least Squares at selected linearization points. A neural network identification is also given for comparison purposes. Results show that the explored methods offer a suitable alternative to identify the dynamics of the neck that allows their implementation for simulation and future control.
Wrist Exoskeleton for Teleoperation of an Ankle Prosthesis

Despite advances in mechanical design, lower limb prostheses are still limited by their control. Amputees frequently list the desire for more complete control over their devices. Similarly, these devices are limited by their lack of sensory feedback. Bilateral teleoperation of prostheses has the potential to provide direct user control and sensory feedback without having to integrate these design needs into lower limb prostheses. Exoskeletons have recently developed as a technology that allows for forces to reliably be applied to users. Therefore, we propose the design of a wrist exoskeleton for the teleoperation of a lower limb prosthesis. The device measures wrist angle to control the angle of a slave device, and can apply forces to the wrist based on position deviations of the slave. Force feedback was modeled as a virtual wall at the locations of heel and toe contact. To validate the design of our device, and to examine if sensory feedback could be beneficial in teleoperating a slave device, we conducted a study of N=6 subjects. We examine if haptic feedback, with or without visual feedback, could improve position tracking in a point-to-point test from -10 degrees wrist flexion to 15 degrees wrist extension. We found that haptic feedback significantly reduced tracking error in the task compared to strictly visual feedback (p<0.05), while including visual feedback along with haptic feedback did not significantly affect tracking (p=0.12). This experiment indicates that haptic feedback could be useful in teleoperation of lower limb prostheses and it will inform the design of future teleoperation devices.
Chronic granulomatous disease (CGD): Epidemiology, Pathogenesis, Clinical Phenotype, Diagnosis, Prognosis and Management

Chronic granulomatous disease (CGD) is a relatively rare inborn error of immune system caused by defects in the phagocyte nicotinamide adenine dinucleotide phosphate (NADPH) oxidase complex which leads to impaired production of reactive oxygen species (ROS) and ineffective phagocyte function. Genetic defects of any of proteinaceous components of NADPH oxidase complex results in CGD. The most common type of CGD (65-70%) is caused by X-linked mutations in the CYBB gene encoding gp91phox, followed by autosomal recessive mutations in the NCF1, NCF2, CYBA and NCF4 genes encoding p47phox, p67phox, p22phox, and p40phox, respectively. Dihydrorhodamine (DHR) 123 oxidation and nitroblue tetrazolium (NBT) tests are both used for the diagnosis of CGD that should be confirmed by genetic testing. CGD patients generally present with recurrent infections caused by uncommon pathogens like aspergillus, staphylococcus aureus, burkholderia cepacia, serratia marcescens, Aspergillus species and nocardia. They manifest with deep seated abscess formation, genitourinary and gastrointestinal granuloma development, autoimmunity and malignancy. Apart from comprehensive treatment of acute infections, management of CGD is based on reducing bacterial and fungal infections in addition to minimizing the inflammatory symptoms. Antibiotics, anti-fungal and IFN-γ are used for prophylaxis. Allogeneic hematopoietic stem cell transplantation from a human leucocyte antigen identical donor is currently the only proven curative treatment for CGD. Gene therapy is considered an alternative, novel therapeutic approach in near future.
Speech Emotion Recognition Based on Acoustic Segment Model

Accurate detection of emotion from speech is a challenging task due to the variability in speech and emotion. In this paper, we propose a speech emotion recognition (SER) method based on acoustic segment model (ASM) to deal with this issue. Specifically, speech with different emotions is segmented more finely by ASM. Each of these acoustic segments is modeled by Hidden Markov Models (HMMs) and decoded into a series of ASM sequences in an unsupervised way. Then feature vectors are obtained from these sequences above by latent semantic analysis (LSA). Finally, these feature vectors are fed to a classifier. Validated on the IEMOCAP corpus, results demonstrate the proposed method outperforms the state-of-the-art methods with a weighted accuracy of 73.9% and an unweighted accuracy of 70.8% respectively.
ENHANCED BUG TRAIGING WITH DATA TRUNCATION

- Software bugs being an inevitable substance ,which is growing up daily as a distractor for the software field. Assigning an efficient and well experienced developer for the bugs are a hazardous task. Bug traiging is a processing that is defined in a manner that to properly select a developer who can handle and fix the bug in an effective manner. Historical dataset plays a major role as a supporter in tariging process, that is we could use it as a reference when a new bug arrives. As per the traditional mannerism, the human traiger does the work of assigning the developer,but it overpowered with automatic startegy. Automatic bug traiging with truncated data source could pave the way much more easier and thus could select the accurate developer for the processing.The Instance and Feature Selection combination help in the case of reduction data.Large amount of data can be truncated throughout.Finally the classifier is demanded for the classification.The most efficient binary classifier(Naive Baye's),moved on to Multi Classifier(Random Forest)is used for classification.Finally it will lead to the assignment of most accurate developer for the present software bug.
Toward Assessing Clinical Trial Publications for Reporting Transparency

Objective. To annotate a corpus of randomized controlled trial (RCT) publications with the checklist items of CONSORT reporting guidelines and using the corpus to develop text mining methods for RCT appraisal. Methods. We annotated a corpus of 50 RCT articles at the sentence level using 37 fine-grained CONSORT checklist items. A subset (31 articles) was double-annotated and adjudicated, while 19 were annotated by a single annotator and reconciled by another. We calculated inter-annotator agreement at the article and section level using MASI (Measuring Agreement on Set-Valued Items)and at the CONSORT item level using Krippendorff's alpha. We experimented with two rule-based methods (phrase-based and section header-based) and two supervised learning approaches (support vector machine and BioBERT-based neural network classifiers), for recognizing 17 methodology-related items in the RCT Methods sections. Results. We created CONSORT-TM consisting of 10,709 sentences, 4,845 (45%) of which were annotated with 5,246 labels. A median of 28 CONSORT items (out of possible 37) were annotated per article. Agreement was moderate at the article and section levels (average MASI: 0.60 and 0.64, respectively). Agreement varied considerably among individual checklist items (Krippendorff's alpha= 0.06-0.96). The model based on BioBERT performed best overall for recognizing methodology-related items (micro-precision: 0.82, micro-recall: 0.63, micro-F1: 0.71). Combining models using majority vote and label aggregation further improved precision and recall, respectively. Conclusion. Our annotated corpus, CONSORT-TM, contains more fine-grained information than earlier RCT corpora. Low frequency of some CONSORT items made it difficult to train effective text mining models to recognize them. For the items commonly reported, CONSORT-TM can serve as a testbed for text mining methods that assess RCT transparency, rigor, and reliability, and support methods for peer review and authoring assistance. Minor modifications to the annotation scheme and a larger corpus could facilitate improved text mining models. CONSORT-TM is publicly available at https://github.com/kilicogluh/CONSORT-TM.
Arm-Hand Boost Therapy During Inpatient Stroke Rehabilitation: A Pilot Randomized Controlled Trial

Objective: It was the aim to assess feasibility, safety, and potential efficacy of a new intensive, focused arm-hand BOOST program and to investigate whether there is a difference between early vs. late delivery of the program in the sub-acute phase post stroke. Methods: In this pilot RCT, patients with stroke were randomized to the immediate group (IG): 4 weeks (4 w) BOOST +4 w CONTROL or the delayed group (DG): 4 w CONTROL +4 w BOOST, on top of their usual inpatient care program. The focused arm-hand BOOST program (1 h/day, 5x/week, 4 weeks) consisted of group exercises with focus on scapula-setting, core-stability, manipulation and complex ADL tasks. Additionally, 1 h per week the Armeo®Power (Hocoma AG, Switzerland) was used. The CONTROL intervention comprised a dose-matched program (24 one-hour sessions in 4 w) of lower limb strengthening exercises and general reconditioning. At baseline, after 4 and 8 weeks of training, the Fugl-Meyer assessment upper extremity (FMA-UE), action research arm test (ARAT), and stroke upper limb capacity scale (SULCS) were administered. Results: Eighteen participants (IG: n = 10, DG: n = 8) were included, with a median (IQR) time post stroke of 8.6 weeks (5–12). No adverse events were experienced. After 4 weeks of training, significant between-group differences were found for FMA-UE (p = 0.003) and SULCS (p = 0.033) and a trend for ARAT (p = 0.075) with median (IQR) change scores for the IG of 9 (7–16), 2 (1–3), and 12.5 (1–18), respectively, and for the DG of 0.5 (−3 to 3), 1 (0–1), and 1.5 (−1 to 9), respectively. In the IG, 80% of patients improved beyond the minimal clinical important difference of FMA-UE after 4 weeks, compared to none of the DG patients. Between 4 and 8 weeks of training, patients in the DG tend to show larger improvements when compared to the IG, however, between-group comparisons did not reach significance. Conclusions: Results of this pilot RCT showed that an intensive, specific arm-hand BOOST program, on top of usual care, is feasible and safe in the sub-acute phase post stroke and suggests positive, clinical meaningful effects on upper limb function, especially when delivered in the early sub-acute phase post stroke. Clinical Trial Registration: www.ClinicalTrials. gov, identifier NCT04584177
New and Increasing Rates of Adverse Events Can be Found in Unstructured Text in Electronic Health Records using the Shakespeare Method

Background: Text in electronic health records (EHRs) and big data tools offer the opportunity for surveillance of adverse events (patient harm associated with medical care) (AEs) in the unstructured notes. Writers may explicitly state an apparent association between treatment and adverse outcome (''attributed'') or state the simple treatment and outcome without an association (''unattributed''). We chose to study EHRs from 2006-2008 because of known heparin contamination during this timeframe. We hypothesized that the prevalence of adulterated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin adverse events, independent of clinicians' documentation of attributed AEs. Objective: Use the Shakespeare Method, a new unsupervised set of tools, to identify attributed and unattributed potential AEs using the unstructured text of EHRs. Methods: We studied 21,287 adult critical care admissions divided into three time periods. Comparisons of period 3 (7/2007 to 6/2008) to period 2 (7/2006 to 6/2007) were used to find admissions notes to review for new or increased clinical events by generating Latent Dirichlet Allocation topics among words in period 3 that were distinct from period 2. These results were further explored with frequency analyses of periods 1 (7/2001 to 6/2006) through 3. Results: Topics represented unattributed heparin AEs, other medical AEs, rare medical diagnoses, and other clinical events; all were verified with EHRs notes review and frequency analysis. The heparin AEs were not attributed in the notes, diagnosis codes, or procedure codes. Somewhat different from our hypothesis, heparin AEs increased in prevalence from 2001 through 2007, and decreased starting in 2008 (when heparin AEs were being published). Conclusions: The Shakespeare Method could be a useful supplement to AE reporting and surveillance of structured EHRs data. Future improvements should include automation of the manual review process.
Variability of Individual Infectiousness Derived from Aggregate Statistics of COVID-19

The quantification of spreading heterogeneity in the COVID-19 epidemic is crucial as it affects the choice of efficient mitigating strategies irrespective of whether its origin is biological or social. We present a method to deduce temporal and individual variations in the basic reproduction number $R$ directly from epidemic trajectories at a community level. Using epidemic data from the 98 districts in Denmark we estimate an overdispersion factor $k$ for COVID-19 to be about 0.11 (95 % confidence interval 0.08 -- 0.18), implying that 10 % of the infected cause between 70 % to 87 % of all infections.
Characteristics of Metals Reinforced with Ceramic Nanoparticles

Metal matrix composites (MMCs) reinforced with continuous ceramic fibers exhibit high specific strength and specific elastic modulus over unreinforced metals/alloys [1]. The high cost of reinforcing fibers and high processing cost of fiber-reinforced MMCs render them uneconomical for technological applications. In contrast, MMCs reinforced with ceramic microparticles are isotropic, easier to manufacture, and lower cost in comparison to the continuous fiber-reinforced composites. MMCs inherent excellent properties from their matrix alloy and the reinforcing phase components, i.e., ductility and toughness of the metal matrix, high modulus and strength of the reinforcement. Therefore, particulate-reinforced MMCs exhibit high strength, superior creep, and wear resistances [2 6]. They find a broad range of applications from structural components in the aerospace, automotive, and transportation industries to the thermal management for electronic devices [7,8]. Generally, the metal matrices used in the particulate-reinforced MMCs include commercially available alloys based on aluminum, magnesium, and titanium. Among them, aluminum-based alloys with face-centered cubic structure are used extensively due to their low density, good workability, and tailored mechanical property. The mechanical strength of heat-treatable aluminum alloys can be increased dramatically through aging heat treatment due to the formation of fine precipitates, i.e., precipitation hardening. However, precipitation-hardened aluminum alloys suffer a large reduction in mechanical strength upon exposure to elevated temperature for long periods because of the precipitate coarsening. Ceramic particles are stable at temperatures up to the melting temperature of the matrix metal and do not coarsen at elevated temperatures. Particulate-reinforced MMCs generally contain large volume fraction of reinforcement to achieve desired mechanical and physical properties. High reinforcement