Miracle-dz/UniFAR-FTUs huggingface dataset:
Push-Button Reliability Testing for Cloud-Backed Applications with Rainmaker

Modern applications have been emerging towards a cloud-based programming model where applications depend on cloud services for various functionalities. Such “cloud native” practice greatly simplifies application deployment and realizes cloud benefits (e.g., availability). Meanwhile, it imposes emerging reliability challenges for addressing fault models of the opaque cloud and less predictable Internet connections. In this paper, we discuss these reliability challenges. We develop a taxonomy of bugs that render cloud-backed applications vulnerable to common transient faults. We show that (mis)handling transient error(s) of even one REST call interaction can adversely affect application correctness. We take a first step to address the challenges by building a “push-button” reliability testing tool named Rainmaker, as a basic SDK utility for any cloud-backed application. Rainmaker helps developers anticipate the myriad of errors under the cloud-based fault model, without a need to write new policies, oracles, or test cases. Rainmaker directly works with existing test suites and is a plug-and-play tool for existing test environments. Rainmaker injects faults in the interactions between the application and cloud services. It does so at the REST layer, and thus is transparent to applications under test. More importantly, it encodes automatic fault injection policies to cover the various taxonomized bug patterns, and automatic oracles that embrace existing in-house software tests. To date, Rainmaker has detected 73 bugs (55 confirmed and 51 fixed) in 11 popular cloud-backed applications.
Detection of anomalous trajectories for vehicle traffic data

Modern GPS recording devices and big data infrastructures enable us to study traffic patterns in a multitude of ways, including trajectory outlier detection. A trajectory is a sequence of consecutive geographical points that can also have timestamps and an outlier is defined as a record, which in our case is a trajectory, that significantly differs from the norm. In this work, we are motivated by the need (i) to encapsulate trajectory outlier detection in real-life Fleet Management Systems (FMSs) and (ii) to improve the performance of existing outlier detection methods. To this end, two trajectory outlier detection methods are proposed, the first one relying on the DBSCAN clustering algorithm and the Hausdorff distance and the second one relying on a Support Vector Machine (SVM) classifier and the Generalized Sequence Pattern algorithm. These two algorithms are evaluated against baselines on two automatically labeled traffic datasets, the former from the Beijing metropolitan area and the latter from a real FMS in Cyprus. Automated labelling process is adopted to both allow for reproducibility and lift the burden of manual annotations from domain experts. The results show that our proposals exhibit better performance than the baselines in terms of accuracy and the F1 score, while, in general, the SVM model performs better than the path clustering one. Finally, traffic clusters and specific outliers are discussed to prove the validity of the models.
Chatbots4Mobile: Feature-oriented Knowledge Base Generation Using Natural Language

Chatbots4Mobile is a research project from the GESSI research group (UPC-BarcelonaTech) which aims at designing and developing a task oriented, knowledge based conversational agent to support mobile users in the process of managing and integrating the functionalities exposed by their own application portfolio. To support the design of the required knowledge base, the project focuses on the application of Natural Language Processing (NLP) techniques to infer extended knowledge about the features exposed by a subset of mobile applications, including feature extraction from app-related documents, syntactic and semantic similarity analysis between features, and intent/entity classification focused on functionality identification from user requests. As next steps, we are focusing on the evaluation of embedded linguistic knowledge in large language models, as well as the application of granular sentiment analysis techniques to discern biased documents and process sentiment-based user feedback.
Parallel algorithms for group word problems

This dissertation contains a study of the direction-finding algorithms commonly known by one of the following terms: signal (or noise) subspace algorithms, invariant subspace algorithms, or super-resolution algorithms. The algorithms derive their names from the fact that they are able to resolve the directions-of-arrival of sinusoidal signals impinging on an array of sensors--or equivalently, the frequencies of sinusoidal signals being sampled in time--by examining a basis of an invariant subspace of a covariance matrix formed from the signal samples, and the resolution of these algorithms is very much better than the Rayleigh resolution criterion, which was thought to be unbeatable for nearly a century. Two prominent algorithms of this type are the multiple signal classification (MUSIC) algorithm, and the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. The ESPRIT algorithm makes use of a basis for the range of a covariance matrix, and the MUSIC algorithm makes use of its orthogonal complement. All of the signal-subspace direction-finding algorithms make use of one of these two subspaces and so MUSIC and ESPRIT are presented in detail as examples of the class of algorithms. The sensitivity of signal-subspace algorithms is explored by using the gap metric as a means of measuring the distance between subspaces, and also by means of the small-sample Monte Carlo condition estimation method which was recently developed by Charles Kenney and Alan Laub. A new resolution criterion for direction-finding methods is given that is similar to the Rayleigh resolution criterion for the separation of sources of nearly equal spatial direction, or equivalently, of nearly equal frequency in the frequency estimation problem. Signal-subspace direction finding is applied to sensor arrays with focusing lenses. This problem is of particular importance to wide-band direction finding. Also, various methods of efficiently computing solutions to the signal-subspace problem are discussed.
Prediction of User-Brand Associations Based on Sentiment Analysis

Finding the right users to be chosen as targets for advertising campaigns is not a trivial task, and it may allow important commercial advantages. A novel approach is presented here for the recommendation of new possible consumers to brands interested in distributing advertising campaigns, ranked according to the “compatibility” between users and brands. A database containing both descriptions associated with different brands, and textual information about users’ opinions on different topics, is required in input. Then, sentiment analysis techniques are applied to measure to what extent the users match with the brands, based on the texts associated with their opinions. The approach has been tested on both synthetic and real datasets, and with two different formulations, showing promising results in all the considered experiments.
Is My Model Up-to-date? Detecting CoViD-19 Variants by Machine Learning

Machine learning extracts models from huge quantities of data. Models trained and validated over past data can be deployed in making forecasts as well as in classifying new incoming data. The real world which generates data may change over time, making the deployed model an obsolete one. To preserve the quality of the currently deployed model, continuous machine learning is required. Our approach retrospectively evaluates in an online fashion the behaviour of the currently deployed model. A drift detector detects any performance slump, and, in case, can replace the previous model with an up-to-date one. The approach experiments on a dataset of 8642 hematochemical examinations from hospitalized patients gathered over 6 months: the outcome of the model predicts the RT-PCR test result about CoViD-19. The method reached an area under the curve (AUC) of 0.794, 6% better than offline and 5% better than standard online-binary classification techniques. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)
Hermit: Low-Latency, High-Throughput, and Transparent Remote Memory via Feedback-Directed Asynchrony

Remote memory techniques are gaining traction in datacenters because they can signiﬁcantly improve memory utilization. A popular approach is to use kernel-level, page-based memory swapping to deliver remote memory as it is transparent, enabling existing applications to beneﬁt without modiﬁ-cations. Unfortunately, current implementations suffer from high software overheads, resulting in signiﬁcantly worse tail latency and throughput relative to local memory. Hermit is a redesigned swap system that overcomes this limitation through a novel technique called adaptive, feedback-directed asynchrony . It takes non-urgent but time-consuming operations ( e.g. , swap-out, cgroup charge, I/O deduplication, etc. ) off the fault-handling path and executes them asynchronously. Different from prior work such as Fastswap, Hermit collects runtime feedback and uses it to direct how asynchrony should be performed— i.e. , whether asynchronous operations should be enabled, the level of asynchrony, and how asynchronous operations should be scheduled. We implemented Hermit in Linux 5.14. An evaluation with a set of latency-critical applications shows that Hermit delivers low-latency remote memory. For example, it reduces the 99 th percentile latency of Memcached by 99.7% from 36 ms to 91 µ s . Running Hermit over batch applications improves their overall throughput by 1.24 × on average. These results are achieved without changing a single line of user code.
Intrinsic Predictability of the 20 May 2013 Tornadic Thunderstorm Event in Oklahoma at Storm Scales

AbstractUsing a high-resolution convection-allowing numerical weather prediction model, this study seeks to explore the intrinsic predictability of the severe tornadic thunderstorm event on 20 May 2013 in Oklahoma from its preinitiation environment to initiation, upscale organization, and interaction with other convective storms. This is accomplished through ensemble forecasts perturbed with minute initial condition uncertainties that were beyond detection capabilities of any current observational platforms. It was found that these small perturbations, too small to modify the initial mesoscale environmental instability and moisture fields, will be propagated and evolved via turbulence within the PBL and rapidly amplified in moist convective processes through positive feedbacks associated with updrafts, phase transitions of water species, and cold pools, thus greatly affecting the appearance, organization, and development of thunderstorms. The forecast errors remain nearly unchanged even when the initial p.
From US to Domain Models: Recommending Relationships between Entities

User stories are a common notation for expressing requirements, especially in agile development projects. While user stories provide a detailed account of the functional requirements, they fail to deliver a holistic view of the domain. As such, they can be complemented with domain models that not only help gain this comprehensive view, but also serve as a basis for model-driven development. We focus on the task of recommending relationships between entities in a domain model, assuming that these entities were previously extracted from a user story collection either manually or through an automated tool. We investigate whether an approach based on supervised machine learning can recommend essential relationships in a domain model more accurately than state-of-the-art rule-based methods. Based on a collection of datasets that we manually labeled and a set of 32 features we engineered, we train a machine learning model by using a random forest classifier. The results indicate that our approach has higher precision and F 1 -score than the baseline rule-based methods. Our findings provide preliminary evidence of the suitability of using machine learning to support the development of domain models, especially in recommending relationships between related entities.
Data migration in column family database evolution using MDE

When software requirements change, databases used by an application may evolve, including the database models (conceptual model and schema), which may require data migrations to maintain data integrity. In some databases such as NoSQL column family databases, the tables of the database often store repeated data, as schema denormalization is encouraged to achieve the best performance. This makes data integrity maintenance more complex, as a single conceptual model change may trigger several changes in the schema. In this work, we propose using a model-driven engineering approach named MoDEvo for data migration in NoSQL column family databases to maintain data integrity after the schema evolves. Using a motivating example as a case study from an open source project that requires data migration, we describe MoDEvo and use it to illustrate how MoDEvo determined these data migrations.
Gems of PODS: Applications of Sketching and Pathways to Impact

Data summaries (a.k.a., sketches) are compact data structures that can be updated flexibly and efficiently to capture certain properties of a data set. Well-known examples include set summaries (Bloom Filters) and cardinality estimators (e.g., Hyperloglog), amongst others. PODS and SIGMOD have been home to many papers on sketching, including several best paper recipients. Sketch algorithms have emerged from the theoretical research community, but have found wide impact in practice. This paper describes some of the impacts that sketches have had, from online advertising to privacy-preserving data analysis. It will consider the range of different strategies that researchers can follow to encourage the adoption of their work, and what has and has not worked for sketches as a case study.
Addressing the unresolved challenge of quantifying skiing exposure—A proof of concept using smartphone sensors

In epidemiological studies related to winter sports, especially alpine skiing, an unresolved methodological challenge is the quantification of actual on-snow activity exposure. Such information would be relevant for reporting meaningful measures of injury incidence, which refers to the number of new injuries that occur in a given population and time period. Accordingly, accurate determination of the denominator, i.e., actual “activity exposure time”, is critical for injury surveillance and reporting. In this perspective article, we explore the question of whether wearable sensors in combination with mHealth applications are suitable tools to accurately quantify the periods in a ski day when the skier is physically skiing and not resting or using a mechanical means of transport. As a first proof of concept, we present exemplary data from a youth competitive alpine skier who wore his smartphone with embedded sensors on his body on several ski days during one winter season. We compared these data to self-reported estimates of ski exposure, as used in athletes' training diaries. In summary, quantifying on-snow activity exposure in alpine skiing using sensor data from smartphones is technically feasible. For example, the sensors could be used to track ski training sessions, estimate the actual time spent skiing, and even quantify the number of runs and turns made as long as the smartphone is worn. Such data could be very useful in determining actual exposure time in the context of injury surveillance and could prove valuable for effective stress management and injury prevention in athletes.
Diverse bone microanatomy in cetaceans from the Eocene of Ukraine further documents early adaptations to fully aquatic lifestyle

Basilosauridae, fully aquatic archaeocetes from the Eocene, had osteosclerotic or pachyosteosclerotic structure of ribs and, sometimes, other bones. Such a structure is far different from osteoporotic-like bones of modern cetaceans. A microanatomical and histological study was conducted on axial and limb skeleton of several basilosaurid specimens assigned to the genus Basilotritus, from Bartonian (late middle Eocene) deposits of Ukraine, remarkable for its pachyostotic bones. The postcranial skeleton of these specimens is a complex mosaic of diverse types of bone structure, which include pachyosteosclerotic, osteosclerotic and cancellous elements. The vertebrae have a pachyostotic layered cortex reaching its greatest thickness in the lumbar region. This cortex was strongly vascularized, and its layered structure is due to concentric circles mostly made by longitudinal vascular canals, in addition to cyclical growth lines. Heavy bones are concentrated in the dorsal and ventral areas. Swollen distal ends of thoracic ribs are interpreted as serving as ballast in the ventral area, as also previously proposed for Basilosaurus cetoides. Cortical bone tissue in vertebrae and ribs showed signs of intensive resorption and remodeling. This indicates the use of the axial skeleton not only for buoyancy control but also possibly for calcium and phosphorus recycling.
Neural induction drives body axis formation during embryogenesis, but a neural induction-like process drives tumorigenesis in postnatal animals

Characterization of cancer cells and neural stem cells indicates that tumorigenicity and pluripotency are coupled cell properties determined by neural stemness, and tumorigenesis represents a process of progressive loss of original cell identity and gain of neural stemness. This reminds of a most fundamental process required for the development of the nervous system and body axis during embryogenesis, i.e., embryonic neural induction. Neural induction is that, in response to extracellular signals that are secreted by the Spemann-Mangold organizer in amphibians or the node in mammals and inhibit epidermal fate in ectoderm, the ectodermal cells lose their epidermal fate and assume the neural default fate and consequently, turn into neuroectodermal cells. They further differentiate into the nervous system and also some non-neural cells via interaction with adjacent tissues. Failure in neural induction leads to failure of embryogenesis, and ectopic neural induction due to ectopic organizer or node activity or activation of embryonic neural genes causes a formation of secondary body axis or a conjoined twin. During tumorigenesis, cells progressively lose their original cell identity and gain of neural stemness, and consequently, gain of tumorigenicity and pluripotency, due to various intra-/extracellular insults in cells of a postnatal animal. Tumorigenic cells can be induced to differentiation into normal cells and integrate into normal embryonic development within an embryo. However, they form tumors and cannot integrate into animal tissues/organs in a postnatal animal because of lack of embryonic inducing signals. Combination of studies of developmental and cancer biology indicates that neural induction drives embryogenesis in gastrulating embryos but a similar process drives tumorigenesis in a postnatal animal. Tumorigenicity is by nature the manifestation of aberrant occurrence of pluripotent state in a postnatal animal. Pluripotency and tumorigenicity are both but different manifestations of neural stemness in pre- and postnatal stages of animal life, respectively. Based on these findings, I discuss about some confusion in cancer research, propose to distinguish the causality and associations and discriminate causal and supporting factors involved in tumorigenesis, and suggest revisiting the focus of cancer research.
Application of Yarrowia lipolytica in fermented beverages

Yarrowia lipolytica is a non-conventional non-pathogenic, generally regarded as safe yeast. It has been isolated from a wide variety of places, from foodstuffs like beer, cheese and sausages to beetle guts and human mouths. It is strictly aerobic and Crabtree-negative. Y. lipolytica harbours various biochemical and physiological traits that make it relevant for biotechnological and food-related applications. Until recently, the application and effect of Y. lipolytica on lipid-containing foodstuff, that is, meat and dairy, have been researched and discussed meticulously. The yeasts’ potential as a synthesiser of several high-value food ingredients, such as organic acids, aromas, and emulsifiers from a range of diverse substrates, from ethanol to olive oil waste, is of interest in a biorefinery context. Interestingly the use of Y. lipolytica as a starter culture in foodstuffs beyond meat and dairy is minimal, despite its ability to synthesise interesting aromas and organic acids that could increase the organoleptic quality of fermented beverages. Besides the indulgence factor, Y. lipolytica synthesises a wide range of functional and bioactive compounds that can act as active ingredients in functional beverages, adding to its potential in producing novel beverages.
Capturing agro-morphological variability for tolerance to terminal heat and combined heat–drought stress in landraces and elite cultivar collection of wheat

Climate change has resulted in extreme temperature and drought around the globe, which poses serious threat to food security. Both heat and drought stress affects the production and productivity of wheat crop. The present study was undertaken to evaluate 34 landraces and elite cultivars of Triticum spp. for phenological and yield-related traits under optimum, heat, and combined heat–drought stress environments during 2020–2021 and 2021–2022. The pooled analysis of variance showed significant genotype × environment interaction, suggesting an influence of stress on trait expression. The trait performance of genotypes exhibited significant reduction under combined heat–drought stress as compared to optimum and heat stress environments. The maximum seed yield penalty was observed under combined heat–drought stress environment as compared to heat stress alone. Regression analysis indicated significant contribution of number of grains per spike towards stress tolerance. Based on Stress Tolerance Index (STI), genotypes Local-17, PDW 274, HI-8802, and HI-8713 were identified to be tolerant to both heat and combined heat and drought stress at Banda, whereas genotypes DBW 187, HI-8777, Raj 4120, and PDW 274 were tolerant at Jhansi location. The genotype PDW 274 showed stress tolerance under all treatments at both the locations. The genotypes PDW 233 and PDW 291 showed highest stress susceptibility index (SSI) across the environments. The number of grains per spike and test kernel weight were positively associated with seed yield across the environments and locations. The selected genotypes Local-17, HI 8802, and PDW 274 were identified to be the potential sources of heat and combined heat–drought tolerance, which may be utilized in hybridization to develop tolerant wheat genotypes and also for mapping of underlying genes/quantitative trait loci (QTLs).