Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging User Feedback for Requirements Through Trend and Narrative Analysis

With the rapid rise of new mediums and surge of user discussion regarding software products, it is increasingly important to consider these user concerns to fulfill user needs, otherwise users may opt for alternatives. Traditional requirements elicitation approaches relied on interviews and surveys with stakeholders to elicit key and important requirements. Despite the advances from recent studies to leverage the “crowd” via increased involvement of crowd based discussions, we still lack empirical structured guidance and approaches to handle feedback from multiple sources and synthesize the themes that emerge from these sources. As providing feedback becomes more accessible for users, managing the volume of feedback is correspondingly challenging. Since development resources are often limited, organizations need to make trade-offs between different user concerns. Gaining more insights on the themes and the trends of these themes from feedback should help organizations conduct these requirement trade-offs. To better explain the emergent trends of user feedback, I use the concept of narratives from economics to explain the phenomenon of what and when users change in their user discussions. Narrative analysis can help explain the causes of feedback trends, which can support organizations determining the priority and validity of various themes from feedback. In this work, I describe my preliminary findings which indicate the profound role that trends and narratives in user feedback have on user perception and concerns. My work has shown that feedback sources, like social media, can provide an avenue to identify requirements. Finally, I outline my plan towards developing a framework to identify trends and narratives, and a tool to support automated identification of requirements in the form of user stories.
Traffic Event Detection from Consumer Vehicle Sensor Data: An Autonomous Vehicle Study

Traffic event detection from vehicles’ on-board sensors can detect the road information and improve traffic management and safety. Current sensor-based traffic event detection is mainly based on probe vehicles, test vehicles, or other designated vehicles, which is costly and cannot be deployed on a large scale. With the fast development of on-board equipment, data collection from consumer vehicle sensors is becoming popular and can cover a large geographic scale with almost no equipment or labor cost. However, there are very few studies of the data features and potential for application. This paper presents a pipeline for employing consumer vehicle sensors to detect roadworks. The unique data features and deficiencies of the consumer vehicle sensor are discussed and summarized. A clustering method is employed to distinguish the roadwork sites. A route builder method is proposed to reconstruct the routes of the roadworks and to extract the corresponding start and end locations. Compared with ground truth, roadworks detection from consumer vehicle sensors can cover up to 86% of the roadworks on freeways and over 40% of the roadworks on non-freeway roads. The average offset errors are 4.7% on freeways, and 24.9% on non-freeway roads. Compared with point-based roadworks detection from “ViaMichelin Traffic information,” the results from consumer vehicle sensors achieved a matching ratio of nearly 90% and were advantageous in extracting the roadworks route information. This study proves the possibility of employing consumer vehicle sensors for route-based traffic event detection and provides insights for countering uneven distribution and trajectory truncation issues related to privacy protection.
Towards the Quantitative Interpretability Analysis of Citizens Happiness Prediction

Evaluating the high-effect factors of citizens' happiness is beneficial to a wide range of policy-making for economics and politics in most countries. Benefiting from the high-efficiency of regression models, previous efforts by sociology scholars have analyzed the effect of happiness factors with high interpretability. However, restricted to their research concerns, they are specifically interested in some subset of factors modeled as linear functions. Recently, deep learning shows promising prediction accuracy while addressing challenges in interpretability. To this end, we introduce Shapley value that is inherent in solid theory for factor contribution interpretability to work with deep learning models by taking into account interactions between multiple factors. The proposed solution computes the Shapley value of a factor, i.e., its average contribution to the prediction in different coalitions based on coalitional game theory. Aiming to evaluate the interpretability quality of our solution, experiments are conducted on a Chinese General Social Survey (CGSS) questionnaire dataset. Through systematic reviews, the experimental results of Shapley value are highly consistent with academic studies in social science, which implies our solution for citizens' happiness prediction has 2-fold implications, theoretically and practically.
Shannon Entropy Index and a Fuzzy Logic System for the Assessment of Stator Winding Short-Circuit Faults in Induction Motors

The induction motor (IM) is one of the most important elements in industry. Although IMs are robust machines, they are susceptible to faults, where the stator winding short-circuit fault is one of the most common ones. In this work, the Shannon entropy (SE) index and a fuzzy logic (FL) system are proposed to diagnose short-circuit faults, considering both different severity levels and different load conditions. In the proposed methodology, a filtering stage based on brick-wall band-pass filters is firstly carried out. After this stage, the SE index is computed to quantify the fault severity and a FL system is applied to diagnose the IM condition in an automatic way. Unlike other works that propose some types of space transformations, the proposal is only based on a filtering stage and a time domain index, requiring low computational resources. The obtained results demonstrate the effectiveness of the proposal, i.e., the SE index quantifies the fault severity, regardless of the mechanical load, and the proposed FL system achieves a positive classification rate of 98%.
sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)

SUNNY is a k-nearest neighbors based Algorithm Selection (AS) approach that schedules and runs a number of solvers for a given unforeseen problem. In this work we present sunny-as2, an enhancement of SUNNY for generic AS scenarios that advances the original approach with wrapper-based feature selection, neighborhood-size configuration and a greedy approach to speed-up the training phase. Empirical evidence shows that sunny-as2 is competitive w.r. t. state-of-the-art AS approaches.
From CDIO to challenge-based learning experiences – expanding student learning as well as societal impact?

ABSTRACT Challenge-based learning (CBL) is a multidisciplinary approach that encourages students to work actively with peers, teachers and stakeholders in society to identify complex challenges, formulate relevant questions and take action for sustainable development. In this paper, it is argued that CBL can be viewed as an evolution of the Conceive, Design, Implement, Operate concept, expanding as well as deepening the learning experience. The study reported on investigates the multiple aims of a particular CBL environment (the Challenge Lab at Chalmers University of Technology), which are to combine significant student learning and societal transformation. The results show that the students perceive that they have developed deep skills in problem formulation and sustainable development, as well as working across disciplines and with different stakeholders. Moreover, the study shows that although few student projects reach implementation stage, there is a potential for societal impact both during and after the Challenge Lab learning experience.
Light Agents Searching for Hot Information

Agent-based crawlers are commonly used in network maintenance and information gathering. In order not to disturb the main functionality of the system, whether acting at nodes or being in transit, they need to operate online, perform a single operation fast and use small memory. They should also be preferably deterministic, as crawling agents have limited capabilities of generating a large number of truly random bits. We consider a system in which an agent receives an update, typically an insertion or deletion, of some information upon visiting a node. On request, the agent needs to output hot information, i.e., with the net occurrence above certain frequency threshold. A desired time and memory complexity of such agent should be poly-logarithmic in the number of visited nodes and inversely proportional to the frequency threshold. Ours is the first such agent with rigorous analysis and a complementary almost-matching lower bound.
High-Density Large-Scale TMR Sensor Array for Magnetic Field Imaging

Magnetic field imaging (MFI) is widely used in electromagnetic nondestructive evaluation (ENDE). This paper presents a high-density, large-scale tunneling magnetoresistance (TMR) sensor array for MFI applied in ENDE. The TMR array consists of 180 sensing elements with the distance of 0.5 mm between any pair of TMR sensors adjacent to each other, which enables to acquire an image with high spatial resolution. With a total length of 90 mm, the TMR array is capable of imaging a rectangular area as wide as 90 mm in a single scan resulting in a fast inspection speed for the evaluation of structure with a large surface area. The proposed TMR sensor array has high sensitivity to the magnetic field in a wide frequency range. Therefore, it can be used for the detection of deeply embedded defects in multilayer riveted structures with excitation currents at ultralow frequencies. Experimental results show that machined notches under fasteners head at a distance of 8 mm from the top surface of a multilayer riveted aluminum structure can be detected with a proper fusion of the magnetic field images at 100 and 1000 Hz. Furthermore, surface notch with dimension $300\,\,\mu \text {m} \times 300\,\,\mu \text {m} \times 400\,\,\mu \text{m}$ on an aluminum sample can be detected from the obtained magnetic field image with excitation current frequency 20 kHz.
Captioning Bosch: A Twitter Bot

The artworks by Dutch painter Hieronymus Bosch are well known for their incredible wealth of details. The popular BoschBot regularly posts small segments of the digitized paintings on Twitter, thus relieving their density and making them more accessible. CaptioningBoschBot, the Twitter bot presented in this demo, reverses the creative process of the artist: It uses the out-of-context painting segments as input for an encoder-decoder model to generate captions that interpret the painted objects. As the model was only trained on realistic, photographic images, curious interpretations of the otherworldly details can be observed. The generated captions are again posted on Twitter to encourage discussions about Bosch's masterpieces and the AI technology in general.
Cervical Cancer - Its Prevalence, Risk factors, Imaging, and Treatment

Cervix uteri is a leading type of cancer exclusive to women. It is gradually spread-ing over different regions of the world without any prominent cause or reason behind it. This disease affects the lower part of the uterus (start of the cervix area). Most reports advocate the role of Human Papilloma Virus (HPV) infection in developing this cancer. Environmental and age-related factors also play an important role, and these areas are open for research to discover exact relationships. However, in this article, we have summarized and revealed some important statistics about cervix uteri cancer that describe its epidemiology, risk factors, relation to age, treatments, staging and similar aspects.
Thermal architecture of the ESA ARIEL payload

The Atmospheric Remote-sensing Infrared Exoplanets Large-survey (ARIEL) is a space project selected by the European Space Agency for the Phase A study in the context of the M4 mission within the Cosmic Vision 2015-2025 programme. ARIEL will probe the chemical and physical properties of a large number of known exoplanets by observing spectroscopically their atmosphere, to extend our knowledge of how planetary systems form and evolve. To achieve its scientific objectives, the mission is designed as a dedicated 3.5-years survey for transit and eclipse spectroscopy, with an instrumental layout based on a 1-m class telescope feeding two spectrometer channels that cover the band 1.95 to 7.8 μm and four photometric channels in the visible to near-IR range. The high sensitivity requirements of the mission need an extremely stable thermo-mechanical platform. In this paper we describe the thermal architecture of the payload and discuss the main requirements that drive the design. The ARIEL thermal configuration is based on a passive and active cooling approach. Passive cooling is achieved by a V-Groove based design that exploits the L2 orbit favorable thermal conditions. The telescope and the optical bench are passively cooled to a temperature close to 50K to achieve the required sensitivity and stability. The photometric detectors are maintained at the operating temperature of 50K by a dedicated radiator coupled to cold space. The IR spectroscopic channel detectors require a lower temperature reference. This colder stage is provided by an active cooling system based on a Neon Joule-Thomson cold end, fed by a mechanical compressor, able to reach temperatures lower than 30K. Thermal stability of the telescope and detector units is one of the main drivers of the design. The periodical perturbations due to orbital changes, to the active cooling or to other internal instabilities make the temperature control one of the most critical issues of the whole architecture. The thermal control system design, based on a combination of passive and active solutions aimed at maintaining the required stability at the telescope and detector stages level, is described. We report here about the baseline thermal architecture at the end of the Phase A, together with the main trade-offs needed to enable the ARIEL exciting science in a technically feasible payload design. Thermal modeling results and preliminary performance predictions in terms of steady state and transient behavior are also discussed.
A Novel Autosomal Recessive Candidate Gene Responsible for RASopathy-Like Phenotype and Bone Marrow Failure: RASA3

Abstract Although many genetic etiologies, such as Fanconi anemia, Shwachman–Diamond syndrome, dyskeratosis congenita, and Diamond–Blackfan anemia, from hereditary bone marrow failure are known today, the responsible gene remains unknown in a significant part of these patients. A 6-year-old girl, whose parents were first-cousin consanguineous, was referred to the pediatric hematology department due to growth retardation, thrombocytopenia, neutropenia, and anemia. The patient had low-set ears, pectus excavatum inferiorly, and cafe-au-lait spots. In whole-exome analysis, p.K385T (c.1154A > C) variant in the RASA3 gene was detected as homozygous. The amino acid position of the alteration is located in the conserved and ordered region, corresponding to the Ras GTPase activation domain (Ras-GAP) in the center of the protein. Importantly, most of in silico prediction tools of pathogenicity predicts the variant as damaging. RASopathies, which are characterized by many common clinical findings, such as atypical facial features, growth delays, and heart defects, are a group of rare genetic diseases caused by mutations in the genes involved in the Ras-MAPK pathway. The findings in this patient were consistent with the RASopathy-like phenotype and bone marrow failure. Interestingly, enrichment of RASopathy genes was observed in the RASA3 protein–protein interaction network. Furthermore, the subsequent topological clustering revealed a putative function module, which further implicates RASA3 in this disease as a novel potential causative gene. In this context, the detected RASA3 mutation could be manifesting itself clinically as the observed phenotype by disrupting the functional cooperation between the RASA3 protein and its interaction partners. Relatedly, current literature also supports the obtained findings. Overall, this study provides new insights into RASopathy and put forward the RASA3 gene as a novel candidate gene for this disease group.
Applications and new developments in phenological research: monitoring, modelling and outlook

The importance of long-term plant phenological time series is growing in monitoring of climate change impacts worldwide. To detect trends and assess possible influences of climate in Hungary, we studied flowering phenological records for 6 species (Convallaria majalis, Taraxacum officinale, Syringa vulgaris, Sambucus nigra, Robinia pseudoacacia, Tilia cordata) based on phenological observations from the Hungarian Meteorological Service recorded between 1952 and 2000. Altogether 4 from the 6 examined plant species showed significant advancement in flowering onset with an average rate of 1.9–4.4 days per decade. We found that it was the mean temperature of the 2–3 months immediately preceding the mean The paper is published in the International Journal of Biometeorology (Szabó et al., 2016) and is co-authored with Enikő Vincze and Bálint Czúcz.
Mathematical Model of Routing Protocol Optimization in Wireless Sensor Networks Based on Improved Particle Swarm Optimization

Energy consumption is very important for the performance of wireless sensor networks. In this paper, we fully consider the constraints of bit error rate, power constraints and competition radius of wireless sensor networks (WSN). , which are in line with the actual network conditions. With the goal of optimizing the routing protocol and reducing the total energy consumption, a mathematical model is established. As a heuristic algorithm, particle swarm optimization (PSO) does not require the form of the objective function or the continuity of the optimization problem, which is simple and can more effectively solve the optimization problem. To improve the tendency of particle swarm optimization algorithm to fall into local optima, the weight factor, cognitive learning factor. , and social learning factor are improved in PSO. Simulation results show that the proposed routing protocol optimization algorithm has better convergence, which can more effectively reduce the energy consumption and optimize the network lifetime of wireless sensor networks.
A Modulation Reference Compensation Method to Realize Fully Decoupled Power Control for Modular Multilevel Converters

This paper proposes a modulation reference compensation method to completely eliminate the intercoupling between the output active and reactive powers of modular multilevel converters (MMCs). Different from the existing methods, it is realized not based on an accurate control model, but by directly amending the initial arm modulation references of MMCs. Therefore, the proposed method is essentially a compensation algorithm, which is extremely easy to implement in a microcontroller with low calculation burden. In addition, it does not need any additional closed-loop controllers or voltage/current sensors, which is very beneficial to practical projects. The proposed method finally realizes accurate and fully decoupled active and reactive power regulation, avoids overload operation and protection malfunction, and significantly improves the dynamic performance of MMCs. The effectiveness and accuracy of the proposed analysis and compensation method are carefully verified by both simulation and experimental results.
Improving Continuous-Variable Measurement-Device-Independent Multipartite Quantum Communication with Optical Amplifiers

The developing tendency of continuous-variable (CV) measurement-device-independent (MDI) quantum cryptography is to cope with the practical issue of implementing scalable quantum networks. Up to now, most theoretical and experimental researches on CV-MDI QKD are focused on two-party protocols. However, we suggest a CV-MDI multipartite quantum secret sharing (QSS) protocol use the EPR states coupled with optical amplifiers. More remarkable, QSS is the real application in multipartite CV-MDI QKD, in other words, is the concrete implementation method of multipartite CV-MDI QKD. It can implement a practical quantum network scheme, under which the legal participants create the secret correlations by using EPR states connecting to an untrusted relay via insecure links and applying the multi-entangled Greenberger–Horne–Zeilinger (GHZ) state analysis at relay station. Even if there is a possibility that the relay may be completely tampered, the legal participants are still able to extract a secret key from network communication. The numerical simulation indicates that the quantum network communication can be achieved in an asymmetric scenario, fulfilling the demands of a practical quantum network. Additionally, we illustrate that the use of optical amplifiers can compensate the partial inherent imperfections of detectors and increase the transmission distance of the CV-MDI quantum system.
Aldehyde Dehydrogenase Isoform 1 Predicts a Poor Prognosis of Acute Cerebral Infarction

To investigate the prognostic potential of serum aldehyde dehydrogenase isoform 1 (ALDH1) level in acute cerebral infarction, and the molecular mechanism in mediating neurological deficits, a total of 120 acute cerebral infarction cases within 72 h of onset were retrospectively analyzed. Serum ALDH1 level in them was detected by qRT-PCR. Receiver operating characteristic (ROC) and Kaplan–Meier curves were depicted for assessing the diagnostic and prognostic potentials of ALDH1 in acute cerebral infarction, respectively. An in vivo acute cerebral infarction model in rats was established by performing MCAO, followed by evaluation of neurological deficits using mNSS and detection of relative levels of ALDH1, Smad2, Smad4, and p21 in rat brain tissues. Pearson's correlation test was carried out to verify the correlation between ALDH1 and mNSS and relative levels of Smad2, Smad4, and p21. Serum ALDH1 level increased in acute cerebral infarction patients. A high level of ALDH1 predicted a poor prognosis of acute cerebral infarction patients. In addition, ALDH1 was sensitive and specific in distinguishing acute cerebral infarction cases, presenting a certain diagnostic potential. mNSS was remarkably higher in acute cerebral infarction rats than that of controls. Compared with sham operation group, relative levels of ALDH1, Smad2, and Smad4 were higher in brain tissues of modeling rats, whilst p21 level was lower. ALDH1 level in brain tissues of modeling rats was positively correlated to mNSS, and mRNA levels of Smad2 and Smad4, but negatively correlated to p21 level. Serum ALDH1 level is a promising prognostic and diagnostic factor of acute cerebral infarction, which is correlated to 90-day mortality. Increased level of ALDH1 in the brain of cerebral infarction rats is closely linked to neurological function, which is associated with the small mothers against decapentaplegic (Smad) signaling and p21.