Miracle-dz/UniFAR-FTUs huggingface dataset:
Compiler-Directed High-Performance Intermittent Computation with Power Failure Immunity

This paper introduces power failure immunity (PFI), an essential program execution property for energy harvesting systems to achieve efficient intermittent computation. PFI ensures program code regions never fail more than once i.e., at most single in-region outage, during intermittent computation as if they are immunized after the first power outage. To enforce PFI automatically for such batteryless systems that use a tiny energy buffer instead, we present its compiler-directed enforcement. The compiler leverages a precise static analysis to partition the program into recoverable regions with the energy buffer size in mind so that their execution can be completed—using the full energy buffered in a single charge cycle—regardless of program execution paths. In this way, no matter how unstable the energy harvesting source is, no region fails more than once.In the virtue of PFI, this paper presents ROCKCLIMB, a high-performance and rollback-free intermittent computation scheme. It guarantees that PFI-enforced regions never fail, i.e., there is no in-region outage at all. To achieve this, ROCKCLIMB checks if the fully buffered energy is secured at each region boundary. If it is not secured, ROCKCLIMB waits until the energy buffer is fully charged, before executing the following region. In particular, the rollback-free nature of ROCKCLIMB obviates the need to log memory writes—required for rollback recovery—since no region is power-interrupted. As a result, PFI+ROCKCLIMB achieves rollback-free and memory-log-free intermittent computation, ensuring forward execution progress and maximizing it even in the presence of frequent power outages. Our real board experiments demonstrate that PFI+ROCKCLIMB outperforms the state-of-the-art work by 5%—550% on average in various energy harvesting conditions.
Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange

Context: Technical debt (TD) can bring short-term benefits to software projects, but its presence is also associated with is-sues such as decreasing product quality. Recent literature has proposed indicator-based strategies for TD identification and different approaches for TD management, but most of them focused on the point of view of software developers. Little is still known about how project management practition-ers actually discuss, experience, and manage TD. Goal: This work investigates, from the point of view of project manage-ment practitioners, how they commonly discuss, experience, and manage TD. Method: We mined, curated, and selected a total of 42 TD discussions on Stack Exchange Project Man-agement (SEPM), totaling 583 messages. We analyzed this data set quantitatively and qualitatively. Results: The most commonly discussed types of debt are process and people, revealing 47 indicators for recognizing debt items. We also found 72 practices related to TD management. Conclusion: The perspective considered by project management practitioners to analyze the TD phenomenon is different from the one considered by other roles in the software development process. This work organizes the TD indicators and manage-ment practices identified in SEPM into a Sankey diagram, which may assist practitioners and serve as guidance for future research.
Cross-Modal Learning for Audio-Visual Emotion Recognition in Acted Speech

Human affects and automatic emotions detection has been an active research topic for several years, with outcomes that have been beneficial to a number of different applications, including human-machine interaction, health-care, e-education, etc. Indeed, humans perceive and express emotions in a multi-modal manner. In order to capture this multimodal emotional content, robust features need to be extracted and combined efficiently. In this paper, we propose a cross-modal deep learning framework that leverages audio and visual information (speech and facial emotions) for emotion recognition in acted speech. The proposed method learns the spatio-temporal information of facial expressions and the audio features in an end-to-end fashion. The experiments were conducted on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Proposed method achieves a classification accuracy of 87.5% outperforming state-of-the-art works on RAVDESS.
Modeling and Operator Control of a Robotic Tool for Bidirectional Manipulation in Targeted Prostate Biopsy

This work introduces design, manipulation, and operator control of a bidirectional robotic tool for minimally invasive targeted prostate biopsy. The robotic tool is purposed to be used as a compliant flexure section of active biopsy needles. The design of the robotic tool comprises of a flexure section fabricated on a nitinol tube that enables bidirectional bending via actuation of two internal tendons. The statics of the flexure section is presented and validated with experimental data. Finally, the capability of the robotic tool to reach targeted positions inside prostate gland is evaluated.
Risk prioritization in autonomous ship operational modes using FMEA and interval numbers

The Maritime Safety Committee of the International Maritime Organization named autonomous ships as Maritime Autonomous Surface Ships. For Maritime Autonomous Surface Ships, it is of utmost importance to remain at a required safety level, or acceptable risk, in ad hoc operational modes. Recently, the concept of risk has been increasingly adopted to measure the safety of socio-technical systems. In the case of Maritime Autonomous Surface Ships, a continuous risk assessment may be required, with respect to a predefined set of unwanted events associated with a corresponding operational modes. Despite the growing number of research works related to autonomous ships and their safety, an approach to compare the operating regimes from the point of risk assessment is still lacking in literature. Therefore, this paper proposes a three-step framework, aiming to assess the risk index and its uncertainty for a Maritime Autonomous Surface Ships in a given scenario, in three operational modes: Manual Control, Remote Control, and Autonomous Control. The applicability of the proposed framework is demonstrated on conventional ship grounding accidents, assuming the ships as being Maritime Autonomous Surface Ships. The results reveal that in a same scenario, the risk of Maritime Autonomous Surface Ships grounding and the inherent uncertainty vary across the operational modes.
Merging Smell Detectors: Evidence on the Agreement of Multiple Tools

Technical Debt estimation relies heavily on the use of static anal-ysis tools looking for violations of pre-defined rules. Largely, Technical Debt principal is attributed to the presence of low-level code smells, unavoidably tying the effort for fixing the problems with mere coding inefficiencies. At the same time, despite their simple definition, the detection of most code smells is non-trivial and subjective, rendering the assessment of Technical Debt prin-cipal dubious. To this end, we have revisited the literature on code smell detection approaches backed by tools and developed an Eclipse plugin that incorporates six code smell detection ap-proaches. The combined application of various smell detectors can increase the certainty of identifying actual code smells that matter to the development team. We also conduct a case study to investigate the agreement among the employed code smell detec-tors. To our surprise the level of agreement is quite low even for relatively simple code smells, threating the validity of existing TD analysis tools and calling for increased attention to the precise specification of code and design level issues. Source code: https://github.com/apostolisich/SmellDetectorMerger
Survey of Component-Based Software Engineering within IoT Development

The Internet of Things (IoT) is a growing area in everyday life. New applications under the umbrella term IoT are being developed continuously. A typical IoT system consists of quite a large set of interchangeable components. In prototype development the Raspberry Pi and Arduino have become core components of wireless sensor network solutions. This research looks at how component-based software engineering (CBSE) and off-the-shelf components can be taken advantage of when defining IoT systems. We will attempt to identify the common properties of IoT systems and compare how well these properties relate to the CBSE component characteristics: composability, deployability, comprehensive documentation, independence, and standardization. The research methodology used in this study is a literature survey. The main results of the study show that CBSE is not widely used. There has been very little research on the software side of applications, as most studies have focused on describing the hardware side of implementations. The conclusion can be made that, in order for studies to be reproducible, the software side should be described in more detail.
Inherent Characteristics of 2D Alluvial Formations Subjected to In-Plane Motion

ABSTRACT We investigate the inherent characteristics of 2D basins subjected to vertically propagating P-SV waves using the finite difference approach. The ratio of fundamental frequencies between an inhomogeneous basin (f0) and its equivalent flat layers (fh) is found to be a function of both its outer overall geometry and inner geometric formation. 2D resonance can occur in inhomogeneous basins under certain conditions, and the f0/fh can be used to demarcate deep from shallow basin behaviors. With other conditions remaining the same, changing a basin from homogeneity to inhomogeneity results in a lower f0/fh and makes 2D resonance less likely to occur.
Influencing Factors and Variation of Subjective Workload in the Process of Takeover During Autonomous Driving

Different factors have different influences on the cognitive load variations of drivers during autonomous driving. To explore the correlation between different factors and the cognitive load of drivers, the influence of different factors on the cognitive load variations is subjectively considered. NASA- TLX questionnaire data is obtained by organizing shared control driving simulation experiment, the total subjective workload score and the distraction degree score are calculated according to the weight calculation result and the scores of 6 dimensions. The research results show that the total score of subjective workloads under the influence of different factors is as follows: obstacle avoidance scene> lane keeping scene, no significant difference between TB=7s and TB=3s, mistake finding> chatting> texting> monitoring. This research could provide references for predictive modeling of takeover safety based on cognitive load.
Deep Ensemble CNN Method Based on Sample Expansion for Hyperspectral Image Classification

With the continuous progress of computer deep learning technology, convolutional neural network (CNN), as a representative approach, provides a unique solution for hyperspectral image (HSI) classification. However, the parameters of CNN cannot be well-tuned when the number of training samples is insufficient, resulting in unsatisfactory classification performance. To tackle the thorny problem, a deep ensemble CNN method based on sample expansion for HSI classification is studied in this article. In particular, spatial information is first extracted and fused with original spectral bands to help classifiers obtain discriminant spectral–spatial features. Then, we use the pixel-pair feature (PPF) to expand the number of training samples so that the parameters of CNN structure can be fully trained. In addition, deep ensemble CNN is employed in this article, enabling the trained model to obtain better generalization ability and more robust classification results. Ultimately, the proposed method is applied to classify four widely used hyperspectral datasets. Experimental results show that the studied approach yields higher classification accuracy than some CNN-based methods even under the condition of small-size training set.
The Influence Path of Safety Culture on Pilot's Safety Behavior

Compared with other industries, consequence caused by unsafe behavior of pilots are more serious. Tremendous effort is required to strive towards zero accidents. In order to explore the influence of pilot situation safety culture on pilots' safety behavior and highlight the importance of pilot safety culture construction, relevant research scale is developed to conduct a survey on pilot nationwide. Sample data are obtained from pilot stations nationwide, with 272 valid collected results. After validity and reliability test of the sample data, structural equation model is performed to analyze the relationship between safety culture, safety attitude, safety cognition and pilot safety behavior. The result show that safety culture has a positive impact on pilot's safety behavior; safety attitude and safety cognition have a significant mediating effect between safety culture and safety behavior.
Efficient Extreme Gradient Boosting Based Algorithm for QoS Optimization in Inter-Radio Access Technology Handoffs

The deployment of many base stations within a small network coverage area can potentially increase network capacities. However, this implies frequent handoffs as the users move within the small tracking areas. An effective hand off strategy is therefore required to boost quality of service and quality of experience during the handoff process. Unfortunately, most of the conventional handoff strategies are reactive and tend to deploy single or few parameters as inputs to the decision-making process. As such, handoffs are executed without fully considering user satisfaction as well as application, device and service requirements. This results in deterioration of both quality of service and quality of experience after the handoff procedures. In this paper, extreme gradient boosting based algorithm is presented. The results show that it has minimal handoff failure and ping pong rates, while exhibiting very high handoff success rates. In addition, its receiver operating characteristic-area under curve value of 0.97627 exceeds the 0.7 threshold. Consequently, it is capable of predicting the success of the inter-radio access technology handoff with high probability.
Long-Term Wind Speed and Power Forecasting Based on LSTM: A Comprehensive Study

One of the aspects of sustainable development is attaining grid operation safety management at the right quality level. Since wind energy is also based on renewable energy, its development and improvement are in line with sustainable development goals. However, there are challenges to managing this energy that needs to be addressed. Considering the behavior of wind speed and power of turbines are two effective issues in controlling wind power since lack of this awareness leads to a decline in the quality of customer service or energy waste. Predicting these two factors is one of the fundamental issues in the design and operation of power generation and distribution systems. In this paper, the wind speed and wind power of wind turbines are predicted based on the combined model AM CNN Bi-LSTM. To show the performance of this model, the results of their accuracy and prediction error are compared with the results of LSTM and CNN-LSTM. In all datasets, the results of the proposed model were more successful than other current models, and in the best case, an accuracy of 98.17% was obtained for power forecasting.
Analysis of multiple chronic disease characteristics in South Koreans by age groups using association rules analysis

The prevalence of MCD (multiple chronic disease) is increasing due to increased life expectancies and aging populations. Individual and socioeconomic burdens of MCD are also increasing. To reduce these burdens, it is necessary to establish policies to prevent MCD; therefore, it is important to understand the characteristics of MCD in the current population. In this study, the combinations of MCD that affect hypertension, which has the highest prevalence, were examined according to different age groups. The combinations of diseases were identified utilizing association rules analysis, using the Community Health Survey as the raw data. Two association rules were determined in young adult group, 18 in the middle-aged group, and 30 in the senior group, showing that the number of rules increases with age. Association rules of this study mean that combined chronic diseases are highly associated with hypertension. Then logistic regression analysis was performed on the MCD combinations with highest lift value in each age group to identify relationships between health behavior and MCD combinations. Especially, alcohol consumption was found to be a common factor affecting MCD prevalence in three combinations. On the contrary, sleep habit did not have a statistically significant influence on any combination.
Hybrid field oriented and direct torque control for sensorless BLDC motors used in aerial drones

In this study, a sensorless hybrid control scheme for brushless direct current (BLDC) motors for use in multirotor aerial vehicles is introduced. In such applications, the control scheme must satisfy high-performance demands for a wide range of rotor speeds and must be robust to motor parameter uncertainties and measurement noise. The proposed controller combines field-oriented control (FOC) and direct torque control (DTC) techniques to take benefit of the advantages offered by each of these techniques individually. Simulation results demonstrate the effectiveness of the proposed control scheme over a wide range of rotor speeds as well as good robustness against parameter uncertainties within -5to + 10% for inductance and -5to + 5% for resistance parameters. The proposed hybrid controller is robust also against noise in voltage and current measurements. In order to verify the results from simulation, the proposed hybrid controller is implemented in hardware using the TI C2000 Piccolo Launchpad and TI BOOSTXL-DRV8305EVM BoosterPack. Testing is done with a Bull Running motor typically used in aerial drones. Testing experiments demonstrate that the hybrid controller reduces the rotor speed ripple when compared to DTC while operating in steady-state mode and decreases the response time to desired speed changes when compared to FOC.
Global linear instability analysis of thermal convective flow using the linearized lattice Boltzmann method

Abstract Modal global linear stability analysis of thermal convection is performed with the linearized lattice Boltzmann method (LLBM). The onset of Rayleigh–Bénard convection in rectangular cavities with conducting and adiabatic sidewalls and the instability of two-dimensional (2-D) and three-dimensional (3-D) natural convection in cavities are studied. The method of linearizing the local equilibrium probability distribution function that was first proposed by Pérez et al. (Theor. Comp. Fluid Dyn., vol. 31, 2017, pp. 643–664) is extended to solve the coupled linear Navier–Stokes equations together with the linear energy equation in this work. A multiscale analysis is also performed to recover the macroscopic linear Navier–Stokes equations from the discrete lattice Boltzmann equations for both the single and multiple relaxation time models. The present LLBM is implemented in the framework of the Palabos library. It is validated by calculating the linear critical value of 2-D natural convection that the LLBM with the multiple relaxation time model has an error less than 1 % compared with the spectral method. The instability mechanism of the flow is explained by kinetic energy transfer analysis. It is shown that the buoyancy mechanism and inertial mechanism tend to stabilize the Hopf bifurcation of the 2-D natural convection at Pr < 0.08 and Pr > 1, respectively. For 3-D natural convection, subcritical bifurcation of the Hopf type is found for low-Prandtl-number fluids (Pr < 0.1).
How internet helped firms to cope with COVID-19*

This paper questions whether familiarity with digital technologies helped firms cope with the COVID-19 pandemic. Using data on 31,387 firms from 39 developing and developed economies, our results stress that businesses using website before the crisis showed higher absorption capacities than other firms during the crisis. The positive role of website use was mainly through the adoption of coping strategies, in particular home-delivery services, online sales or remote work. In contrast, the positive effect played by the prior use of internet is not explained by better access to external public (government support) or private (bank loan) financial resources.
The Relationship Between Self-Esteem and Approval Dependence in University Students: The Serial Mediation of Interaction Anxiety and Insight

This research examined the serial mediating effect of interaction anxiety and insight into the relationship between self-esteem and approval dependence in university students. The research group comprised 511 volunteer university students, of whom 78,7% were females and 21,3% were males. Research data was collected via “The Two -Dimensional Self- Esteem Scale”, “The Scale of Interpersonal Relationship Dimensions”, “Interaction Anxiousness Scale”, and “Insight Scale”. In analysing the data, descriptive statistics were examined and the relationship between variables was calculated using the Pearson correlation coefficient. The bootstrap method was used to test the mediation model.The mediation analysis results revealed that interaction anxiety and insight functioned as mediation variables in the relationship between self-esteem and approval dependence. According to the findings, an increase in self-esteem causes a decline in interaction anxiousness, which causes an increase in insight, which in turn leads to a decline in approval dependence. Findings related to the model that was tested were discussed following the literature. Suggestions for researchers and field practitioners were listed.
Comparing Training Effects of Virtual Reality Flight Simulation to Conventional PC-Based Flight Simulation

The purpose of the current project was to investigate the effect of utilizing Virtual Reality (VR) technologies for flight training by comparing the training results when using conventional desktop flight simulation versus VR flight simulation. Additionally, this project examined the user experience of VR flight simulation and how users’ motivation and satisfaction with VR simulations. This research employed a quasi-transfer of training experiment including 48 participants. Analyses indicated that VR group participants performed better in the post-training maneuver performance on an FTD than in the conventional desktop simulation group. Findings also supported that VR flight simulation could provide a better user experience and generate a higher motivation for usage. This work contributed positive evidence that VR flight simulation has a large potential to be an effective flight training and provided a foundation for future research to continue exploring the training effect of VR flight simulation.