Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV-Assisted Content Delivery in Intelligent Transportation Systems-Joint Trajectory Planning and Cache Management

Unmanned Aerial Vehicles (UAVs) are gaining growing interests due to the paramount roles they play, particularly these days, in enabling new services that help modernize our transportation, supply chain, search and rescue, among others. They are capable of positively influencing wireless systems through enabling and fostering emerging technologies such as autonomous driving, vertical industries, virtual reality and so many others. The Internet of Vehicles is a prime sector benefiting from the services offered by future cellular systems in general and UAVs in particular, and this paper considers the problem of content delivery to vehicles on road segments with either overloaded or no available communication infrastructure. Incoming vehicles demand service from a library of contents that is partially cached at the UAV; the content of the library is also assumed to change as new vehicles carrying more popular contents arrive. Each inbound vehicle makes a request and the UAV decides on its best trajectory to provide service while maximizing a certain operational utility. Given the energy limitation at the UAV, we seek an energy efficient solution. Hence, our problem consists of jointly finding caching decisions, UAV trajectory and radio resource allocation which is formulated mathematically as a Mixed Integer Non-Linear Problem (MINLP). However, owing to uncertainties in the environment (e.g., random arrival of vehicles, their requests for contents and their existing contents), it is often hard and impractical to solve using standard optimization techniques. To this end, we formulate our problem as a Markov Decision Process (MDP) and we resort to tools such as Proximal Policy Optimization (PPO), a very promising Reinforcement Learning method, along with a set of crafted algorithms to solve our problem. Finally, we conduct simulation-based experiments to analyze and demonstrate the superiority of our solution approach compared with four counterparts and baseline schemes.
Reconfigurable 2T2R ReRAM Architecture for Versatile Data Storage and Computing In-Memory

Nonvolatile memory (NVM)-based computing in-memory (CIM) is a promising solution to data-intensive applications. This work proposes a 2T2R resistive random access memory (ReRAM) architecture that supports three types of CIM operations: 1) ternary content addressable memory (TCAM); 2) logic in-memory (LiM) primitives and arithmetic blocks such as full adder (FA) and full subtractor; and 3) in-memory dot-product for neural networks. The proposed architecture allows the NVM operations in both 2T2R and conventional 1T1R configurations. The proposed LiM full adder (LiM-FA) improves the delay, the static power, and the dynamic power by <inline-formula> <tex-math notation="LaTeX">$3.2\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.2\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$1.6\times $ </tex-math></inline-formula>, respectively, compared with state-of-the-art LiM-FAs. Furthermore, based on different optimization techniques and robustness analysis, a lower precharge voltage is set for each mode. This reduces the TCAM search energy and 1T1R ReRAM access energy by <inline-formula> <tex-math notation="LaTeX">$1.6\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1.14\times $ </tex-math></inline-formula>, respectively, compared with the case without optimizations.
Dialogue systems for language learning: a meta-analysis

The present study offers a meta-analysis of effectiveness studies on dialogue-based CALL, systems affording a learner practice in a foreign language (L2) by interacting with a conversational agent (“bot”). Through a systematic inclusion and exclusion process, we identified 17 relevant meta-analyzable studies. We made use of Morris and DeShon’s (2002) formulas to compute comparable effect sizes across designs, including k = 100 individual effect sizes, which were analyzed through a multilevel random-effects model. Results confirm that dialogue-based CALL practice had a significant medium effect size on L2 proficiency development ( d = 0.58). We performed extensive moderator analyses to explore the relative effectiveness on several learning outcomes of different types and features of dialogue-based CALL (type of interaction, modality, constraints, feedback, agent embodiment, gamification). Our study confirms the effectiveness of form-focused and goal-oriented systems, system-guided interactions, corrective feedback provision, and gamification features. Effects for lower proficiency learners, and on vocabulary, morphosyntax, holistic proficiency, and accuracy are established. Finally, we discuss expected evolutions in dialogue-based CALL and the language learning opportunities it offers.
Missing Data Problem in the Monitoring System: A Review

Missing data is a common phenomenon in sensor networks, especially in the large-scale monitoring system. It can be affected by various kinds of reasons. Moreover, incomplete data or information may affect the subsequent data processing and reasoning, resulting in a wrong decision. Hence, missing data recovery has always been a hot topic in the literature. However, although researchers have developed many different methods to recover missing data, this problem is still far away from being solved. To better track research progress and identify potential challenges, in this paper, we give a detailed review in the context of large-scale monitoring system. Mainly, we first introduce the basic concept of missing data, including the definition, causes, types, and performance evaluation. Then, a series of traditional and classical missing data recovery methods are analyzed and compared where their characteristics and scope of application are given. Furthermore, we present two current mainstream approaches from methodology to the existing literature, which are data recovery based on data mining algorithms and low rank algorithms, respectively. Finally, we conclude this paper with several promising directions for future research.
A Multi-Folded MCML for Ultra-Low-Voltage High-Performance in Deeply Scaled CMOS

In this paper we propose a novel approach called <italic>Multi-Folded (MF)</italic> MOS Current Mode Logic (MCML) which can be applied to a generic MCML gate (i.e., with a fan-in higher than two). The idea is implemented by alternating NMOS and PMOS differential pairs and properly introducing current mirrors between the adjacent levels of logic. The proposed approach allows a minimum power supply equal to the one of a MCML inverter and we show analytically the advantages in terms of speed and power consumption against the conventional implementation. The approach has been validated with post-layout simulations considering a commercial 28nm FD-SOI CMOS technology and a supply voltage as low as 0.6V. In particular, <italic>OR/NOR</italic> gates with 3, 4, and 5 inputs implemented both with the conventional 2-inputs MCML gates implementation (<italic>Fin2)</italic> and the proposed <italic>MF</italic> MCML have been compared. Results show a reduction in the power consumption of the <italic>MF</italic> MCML equal to 3/4, 2/3 and 5/8 for the OR3, OR4 and OR5 logic functions respectively. Moreover, in terms of speed the <italic>Fin2</italic> implementation has a delay at least 1.4 worst than the <italic>MF</italic> MCML, but is generally 1.7 worst than the proposed <italic>MF</italic> MCML gate. In this paper we have presented a novel approach which allows to implement MCML gates with a fan-in higher than 2 while keeping the minimum supply voltage as low as the one of a conventional MCML inverter. The proposed methodology, named Multi Folded MCML, born out by a generalization of the Folded MCML previously proposed by authors. The approach has been compared against the conventional <italic>Fin2</italic> implementation (suited to implement arbitrarily logic function with a low power supply) both in terms of power consumption and propagation delay. The results have clearly demonstrated the advantages provided by the <italic>MF</italic> MCML. In particular, OR/NOR gates with 3-, 4-, and 5-inputs have been compared considering a commercial 28nm FD-SOI CMOS technology and a supply voltage as low as 0.6V. Post layout simulation results have shown that the <italic>MF</italic> gates outperform the conventional implementations in terms of propagation delay for all the considered logic functions. In particular, the propagation delay of the <italic>Fin2</italic> implementation has been shown to be at least 1.4 times higher than the one of the MF MCML implementation and even 2 times higher for other logic functions. The power consumption of the <italic>MF</italic> gates has been shown to be <italic>3/4</italic>, <italic>2/3</italic> and <italic>5/8</italic> than the one of the <italic>Fin2</italic> implementations for the <italic>OR3</italic>, <italic>OR4</italic> and <italic>OR5</italic> logic functions respectively. Hence, the only drawback of the propose techniques seems due to a more complex design procedure.
Learning-Based Terrain Identification With Proprioceptive Sensors for Mobile Robots

High accuracy of terrain identification is essential for intelligent control of tracked mobile robots. In this article, a learning-based identification framework is proposed to achieve precise driving torque prediction. Experiments under straight-line and steady-state turning maneuvers were conducted to develop terrain identification in three similar terrains. A multiple deep belief networks is applied as the identification layer with three kinds of signal sources. An equivalent weight algorithm with training experience effectively integrates the results from different signal sources to improve the identification accuracy. With the experiment and identification results, a method combining numerical approximation and Gaussian process (GP) is presented to predict driving torque. A combined Gaussian kernel with long- and short-term characteristics is selected to enhance prediction performance. The results from combined signal sources under straight-line maneuvers yield over 98% accuracy, which exceeds that from other sources. The integration algorithm obviously improves the identification accuracy and stability compared with a single signal source. The influence of window length in GP is explored with hyperparameters and results. The performance of torque prediction in different terrains is analyzed with inner and outer tracks. Compared with existing methods, the results validate the effectiveness and superior performance of the proposed framework.
Rotor Topology Optimization of Interior Permanent Magnet Synchronous Motor With High-Strength Silicon Steel Application

This article presents the rotor optimization in an interior permanent magnet synchronous machine (IPMSM) with high-strength non-grain-oriented (NGO) silicon steel material. Different from simple replacement with high-strength NGO, this article presents a customized design to make the best use of rotor iron core material. Specific optimization of the rotor topology is presented, followed with simulation verification by finite element method (FEM). Based on the simulation results, the benefits and limitations of the optimization design with the replacement of high-strength NGO are listed. In order to further overcome the limitations of the presented optimization design, magnetic characteristics of the high-strength NGO laminations subjected to various tensile stresses are measured in the experiment. Finally, experimental results are analyzed, and the conclusions to employ high-strength NGO for IPMSM are summarized.
Cognitive processing of sexual cues in asexual individuals and heterosexual women with desire/arousal difficulties

Asexuality is defined as a unique sexual orientation characterized by a lack of sexual attraction to others. This has been challenged, with some experts positing that it is better explained as a sexual dysfunction. Sexual Interest/Arousal Disorder (SIAD) is characterized by absent/reduced sexual interest/arousal paired with personal distress, with two subtypes: acquired and lifelong. Research suggests that while asexuality and acquired SIAD are distinct entities, there may be overlap between asexuality and lifelong SIAD. Findings from studies using eye-tracking and implicit association tasks suggest that these methodologies might differentiate these groups on the basis of their neural mechanisms. However, no study has compared their cognitive processing of sexual cues, and the literature on lifelong SIAD is minimal. The current study tested differences in the cognitive processing of sexual cues between asexual individuals and women with SIAD (lifelong and acquired). Forty-two asexual individuals and 25 heterosexual women with SIAD (16: acquired; 9: lifelong) completed three study components: a visual attention task, a Single Category-Implicit Association Task, and the sex semantic differential. ANOVAs examined group differences in: 1) visual attention to erotic cues, 2) implicit appraisals of sexual words, and 3) explicit appraisals of sex. Women with SIAD displayed a controlled attention preference for erotic images and areas of sexual contact, with longer dwell times to these areas relative to asexual individuals, who did not gaze preferentially at erotic cues. For implicit appraisals, all groups demonstrated negative—neutral implicit associations with sexual words. For explicit appraisals, women with acquired SIAD reported more positive evaluations of sex relative to asexual individuals and women with lifelong SIAD. This project sheds light on key differences between asexuality and low desire, and has implications for best clinical practice guidelines for the assessment of lifelong SIAD.
Optimising airline maintenance scheduling decisions

Airline maintenance scheduling (AMS) studies how plans or schedules are constructed to ensure that a fleet is efficiently maintained and that airline operational demands are met. Additionally, such schedules must take into consideration the different regulations airlines are subject to, while minimising maintenance costs. In this thesis, we study different formulations, solution methods, and modelling considerations, for the AMS and related problems to propose two main contributions. First, we present a new type of multi-objective mixed integer linear programming formulation which challenges traditional time discretisation. Employing the concept of time intervals, we efficiently model the airline maintenance scheduling problem with tail assignment considerations. With a focus on workshop resource allocation and individual aircraft flight operations, and the use of a custom iterative algorithm, we solve large and long-term real-world instances (16000 flights, 529 aircraft, 8 maintenance workshops) in reasonable computational time. Moreover, we provide evidence to suggest, that our framework provides near-optimal solutions, and that inter-airline cooperation is beneficial for workshops. Second, we propose a new hybrid solution procedure to solve the aircraft recovery problem. Here, we study how to re-schedule flights and re-assign aircraft to these, to resume airline operations after an unforeseen disruption. We do so while taking operational restrictions into account. Specifically, restrictions on aircraft, maintenance, crew duty, and passenger delay are accounted for. The flexibility of the approach allows for further operational restrictions to be easily introduced. The hybrid solution procedure involves the combination of column generation with learning-based hyperheuristics. The latter, adaptively selects exact or metaheuristic algorithms to generate columns. The five different algorithms implemented, two of which we developed, were collected and released as a Python package (Torres Sanchez, 2020). Findings suggest that the framework produces fast and insightful recovery solutions.
Blockchain effect on the New Connect Stock Exchange

Abstract Aim/purpose – Blockchain is a distributed database system widely popular in social and business spheres. As a result, interest in blockchain technology is utilised by listed companies, who, thanks to the announcement of blockchain application, record a significant increase in the share value. The aim of this publication is to analyse the situation with regard to companies listed on the New Connect market, who over the last two years, expressed an interest in blockchain technology in their communication with investors. Design/methodology/approach – Observing the range of applications of this technology in business, a study was conducted to check if and how the information on using the blockchain technology affects the quotations of the New Connect market listed companies that applied it. Qualitative comparative analysis (QCA), a method developed by Ragin was chosen as a complementary research method. An underlying assumption of QCA is that social phenomena involve complex causality. Findings – In most cases, companies releasing blockchain application announcements counted on fast and high growth of the quotations. In most of the analysed cases, blockchain technology utilisation announcements were not fulfilled. This could be a result of conscious tactics towards investors, a rapid increase in quotations and/or a lack of competence and resources to implement blockchain technology. Investors should analyse the business activity of a company before investing. Research implications/limitations – Research is conducted with a small sample of 11 companies from the New Connect market in Poland. Consequently, to obtain more general results, it is recommended for future research to use a larger sample such as other stock exchanges in Europe and USA. Originality/value/contribution – The research revealed that new blockchain technology is used by some listed companies on the New Connect market for short-term share growth. Investors are becoming increasingly more attracted to the fashion for the new blockchain technology without understanding its mechanism. Accordingly, this study will also attempt to explain such a mechanism.
Regression Models and Correlation Analysis for Predicting Body Weight of Female Ettawa Grade Goat using its Body Measurements

| This research aimed to analyse the correlation and regression model and also to find the best regression model for predicting body weight of female Ettawa Grade (EG) goat using its body measurements. This research used 41 female EG goat aged 3–4 years. The method used was a survey with data collection by census, all female EG goats aged 3-4 years in research location were collected. Body weight (BW) data were regressed and correlated to body measurements (body length = BL, chest girth = CG and shoulder height = SH) using linear and multiple linear regression using R program. Pearson correlation was used to calculate correlation coefficient (r). Coefficient of determination (R2), adjusted R2, residual standard error (RSE), Akaike information criterion (AIC), Bayesian information criterion (BIC) and step wise regression analysis were used to analyse and find the best and parsimonious model for predicting BW. The results showed that body measurements had positive correlation with the BW, which CG had the highest correlation (0.838); followed by BL (0.744) and SH (0.543). Results also showed that CG was the best predictor for BW compared to BL and SH if using single predictor. Combination of CG and BL resulted in the fittest prediction of BW with model regression BW = -67.86 + 0.87*CG + 0.51*BL with the highest correlation coefficient (r = 0.87), R2 (0.76), adjusted R2 (0.75) and the lowest RSE (2.795), AIC (205.51) and BIC (212.36). The results of this study suggested that CG and BL could be used as predictor for body weight and as indicator of indirect selection to improve genetic merit in body weight of EG goat.
SMAF-Net: Sharing Multiscale Adversarial Feature for High-Resolution Remote Sensing Imagery Semantic Segmentation

Semantic segmentation of high-resolution remote sensing imagery (HRSI) is a major task in remote sensing analysis. Although deep convolutional neural network (DCNN)-based semantic segmentation models have powerful capacity in pixel-wise classification, they still face challenge in obtaining intersemantic continuity and extraboundary accuracy because of the geo-object’s characteristic feature of diverse scales and various distributions in HRSI. Inspired by the transfer learning, in this study, we propose an efficient semantic segmentation framework named SMAF-Net, which shares multiscale adversarial features into a U-shaped semantic segmentation model. Specifically, it uses multiscale adversarial feature representation obtained from a well-trained generative adversarial network to grasp the pixel correlation and further improve the boundary accuracy of multiscale geo-objects. Comparison experiments on the Potsdam and Vaihingen data sets demonstrate that the proposed framework can achieve considerable improvement in the semantic segmentation of HRSI.
The exploitation of CFD legacy for the meridional analysis and design of modern gas and steam turbines

In the last decades, the consolidation of 3D CFD approaches in the industrial design practices has progressively moved throughflow codes from the top of design systems to somewhere in between first development stages and the final aerodynamic optimizations. Despite this trend and the typical limitations of traditional throughflow methods, designers tend to still consider such methods as fundamental tools for drafting a credible aero-design in a short turnaround time. Recently a considerable attention has been devoted to CFDbased throughflow codes as suitable means to widen the range of applicability of these tools while smoothing the predictive gap with successive threedimensional flow analyses. The present paper retraces the development and some applications of a modern and complete CFD-based throughflow solver specifically tuned for multistage axial turbine design. The code solves the axisymmetric Euler equations with an original treatment of tangential blockage and body force. It inherits its numerical scheme from a state-of-the-art CFD solver (TRAF code) and incorporates real gas capabilities, three-dimensional flow features (e.g. secondary flows, tip leakage effects), coolant flow injections, and radial mixing models. Also geometric features of actual blades, like fillets, part-span shrouds, and snubbers, are accounted for by suitable models. The capabilities of the code are demonstrated by discussing a significant range of test cases and industrial applications. They include single stage configurations and entire multistage modules of steam turbines, with flow conditions ranging from subsonic to supersonic. Computational strategies for design and off-design analyses will be presented and discussed. The reliability and accuracy of the method is assessed by comparing throughflow results with 3D CFD calculations and experimental data. A good agreement in terms of overall performance and spanwise distributions is achieved in both design and off-design operating conditions.