Miracle-dz/UniFAR-FTUs huggingface dataset:
On a type of Static Equation on Certain Contact Metric Manifolds

This paper deals with the investigation of $K$-contact and $(\kappa,\mu)$-contact manifolds admitting a positive smooth function $f$ satisfying the equation: $$f\mathring{Ric}=\mathring{\nabla}^2f$$ where $\mathring{Ric}$, $\mathring{\nabla}^2f$ are traceless Ricci tensor and Hessian tensor respectively. We proved that if a complete and simply connected $K$-contact manifold admits such a smooth function $f$, then it is isometric to the unit sphere $\mathbb{S}^{2n+1}$. Next, we showed that if a non-Sasakian $(\kappa,\mu)$-contact metric manifold admit such a smooth function $f$, then it is locally flat for $n=1$ and for $n>1$ is locally isometric to the product space $E^{n+1}\times S^n(4)$.
Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

This work investigates the problem of unmanned aerial vehicles (UAVs) identification using their radar crosssection (RCS) signature. The RCS of six commercial UAVs are measured at 15 GHz and 25 GHz in an anechoic chamber, for both vertical-vertical and horizontal-horizontal polarization. The RCS signatures are used to train 15 different classification algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that while the classification accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3 dB SNR using the 15 GHz VV-polarized RCS test data from the UAVs. We investigate the classification accuracy using Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by the Peter Swerling statistical models and the discriminant analysis ML model. In general, the classification accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that while the SL algorithms achieved good classification accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average classification time of about 0.46 ms.
Hybridization of volumetric and surface models for the computation of the T/R EC probe response due to a thin opening flaw

The purpose of this paper is to describe the development of simulation tools dedicated to eddy current non destructive testing (ECNDT) on planar structures implying planar defects. Two integral approaches using the Green dyadic formalism are considered. The surface integral model (SIM) is dedicated to ideal cracks, whereas the volume integral method is adapted to general volumetric defects. The authors observed that SIM provides satisfactory results, except in some critical transmitting/receiving (T/R) configurations. This led us to propose a hybrid method based on the combination of the two previous ones. This method enables to simulate ECNDT on planar structures implying defects with a small opening using T/R probes.
Evolutionary Hierarchical Harvest Schedule Optimization for Food Waste Prevention

In order to avoid disadvantages of monocropping for soil and environment, it is advisable to practice intercropping of various plant species whenever possible. However, intercropping is challenging as it requires a balanced planting schedule due to individual cultivation time frames. Maintaining a continuous harvest reduces logistical costs and related greenhouse gas emissions, and contributes to food waste prevention. In this work, we address these issues and propose an optimization method for a full harvest season of large crop ensembles that complies with given constraints. By using an approach based on an evolutionary algorithm combined with a novel hierarchical loss function and adaptive mutation rate, we transfer the multi-objective into a pseudo-single-objective optimization problem and obtain faster convergence and better solutions than for conventional approaches.
Redycler: Daily Outfit Texture Fabrication Appliance Using Re-Programmable Dyes

We present a speculative design for a novel appliance for future fabrication in the home to revitalize textiles using re-programmable multi-color textures. Utilizing colored photochromic dyes activated by ultraviolet (UV) light, we can selectively deactivate hues using complementary colors in visible light to result in the final desired dye pattern. Our proposed appliance would automate this process within a box placed in the bedroom. We envision a future where people are able to transform old apparel into unique and fashionable pieces of clothing. We discuss how the user would interact with the appliance and how this device elongates the life-cycle of clothing through modification. We also outline the central issues to integrate such a concept into the home. Finally, we analyze how this device fits into personal modification trends in HCI to show how this device could change existing conceptions around sustainable fashion and personal style.
Spatial and multi-scale variations in key factors of maritime accident consequences: A case in the East China Sea

Guorong Lia,b, Kun Gaob,*, Jinxian Wenga, Xiaobo Qub a College of Transport and Communications, Shanghai Maritime University, Shanghai, 201306, China. b Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden. Email: gkun@chalmers.se Abstract Maritime accidents and corresponding consequences vary substantially across spatial dimension as affected by ship navigation environment such as hydrological factors, geological factors, and ship density. Understanding these variations in key factors contributions to maritime accidents would be of great benefit to prevent the occurrence or reduce the consequences of maritime accidents. Based on unique maritime accident data with geographical information covering ten years in the East China Sea, a multi-scale geographically weighted regression (MGWR) model is employed to quantify the influences of different factors as well as the spatial heterogeneity in the effects of key factors. MGWR outputs are compared with the results from multiple linear regression (MLR) and geographically weighted regression (GWR). Especially, MGWR considers the multi-scale spatial variation during modeling process and outperforms the other two models in terms of modeling fitness and clearly capturing the unobserved spatial heterogeneity. Our results reveal notably distinct and even inverse influences of some factors in different water areas on maritime accident consequences. Approximately 50% of the accident locations present positive coefficients of visibility while other locations are negative, which are ignored by MLR. The outcomes provide insights for making appropriate safety countermeasures and policies customized for different geographic areas.
What Tools Do We Need to Improve Identification of Child Abuse

Child protection work is being transformed by the introduction of information and communication technology (ICT) and other tools to improve frontline work. This article argues that current innovations are being developed without sufficient attention to understanding the needs of frontline workers. Taking the identification of child abuse as an example, the article shows how beginning with the question 'What tools do we need? ' produces radically different answers from the current proposed tools such as the Information Sharing and Assessment database (ISA). The approach advocated here involves examining what aspects of the task frontline workers find difficult and identifying where they would most appreciate help. In relation to the problem of sharing information between professionals to ensure accurate assessment of risk, it is argued that the key problems do not lie in the technical process of sharing data but in professionals' ability to collect the necessary information, to interpret it accurately and to communicate it clearly.
panda-gym: Open-source goal-conditioned environments for robotic learning

This paper presents panda-gym, a set of Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym. Five tasks are included: reach, push, slide, pick&place and stack. They all follow a Multi-Goal RL framework, allowing to use goal-oriented RL algorithms. To foster open-research, we chose to use the open-source physics engine PyBullet. The implementation chosen for this package allows to define very easily new tasks or new robots. This paper also presents a baseline of results obtained with state-of-the-art model-free off-policy algorithms. panda-gym is open-source and freely available at https://github.com/qgallouedec/panda-gym.
Auto Guided Oil Palm Planter by using multi-GNSS

Planting is one of the most important operations in plantation because it could affect the total area of productivity since it is the starting point in cultivation. In oil palm plantation, lining and spacing of oil palm shall be laid out and coincided with the topographic area and a system of drains. Conventionally, planting of oil palm will require the polarization process in order to prevent and overcome the lack of influence of the sun rise and get a regular crop row. Polarization is done after the completion of the opening area by using the spike wood with 1 m length painted at the top and 100 m length of wire. This process will generally require at least five persons at a time to pull the wire and carry the spikes while the other two persons will act as observer and spikes craftsmen respectively with the ability of the team is 3ha/day. Therefore, the aim of this project is to develop the oil palm planting technique by using multi- GNSS (Global Navigation Satellite System). Generally, this project will involve five main steps mainly; design of planting pattern by using SOLIDWORKS software, determine the boundary coordinate of planting area, georeference process with ArcGIS, stakeout process with Tracy software and finally marking up the location with the wooden spikes. The results proved that the multi- GNSS is capable to provide the high accuracy with less than 1 m in precise positioning system without augmentation data. With the ability of one person, time taken to complete 70 m × 50 m planting area is 290 min, which is 25 min faster than using GPS (Global Positioning System) only.
An empirical nonlinear power spectrum overdensity response

Context. The overdensity inside a cosmological sub-volume and the tidal fields from its surroundings affect the matter distribution of the region. The resulting difference between the local and global power spectra is characterized by the response function. Aims. Our aim is to provide a new, simple, and accurate formula for the power spectrum overdensity response at highly nonlinear scales based on the results of cosmological simulations and paying special attention to the lognormal nature of the density field. Methods. We measured the dark matter power spectrum amplitude as a function of the overdensity ($\delta_W$) in $N$-body simulation subsamples. We show that the response follows a power-law form in terms of $(1+\delta_W)$, and we provide a new fit in terms of the variance, $\sigma(L)$, of a sub-volume of size $L$. Results. Our fit has a similar accuracy and a comparable complexity to second-order standard perturbation theory on large scales, but it is also valid for nonlinear (smaller) scales, where perturbation theory needs higher-order terms for a comparable precision. Furthermore, we show that the lognormal nature of the overdensity distribution causes a previously unidentified bias: the power spectrum amplitude for a subsample with an average density is typically underestimated by about $-2\sigma^2$. Although this bias falls to the sub-percent level above characteristic scales of $200Mpch^{-1}$, taking it into account improves the accuracy of estimating power spectra from zoom-in simulations and smaller high-resolution surveys embedded in larger low-resolution volumes.
Antenna Modification Leads to Enhanced Nitrogenase Activity in a High Light-Tolerant Cyanobacterium

The function of phycobilisomes, the large antenna protein complexes in heterocysts has long been debated. This study provides direct evidence of the involvement of these proteins in supporting nitrogenase activity in Anabaena 33047, a heterocystous cyanobacterium that has an affinity for high light intensities. ABSTRACT Biological nitrogen fixation is an energy-intensive process that contributes significantly toward supporting life on this planet. Among nitrogen-fixing organisms, cyanobacteria remain unrivaled in their ability to fuel the energetically expensive nitrogenase reaction with photosynthetically harnessed solar energy. In heterocystous cyanobacteria, light-driven, photosystem I (PSI)-mediated ATP synthesis plays a key role in propelling the nitrogenase reaction. Efficient light transfer to the photosystems relies on phycobilisomes (PBS), the major antenna protein complexes. PBS undergo degradation as a natural response to nitrogen starvation. Upon nitrogen availability, these proteins are resynthesized back to normal levels in vegetative cells, but their occurrence and function in heterocysts remain inconclusive. Anabaena 33047 is a heterocystous cyanobacterium that thrives under high light, harbors larger amounts of PBS in its heterocysts, and fixes nitrogen at higher rates compared to other heterocystous cyanobacteria. To assess the relationship between PBS in heterocysts and nitrogenase function, we engineered a strain that retains large amounts of the antenna proteins in its heterocysts. Intriguingly, under high light intensities, the engineered strain exhibited unusually high rates of nitrogenase activity compared to the wild type. Spectroscopic analysis revealed altered PSI kinetics in the mutant with increased cyclic electron flow around PSI, a route that contributes to ATP generation and nitrogenase activity in heterocysts. Retaining higher levels of PBS in heterocysts appears to be an effective strategy to enhance nitrogenase function in cyanobacteria that are equipped with the machinery to operate under high light intensities. IMPORTANCE The function of phycobilisomes, the large antenna protein complexes in heterocysts has long been debated. This study provides direct evidence of the involvement of these proteins in supporting nitrogenase activity in Anabaena 33047, a heterocystous cyanobacterium that has an affinity for high light intensities. This strain was previously known to be recalcitrant to genetic manipulation and, hence, despite its many appealing traits, remained largely unexplored. We developed a genetic modification system for this strain and generated a ΔnblA mutant that exhibited resistance to phycobilisome degradation upon nitrogen starvation. Physiological characterization of the strain indicated that PBS degradation is not essential for acclimation to nitrogen deficiency and retention of PBS is advantageous for nitrogenase function.
Evaluating machine learning algorithms for intrusion detection systems using the dataset CIDDS-002

Intrusion detection systems play a vital role in protecting computer systems from external attacks. In recent years, many open-source intrusion datasets have been released to let researchers study and evaluate their detection methods. These datasets are presented with a comprehensive feature set. In this paper, we study the intrusion detection problem using a dataset CIDDS-002 that is provided with a small number of features. Our experimental using different classification algorithms including Logistic Regression, xgboost, Random Forest and catboost show that we can achieve very high predictive performance without using a lot of features generated in other datasets.
Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network

Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion (BFTD) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average.
Video Games for Learning: Design considerations and its application in the video game “The Time Invaders”: Video Games for Learning: Design considerations and application

Educational video games require problem-solving within a narrative context which situates the actions of the game and establishes rules of engagement to prompt certain learning. The design of the educational game must present a series of characteristics to lead to a really effective learning. However, it is not an easy task for an inexperienced team to develop an educational video game with a suitable fun-learning balance. With this aim, in this work, the four main axes to design successful educational games are identified and argued. As a study case, the design of the game The Time Invaders is described.
LCW: A Lightweight Recommendation Framework for Non-profit Crowdfunding Projects

In recent years, crowdfunding platform has gradually emerged as a new economic model, and more and more people choose crowdfunding to obtain the initial funds needed by the project. However, with the increasing number of projects, it is difficult for sponsors to find suitable projects they want to invest in. Therefore, it is very important to build an efficient recommendation system on the crowdfunding platform. Different from previous crowdfunding recommendation systems designed to improve returns, this paper designs a lightweight recommendation framework LCW for non-profit crowdfunding platforms. A large number of experiments on real kiva data sets have proved the effectiveness of this method and it obtains the SOTA results.
A new process algebra more suitable for formal specification

The paper shows that some usual specifications cannot be conveniently expressed in traditional process calculi. To solve the problem in formal specification, a new process calculus based on value-passing CCS, Calculus of Communicating Systems with Sequential Composition (CCSSC), is proposed, which features a unique sequential composition operator and replaces action prefix with sequential composition in CCS, together with static semantics constraints ensuring that data variables in process expressions properly obtain values. CCSSC successfully solves the above problem on the premise of keeping the language concise.
Visualization tool for teaching and learning Artificial Neural Networks

In this article, we describe a visualization tool for teaching and learning the basics of artificial neural networks with a user interface and a mobile robot. The participants are given a brief introduction to artificial neural networks and are introduced to the visualization tool. Participants had different educational backgrounds including Computer Science, Information Technology, Language and Translation, Law and Psychology. Most of the participants were excited to be a part of the study and they were able to relate the artificial neural networks to real-life applications. The study suggests that visualization tools can increase interest in teaching and learning certain topics regardless of its complexity. It also suggests that similar tools can be utilized in teaching topics related to Artificial Intelligence.
Adaptation of honeypot solutions for WSN

The article is devoted to security aspects of Wireless Sensor Networks. Starting with review and classification of existing threats and methods of improving security, the paper presents an idea of securing WSNs using honeypots for verification of its proper functioning. Several implementation possibilities are presented, as well as identification of the services which lend themselves to proposed mode of
Data Analytics for Predicting Dropout

Massive open online courses (MOOCs) offer multiple advantages and vast training possibilities in diverse topics for millions of people worldwide to continue their education. However, dropout rates are high; thus, it is important to continue investigating the reasons for dropout to implement new and better strategies to increase course completions. The present study aimed to analyze the data of a MOOC class on energy sustainability to know why students drop out, identify causes, and predict dropouts in future courses. The method used was Knowledge Discovery in Databases to analyze association rules in the data. Using the Mexico X platform, an initial, validated survey instrument was applied to 1506 students enrolled in the MOOC course "Conventional Clean Energy and its Technology. " The results indicated that association rules allowed identifying participants’ behavior according to the type of responses with a determined confidence level. Also, the association rules were appropriate for working with a large amount of data. In the present case, results of up to 86% confidence were obtained based on the rules. This research can be of value to decision-makers, teachers, researchers, designers, and those interested in large-scale training environments.
Motivation and expectation in a training course on Flipped Classroom

In this work, we analyze the participant's profile in a MOOC on the Inverted Classroom model by means of two independent surveys carried out at the beginning and at the end of the course. We study the motivation for taking the course, the modules fulfilled, and the characteristics internalized that allow them to implement teaching innovation in a sustainable and transferable in their teaching workflow. This information will be useful in future editions of the course, which will contain activities and contents adapted to the different motivational profiles.
A Review of Fatigue Driving Detection Based on Electrooculogram

Fatigue driving is one of the main causes of traffic collision, which causes great loss of life and property. Therefore, the study of fatigue driving is of great significance for preventing fatigue-related road accidents and reducing the number of injuries. This paper presents an in-depth review of the scientific research and existing techniques for fatigue driving detection based on electrooculogram (EOG). These include: 1) acquisition of EOG; 2) preprocessing of EOG; 3) feature extraction of EOG; 4) training and prediction model of EOG; and 5) research on fatigue relief. The various methods of fatigue driving detection based on EOG are compared, and the areas for improvement are pointed out. Finally, we think about the future development direction. This paper can help us better study the application of EOG in fatigue driving.