Miracle-dz/UniFAR-FTUs huggingface dataset:
Limitation of seedling growth by potassium and magnesium supply for two ectomycorrhizal tree species of a Central African rain forest and its implication for their recruitment

Abstract In the ectomycorrhizal caesalpiniaceous groves of southern Korup National Park, the dominant tree species, Microberlinia bisulcata, displays very poor in situ recruitment compared with its codominant, Tetraberlinia bifoliolata. The reported ex situ experiment tested whether availabilities of soil potassium and magnesium play a role. Seedlings of the two species received applications of K and Mg fertilizer in potted native soil in a local shade house, and their responses in terms of growth and nutrient concentrations were recorded over 2 years. Amended soil concentrations were also determined. Microberlinia responded strongly and positively in its growth to Mg, but less to K; Tetraberlinia responded weakly to both. Added Mg led to strongly increased Mg concentration for Microberlinia while added K changed that concentration only slightly; Tetraberlinia strongly increased its concentration of K with added K, but only somewhat its Mg concentration with added Mg. Additions of Mg and K had small but important antagonistic effects. Microberlinia is Mg‐demanding and apparently Mg‐limited in Korup soil; Tetraberlinia, whilst K‐demanding, appeared not to be K‐limited (for growth). Added K enhanced plant P concentrations of both species. Extra applied Mg may also be alleviating soil aluminum toxicity, and hence improving growth indirectly and especially to the benefit of Microberlinia. Mg appears to be essential for Microberlinia seedling growth and its low soil availability in grove soils at Korup may be an important contributing factor to its poor recruitment. Microberlinia is highly shade‐intolerant and strongly light‐responding, whilst Tetraberlinia is more shade‐tolerant and moderately light‐responding, which affords an interesting contrast with respect to their differing responses to Mg supply. The study revealed novel aspects of functional traits and likely niche‐partitioning among ectomycorrhizal caesalps in African rain forests. Identifying the direct and interacting indirect effects of essential elements on tropical tree seedling growth presents a considerable challenge due the complex nexus of causes involved.
A Multi-center Clinical Trial for Camera-based Infant Sleep and Awake Detection in Neonatal Intensive Care Unit

Infants need adequate sleep to develop their brain and cardiovascular systems, especially for preterm infants in the Neonatal Intensive Care Unit. Camera-based infant monitoring is an emerging direction of research in video health monitoring. Intuitively, camera can easily identify the sleep-awake stage of infants by detecting the state of eyes, e.g. closed or opening. Thus in this paper, we propose to explore the unique advantage of camera-based facial analysis for sleep-awake detection, as a fundamental step toward infant sleep monitoring. A multi-center clinical trial was conducted to collect infant videos for investigating the feasibility of our proposal. A benchmark including four machine learning methods of SVM, KNN, MLP, and CNN (ResNet18) was set up to classify the sleep/awake stage of infants. To alleviate the overfitting issue caused by over-sampling of a sleeping infant, we propose to integrate ResNet18 with the contrastive learning strategy to strengthen the consistency of facial features learned from different infants. The clinical evaluation shows that all benchmarked methods obtained an accuracy above 75% while the proposed method achieved the best accuracy of 86%. This invokes further explorations of using facial/eye features of infants for sleep-awake staging, towards intelligent contactless sleep analysis of infants in combination with camera-based vital signs monitoring.
NTRU PRIME

Investigations of perspective (including post-quantum) asymmetric cryptanalytic transforms such as asymmetric code (ASC), key encapsulation protocol (KEP) and digital signature (DS) confirm the promising use for constructing post-quantum transformation standards in polynomial rings over finite fields [1 – 4]. The main candidates for constructing the asymmetric cryptographic transforms are NTRUEncrypt ANSI X9.98 [1], NTRU Prime [2] and NTRU Prime Ukraine [3]. Special attention was paid to the ASC and KEP construction in [1], since they provide cryptographic stability up to 256 bits of classical stability and up to 128 bits of quantum stability. At the same time, the standards of symmetric cryptographic transformations have been constructed and used, which provide cryptographic stability of 512 bits of classical and 256 bits of quantum stability [5, 6]. Therefore, in our opinion, in the long run, the ASC, KEP and DS of 6-7 levels of stability are needed. Moreover, under the 6th level of security it is proposed to understand resistance against 384 bits of classical cryptographic stability and 192 bits of quantum cryptographic stability, respectively, and under the 7th level of security it is proposed to understand resistance against 512 bits of classical cryptographic stability and 256 bits of quantum cryptographic stability, respectively. The implementation of 6-7 levels of stability is associated with the complex problematic task of constructing common parameters and keys for cryptographic transformations in a ring of polynomials over finite fields for recognized and accepted security models [4, 9]. The purpose of this paper is to carry out research and develop an effective practical algorithm for construction and experimental confirmation of the built-in system-wide parameters and keys of cryptographic transformations of the ASC and KEP of 6-7 levels of stability based on transformations in a ring of polynomials over finite fields.
Input and Editing of Force Profiles of In-Contact Robot Motions via a Touch Graphical User Interface

Various automation tasks require the precise specification of forces/torques over time and space to be applied to the environment. Traditionally, only experts can provide such specifications, contrasting the need for intuitive robot programming in small and medium-sized enterprises. This paper presents a concept for extending kinesthetic robot programming to allow users to program motions in contact with the environment. In this context, four novel input methods enable non-experts to input and edit force/torque profiles graphically via a touch device as a function graph. A user study compares the input methods regarding duration, accuracy, and workload. In addition, the system's intuitiveness and usability for programming in-contact motions are evaluated. Even for a small sample set (N = 20), the evaluation of the System Usability Scale (SUS) showed a high median result (82.5). Thus, the promising results indicate that the presented programming system is usable and intuitive for non-experts.
The Toll and Imd Pathways Are Not Required for Wolbachia-Mediated Dengue Virus Interference

ABSTRACT Wolbachia blocks dengue virus replication in Drosophila melanogaster as well as in Aedes aegypti. Using the Drosophila model and mutations in the Toll and Imd pathways, we showed that neither pathway is required for expression of the dengue virus-blocking phenotype in the Drosophila host. This provides additional evidence that the mechanistic basis of Wolbachia-mediated dengue virus blocking in insects is more complex than simple priming of the host insect innate immune system.
Classification of Malware Using Deep Learning: A Study

Malware, or malicious software, significantly threatens data privacy and system security. This study delves into image-based malware classification, employing deep learning techniques. This research addresses the early stages of the classification of malware using image-based deep learning approaches. Our study emphasizes the exploration of diverse methodologies to enhance malware classification efficiency. We acknowledge image-based classification, with substantial potential for improved accuracy and robustness in malware classification. Additionally, we conduct a comprehensive comparative analysis, contrasting our image-based approach with existing methods. Through rigorous experimentation, we achieve 97.3% accuracy rate, highlighting the effectiveness of our approach. In conclusion, the study aims to contribute to evolving cybersecurity practices by exploring this domain for more precise and efficient malware classification.
Curiosity-Driven Exploration with Planning Trajectories

Reinforcement learning (RL) agents can reduce learning time dramatically by planning with learned predictive models. Such planning agents learn to improve their actions using planning trajectories, sequences of imagined interactions with the environment. However, planning agents are not intrinsically driven to improve their predictive models, which is a necessity in complex environments. This problem can be solved by adding a curiosity drive that rewards agents for experiencing novel states. Curiosity acts as a higher form of exploration than simple random action selection schemes because it encourages targeted investigation of interesting situations. In a task with multiple external rewards, we show that RL agents using uncertainty-limited planning trajectories and intrinsic curiosity rewards outperform non-curious planning agents. The results show that curiosity helps drive planning agents to improve their predictive models by exploring uncertain territory. To the author’s knowledge, no previous work has tested the benefits of curiosity with planning trajectories.
Automatic Crossover Detection of Long-Range Correlation Analysis Based on Detrending Operation

A crossover of two scaling regimes is often observed in long-range correlation analysis of time series. This paper proposes a method to accurately detect such crossover points in detrended fluctuation analysis (DFA) and detrending moving average analysis (DMA), which have been widely used to assess long-range correlations. It is known that the detrending operation used in those methods induces time-scale distortion. Our method corrects the scale distortion based on the frequency response of the detrending operation in DFA and DMA and automatically detects the crossover using segmented regression and kernel density estimation. Using the auto-regressive fractionally integrated moving average process, we generate numerical time series showing crossover phenomena and demonstrate the superior performance of our method.
Cognitive authentication schemes safe against spyware

Can we secure user authentication against eavesdropping adversaries, relying on human cognitive functions alone, unassisted by any external computational device? To accomplish this goal, we propose challenge response protocols that rely on a shared secret set of pictures. Under the considered brute-force attack the protocols are safe against eavesdropping, in that a modestly powered adversary who fully records a series of successful interactions cannot compute the user's secret. Moreover, the protocols can be tuned to any desired level of security against random guessing, where security can be traded-off with authentication time. The proposed protocols have two drawbacks: First, training is required to familiarize the user with the secret set of pictures. Second, depending on the level of security required, entry time can be significantly longer than with alternative methods. We describe user studies showing that people can use these protocols successfully, and quantify the time it takes for training and for successful authentication. We show evidence that the secret can be maintained for a long time (up to a year) with relatively low loss
Effect of Thermotherapy on Clinical Outcomes among Patients with Osteoarthritis

Background : The most common and crippling type of arthritis is osteoarthritis (OA), which is characterized as a degenerative disorder targeting synovial joints. Patients with osteoarthritis frequently lament a dull aching pain that worsens with movements. When physical agents as heat therapy are used properly, they can combat unpleasant conditions. Aim: This study was carried out to determine the effect of thermotherapy on Clinical Outcomes among Patients with Osteoarthritis. Design: A quasi-experimental research design (pre-post study/control) was used to achieve the study's aim. Setting: The study was conducted in the orthopedic outpatient department of the shebin EL-Kom Teaching Hospital and Menoufia University Hospital, Menoufia Governorate
Ground State Properties of Even-Even 30−92Ca Isotopes Using HFB Theory.

Ground State (GS) Properties of Even-Even 30 − 9220 Ca isotopes have been studied in frame of Skyrme Hartree-Fock-Bogoliubov using computer program HFBTHO V3.00 with three types of Skyrme interactions (HFB9, SLY4 and SLY5). The calculated nuclear properties including (average binding energy ( BE ⁄ A ), two nucleon separation energy ( S 2 N ) , two neutron shell gap ( δ 2 N ) , quadrupole deformation parameter ( β 2 ) , charge radii ( R ch ) , neutron and proton radii ( R N , R P ) and skin thickness) were compared with the available experimental data and with the results of Finite Range Droplet Macroscopic method (FRDM) and Relativistic Mean Field (RMF) theory. According to Binding energy ( BE ⁄ A ), it turns out that it increases directly with increase directly
Capacity bounds for peak-constrained multiantenna wideband channels

Bounds are derived on the noncoherent capacity of a very general class of multiple-input multiple-output fading channels that are selective in time and frequency as well as correlated in space. The bounds apply to peak-constrained inputs; they are explicit in the channel's scattering function, are useful for a large range of bandwidth, and allow one to coarsely identify the capacity-optimal combination of bandwidth and number of transmit antennas. Furthermore, a closed-form expression is obtained for the first-order Taylor series expansion of capacity in the limit of infinite bandwidth. From this expression, it is concluded that in the wideband regime: (i) it is optimal to use only one transmit antenna when the channel is spatially uncorrelated; (ii) rank-one statistical beamforming is optimal if the channel is spatially correlated; and (iii) spatial correlation, be it at the transmitter, the receiver, or both, is beneficial.
Content adaptive screen image scaling

This paper proposes an efficient content adaptive screen image scaling scheme for the real-time screen applications like remote desktop and screen sharing. In the proposed screen scaling scheme, a screen content classification step is first introduced to classify the screen image into text and pictorial regions. Afterward, we propose an adaptive shift linear interpolation algorithm to predict the new pixel values with the shift offset adapted to the content type of each pixel. The shift offset for each screen content type is offline optimized by minimizing the theoretical interpolation error based on the training samples respectively. The proposed content adaptive screen image scaling scheme can achieve good visual quality and also keep the low complexity for realtime applications.
Detrital zircon geochronology of sandstones of the 3.6-3.2 Ga Barberton greenstone belt: No evidence for older continental crust

Analytical Methods for University of Arizona LaserChron Center (ALC) LA-ICP-MS zircon dating Zircons were extracted from 2-10kg sandstone samples using standard size, hydrodynamic and density mineral separation techniques at Stanford University. For U-Pb analysis of spherule bed and detrital zircons in the Northern, Southern, and West-Central Domain only the most magnetic fraction (0.4 MAG) was excluded during Frantz mineral separation to avoid bias. Detrital samples from the East-Central Domain were magnetically separated at 1.2 MAG. Samples were then hand-picked onto 1” epoxy mounts together with ALC’s Sri Lanka, FC-1, R33 and OG1 standards and then polished.
Soil Physical Properties and Grain Yield Influenced by Cover Crops and Crop Rotation

The crop yield is related to several factors, among these, soil tillage, soil compaction and crop rotation. This study aimed to evaluate the winter cover crops and crop rotation influence on soil physical properties and grain yield of dry beans, maize and soybean for two growing seasons. Three experiments were conducted, corresponding to dry beans, maize and soybean crops. It was used the randomized block design with three treatments and four replications consisted by 3 × 10 m plots. The treatments were: two cover crops systems and crop rotation in no-till, and the control, consisting of winter fallow and conventional tillage. The cover crop dry matter, soil physical properties and grain yield for dry beans, maize and soybean in the two growing seasons were evaluated. Crop rotation systems and cover crops showed a trend to increase maize and soybean yields. Crop rotation in no-till increases soil compaction in the superficial layer compared to conventional tillage, but does not reduce the dry beans, maize and soybean yields.
Automation Possibilities in Information Security Management

Information security management, as defined in ISO 27001, deals with establishing, implementing, operating, monitoring, reviewing, maintaining, and improving an information security management system. This paper provides an analysis about the automation possibilities in information security management. The analysis takes into account the potential of using (i) security ontologies in risk management, (ii) hard- and software systems for the automatic operation of certain security controls, and (iii) the Security Control Automation Protocol (SCAP) for automatically checking compliance and security configurations. The analysis results support organizations and security managers at identifying systems they can use to achieve greater efficiency in the information security management process.
Cost-optimized scheduling for Microservices in Kubernetes

The usage of Kubernetes for running microservices applications is increasing nowadays. In a particular application, all microservices do not have the same priority. Hence it is costly to allocate the same resources to both high and low-priority services. This research aims to utilize spot instances to run low-priority services with the intention of reducing the cloud cost and providing overall high availability to the application. A service called KubeEconomy has been proposed to monitor and manage Kubernetes worker nodes. Three functionalities of the KubeEconomy service have been explained and it is shown that it is possible to reduce the cloud cost while maintaining high availability for the microservices.
Academic Writing Training Programs to Developing Teachers’ Writing Skills for Publication

This action research-based writing training is a component of the community service initiative organized by social studies lecturers at the Teacher Training Faculty of Tanjungpura University. The training adopted an action research approach, which involved some cycles, each comprising planning, implementation, observation, and reflection stages. This approach was chosen to encourage participants to share their insights to improve the training quality. The research goal was to gather teachers' perceptions and recommendations for enhancing the quality of action research-based academic writing training. The study involved 33 secondary school Social Studies teachers taking place at SMK 2 Singkawang. To collect data, an open-ended questionnaire was utilized. The results revealed that teachers faced challenges in accessing internet resources and suggested extending the duration of the training. They also recommended providing more comprehensive information and examples for each section of the research article. Lastly, the teachers expressed a positive view of the training.
Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains

This paper introduces a evolutionary computation method that applies Bayesian classifiers to optimization problems. This approach is based on Estimation of Distribution Algorithms (EDAs) in which Bayesian or Gaussian networks are applied to the evolution of a population of individuals (i.e. potential solutions to the optimization problem) in order to improve the quality of the individuals of the next generation. Our new approach, called Evolutionary Bayesian Classifier-based Optimization Algorithm (EBCOA), employs Bayesian classifiers instead of Bayesian or Gaussian networks in order to evolve individuals to a fitter population. In brief, EBCOAs are characterized by applying Bayesian classification techniques – usually applied to supervised classification problems – to optimization in continuous domains. We propose and review in this paper different Bayesian classifiers for implementing our EBCOA method, focusing particularly on EBCOAs applying naïve Bayes, semi-na¨ive Bayes, and tree augmented na¨ive Bayes classifiers. This work presents a deep study on the behavior of these algorithms with classical optimiztion problems in continuous domains. The different parameters used for tuning the performance of the algorithms are discussed, and a comprehensive overview of their influence is provided. We also present experimental results to compare this new method with other state of the art approaches of the evolutionary computation field for continuous domains such as Evolutionary Strategies (ES) and Estimation of Distribution Algorithms (EDAs).
TRANSIENT ANALYSIS OF THE BMAP/PH/1 QUEUE

We derive in this paper the transient queue length and the busy period distributions of a single server queue with phase-type (PH) service times and a batch Markovian arrival process (BMAP). These distributions are obtained by the uniformization technique which gives simple recurrence relations. The main advantage of this technique is that it leads to stable algorithms for which the precision of the result can be given in advance.
Probabilistic Combination of Visual Context Based Attention and Object Detection

Visual context provides cues about an object’s presence, position and size within the observed scene, which are used to increase the performance of object detection techniques. However, state-of-theart methods for context aware object detection could decrease the initial performance. We discuss the reasons for failure and propose a concept that overcomes these limitations. Therefore, we introduce the prior probability function of an object detector, that maps the detector’s output to probabilities. Together, with an appropriate contextual weighting a probabilistic framework is established. In addition, we present an extension to state-of-the-art methods to learn scale-dependent visual context information and show how this increases the initial performance. The standard methods and our proposed extensions are compared on a novel demanding image data set.
Identification of the dynamic parameters of the Orthoglide

This paper presents the experimental identification of the dynamic parameters of the Orthoglide, a 3-DOF parallel mechanism. The dynamic identification model is based on the inverse dynamic model, which is linear in the parameters. The model is computed in a closed form in terms of the Cartesian dynamic model elements of the legs and of the Newton-Euler equation of the platform. The base inertial parameters of the robot, which constitute the identifiable parameters, are given.