Miracle-dz/UniFAR-FTUs huggingface dataset:
FUZZY CONTROLLER TECHNIQUE DEPENDENT MPPT FOR PV SCHEME

Fuzzy MPPT photovoltaic control technology is the main focus of this article. The maximum power point technology (MPPT) is employed for photovoltaic systems to make optimum use of PV output. The output power of the PV arrays also varies for environmental conditions including solar irradiation and ambient temperatures. This paper is intended to increase the performance of a self-sufficient solar energy system by means of fluid logic control (FLC). Fuggiest logic controllers are the intelligent way to handle full power (MPP). The Fuzzy logic control is easy to monitor the peak power in order to achieve better output in comparison with other MPPT approaches. The performance of the device was simulated in Matlab / Simulink at different solar radiation and temperature.
Optimization Design of a Novel Partitional Stator Flux Modulated Memory Machine

This digest proposes the optimization design of a novel partitional stator flux modulated memory machine (PS-FMMM). Compared with traditional variable flux memory machine, it can significantly improve the torque density due to magnetic gear effect, and has good flux adjustable capacity with low coercive force (LCF) PMs. Firstly, the basic principle of the proposed PS-FMMM is introduced, and different combinations of stator poles and rotor poles are analyzed and compared. Then the torque regulating characteristics of the proposed PS-FMMM with different field currents is achieved. Various FEA simulation, optimization and comparison studies of the proposed machine are performed. The results verified the effectiveness of the proposed structure.
Antixenosis in cotton genotypes (Gossypium hirsutum var. marie galante) to Spodoptera frugiperda (Lepidoptera: Noctuidae) mediated by trichome and gossypol densities

Cotton (Gossypium hirsutum L.) is one of the most important crops in Brazil and pest incidence limits its yields. The fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), native to tropical and subtropical America, spread and rapidly became an invasive pest worldwide. It is a polyphagous pest that feeds on up to 353 plant species including cotton. The objective of this study was to evaluate the antixenosis to S. frugiperda in 27 cotton genotypes, comparing varieties with non- commercial genotypes. The attractiveness was estimated in no-choice and free-choice tests, offering leaf discs of the 27 cotton genotypes to S. frugiperda, and counting the number of caterpillars feeding at 3, 5, 10, 15, and 30 minutes and 1, 2, 6, 12, and 24 hours after their release. The numbers of trichomes and gossypol glands per cm2 were measured on abaxial and adaxial surface. The free-choice test was performed in a randomized block design with the 27 genotypes (treatments) and 20 arenas (replicates). The no-choice assay was conducted as a completely randomized design with 18 genotypes and 20 replicates. The non-commercial genotypes PI0440, CE0467, PI0416, BA0512 and MA0425 presented antixenosis against S. frugiperda. In the free-choice test the cultivar BRS Buriti and the genotype PI0429 were the most attractive to S. frugiperda, while in no-choice test, the genotype PI0429 was the most attractive. High trichome density and gossypol glands did not influence the attractiveness of S. frugiperda in cotton genotypes, while the absence of trichomes was repellent to the caterpillars. There is polymorphism among cotton genotypes to attractiveness to the pest, and the genotypes with antixenosis can be used in future study in plant breeding program as donor source of resistance to S. frugiperda
Who’s Got the Groove? Classifying Dance Style from Video

Many video action recognition models rely on background information or context clues; these models are nat-ural extensions of image processing, where only spatial information is needed. Our project focuses on classiﬁcation of dance styles from video, a domain that requires representation of motion because contextual information such as scenery or props is often sparse. We use Castro et al.’s Let’s Dance dataset, designed speciﬁcally for this purpose. Our contribution is the application of sequential models to this dataset. We show that, while sequential models out-perform purely spatial models by roughly 10% accuracy, they are not as successful in dance style recognition as Castro’s models that use optical ﬂow to explicitly encode motion
Evaluating HWE and Association in Genome Wide Association Studies: A Unified Procedure

In current genome wide association studies (GWASs) based on a case-control design, single nucleotide polymorphisms (SNPs) are typically evaluated for association as well as for Hardy-Weinberg equilibrium (HWE) in the control group by performing two separate statistical tests. Since HWE is expected to hold in the control group, SNPs for which the p-value of the HWE test is below some threshold value are excluded from further analysis. For the other SNPs, the value of the HWE statistic is subsequently ignored. This standard approach has several quite obvious drawbacks. First, the threshold value for the HWE test has to be chosen rather arbitrarily. Second, for SNPs which are not excluded the HWE statistic is completely ignored. Thus, the dependency of the two tests is not taken into account in an appropriate manner. Further, we shall argue that such a procedure is not optimal for detecting or avoiding effects of systematic error. To overcome these drawbacks, we propose a conditional genotype-based test that conditions the Pearson Chi-Square test for association in the 3x2 contingency table on the HWE statistic in the control group. The asymptotic distribution of the conditional test is derived. Here, the main technical tool is a new parametrization for the genotype distribution. The properties of our test, as compared to two competing procedures, are investigated in an extensive simulation study. After comparing power and robustness properties in single locus simulations, our main concern is for the properties of the tests in GWASs simulations. Here, we propose an elaborate simulation setting and study several measures for performance in GWASs, in particular average ranking of alternative SNPs, number of rejected alternative SNPs under a false discovery rate criterion (FDR) and probability of rejecting any alternative SNP (while controlling the FDR). It turns out that the proposed conditional test strongly outperforms its competitors for these performance measures in several distinct GWASs settings. Finally, we illustrate the new procedure by an application to a data set in an alopecia study. This data set shows mild deviation from HWE in controls and therefore offers interesting insights into behavior under misspecification. In conclusion, our test makes separate HWE testing superfluous by providing a unified framework and strictly improves on the standard procedure in terms of power and interpretability, thereby making replication more cost effective and improving subsequent fine mapping.
Clustering Principles and Empirical Risk Approximation

Data Clustering is one of the fundamental techniques in pattern recognition to extract structure from data. We discuss a maximum entropy approach to clustering for diﬀerent clustering cost functions and relate it to the estimation principle of Empirical Risk Approximation . Large deviation techniques from statistical learning theory provide guarantees for the stability of clustering solutions
Office Information Systems: The Challenges of Detecting Malware in Encrypted Network Traffics for Smooth Information Processing

In recent years there has been a dramatic increase in the number of malware attacks that use encrypted HTTP traffic for self-propagation and communication. Due to the volume of legitimate encrypted data, encrypted malicious traffic resembles legitimate traffic. As the malicious and legitimate traffic looks similar, it poses challenges for antivirus software and firewalls. Since antivirus software and firewalls will not typically have access to encryption keys, detection techniques are needed that do not require decrypting the traffics In this research, we will survey Intrusion Detection Systems (IDS) that can be deployed to work efficiently regardless of encryption and then attempt to apply machine learning technique to the problem of distinguishing malicious encrypted Hyper Text Transfer Protocol (HTTP) traffics from legitimate encrypted traffics without decryption. The effectiveness of machine-learning approaches crucially depends on the availability of large amounts of labelled training data. However, obtaining ground-truth class labels for Hyper Text Transfer protocol secured (HTTPS) trafﬁc is a difﬁcult problem when the Hyper Text Transfer protocol (HTTP) payload is encrypted, generally one cannot determine whether it originates from malware by analyzing the network trafﬁc in isolation. We develop an approach to collecting training data based on a VPN client that is able to observe the associations between executable ﬁles and TCP/IP packets on a large number of client computers. One of the few observable features of Hyper Text Transfer protocol secured (HTTPS) trafﬁc is the host IP address and, if a DNS entry exists for that address, the domain name. In order to extract features from the domain name, we explore neural language models (CISCO, 2017) which use neural networks to derive a low-dimensional, continuous state representations of a text. As a baseline, we also study manually-engineered domain features (A. Özgür. , 2016
Thunderstorm Event Simulation over the Balasore (Odisha) Using WRF-ARW Model

The vast majority of extreme weather occurrences are convective in origin. The Advance Research dynamic core of the Weather Research and Forecasting Model was used to simulate the thunderstorm event that happened over Balasore District, Odisha near the Bay of Bengal on 07 August 2021, which resulted in 5 deaths (WRF-ARW). The WRF model was run using 3 hourly NCEP-GFS datasets from 0000UTC of 07 August to 0000UTC of 08 August 2021 as initial and boundary conditions, with a domain of 500 m horizontal resolution. The model's performance has been evaluated using hourly outputs. With certain spatial and temporal biases in the data, the WRF model captured the analyzed thunderstorm event on 07 August 2021 very well. However, during 0900 UTC, the model simulated 24hour rainfall over the Balasore district increased significantly. Mesoscale models are required for accurate and precise forecasting of high-impact weather phenomena. A thunderstorm event that happened on 07 August 2021 at Balasore, Odisha (21.50°N, 86.90°E) was attempted to be simulated in this study. The National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction (NOAA/NCEP) devised and developed this model. The findings of these investigations demonstrate that the 500 m resolution WRF-ARW has superior thunderstorm modeling capacity. As a result, high-resolution models have the potential to give severe thunderstorm forecasters with unique and reliable data.
New silvicultural approaches for multipurpose management in beech forests

Traditional thinning and stand silviculture had positive effects on tree stability, stand efficiency and tree growth if compared to the lack of management. Nevertheless, the tree oriented silviculture has proven to be a type of management best meets the multifunctionality pourpouses required by forests today. In Italy beech high forests are generally managed with traditional thinning from below ascribable to stand silviculture; in the last decades tree oriented silviculture, an innovative management system is spreading in Europe. The aim of the study is to compare two thinning types applied in two localities of central Italy, in beech stands deriving from shelterwood system, aged 45-50 years: stand silviculture (SS), tree-silviculure (TS) and control (C). All trees were periodically measured in order to assess growth, mortality and production from 2008 onwards. Crop trees DBH, selected for each silvicultural approach, was annually measured while, an evaluation of the stem quality, crown development and stability were carried out between the 1st and last thinnings. Differences in vertical and horizontal diversity stand structures were assessed. Results pointed out similar growth rate at stand level; while a superior growth in diameter, in dimension of the crown and stability of the crop trees were observed in TS. The TS has proven to be the management option that best meets the multifunctionality required by forests today.
Deep Learning Based Rumor Detection for Arabic Micro-Text

Nowadays microblogs have become the most popular platforms to obtain and spread information. Twitter is one of the most used platforms to share everyday life event. However, rumors and misinformation on Arabic social media platforms has become pervasive which can create inestimable harm to society. Therefore, it is imperative to tackle and study this issue to distinguish the verified information from the unverified ones. There is an increasing interest in rumor detection on microblogs recently, however, it is mostly applied on English language while the work on Arabic language is still ongoing research topic and need more efforts. In this paper, we propose a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to detect rumors on Twitter dataset. Various experiments were conducted to choose the best hyper-parameters tuning to achieve the best results. Moreover, different neural network models are used to evaluate performance and compare results. Experiments show that the CNN-LSTM model achieved the best accuracy 0.95 and an F1-score of 0.94 which outperform the state-of-the-art methods.
Analysis and Simulation of a High Step-Up DC-DC Converter for Renewable Energy System Applications

This paper proposes a new step-up DC-DC converter topology with a high voltage conversion ratio for renewable system applications. The desired high voltage gain and satisfactory performance can be achieved by employing only a single power switch with simple control technique. The proposed converter can be used to step up the low voltage generated from renewable energy sources, such as solar photovoltaic modules, to the high level of the dc bus voltage, obtaining low-to-high voltage conversion ratios of approximately 30 times without adopting extremely large duty cycle. Employing the proposed converter the low input voltage (20-50Vdc) can be boosted up to the high output voltage level about 600Vdc at the dc-link bus required for the three-phase inverter feeding the three-phase motor drive system. In this paper, the proposed converter configuration with only one active power switch is presented. The operating principleincluding analysis of the steady-state performance characteristics under continuous conduction mode (CCM) operating conditions is described. In addition, the control strategy is developed in order to obtain the satisfactory output voltage regulation. Finally, simulation results are shown to demonstrate the effectiveness of the proposed converter with its control strategy to achievehigh step-up conversion ratios under different operating conditions.
Behavior Recognition Based on the Integration of Multigranular Motion Features

The recognition of behaviors in videos usually requires a combinatorial analysis of the spatial information about objects and their dynamic action information in the temporal dimension. Specifically, behavior recognition may even rely more on the modeling of temporal information containing short-range and long-range motions; this contrasts with computer vision tasks involving images that focus on the understanding of spatial information. However, current solutions fail to jointly and comprehensively analyze short-range motion between adjacent frames and long-range temporal aggregations at large scales in videos. In this paper, we propose a novel behavior recognition method based on the integration of multigranular (IMG) motion features. In particular, we achieve reliable motion information modeling through the synergy of a channel attention-based short-term motion feature enhancement module (CMEM) and a cascaded long-term motion feature integration module (CLIM). We evaluate our model on several action recognition benchmarks such as HMDB51, Something-Something and UCF101. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods, which confirms its effectiveness and efficiency.
Efficient perturbative framework for coupling of radiative and guided modes in nearly periodic surfaces

We present a semi-analytical framework for computing the coupling of radiative and guided waves in slowly varying (nearly uniform or nearly periodic) surfaces, which is especially relevant to the exploitation of nonlocal effects in large-area metasurfaces. Our framework bridges a gap in the theory of slowly varying surfaces: aside from brute-force numerical simulations, current approximate methods can model either guided or radiative waves, but cannot easily model their coupling. We solve this problem by combining two methods: the locally periodic approximation, which approximates radiative scattering by composing a set of periodic scattering problems, and spatial coupled-wave theory, which allows the perturbative modeling of guided waves using an eigenmode expansion. We derive our framework for both nearly uniform and nearly periodic surfaces, and we validate each case against brute-force finite-difference time-domain simulations, which show increasing agreement as the surface varies more slowly.
The Infrared Database of Extragalactic Observables from Spitzer. II. The Database and Diagnostic Power of Crystalline Silicate Features in Galaxy Spectra

We present the Infrared Database of Extragalactic Observables from Spitzer (IDEOS), a homogeneous, publicly available, database of 77 fitted mid-infrared observables in the 5.4–36 μm range, comprising measurements for 3335 galaxies observed in the low-resolution staring mode of the Infrared Spectrometer on board the Spitzer Space Telescope. Among the included observables are polycyclic aromatic hydrocarbon fluxes and their equivalent widths, the strength of the 9.8 μm silicate feature, emission-line fluxes, solid-state features, rest-frame continuum fluxes, synthetic photometry, and a mid-infrared spectral classification. The IDEOS spectra were selected from the Cornell Atlas of Spitzer-IRS Sources. To our surprise, we have detected at a >95% confidence level crystalline silicates in the spectra of 786 IDEOS galaxies. The detections range from single-band detections to detections of all fitted crystalline bands (16, 19, 23, 28, and 33 μm). We find the strength of the crystalline silicate bands to correlate with the amorphous silicate strength and the change from an emission to an absorption feature to occur at higher obscuration as the wavelength of the crystalline silicate band is longer. These observed characteristics are consistent with an origin for the amorphous and crystalline silicate features in a centrally heated dust geometry, either an edge-on disk or a cocoon. We find the 23 and 33 μm crystalline silicate bands to be well suited to classify the obscuration level of galactic nuclei, even in the presence of strong circumnuclear star formation. Based on our detection statistics, we conclude that crystalline silicates are a common component of the interstellar medium of galactic nuclei.
Online Matching with Convex Delay Costs

We investigate the problem of Min-cost Perfect Matching with Delays (MPMD) in which requests are pairwise matched in an online fashion with the objective to minimize the sum of space cost and time cost. Though linear-MPMD (i.e., time cost is linear in delay) has been thoroughly studied in the literature, it does not well model impatient requests that are common in practice. Thus, we propose convex-MPMD where time cost functions are convex, capturing the situation where time cost increases faster and faster. Since the existing algorithms for linear-MPMD are not competitive any more, we devise a new deterministic algorithm for convex-MPMD problems. For a large class of convex time cost functions, our algorithm achieves a competitive ratio of $O(k)$ on any $k$-point uniform metric space, or $O(k\Delta)$ when the metric space has aspect ratio $\Delta$. Moreover, we prove lower bounds for the competitive ratio of deterministic and randomized algorithms, indicating that our deterministic algorithm is optimal. This optimality uncover a substantial difference between convex-MPMD and linear-MPMD, since linear-MPMD allows a deterministic algorithm with constant competitive ratio on any uniform metric space.
Recent Advances in Neural Text Generation: A Task-Agnostic Survey

In recent years, considerable research has been dedicated to the application of neural models in the field of natural language generation (NLG). The primary objective is to generate text that is both linguistically natural and human-like, while also exerting control over the generation process. This paper offers a comprehensive and task-agnostic survey of the recent advancements in neural text generation. These advancements have been facilitated through a multitude of developments, which we categorize into four key areas: data construction, neural frameworks, training and inference strategies, and evaluation metrics. By examining these different aspects, we aim to provide a holistic overview of the progress made in the field. Furthermore, we explore the future directions for the advancement of neural text generation, which encompass the utilization of neural pipelines and the incorporation of background knowledge. These avenues present promising opportunities to further enhance the capabilities of NLG systems. Overall, this survey serves to consolidate the current state of the art in neural text generation and highlights potential avenues for future research and development in this dynamic field.
What does it mean to represent? Mental representations as falsifiable memory patterns

Representation is a key notion in neuroscience and artificial intelligence (AI). However, a longstanding philosophical debate highlights that specifying what counts as representation is trickier than it seems. With this brief opinion paper we would like to bring the philosophical problem of representation into attention and provide an implementable solution. We note that causal and teleological approaches often assumed by neuroscientists and engineers fail to provide a satisfactory account of representation. We sketch an alternative according to which representations correspond to inferred latent structures in the world, identified on the basis of conditional patterns of activation. These structures are assumed to have certain properties objectively, which allows for planning, prediction, and detection of unexpected events. We illustrate our proposal with the simulation of a simple neural network model. We believe this stronger notion of representation could inform future research in neuroscience and AI.
Chance-Constrained Optimization in Contact-rich Systems

This paper presents a chance-constrained formulation for robust trajectory optimization during manipulation. In particular, we present a chance-constrained optimization for Stochastic Discrete-time Linear Complementarity Systems (SDLCS). To solve the optimization problem, we formulate Mixed-Integer Quadratic Programming with Chance Constraints (MIQPCC). In our formulation, we explicitly consider joint chance constraints for complementarity as well as states to capture the stochastic evolution of dynamics. We evaluate robustness of our optimized trajectories in simulation on several systems. The proposed approach outperforms some recent approaches for robust trajectory optimization for SDLCS.