Reviewerly/exHarmony huggingface dataset:
Factors Influencing the Use of Mobile Phone-Enabled Services in Accessing Agricultural Information by Smallholder Farmers in Bungoma County, Kenya

The agriculture sector is the backbone of Kenya’s economy, contributing approximately 33 percent of Kenya’s gross domestic product (GDP). The agriculture sector employs more than 40 percent of the total population and 70 percent of the rural population. Kenya’s agriculture is predominantly smallholder farming and is carried out on farms averaging 0.2–3 ha, mostly on a subsistence basis. The objective of this study was to determine factors influencing the use of mobile phone-enabled services in accessing agricultural information by smallholder farmers in Bungoma County, Kenya. The study adopted a cross-sectional and correlational research design. Using simple random sampling, 400 respondents were selected to participate in the study. Data was collected using questionnaires, interview guides, and focus group discussions. Findings revealed that 71.1% of smallholder farmers indicated that it was costly to use mobile phone-enabled services to access agricultural information. Another 57% of the farmers perceived that socio-economic characteristics (age, education, and income) were factors determining the use of mobile phone-enabled services for accessing agricultural information. About 75.8% of the smallholder farmers did not use mobile phones to communicate agricultural information needed for their farming activities for various reasons, including: having no one to communicate with, due to the complexity of mobile phone technology, not being able to pay for the cost of both the purchase and use of mobile phones, and due to network problems. Further evidence showed that the factors affecting the use of mobile phone-enabled services have a significant impact on accessing agricultural information. The fact that socio-demographic factors significantly affect the use of mobile phone-enabled services in accessing agricultural information It is recommended that mobile phone service providers such as Safaricom, Airtel, and Orange need to understand the user’s characteristics, their needs, dynamics, and challenges, as well as potential changes in the future.
Phase sensitivity of spatially broadband high-gain <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>SU</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:math> interferometers

Nonlinear interferometers are promising tools for quantum metrology, as they are characterized by an improved phase sensitivity scaling compared to linear interferometers operating with classical light. However, the multimodeness of the light generated in these interferometers results in the destruction of their phase sensitivity, requiring advanced interferometric configurations for multimode light. Moreover, in contrast to the single-mode case, time-ordering effects play an important role for the high-gain regime in the multimode scenario and must be taken into account for a correct estimation of the phase sensitivity. In this paper, we present a theoretical description of spatially multimode $\mathrm{SU}(1,1)$ interferometers operating at low and high parametric gains. Our approach is based on a step-by-step solution of a system of integro-differential equations for each nonlinear interaction region. We focus on interferometers with diffraction compensation, where focusing elements such as a parabolic mirror are used to compensate for the divergence of the light. We investigate plane-wave and Gaussian pumping and show that for any parametric gain, there exists a region of phases for which the phase sensitivity surpasses the standard shot-noise scaling and discuss the regimes where it approaches the Heisenberg scale. Finally, we arrive at insightful analytical expressions for the phase sensitivity that are valid for both low and high parametric gain and demonstrate how it depends on the number of spatial modes of the system.
Optimal quantum resource generation by coupled transmons immersed in Markovian baths

We analyze quantum resource generation of capacitively coupled multilevel transmon circuits surrounded by bosonic baths, within the Markovian limit. Superconducting circuit elements are in practice usually part of a larger circuit, constructed with many other elements, which along with their environment is assumed to be mimicked by the baths. We study the response, to variation of the temperature of the thermal baths and the coupling strength, of resource generation for the system prepared in zero-resource initial states. We focus, in particular, on entanglement and quantum coherence as resources. We find that capacitive coupling, even if weak, can compete with Markovian decay of quantum resources and lead to significant steady-state values of the same. At short and moderate timescales, the resources exhibit nonmonotonic oscillatory behavior, reminiscent of the same in non-Markovian environs in absence of intrasystem coupling. We quantify the entanglement generation power of coupled transmon qutrits, taking into account the maximum entanglement the system can generate and the timescale over which the system can sustain a significant amount of the same. We identify the optimal initial separable states leading to maximum entanglement generating power.
Contextual Refinement Types

We develop an extension of the proof environment BELUGA with datasort refinement types and study its impact on mechanized proofs.In particular, we introduce refinement schemas, which provide finegrained classification for the structures of contexts and binders.Refinement schemas are helpful in concisely representing certain proofs that rely on relations between contexts.Our formulation of refinements combines the type checking and sort checking phases into one by viewing typing derivations as outputs of sorting derivations.This allows us to cleanly state and prove the conservativity of our extension.
LEIA: Linguistic Embeddings for the Identification of Affect

The wealth of text data generated by social media has enabled new kinds of analysis of emotions with language models. These models are often trained on small and costly datasets of text annotations produced by readers who guess the emotions expressed by others in social media posts. This affects the quality of emotion identification methods due to training data size limitations and noise in the production of labels used in model development. We present LEIA, a model for emotion identification in text that has been trained on a dataset of more than 6 million posts with self-annotated emotion labels for happiness, affection, sadness, anger, and fear. LEIA is based on a word masking method that enhances the learning of emotion words during model pre-training. LEIA achieves macro-F1 values of approximately 73 on three in-domain test datasets, outperforming other supervised and unsupervised methods in a strong benchmark that shows that LEIA generalizes across posts, users, and time periods. We further perform an out-of-domain evaluation on five different datasets of social media and other sources, showing LEIA's robust performance across media, data collection methods, and annotation schemes. Our results show that LEIA generalizes its classification of anger, happiness, and sadness beyond the domain it was trained on. LEIA can be applied in future research to provide better identification of emotions in text from the perspective of the writer.
A note on compositional data gauge R&amp;R studies

This article considers measurement system assessment for compositional data, component proportions that sum to one. The data are typically collected from a gauge R&R study, that is, an experiment. We propose a model and use it to analyze real data from a glass composition study. We also simulate data to assess the impact of the gauge R&R study design.
Non–Pauli Errors Can Be Efficiently Sampled in Qudit Surface Codes

Surface codes are the most promising candidates for fault-tolerant quantum computation. Single qudit errors are typically modeled as Pauli operators, to which general errors are converted via randomizing methods. In this Letter, we quantify remaining correlations after syndrome measurement for a qudit 2D surface code subject to non--Pauli errors via loops on the lattice, using percolation theory. Below the error correction threshold, remaining correlations are sparse and locally constrained. Syndromes for qudit surface codes are therefore efficiently samplable for non--Pauli errors, independent of the exact forms of the error and decoder.
Improved End-to-End Data Security Approach for Cloud Computing

Cloud computing is one of the major cutting-edge technologies that is growing at a gigantic rate to redefine computation through service-oriented computing. It has addressed the issue of owning and managing computational infrastructure by providing service through a pay-and-use model. However, a major possible hindrance is security breaches, especially when the sender uploads or the receiver downloads the data from a remotely accessed server. It is a very generic approach to ensuring data security through different encryption techniques, but it might not be able to maintain the security standard. This paper proposes an end-to-end data security approach from the sender side to the receiver side by adding extra padding sequences, as well as randomized salting, followed by hashing and an encryption technique. The effectiveness of the proposed method was established using both a simulated system and mathematical formulations with different performance metrics. Furthermore, its performance was compared with those of contemporary algorithms, showing that the proposed algorithm creates a larger ciphertext that is almost impossible to crack due to randomization modules. However, it has significantly longer encryption and decryption times, although our primary concern is ensuring security, not reducing time.
Research on color image encryption approach based on chaotic Duffing map

Abstract In this study, we propose a novel image encryption approach that utilizes the chaotic dynamics of the Duffing map and Moore-penrose pseudo inverse to obscure images. Chaotic maps have been widely used in the field of image encryption due to their sensitive dependence on initial conditions, enabling them a suitable candidate for generating pseudo-random sequences that can be used for encryption tasks. The Duffing map, known for its complex nonlinear behavior, is highly preferred for developing robust and secure encryption schemes. The proposed approach aims to develop a balance between encryption efficiency and cryptographic security while maintaining a straightforward implementation. The analytical results and comparison of suggested approach with some existing approaches exhibit that it is a robust and secure method for real-time image encryption.
Outlook on the Brazilian scenario of floating photovoltaic solar energy

Brazil offers significant potential for installing floating photovoltaic systems in artificial reservoirs, as it represents the world's second-largest installed hydroelectric capacity, corresponding to 56.8% of the Brazilian electrical energy matrix. The current paper highlights the potential contributions of floating photovoltaic solar energy to the Brazilian renewable energy matrix, specifically regarding land use efficiency and water resource management. In addition, through a comparative analysis with a global scenario, this work shows the importance of Brazil's water bodies and sunlight for renewable energy generation. It shows that using 1% of surface areas in artificial water bodies in Brazil can generate 57,384 GWh/year, reaching up to 5 times the generation capacity, as indicated by more recent studies. Moreover, analyzing data for one-day hourly generation considering a hybrid system would result in an increase of approximately 4% in electricity generation. By leveraging its favorable conditions and addressing the challenges, Brazil has the opportunity to establish itself as a pioneer in the (FPV) sector, contributing to its sustainable energy future and the global transition to renewable sources.
Impoliteness and morality as instruments of destructive informal social control in online harassment targeting Swedish journalists

This study investigates the interplay between morality, impoliteness, and moral order in the online harassment of Swedish journalists on Twitter. It reveals how impoliteness serves as a tool to harm the media's epistemic credibility, rooted in anti-press and populist rhetoric, and exert destructive informal social control. The highlighted paradox is that provisions for freedom of speech, designed to protect, are paradoxically used to suppress journalists' voices through targeted insults and derogatory language. The study uncovers that such harassment is systematic, politically motivated, and morally grounded. We emphasize the urgent need to recognize and confront these subtle tactics that threaten journalistic freedom and, consequently, access to information in Sweden and internationally under growing criticism that seeks to delegitimize the media.
HDR Challenges and Solutions

For content producers who need to make drastic choices to allocate limited contrast and colors in standard dynamic range (SDR), high dynamic range (HDR) offers the opportunity to show more. This means greater freedom and flexibility for their storytelling. However, future-proof HDR content presents its own challenges, for example, the wide variation in HDR display capabilities and the desire to have any production, HDR or SDR, play optimally on any display, HDR or SDR. The typical solution to this issue is tone mapping, whether a tone compression to map from HDR to a lower HDR or SDR luminance, or tone expansion to map from SDR or HDR to a higher HDR luminance. Various techniques of tone mapping, including hybrid log-gamma (HLG), static look-up tables (LUTs) and dynamic metadata, are considered, and the relative advantages and performance are analyzed. A proposed solution that has been introduced in practice, “Advanced HDR by Technicolor,” is described.
Intelligent optimization: Literature review and state-of-the-art algorithms (1965–2022)

Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: "Proceedings of the Congress" (33, 10.09%), "Applied Soft Computing" (19, 5.81%), "Expert Systems with Applications" (18, 5.51%), "Knowledge-Based Systems" (12, 3.67%), "Engineering Applications of Artificial Intelligence" (12, 3.67%), "Advances in Engineering Software" (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece.
Image caption generation using transfer learning

This paper describes an image caption generation system using deep neural networks. The model is trained to maximize the probability of generated sentence, given the image. The model utilizes transfer learning in the form of pretrained convolutional neural networks to preprocess the image data. The datasets are composed of a still photographs and associated with it, five captions in English language. Constructed model is compared to other similarly constructed models using BLEU score system and ways to further improve its performance are proposed.
Overview of selected reinforcement learning solutions to several game theory problems

This paper collects several applications of reinforcement learning in solving some problems related to game theory. The methods were selected to possibly show variety of problems and approaches. Selections includes Thompson Sampling, Q-learning, DQN and AlphaGo Zero using Monte Carlo Tree Search algorithm. Paper attempts to show intuition behind proposed algorithms with shallow explaining of technical details. This approach aims at presenting overview of the topic without assuming deep knowledge about statistics and artificial intelligence.
Adaptive Stanley Control Method Based on Dynamic Window Approach

Stanley algorithm is a classical algorithm in automatic driving path tracking algorithm, which has the advantages of low computational complexity and good tracking effect. However, the tracking accuracy of the traditional Stanley algorithm is limited by the gain parameters and the gain coefficients cannot be dynamically adjusted according to the road conditions. To improve the tracking accuracy and driving stability, an adaptive Stanley control algorithm based on DWA (DWA-Stanley) is proposed, which is used to sampling the velocity vector space and selecting the evaluation function, the vehicle motion planning is based on the best set of velocity values. In this paper, the DWA-Stanley algorithm is validated based on three different driving speeds. Simulation results show that an racecar using the DWA-Stanley algorithm has higher tracking accuracy and smoothness than a conventional Stanley.
Physics-Driven Deep Panoramic Imaging for High Dynamic Range Scenes

Due to saturated regions of low dynamic range (LDR) images and large intensity changes among them, it is challenging to produce an information-enriched panoramic LDR image without visual artifacts from multiple geometrically synchronized LDR images with different exposures and piecewise overlapping fields of views for a high dynamic range (HDR) scene. Fortunately, the stitching of such images is innately a perfect scenario for the fusion of physics-driven and data-driven methods. Based on the insight, a novel neural augmented HDR panoramic stitching algorithm is proposed in this paper. Differently exposed panoramic LDR images are initialized by using a physics-driven method on top of the piecewise overlapping fields of views. They are then refined by a data-driven one, and finally merged together via a multi-scale exposure fusion algorithm to produce the desired panoramic LDR image. Experimental results validate the proposed algorithm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The source code and trained model will be publicly available upon the acceptance..
Use K-Means-Generated Nodes to Distinguish Learned from Non-Learned Exercises

In recent years, exercise recognition has become increasingly popular for exercise monitoring and rehabilitation for older adults. However, to identify non-learned exercises, another dataset needs to be collected for differentiation purposes. This study aims to provide an add-on technique to the encoder model to distinguish between learned and non-learned exercises without training with non-learned exercise data. First, we form a list of activation nodes based on the output of the encoder by using the k-means algorithm. Afterward, these nodes are used to compute activation scores from encoded features. These activation scores are used to differentiate non-learned exercises by a threshold value. After differentiation, the activation scores are then passed to a multi-layer perceptron (MLP) for exercise classification. Meanwhile, we proposed a unique method to compute a distinguishing score to find the optimal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k-\mathbf{nodes}$</tex> . We demonstrate the proposed method using the MM-Fit dataset, showing that it can identify fitness exercises and distinguish non-learned exercises without much performance loss. Lastly, we collected a dataset about Chair-Fitness activity to validate the proposed method's effectiveness further and welcome other researchers to utilize the dataset.
State Estimation Based on TS Fuzzy Models for Networked Systems: A Survey

The networked system is becoming more and more complex and hence cannot be described by an accurate mathematical model. To address the issue, the Takagi-Sugeno (TS) fuzzy model has gained attention as a promising tool for achieving efficient and flexible design requirements. This survey paper aims to provide a thorough overview of the latest advancements of state estimation based on TS fuzzy models. A brief historical overview is proposed for the evolution of T-S fuzzy models and then the T-S fuzzy model-based filtering is systematically reviewed alongside their corresponding analytical and design techniques. Lastly, we a variety of challenging areas are identified to highlight future research efforts.
Drone Classification Using Gated Recurrent Unit

Nowadays, the trend of using unmanned aerial vehicles (UAVs) is increasing dramatically due to commercial, engineering, medical, logistical, security, and military applications. This paper proposes the use of Gated Recurrent Unit (GRU) to classify drones based on their sound, and methods to collect drone sounds for machine learning applications. This paper concludes that the performance of GRU is better than traditional RNN and is on par with papers using CNN and CRNN networks even though the GRU network has 4 times less parameters than RNN, CNN, and CRNN networks. We also found out that while collecting sound for machine learning applications, it is important to include environmental noise to make it more robust. Our approach is a strategic parameter optimization, allowing us to streamline the model's complexity and speed up training without compromising accuracy. The paper proceeds with an introduction to the motivation and background of the research, followed by a summary of related works and challenges in drone sound classification. We then describe the GRU architecture, parameter optimization, and the data collection process involving environmental noise. Experimental results and comparative analysis are presented, along with potential applications and future research directions. Overall, this research presents a compelling solution for drone sound classification, leveraging the advantages of the GRU model, optimizing its parameters for improve efficiency, and emphasizing the importance of environmental noise during data collection. We believe that our work will contribute significantly to the field of drone-related research and inspire further advancements in UAV classification and sound-based machine learning applications.