Reviewerly/exHarmony huggingface dataset:
Monitoring and Assessment of Salinity and Chemicals in Agricultural Lands by a Remote Sensing Technique and Soil Moisture with Chemical Index Models

Agricultural land in the south of Iraq provides habitat for several types of living creatures. This land has a significant impact on the ecosystem. The agricultural land of Al-Hawizeh marsh covers an area of more than 3500 km2 and is considered an enriched resource to produce several harvests. A total of 74% of this area suffers from a high degree of salinity and chemical pollution, which needs to be remedied. Several human-made activities and post-war-related events have caused radical deterioration in soil quality in the agricultural land. The goal of this research was to integrate mathematical models, remote sensing data, and GIS to provide a powerful tool to predict, assess, monitor, manage, and map the salinity and chemical parameters of iron (Fe), lead (Pb), copper (Cu), chromium (Cr), and zinc (Zn) in the soils of agricultural land in Al-Hawizeh marsh in southern Iraq during the four seasons of 2017. The mathematical model consists of four parts. The first depends on the B6 and B11 bands of Landsat-8, to calculate the soil moisture index (SMI). The second is the salinity equation (SE), which depends on the SMI result to retrieve the salinity values from Landsat-8 images. The third part depends on the B6 and B7 bands of Landsat-8, which calculates the clay chemical index (CCIs). The fourth part is the chemical equation (CE), which depends on the CCI to retrieve the chemical values (Fe, Pb, Cu, Cr, and Zn) from Landsat-8 images. The average salinity concentrations during autumn, summer, spring, and winter were 1175, 1010, 1105, and 1789 mg/dm3, respectively. The average Fe concentrations during autumn, summer, spring and winter were 813, 784, 842, and 1106 mg/dm3, respectively. The average Pb concentrations during autumn, summer, spring, and winter were 4.85, 3.79, 4.74, and 7.2 mg/dm3, respectively. The average Cu concentrations during autumn, summer, spring, and winter were 3.9, 3.1, 4.45, and 7.5 mg/dm3, respectively. The average Cr concentrations during autumn, summer, spring, and winter seasons were 1.28, 0.73, 1.03, and 2.91 mg/dm3, respectively. Finally, the average Zn concentrations during autumn, summer, spring, and winter were 8.25, 6, 7.05, and 12 mg/dm3, respectively. The results show that the concentrations of salinity and chemicals decreased in the summer and increased in the winter. The decision tree (DT) classification depended on the output results for salinity and chemicals for both SE and CE equations. This classification refers to all the parameters simultaneously in one stage. The output of DT classification results can display all the soil quality parameters (salinity, Fe, Pb, Cu, Cr, and Zn) in one image. This approach was repeated for each season in this study. In conclusion, the developed systematic and generic approach may constitute a basis for determining soil quality parameters in agricultural land worldwide.
Vista: Optimized System for Declarative Feature Transfer from Deep CNNs at Scale

Scalable systems for machine learning (ML) are largely siloed into dataflow systems for structured data and deep learning systems for unstructured data. This gap has left workloads that jointly analyze both forms of data with poor systems support, leading to both low system efficiency and grunt work for users. We bridge this gap for an important class of such workloads: feature transfer from deep convolutional neural networks (CNNs) for analyzing images along with structured data. Executing feature transfer on scalable dataflow and deep learning systems today faces two key systems issues: inefficiency due to redundant computations and crash-proneness due to mismanaged memory. We present Vista, a new data system that resolves these issues by elevating this workload to a declarative level on top of dataflow and deep learning systems. Vista automatically optimizes the configuration and execution of this workload to reduce both computational redundancy and the potential for workload crashes. Experiments on real datasets show that apart from making feature transfer easier, Vista avoids workload crashes and reduces runtimes by 58% to 92% compared to baselines.
Sample Debiasing in the Themis Open World Database System

Open world database management systems assume tuples not in the database still exist and are becoming an increasingly important area of research. We present Themis, the first open world database that automatically rebalances arbitrarily biased samples to approximately answer queries as if they were issued over the entire population. We leverage apriori population aggregate information to develop and combine two different approaches for automatic debiasing: sample reweighting and Bayesian network probabilistic modeling. We build a prototype of Themis and demonstrate that Themis achieves higher query accuracy than the default AQP approach, an alternative sample reweighting technique, and a variety of Bayesian network models while maintaining interactive query response times. We also show that Themis is robust to differences in the support between the sample and population, a key use case when using social media samples.
Dynamics of Aimed Mid-air Movements

Mid-air arm movements are ubiquitous in VR, AR, and gestural interfaces. While mouse movements have received some attention, the dynamics of mid-air movements are understudied in HCI. In this paper we present an exploratory analysis of the dynamics of aimed mid-air movements. We explore the 3rd order lag (3OL) and existing 2nd order lag (2OL) models for modeling these dynamics. For a majority of movements the 3OL model captures mid-air dynamics better, in particular acceleration. The models can effectively predict the complete time series of position, velocity and acceleration of aimed movements given an initial state and a target using three (2OL) or four (3OL) free parameters.
Parametric Signal Estimation Using the Cumulative Distribution Transform

We present a new method for estimating signal model parameters using the Cumulative Distribution Transform (CDT). Our approach minimizes the Wasserstein distance between measured and model signals. We derive some useful properties of the CDT and show that the resulting estimation problem, while nonlinear in the original signal domain, becomes a linear least squares problem in the transform domain. Furthermore, we discuss the properties of the estimator in the presence of noise and present a novel approach for mitigating the impact of the noise on the estimates. The proposed estimation approach is evaluated by applying it to a source localization problem and comparing its performance against traditional approaches.
Theoretical design and circuit implementation of novel digital chaotic systems via hybrid control

Chaos is a paradigm shift of all science, which provides a collection of concepts and methods to analyze a novel behavior that can arise in a wide range of disciplines. However, most of researches in simulations and applications of chaos are performed on finite-state automata, which inevitably causes chaos to collapse. Here we present a hybrid model by controlling digital system with continuous chaotic system to construct chaos on finite-state automata. A new concept and method named Generalized Symbolic Dynamics (GSD) is proposed to target the hybrid system. Based on GSD, a rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney. Moreover, analog-digital hybrid circuit is built for the digital chaotic system. Finally, a simple pseudorandom number generator is designed as a proof of concept. Results show that the proposed generator has good performance for cryptography. Such digital chaotic systems, which are not subject to degradation, could pave the way for widespread applications of chaos.
A Cascaded Feature Pyramid Network With Non-Backward Propagation for Facial Expression Recognition

In this work we propose a novel cascaded feature pyramid network with non-backward propagation (CFPN-NBP) for facial expression recognition (FER) that addresses the problems inherent in traditional backward propagation (BP) algorithms in the training process by using the Hilbert-Schmidt independence criterion (HSIC) bottleneck. The proposed algorithm is developed at two different levels. At the first level, a novel training method HSIC bottleneck is considered as an alternative to traditional BP optimization, where the correlation between the output of the hidden layers and the input, and the correlation between the output of the hidden layers and its label are calculated to reduce redundant information; hence, the least information is used to predict the results. At the second level, a novel architecture is designed in the feature extraction process. The convolutional layers with the same resolutions are densely connected and introduced into the attention mechanism, so that the model can focus on more important information. The convolutional layers with different resolutions are combined by three cascaded pyramid networks; in this way, the shallow features and the deep features can be further fused, and; therefore, the semantic information and the content information can both be reserved. To further reduce the number of parameters, the operation of separable convolution instead of traditional convolution is utilized. Experiments on the challenging FER2013 dataset show that the proposed CFPN-NBP algorithm improves the accuracy of the FER task and outperforms the related state-of-the-art methods.
Classifying Wastes Using Random Forests, Gaussian Naïve Bayes, Support Vector Machine and Multilayer Perceptron

Abstract As the population of the Philippines increases in size, so does the amount of garbage that it generates. A lot of this garbage is not properly segregated when dumped andmany citizens are not aware of the effects of unsegregated garbage which leads to them simply disposing mixed waste in dump sites. A method to help classify garbage can help address this issue. This paper presents a comparison of the efficiency of four commonly used machine learning models, Random Forests, Gaussian Naïve Bayes, Support Vector Machines and Multilayer Perceptron in classifying biodegradable and non-biodegradable waste. The data used for training and testing was collected from various sources and then augmented to increase the size of the dataset resulting in 15,000 images of biodegradable and non-biodegradable trash used for training. The four models were trained using the K-Fold cross validation technique with the dataset being split ten times. The results indicate that the four models were able to achieve a relatively high accuracy in classifying images of biodegradable and non-biodegradable trash with the Random Forests, Gaussian Naïve Bayes, Support Vector Machines and Multilayer Perceptron models achieving an accuracy of 97.49%, 81.46%, 89.51% and 96.44% respectively.
Improved noise covariance estimation in visual servoing using an autocovariance least-squares approach

Position based visual servoing is a widely adopted tool in robotics and automation. While the extended Kalman filter (EKF) has been proposed as an effective technique for this, it requires accurate noise covariance matrices to render desirable performance. Although numerous techniques for updating or estimating the covariance matrices have been developed in the literature, many of these suffer from computational limits or difficulties in imposing structural constraints such as positive semi-definiteness (PSD). In this paper, a relatively new framework, namely the autocovariance least-squares (ALS) method, is applied to estimate noise covariances using real world visual servoing data. To generate the innovations data required for the ALS method, we utilize standard position based visual servoing methods such as EKF, and also an advanced optimization-based framework, namely moving horizon estimation (MHE). A major advantage of the proposed method is that the PSD and other structural constraints on the noise covariances can be enforced conveniently in the optimization problem, which can be solved efficiently using existing software packages. Our results show that using the ALS estimated covariances in the EKF, instead of hand-tuned covariances, gives more than 20% mean error reduction in visual servoing, while utilising MHE to generate the ALS innovations provides a further 21% accuracy improvement.
Verifying Voter Registration Records

This study investigates the reliability of Florida’s voter registration files through a phone survey, asking respondents to verify their records. We find 17.7% of registrants fail to verify at least one identifying piece of information. Applying the total survey error (TSE) framework, we classify these errors as due to coverage error, measurement error, or processing error. These inconsistencies create election administration and campaign inefficiencies, which lead to poorer voter experiences, and challenge the validity of some research based on these data. Furthermore, if registration records do not accurately capture the members of protected groups, the data are less helpful in both government monitoring and enforcement. We suggest voter registration forms should be treated like survey questionnaires so as to improve data quality with better form design, and that some vote overreport bias is attributable to limitations of voter file data, not to respondents’ vote misreporting.
A New Acoustic-Based Pronunciation Distance Measure

We present an acoustic distance measure for comparing pronunciations, and apply the measure to assess foreign accent strength in American-English by comparing speech of non-native American-English speakers to a collection of native American-English speakers. An acoustic-only measure is valuable as it does not require the time-consuming and error-prone process of phonetically transcribing speech samples which is necessary for current edit distance-based approaches. We minimize speaker variability in the data set by employing speaker-based cepstral mean and variance normalization, and compute word-based acoustic distances using the dynamic time warping algorithm. Our results indicate a strong correlation of r = -0.71 (p < 0.0001) between the acoustic distances and human judgments of native-likeness provided by more than 1,100 native American-English raters. Therefore, the convenient acoustic measure performs only slightly lower than the state-of-the-art transcription-based performance of r = -0.77. We also report the results of several small experiments which show that the acoustic measure is not only sensitive to segmental differences, but also to intonational differences and durational differences. However, it is not immune to unwanted differences caused by using a different recording device.
How Domain Experts Create Conceptual Diagrams and Implications for Tool Design

Conceptual diagrams are used extensively to understand abstract relationships, explain complex ideas, and solve difficult problems. To illustrate concepts effectively, experts find appropriate visual representations and translate concepts into concrete shapes. This translation step is not supported explicitly by current diagramming tools. This paper investigates how domain experts create conceptual diagrams via semi-structured interviews with 18 participants from diverse backgrounds. Our participants create, adapt, and reuse visual representations using both sketches and digital tools. However, they had trouble using current diagramming tools to transition from sketches and reuse components from earlier diagrams. Our participants also expressed frustration with the slow feedback cycles and barriers to automation of their tools. Based on these results, we suggest four opportunities of diagramming tools — exploration support, representation salience, live engagement, and vocabulary correspondence — that together enable a natural diagramming experience. Finally, we discuss possibilities to leverage recent research advances to develop natural diagramming tools.
The perspective of undergraduate students on information needs and seeking behavior through YouTube

This study examined the perspective of university of Ilorin Library and Information Science Undergraduate Students on Information Needs and Seeking Behavior through YouTube. Total enumeration method was used to involve all the available students in the department. Questionnaire developed by the researcher complemented with a focused group interview were used for the collection of data. Five research questions were raised and answered. The results demonstrated that LIS undergraduates search through YouTube for information on latest development in the discipline, news and documentary videos on the practical aspects of the discipline, information on assignments and research, and information to complement class notes.
Entity Summarization with User Feedback

Semantic Web applications have benefited from entity summarization techniques which compute a compact summary for an entity by selecting a set of key triples from underlying data. A wide variety of entity summarizers have been developed. However, the quality of summaries they generate are still not satisfying, and we lack mechanisms for improving computed summaries. To address this challenge, in this paper we present the first study of entity summarization with user feedback. We consider a cooperative environment where a user reads the current entity summary and provides feedback to help an entity summarizer compute an improved summary. Our approach represents this iterative process as a Markov decision process where the entity summarizer is modeled as a reinforcement learning agent. To exploit user feedback, we represent the interdependence of triples in the current summary and the user feedback by a novel deep neural network which is incorporated into the policy of the agent. Our approach outperforms five baseline methods in extensive experiments with both real users and simulated users.
Using Eye Gaze to Enhance Generalization of Imitation Networks to Unseen Environments

Vision-based autonomous driving through imitation learning mimics the behavior of human drivers by mapping driver view images to driving actions. This article shows that performance can be enhanced via the use of eye gaze. Previous research has shown that observing an expert's gaze patterns can be beneficial for novice human learners. We show here that neural networks can also benefit. We trained a conditional generative adversarial network to estimate human gaze maps accurately from driver-view images. We describe two approaches to integrating gaze information into imitation networks: eye gaze as an additional input and gaze modulated dropout. Both significantly enhance generalization to unseen environments in comparison with a baseline vanilla network without gaze, but gaze-modulated dropout performs better. We evaluated performance quantitatively on both single images and in closed-loop tests, showing that gaze modulated dropout yields the lowest prediction error, the highest success rate in overtaking cars, the longest distance between infractions, lowest epistemic uncertainty, and improved data efficiency. Using Grad-CAM, we show that gaze modulated dropout enables the network to concentrate on task-relevant areas of the image.
Getting out of Out of Sight: Evaluation of AR Mechanisms for Awareness and Orientation Support in Occluded Multi-Room Settings

Augmented Reality can provide orientation and awareness in situations in which objects or people are occluded by physical structures. This is relevant for many situations in the workplace, where objects are scattered across rooms and people are out of sight. While several AR mechanisms have been proposed to provide awareness and orientation in these situations, little is known about their effect on people's performance when searching objects and coordinating with each other. In this paper, we compare three AR based mechanisms (map, x-ray, compass) according to their utility, usability, social presence, task load and users' preferences. 48 participants had to work together in groups of four to find people and objects located around different rooms. Results show that map and x-ray performed best but provided least social presence among participants. We discuss these and other observations as well as potential impacts on designing AR awareness and orientation support.
Attention‐guided adaptation factors for unsupervised facial domain adaptation

In this Letter, the authors introduce attention-guided domain adaptation networks for face recognition under the unsupervised setting. Recently, there has been a dramatic increase in demand for real-world face recognition problems under severe domain shifts. Adversarial learning of the domain adaptation network has shown promising results for this problem. However, this approach has limitations in manually setting the adaptation factor that controls the trade-off between feature discriminability and domain-invariance. To address this critical issue, the authors propose attention-guided domain adaptation networks that can learn the optimal adaptation factor without the manual configurations. The proposed attention-guided module is applied to both category classifier and domain discriminator in a channel-wise manner. By learning the optimal adaptation factor for each channel, the proposed networks successfully align the source domain to the target domain and show state-of-the-art performance on the public datasets.
Spatial Frequency and the Performance of Image-Based Visual Complexity Metrics

There is a wide range of visual and spatial complexity measurement methods that aim to quantify perceived image complexity.While image-based calculation methods (edge detection, image compression, contrast) characterize a digital image, visual perception studies focus on fundamental visual mechanisms, such as contrast sensitivity and visual task performance.Despite the evidence from several vision studies, spatial frequency information has not been widely utilized to assess image complexity.Previous studies suggest that image-based performance metrics are limited in explaining perceived complexity due to confounding factors, such as context, memory, familiarity, and expectation.Here, a visual experiment is conducted to assess the performance of image-based metrics and spatial frequency information using 16 abstract and natural images.A new image complexity metric (R spt ), based on detectability suprathreshold, was proposed to benchmark the performance of existing measures.Forty-four naïve participants used a 5-point Likert-type scale to judge the visual complexity of the images displayed on a tablet.Results indicate that root-mean-square error (RMSE) and R spt correlate statistically significantly with subjective evaluations.Biological sex did not affect perceived spatial complexity.While RMSE and R spt can potentially be used to estimate the spatial complexity of display images, the performance of spatial frequency information and image assessment measures in immersive viewing conditions require further research.