Reviewerly/exHarmony huggingface dataset:
Factors Affecting the Adoption of Agricultural Automation Using Davis’s Acceptance Model (Case Study: Ardabil)

Abstract Taking into account that traditional agricultural methods reduce the farm performance and make agriculture economically ineffective, development of intelligent machinery is essential for improving the quality of crops and agricultural activities. The most important issue in development of agricultural technologies lies in users’ willingness to adopt it. Therefore, the purpose of this study is to investigate the key factors of Davis’s model in automation acceptance in agriculture. Presented paper describes an applied research with a survey approach through a questionnaire. Questionnaire data were collected from 378 people and respondents include university students, farmers and experts in a randomized sampling from the Ministry of Agriculture. Firstly, the questionnaire data were described in a form of statistical numerical characteristics. Secondly, in order to verify the data normality, the Kolmogorov-Smirnov test was calculated in SPSS and the relationship between the variables was investigated in the conceptual model. Subsequently, hypotheses were tested via appropriate statistical models using LISREL and SPSS software. The results showed that for all hypotheses, the T -test exceeds 1.96 and the significance level is less than 0.05. In such a manner, all hypotheses were confirmed at 95% level, and the path coefficients in the hypotheses H1, H2 and H6 were negative – indicating the negative effect of the independent variable on the dependent variable in the hypothesis. In the other hypotheses, these were positive – indicating the positive effect of the independent variable on dependent variable. By means of modelling, it was found that there was an inverse relationship between social and individual factors with perceived usefulness, as well as an inverse relationship between social factors with perceived ease-of-use, while there was a positive and significant relationship between other factors. According to a set of fitting indices, the research conceptual model was appropriate and Cronbach’s coefficients for each factor were greater than 0.7, suggesting that the questionnaire was valid. On the basis of findings, the better a person understands the usefulness of automation, the more likely this person is to adopt it. Since the risk and issues related to it play an important role in farmers’ decision making, it is recommended that future studies address the issue of risk in adopting precision farming technology.
Perception of means, sums, and areas

In this age of data visualization, it is important to understand our perception of the symbols that are used. For example, does the perceived size of a disc correspond most closely to its area, diameter, circumference, or some other measure? When multiple items are present, this becomes a question of ensemble perception. Here, we compare observers’ performance across three different tasks: judgments of (i) the mean diameter, (ii) the total diameter, or (iii) the total area of (N = 1, 2, 3, or 7) test circles compared with a single reference circle. We draw a parallel between Anne Treisman’s feature integration theory and Daniel Kahneman’s cognitive systems, comparing the preattentive stage to System 1, and the focused attention stage to System 2. In accordance with Kahneman’s prediction, average size (diameter) of the geometric figures can be judged with considerable accuracy, but the total diameter of the same figures cannot. Like the total length, the cumulative area covered by circles was also judged considerably less accurately than the mean diameter. Differences in efficiency between these three tasks illustrate powerful constraints upon visual processing: The visual system is well adapted for the perception of the mean size while there are no analogous mechanisms for the accurate perception of the total length or cumulative area. Thus, in visualizing data, using bubble charts proportional to area may be misleading as our visual system seems better adapted to perceive disc size by the radius rather than the area.
Geometry constrained correlation adjustment for stereo reconstruction in 3D optical deformation measurements

Recovering the geometric shape of deformable objects from images is essential to optical three-dimensional (3D) deformation measurements and is also actively pursued by researchers. Most of the existing techniques retrieve the shape data with triangulation based on pre-estimated stereo correspondences. In this paper, we instead propose to recover depth information directly from images of a binocular vision system for 3D deformation estimation. Given a calibrated geometry of the system, the reprojection error is parameterized by the depth and then described with local intensity dissimilarity between a stereo pair in considering spatial deformation. Afterward, a correlation adjustment model is formulated to estimate the depth parameter by minimizing the error. As a solving strategy, we show the Gauss-Newton linearization of the proposed model and its initialization. 3D displacement estimation based on depth information is also presented. Experiments, including rigid translation and bending deformation measurements, are conducted to verify the performance of the proposed method. Results show that the proposed method is accurate yet precise in 3D deformation estimations. Other underlying developments are underway.
Enhanced Non-Local Cascading Network with Attention Mechanism for Hyperspectral Image Denoising

Because of the complexity of imaging environment, hyper-spectral remote sensing images (HSIs) often suffer from different kinds of noise. Despite the success in natural image denoising, most of the existing CNN-based HSIs denoising methods still suffer from the problem of inadequate noise suppression and insufficient feature extraction. In this paper, a novel HSIs denoising algorithm based on an enhanced non-local cascading network with attention mechanism (ENCAM) is proposed, which can extract the joint spatial-spectral feature more effectively. The main contributions include: (1) the non-local structure is introduced to enlarge the receptive field to extract the spatial features more effectively; (2) multi-scale convolutions and channel attention module are applied to enhance extracted multi-scale features; (3) a cascading residual dense structure is used to extract different frequency features. Both of the theoretical analysis and the experiments indicate that the proposed method is superior to the other state-of-the-art methods on HSIs denoising.
Dynamic 3-D shape measurement in an unlimited depth range based on adaptive pixel-by-pixel phase unwrapping

Pixel-by-pixel phase unwrapping (PPU) has been employed to rapidly achieve three-dimensional (3-D) shape measurement without additional projection patterns. However, the maximum measurement depth range that traditional PPU can handle is within 2 π in phase domain; thus PPU fails to measure the dynamic object surface when the object moves in a large depth range. In this paper, we propose a novel adaptive pixel-by-pixel phase unwrapping (APPU), which extends PPU to an unlimited depth range. First, with PPU, temporary phase maps of objects are obtained referring to the absolute phase map of a background plane. Second, we quantify the difference between the image edges of the temporary phase maps and the practical depth edges of dynamic objects. Moreover, according to the degree of the edge difference, the temporary phase maps are categorized into two classes: failed phase maps and relative phase maps. Third, by combining a mobile reference phase map and the edge difference quantization technique, the failed phase maps are correspondently converted into relative phase maps. Finally, the relative phase maps are innovatively transformed into the absolute phase maps using a new shadow-informed depth estimation method (SDEM). The proposed approach is suitable for high-speed 3-D shape measurement without depth limitations or additional projection patterns.
Hand sign recognition from depth images with multi-scale density features for deaf mute persons

Among many of the fastest growing research fields, sign language recognition is one of the top. Deaf and dumb community uses sign language to express their ideas or views. Sign Language is a methodical coded language where meanings are assigned to every gestures. Many techniques have been developed with the advancement of science and technology to minimize the problem for speech and hearing disabled. The mode of such communication is part of human computer interaction. Hand gesture plays an important role here. The interaction with computer through gesture removes the use of conventional input devices like mouse and keyboards. To create a strong interface between user and computer, recognition of gesture is important. In this paper, a hand gesture recognition method based on multiscale density features is proposed. Depth images of numerals of American Sign Language are considered in this work and recognition rate of 98.20% is obtained, which is comparable with related state-of-the art methods.
Improving the Named Entity Recognition of Chinese Electronic Medical Records by Combining Domain Dictionary and Rules

Electronic medical records are an integral part of medical texts. Entity recognition of electronic medical records has triggered many studies that propose many entity extraction methods. In this paper, an entity extraction model is proposed to extract entities from Chinese Electronic Medical Records (CEMR). In the input layer of the model, we use word embedding and dictionary features embedding as input vectors, where word embedding consists of a character representation and a word representation. Then, the input vectors are fed to the bidirectional long short-term memory to capture contextual features. Finally, a conditional random field is employed to capture dependencies between neighboring tags. We performed experiments on body classification task, and the F1 values reached 90.65%. We also performed experiments on anatomic region recognition task, and the F1 values reached 93.89%. On both tasks, our model had higher performance than state-of-the-art models, such as Bi-LSTM-CRF, Bi-LSTM-Attention, and Vote. Through experiments, our model has a good effect when dealing with small frequency entities and unknown entities; with a small training dataset, our method showed 2–4% improvement on F1 value compared to the basic Bi-LSTM-CRF models. Additionally, on anatomic region recognition task, besides using our proposed entity extraction model, 12 rules we designed and domain dictionary were adopted. Then, in this task, the weighted F1 value of the three specific entities extraction reached 84.36%.
Holographic teaching presence: participant experiences of interactive synchronous seminars delivered via holographic videoconferencing

This study seeks to identify potential advantages of using holographic videoconferencing to deliver seminars within higher education as compared to the use of alternative non-holographic videoconferencing. Holographic videoconferencing offers opportunities to enhance attendees' experience of remotely delivered seminars but has not been widely researched. Data were collected from 127 attendees attending one of three seminars, each of which featured a combination of physically present presenters and remote presenters participating via holographic videoconferencing. In this study, the holographic representations were three-dimensional and life-size. Monitors and holographic images were calibrated in a manner such that the remote presenters were able to point to and achieve eye-contact with members of the audience. Results indicate that the use of holographic videoconferencing can enhance the teaching presence of remote presenters, the engagement between participants and attendees' enjoyment of a seminar. Almost all participants reported this to be their first experience of a holographic event and the positive results are partly explained by a sense of novelty. This suggests that the benefits of holographic videoconferencing may reduce over time. However, we argue that some benefit, resulting from an enhanced degree of teaching presence, will be sustained. The relative impact on learning gain is not explored in the current study. We believe that this would likely require a more controlled experiment in future research.
A study on relationship between human resources and strategic performance: The mediating role of productivity

The objective of the study is to investigate the mediating role of the productivity of the banks sector in Jordan based on the relationship between human resources and strategic performance from the point of view of the senior management managers and functional unit managers (human resources, marketing, finance and accounting).Human resources were measured through human resource activities.The population of the current study represented all the commercial banks in Jordan, the total number of which is 26.A survey questionnaire is prepared based on the published literature, and the data are collected from 371 respondents from the targeted banks.The necessary tests to ensure reliability and validity are performed using SPSS.The results indicate that human resources positively affected strategic performance and productivity.The findings also revealed that productivity positively mediated the relationship between human resources and the commercial banks' strategic performance.
How Accents Confound: Probing for Accent Information in End-to-End Speech Recognition Systems

In this work, we present a detailed analysis of how accent information is reflected in the internal representation of speech in an end-to-end automatic speech recognition (ASR) system. We use a state-of-the-art end-to-end ASR system, comprising convolutional and recurrent layers, that is trained on a large amount of US-accented English speech and evaluate the model on speech samples from seven different English accents. We examine the effects of accent on the internal representation using three main probing techniques: a) Gradient-based explanation methods, b) Information-theoretic measures, and c) Outputs of accent and phone classifiers. We find different accents exhibiting similar trends irrespective of the probing technique used. We also find that most accent information is encoded within the first recurrent layer, which is suggestive of how one could adapt such an end-to-end model to learn representations that are invariant to accents.
Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer

Extracting lexico-semantic relations as graphstructured taxonomies, also known as taxonomy construction, has been beneficial in a variety of NLP applications.Recently Graph Neural Network (GNN) has shown to be powerful in successfully tackling many tasks.However, there has been no attempt to exploit GNN to create taxonomies.In this paper, we propose Graph2Taxo, a GNN-based cross-domain transfer framework for the taxonomy construction task.Our main contribution is to learn the latent features of taxonomy construction from existing domains to guide the structure learning of an unseen domain.We also propose a novel method of directed acyclic graph (DAG) generation for taxonomy construction.Specifically, our proposed Graph2Taxo uses a noisy graph constructed from automatically extracted noisy hyponym-hypernym candidate pairs, and a set of taxonomies for some known domains for training.The learned model is then used to generate taxonomy for a new unknown domain given a set of terms for that domain.Experiments on benchmark datasets from science and environment domains show that our approach attains significant improvements correspondingly over the state of the art.
Ready Policy One: World Building Through Active Learning

Model-Based Reinforcement Learning (MBRL) offers a promising direction for sample efficient learning, often achieving state of the art results for continuous control tasks. However, many existing MBRL methods rely on combining greedy policies with exploration heuristics, and even those which utilize principled exploration bonuses construct dual objectives in an ad hoc fashion. In this paper we introduce Ready Policy One (RP1), a framework that views MBRL as an active learning problem, where we aim to improve the world model in the fewest samples possible. RP1 achieves this by utilizing a hybrid objective function, which crucially adapts during optimization, allowing the algorithm to trade off reward v.s. exploration at different stages of learning. In addition, we introduce a principled mechanism to terminate sample collection once we have a rich enough trajectory batch to improve the model. We rigorously evaluate our method on a variety of continuous control tasks, and demonstrate statistically significant gains over existing approaches.
Moniqua: Modulo Quantized Communication in Decentralized SGD

Running Stochastic Gradient Descent (SGD) in a decentralized fashion has shown promising results. In this paper we propose Moniqua, a technique that allows decentralized SGD to use quantized communication. We prove in theory that Moniqua communicates a provably bounded number of bits per iteration, while converging at the same asymptotic rate as the original algorithm does with full-precision communication. Moniqua improves upon prior works in that it (1) requires zero additional memory, (2) works with 1-bit quantization, and (3) is applicable to a variety of decentralized algorithms. We demonstrate empirically that Moniqua converges faster with respect to wall clock time than other quantized decentralized algorithms. We also show that Moniqua is robust to very low bit-budgets, allowing 1-bit-per-parameter communication without compromising validation accuracy when training ResNet20 and ResNet110 on CIFAR10.
Robustness to Spurious Correlations via Human Annotations

The reliability of machine learning systems critically assumes that the associations between features and labels remain similar between training and test distributions. However, unmeasured variables, such as confounders, break this assumption---useful correlations between features and labels at training time can become useless or even harmful at test time. For example, high obesity is generally predictive for heart disease, but this relation may not hold for smokers who generally have lower rates of obesity and higher rates of heart disease. We present a framework for making models robust to spurious correlations by leveraging humans' common sense knowledge of causality. Specifically, we use human annotation to augment each training example with a potential unmeasured variable (i.e. an underweight patient with heart disease may be a smoker), reducing the problem to a covariate shift problem. We then introduce a new distributionally robust optimization objective over unmeasured variables (UV-DRO) to control the worst-case loss over possible test-time shifts. Empirically, we show improvements of 5-10% on a digit recognition task confounded by rotation, and 1.5-5% on the task of analyzing NYPD Police Stops confounded by location.
Unravelling Ariadne’s Thread: Exploring the Threats of Decentralised DNS

The current landscape of the core Internet technologies shows considerable centralisation with the big tech companies controlling the vast majority of traffic and services. This situation has sparked a wide range of decentralisation initiatives with blockchain technology being among the most prominent and successful innovations. At the same time, over the past years there have been considerable attempts to address the security and privacy issues affecting the Domain Name System (DNS). To this end, it is claimed that Blockchain-based DNS may solve many of the limitations of traditional DNS. However, such an alternative comes with its own security concerns and issues, as any introduction and adoption of a new technology typically does - let alone a disruptive one. In this work we present the emerging threat landscape of blockchain-based DNS and we empirically validate the threats with real-world data. Specifically, we explore a part of the blockchain DNS ecosystem in terms of the browser extensions using such technologies, the chain itself (Namecoin and Emercoin), the domains, and users who have been registered in these platforms. Our findings reveal several potential domain extortion attempts and possible phishing schemes. Finally, we suggest countermeasures to address the identified threats, and we identify emerging research themes.
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks

In the last couple of years, several adversarial attack methods based on different threat models have been proposed for the image classification problem. Most existing defenses consider additive threat models in which sample perturbations have bounded L_p norms. These defenses, however, can be vulnerable against adversarial attacks under non-additive threat models. An example of an attack method based on a non-additive threat model is the Wasserstein adversarial attack proposed by Wong et al. (2019), where the distance between an image and its adversarial example is determined by the Wasserstein metric (earth-mover distance) between their normalized pixel intensities. Until now, there has been no certifiable defense against this type of attack. In this work, we propose the first defense with certified robustness against Wasserstein Adversarial attacks using randomized smoothing. We develop this certificate by considering the space of possible flows between images, and representing this space such that Wasserstein distance between images is upper-bounded by L_1 distance in this flow-space. We can then apply existing randomized smoothing certificates for the L_1 metric. In MNIST and CIFAR-10 datasets, we find that our proposed defense is also practically effective, demonstrating significantly improved accuracy under Wasserstein adversarial attack compared to unprotected models.
A True Concurrent Model of Smart Contracts Executions

The development of blockchain technologies has enabled the trustless execution of so-called smart contracts, i.e. programs that regulate the exchange of assets (e.g., cryptocurrency) between users. In a decentralized blockchain, the state of smart contracts is collaboratively maintained by a peer-to-peer network of mutually untrusted nodes, which collect from users a set of transactions (representing the required actions on contracts), and execute them in some order. Once this sequence of transactions is appended to the blockchain, the other nodes validate it, re-executing the transactions in the same order. The serial execution of transactions does not take advantage of the multi-core architecture of modern processors, so contributing to limit the throughput. In this paper we propose a true concurrent model of smart contracts execution. Based on this, we show how static analysis of smart contracts can be exploited to parallelize the execution of transactions.