Reviewerly/exHarmony huggingface dataset:
The Australian digital Online Farm Trials database increases the quality of systematic reviews and meta-analyses in grains crop research

Synthesis and analysis of past cropping research can provide valuable information to direct future decisions around crop management. Systematic reviews and meta-analyses are considered gold standards in the synthesis and analysis of scientific research because they distil large amounts of information about complex issues, provide a summary of knowledge to date, and identify knowledge gaps. However, several issues concerning the methodologies employed to conduct systematic reviews have been identified; among them is the risk of publication bias when a review relies too heavily on ‘white’ literature from published academic sources and in so doing fails identify relevant ‘grey’ literature. Grey literature is inherently difficult to identify and collect, but forms a large portion of information available in many fields including agricultural-based research within Australia. The Online Farm Trials (OFT) database is a digital database of crop research field trial data from across Australia that has the potential for use as a discipline-specific source of grey literature to inform systematic reviews and meta-analyses. Using a case study approach to investigate the amount of information available on time of sowing (sowing date) on crop yield across Australia, we demonstrate that the OFT database provides easy access to transparent and reproducible search results similar to other commonly used academic databases.
Image-Level Supervised Instance Segmentation Using Instance-Wise Boundary

Recently, most image-level supervised instance segmentation methods extend Class Attention Maps (CAMs) to find the entire instance masks. Inter-pixel Relation Network (IRNet) can effectively generate the class-wise boundary maps for attention score propagation. However, class-wise boundary is likely to cause the failure of segmentation among instances. In this work, we find instance-wise information can be extracted from the displacement field of IRNet. Motivated by the observations, an improved IRNet-based instance segmentation method with instance-wise boundary has been developed. Experimental results based on PASCAL VOC 2012 demonstrate the effectiveness of our proposed method. Compared with the recent state-of-the-art methods, the mean average precision can be increased by 4.3% without any additional annotations.
Reproducibility in Quantum Computing

Reproducibility is important for validating the performance of applications in quantum computing as a measure of consistency in computation. Current noisy, intermediate-scale devices quantum (NISQ) devices are strongly affected by intrinsic noise that leads to a variety of computational error mechanisms. Here we assess reproducibility of NISQ computing by focusing on a specific simple error mechanism that arises during noisy readout. Using an asymmetric channel for binary readout, we develop an analytic bound on the Hellinger distance between computational outputs for different readout parameters. We validate this model using characterization and testing of the IBM toronto device, which displays a range of readout parameters. We find that to ensure reproducibility, one must avoid using register elements characterized by fidelity asymmetry exceeding a threshold.
An Efficient Test for Homogeneity of Mean Directions on the Hyper‐sphere

Summary The paper aims to develop a universally implementable efficient test for testing homogeneity of mean directions of several independent hyper‐spherical populations. Conventional tests are valid only under highly concentrated and/or large‐size groups. Focusing on the popular Langevin distribution on a d ‐hyper‐sphere, the present work extends the very recent results for the circular case. The hurdle of the nuisance non‐location‐scale concentration parameter κ is overcome through a variant of the integrated likelihood ratio test (ILRT), yielding a simple and elegant test statistic. Analytically, second‐order accurate asymptotic chi‐squared distribution of ILRT is established. Extensive simulation study demonstrates that ILRT uniformly outperforms its peers, notably under highly dispersed groups, which is precisely the target parametric region, and is robust under a large class of alternate distributions. Five real‐life data analyses from diverse disciplines, including the emerging field of vectorcardiography and a novel application to compositional data analysis in the context of drug development, illustrate applications of the findings.
Simplifying the A.I. Planning modeling for Human-Robot Collaboration

For an effective deployment in manufacturing, Collaborative Robots should be capable of adapting their behavior to the state of the environment and to keep the user safe and engaged during the interaction. Artificial Intelligence (AI) enables robots to autonomously operate understanding the environment, planning their tasks and acting to achieve some given goals. However, the effective deployment of AI technologies in real industrial environments is not straightforward. There is a need for engineering tools facilitating communication and interaction between AI engineers and Domain experts. This paper proposes a novel software tool, called TENANT (Tool fostEriNg Ai plaNning in roboTics) whose aim is to facilitate the use of AI planning technologies by providing domain experts like e.g., production engineers, with a graphical software framework to synthesize AI planning models abstracting from syntactic features of the underlying planning formalism.
Determining learning direction via multi-controller model for stably searching generative adversarial networks

The data generated by Generative Adversarial Network (GAN) inevitably contains noise, which can be reduced by searching and optimizing the architecture of GAN. To search for generative adversarial networks architectures stably, a neural architecture search (NAS) method, StableAutoGAN, is proposed based on the existing algorithm, AutoGAN. The stability of conventional reinforcement learning (RL)-based NAS methods for GAN is adversely influenced by the uncertainty of direction, where the controller will go forward once receiving inaccurate rewards. In StableAutoGAN, a multi-controller model is employed to mitigate this problem via comparing the performance of controllers after receiving rewards. During the search process, each controller independently learns the sampling policy. Meanwhile, the learning effect is measured by the credibility score, which further determines the usage of controllers. Our experiments show that the standard deviation of Frchet Inception Distance (FID) scores of the GANs discovered by StableAutoGAN is approximately 1/16 and 1/8 of that by AutoGAN on CIFAR-10 and on STL-10 respectively, while the effects remain similar to AutoGAN.
Trust or no trust in chatbots: a dilemma of millennial

The chapter empirically investigates what dilemma millennials have regarding the perception of trust while interacting with chatbots and how the perception of trust can be measured in terms of this research context. The major findings of the study are that millennials’ perception of trust has been measured in two dimensions. The first dimension of trust is cognitive-based trust which represented that “we choose whom we will trust in which respect and under what circumstances, and we base the choice on what we take to be ‘good reasons, constituting evidence of trust-worthiness’ conversing with the chatbot.” The second dimension is affect-based trust “which is made by emotional bonds between individuals that go beyond a regular business or professional relationship.” So, it is recommended for the organizations to implement chatbots in their premises for the initial interaction with the millennials.
Thermal entanglement in a mixed spin Heisenberg XXX chain with DM interaction

We consider a one-dimensional, mixed spin Heisenberg XXX model with an homogeneous external magnetic field and Dzyaloshinskii–Moriya interaction. Alternating spin-[Formula: see text] and spin-1 particles are forming the chain. The effect of the different parameters of the system on the bipartite thermal entanglement is studied. The type of chain used (mixed) and the size of the chain ([Formula: see text]) allow to study three types of bipartite entanglement, the qubit–qubit, qubit–qutrit and qutrit–qutrit thermal entanglement.
Electronic Dictionary Research Corpus

Abstract The development of computer and Internet technology, and the emergence of new technologies such as electronic dictionaries and corpora have made it more convenient, accurate and reliable for students to learn to access materials. Students must master these new technologies in their study and life. The purpose of this article is to focus on the research and analysis of the electronic dictionary corpus, and to understand the influence of the electronic dictionary corpus on students’ study and life. The inquiry method used in this article is to conduct a questionnaire survey on students using the electronic dictionary corpus and then analyze the data obtained through big data technology. At the same time, this article still uses the simulation method to predict the future development prospects of the electronic dictionary corpus with the computer simulation experiment. The electronic dictionary corpus uses a variety of algorithms to query massive databases, and finally can display the data we need in front of us at the fastest speed. The dynamic BP network is used to predict the generalized predictive control prediction error. The dynamic BP network the weight can be adjusted online, which changes the situation that the traditional BP network performs error correction once the network weight is fixed after training. The experimental results show that this article conducted interviews and questionnaire surveys with experimental students and found that more than 90% of the students believe that the electronic dictionary corpus has a positive impact on students’ learning and development.
Sphererpn: Learning Spheres For High-Quality Region Proposals On 3d Point Clouds Object Detection

A bounding box commonly serves as the proxy for 2D object detection. However, extending this practice to 3D detection raises sensitivity to localization error. This problem is acute on flat objects since small localization error may lead to low overlaps between the prediction and ground truth. To address this problem, this paper proposes Sphere Region Proposal Network (SphereRPN) which detects objects by learning spheres as opposed to bounding boxes. We demonstrate that spherical proposals are more robust to localization error compared to bounding boxes. The proposed SphereRPN is not only accurate but also fast. Experiment results on the standard ScanNet dataset show that the proposed SphereRPN outperforms the previous state-of-the-art methods by a large margin while being $2 \times$ to $7 \times$ faster. The code will be made publicly available.
Overcoming Crisis: A Portrait of the Adaptation of Engineering Professors to Remote Teaching in Chile

Lately, Chile has been struck with two significant events that have made its citizens rethink and adapt their worldview and activities in various dimensions. The recent social outbreak and the COVID-19 pandemic have impacted almost any institution, and public higher education is no exception. Emergency remote teaching has arrived and professors have made all the possible efforts to deliver a quality education given the limitations. In this article, we portray teachers' experience belonging to the FCFM, one of the most prestigious Chilean engineering and science schools. We implemented two surveys, one on each semester of the year 2020, to understand their main difficulties, concerns, and appreciations. Frequencies and regression analyses were conducted. Results show that teachers' experience has improved through the year. The gained experience in the first semester has made them adapt their ways of teaching, design a better course and enhance their interaction with students through a combination of synchronous and asynchronous strategies.
Hierarchical Region Proposal Refinement Network for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) has attracted more attention because it only requires image-level annotations to indicate whether a certain class exists. Most WSOD methods utilize multiple instance learning (MIL) to train an object detector where an image is treated as a bag of candidate proposals. Unlike fully supervised object detection (FSOD) that uses the object-aware region proposal network (RPN) to generate effective candidate proposals, WSOD only utilizes region proposal methods (e.g., selective search or edge boxes) due to the lack of instance-level annotations (i.e., bounding boxes). However, the quality of proposals can influence the training of the detector. To solve this problem, we propose a hierarchical region proposal refinement network (HRPRN) to refine these proposals gradually. Specifically, our network contains multiple weakly supervised detectors that are trained stage by stage. In addition, we propose an instance regression refinement model to generate object-aware coordinate offsets to refine proposals at each stage. In order to demonstrate the effectiveness of our method, we conduct experiments on PASCAL VOC 2007 dataset that is the widely used benchmark. Compared with our baseline method, online instance classifier refinement (OICR), our method achieves 9% and 5.6% improvements in terms of mAP and CorLoc, respectively.
Bidirectional typing for Erlang

Erlang is a strict, dynamically typed functional programming language popular for its use in distributed and fault-tolerant applications. The absence of static type checking allows ill-typed programs to cause type errors at run time. The benefits of catching these type errors at compile time are the primary motivation for introducing a static type system for Erlang. The greatest challenge is to find a balance between keeping the type checking sound while retaining the flexibility and philosophy of the Erlang. However, since Erlang allows higher-rank polymorphism, it is unavoidable to require type annotations for some functions to ensure decidability.
AVM Image Quality Enhancement by Synthetic Image Learning for Supervised Deblurring

An Around View Monitoring (AVM) system is widely used to allow a driver to watch the situation around a car. The AVM image is generated by image distortion correction and viewpoint transformation for images captured by wide view-angle cameras installed on the car. However, the AVM image is blurred due to these transformations. This blur impairs the visibility of the driver. While many deblurring methods based on CNN have been proposed, these general-purpose de-blurring methods are not designed for the AVM image. (1) Since the blur level in the AVM image is region-dependent, deblurring for the AVM should also be region-dependent. (2) Furthermore, while supervised deblurring methods require a pair of input-blurred and output-deblurred images, it is not easy to collect the deblurred AVM image. This paper proposes a method for generating the pairs of training images that cope with the aforementioned two problems. These training images are generated by the inverse transformation of the AVM image generation process. Experimental results show that our method can suppress blur on AVM images. We also confirmed that even a very shallow CNN with the inference time of 2.1ms has the same performance as the SoTA model.
Adversarial Cross-Scale Alignment Pursuit For Seriously Misaligned Person Re-Identification

Person re-identification is still a challenging task in actual application due to serious misalignment caused by large scale variation, serious occlusion, or variably truncated body. Conventional holistic methods usually lack the cross-scale aligning ability. Segmentation-based partial methods achieve better aligning performance but generally suffer from the instability of part segmentation. To explicitly address these issues, we define the seriously misaligned Re-ID task and propose a novel framework called adversarial cross-scale alignment pursuit (ACSAP). Instead of dynamically segmenting the feature map for part alignment, the model incorporates the stability of holistic methods and adversarially generates aligned feature maps for similarity metrics. Especially, the spatial reconstruction (SR) module in the generator is proposed for feature filtering and aligning. Then a part visibility calculation (PVC) algorithm is proposed to distinguish the credibility of different generated areas. We propose a novel dataset called Seriously-Misaligned-REID and achieve 10.0% rank-1 outperformance compared to state-of-the-art methods on it. Extensive performance results demonstrate the effectiveness of our framework.
A quantum search algorithm of two-dimensional convex hull

Despite the rapid development of quantum research in recent years, there is very little research in computational geometry. In this paper, to achieve the convex hull of a point set in a quantum system, a quantum convex hull algorithm based on the quantum maximum or minimum searching algorithm (QUSSMA) is proposed. Firstly, the novel enhanced quantum representation of digital images is employed to represent a group of point set, and then the QUSSMA algorithm and vector operation are used to search the convex hull of the point set. In addition, the algorithm is simulated and compared with the classical algorithm. It is concluded that the quantum algorithm accelerates the classical algorithm when the value of the convex hull point is under a certain condition.
Understanding the context of IoT software systems in DevOps

The growing demand for connected devices and the increase in investments in the Internet of Things (IoT) sector induce the growth of the market for this technology. IoT permeates all areas of life of an individual, from smartwatches to entire home assistants and solutions in different areas. The IoT concept is gradually increasing all over the globe. IoT projects induce an articulation of studies in software engineering to prepare the development and operation of software systems materialized in physical objects and structures interconnected with embedded software and hosted in clouds. IoT projects have boundaries between development and operation stages. This study search for evidence in scientific literature to support these boundaries through Development and Operations (DevOps) principles. We rely on a Systematic Literature Review to investigate the relations of DevOps in IoT software systems. As a result, we identify concepts, characterize the benefits and challenges in the context of knowledge previously reported in primary studies in the literature. The main contributions of this paper are: (i) discussion of benefits and challenges for DevOps in IoT software systems, (ii) identification of tools, concepts, and programming languages used, and, (iii) perceived pipeline for this kind of software development.
Two-Stream Boosted TCRNet for Range-Tolerant Infra-Red Target Detection

The detection of vehicular targets in infra-red imagery is a challenging task, both due to the relatively few pixels on target and the false alarms produced by the surrounding terrain clutter. It has been previously shown [1] that a relatively simple network (known as TCRNet) can outperform conventional deep CNNs for such applications by maximizing a target to clutter ratio (TCR) metric. In this paper, we introduce a new form of the network (referred to as TCRNet-2) that further improves the performance by first processing target and clutter information in two parallel channels and then combining them to optimize the TCR metric. We also show that the overall performance can be considerably improved by boosting the performance of a primary TCRNet-2 detector, with a secondary network that enhances discrimination between targets and clutter in the false alarm space of the primary network. We analyze the performance of the proposed networks using a publicly available data set of infra-red images of targets in natural terrain. It is shown that the TCRNet-2 and its boosted version yield considerably better performance than the original TCRNet over a wide range of distances, in both day and night conditions.
Poststack Seismic Data Compression Using a Generative Adversarial Network

This work presents a method for volumetric seismic data compression by coupling a 3-D convolution-based autoencoder to a generative adversarial network (GAN). The main challenge of 3-D convolutional autoencoders for data compression is how to fully exploit volumetric redundancy while keeping reasonable latent representation dimensions. Our method is based on a convolutional neural network for seismic data compression called 3DSC. Its encoder and decoder use 3-D convolutions and are connected by a latent representation with the same dimensions as its 2-D network counterparts. Our main hypothesis is that the 3DSC architecture can be improved by adversarial training. We, thus, propose a new 3-D-based seismic data compression method (3DSC-GAN) by coupling the 3DSC network to a GAN. The seismic data decoder is used as a generator of poststack data that are integrated with a discriminator module to better exploit 3-D redundancy. Results show that our method outperforms previous seismic data compression methods for very low target bit rates, increasing the peak signal-to-noise ratio (PSNR) with fairly high visual quality.
Search-based Kinodynamic Motion Planning for Omnidirectional Quadruped Robots

Autonomous navigation has played an increasingly significant role in quadruped robot system. However, most existing works on quadruped robots navigation using traditional search-based or sample-based methods do not consider the kinodynamic characteristics of quadruped robots, generating kinodynamically infeasible parts, that are difficult to track. In this paper, we introduce a complete navigation system considering the omnidirectional abilities of quadruped robots. First, we use kinodynamic path finding method to obtain smooth, dynamically feasible, time-optimal initial paths and add collision cost as a soft constraint to ensure safety. Then the trajectory is refined by the timed elastic band (TEB) method based on the omnidirectional model of quadruped robots. The superior performance of our work is demonstrated through simulating and real-world experiments on our quadruped robot Jueying Mini.
Super-Resolution for Music Signals Using Generative Adversarial Networks

Super-Resolution (SR) refers to increasing the resolution of a signal in a variety of ways, conventionally employed in the field of image enhancement. Compared with the endeavors for super-resolution in image processing, music signals require supper-resolution to improve their quality or adapt to communication in narrow-band channel, which is also regarded as bandwidth expansion. To this end, we shed light on super-resolution for music signals using the deep learning strategy of Generative Adversarial Networks (GANs). The proposed approach feeds Shot-Time Fourier Transform (STFT) features of low-band signals to the GAN, expecting to obtain their high-band information through jointly considering content and adversarial losses. Then, we carry out experiments on MUSDB18 dataset using mixtures of music sources, in order to show the performance of the proposed approach. The experimental results indicate that the proposed approach achieves better super-resolution performances compared with interpolation and some conventional deep-learning strategies.