Reviewerly/exHarmony huggingface dataset:
GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization

Abstract Background Plant breeding and crop research rely on experimental phenotyping trials. These trials generate data for large numbers of traits and plant varieties that needs to be captured efficiently and accurately to support further research and downstream analysis. Traditionally scored by hand, phenotypic data is nowadays collected using spreadsheets or specialized apps. While many solutions exist, which increase efficiency and reduce errors, none offer the same familiarity as printed field plans which have been used for decades and offer an intuitive overview over the trial setup, previously recorded data and plots still requiring scoring. Results We introduce GridScore which utilizes cutting-edge web technologies to reproduce the familiarity of printed field plans while enhancing the phenotypic data collection process by adding advanced features like georeferencing, image tagging and speech recognition. GridScore is a cross-platform open-source plant phenotyping app that combines barcode-based systems with a guided data collection approach while offering a top-down view onto the data collected in a field layout. GridScore is compared to existing tools across a wide spectrum of criteria including support for barcodes, multiple platforms, and visualizations. Conclusion Compared to its competition, GridScore shows strong performance across the board offering a complete manual phenotyping experience.
Using multispectral variables to estimate heavy metals content in agricultural soils: A case of suburban area in Tianjin, China

Efficient estimation and mapping of soil heavy metal concentrations based on rapid and environmentally friendly multispectral data can provide a reference for the prevention and control of non-point source pollution and safety in ecosystems. In this study, the spatial variation of soil heavy metals (As, Cd, Cr, Cu, Ni, and Zn) in Wuqing was analyzed with 394 and 57 soil samples gathered in 2005 and 2019. Based on these samples, as well as multispectral variables (raw bands and their respective vegetation index and derivative transformation), random forest-ordinary kriging (RFOK) and artificial neural network-ordinary kriging (ANNOK) were combined to predict spatial variations of heavy metal content. Results indicated that the mean values of soil As, Cd, Cr, Cu, and Zn in 2019 exceeded their respective concentrations in 2005 by 1.40, 1.36, 1.12, 1.12, and 1.35 times, and exceeded the standard rate of background values (BGV) in 2019 for all soil heavy metals (except for Ni). Compared to RF, ANN, and RFOK, the ANNOK method exhibited the best estimation performance for As (R 2005 2 = 69.47% andR 2019 2 = 65.03%), Cd (R 2005 2 = 64.48% andR 2019 2 = 64.20%), Cr (R 2005 2 = 72.02% andR 2019 2 = 70.65%), Cu (R 2005 2 = 72.05% andR 2019 2 = 70.53%), Ni (R 2005 2 = 69.37% andR 2019 2 = 66.03%), and Zn (R 2005 2 = 71.16% andR 2019 2 = 67.11%) in the validation dataset. Mapping of heavy metals derived from ANNOK revealed a similar distribution in 2005 and 2019, where the high concentrations of all soil heavy metals were in the southern part of the study area. The effects of multispectral bands as indicated by vegetation index and derivative transformation exhibited sensitivity for predicting soil heavy metals based on hybrid geostatistical methods. Overall, greater efficiency of spatial estimation for the content of soil heavy metals is important for the prevention and control of non-point source pollution. • ANNOK has the best predicted performance for variations of soil heavy metals. • High concentrations of soil heavy metals are the southern area both 2005 and 2019. • Sewage irrigation is a key cause that affects the accumulation of soil heavy metals. • B4-ln, B4-fd, B4-sd, and B4-td are important driving factors.
On the Use of Efficient Projection Kernels for Motion-Based Visual Saliency Estimation

In this paper, we investigate the potential of a family of efficient filters—the Gray-Code Kernels (GCKs)—for addressing visual saliency estimation with a focus on motion information. Our implementation relies on the use of 3D kernels applied to overlapping blocks of frames and is able to gather meaningful spatio-temporal information with a very light computation. We introduce an attention module that reasons the use of pooling strategies, combined in an unsupervised way to derive a saliency map highlighting the presence of motion in the scene. A coarse segmentation map can also be obtained. In the experimental analysis, we evaluate our method on publicly available datasets and show that it is able to effectively and efficiently identify the portion of the image where the motion is occurring, providing tolerance to a variety of scene conditions and complexities.
A Novel Vulnerable Code Clone Detector Based on Context Enhancement and Patch Validation

With the rapid growth of open-source software, code cloning has become increasingly prevalent. If there are security vulnerabilities in a cloned code segment, those vulnerabilities may spread in the related software to potentially lead to security incidents. The existing methods of vulnerable code detection are performed on the condition that the source code is converted into an intermediate representation. However, these methods do not fully consider the rich semantic knowledge and patch information available for vulnerable codes, which can induce a high false positive rate (FPR). To address this problem, this paper proposes a vulnerable code clone detection method based on code fingerprints, named the Context-enhanced and Patch-validation-based Vulnerable code clone Detector (CPVDetector). A fingerprint database is built for functions, code snippets, and patches derived from preprocessed vulnerable source code. The target code to be detected is firstly transformed into function-level fingerprints. If clone detection fails at this coarse granularity, the detector is then applied at the finer line-level granularity. When fingerprint matching is successful between the target code and the vulnerable code segments, the detector will proceed to verify the context of vulnerable codes. Finally, CPVDetector can verify the fingerprints of patches corresponding to vulnerable codes to further reduce the FPR. Based on the generally accepted classification of code clones, CPVDetector can identify Type 1 and Type 2 vulnerable code clones at the coarse-grained level and offers significantly improved detection sensitivity for Type 3 and Type 4 code clones at the fine-grained level. Experimental results show that the proposed method can achieve high accuracy with a fast detection speed, and the FPR is as low as 2.35%, which is less than one-third of that of other existing methods. In view of its competitive performance and efficiency, CPVDetector can be applied in large-scale vulnerable code detection scenarios.
Children’s cognitive abilities in the context of the digital transformation of basic education

The nine-year compulsory education policy is a priority state policy in China. The transition to online learning caused by the COVID-19 pandemic turned out to be especially difficult for elementary school students, primarily in the organizational aspect. The need to ensure quality education and effective learning outcomes regardless of its form determined the research relevance. Thus, the purpose of the study is to consider the impact of the digital transformation of basic education on the development of first-graders’ cognitive abilities. The study involved 205 first-grade students: 112 girls and 93 boys, their average age was 6.4 years. The experimental group consisted of three student groups (98 first-graders). The control group also involved three student groups (115 children). The null hypothesis that both online learning and traditional classroom activities have the same effect on the development of cognitive abilities in first graders was statistically confirmed.
LSRML: A latent space regularization based meta-learning framework for MR image segmentation

Data sources for medical image segmentation can be quite extensive, and models trained with data from a source domain may perform poorly on data from the target domain owing to domain shift issues. To overcome the impact of domain shift, we propose a novel meta-learning-based multi-source domain adaptation framework for medical image segmentation. Specifically, we designed a domain discriminator module to produce category prediction over the latent features, and an image reconstruction module to reconstruct the foreground and background of the target domain image separately. Furthermore, we constructed a large-scale multi-modal prostate dataset, which contained 495,902 magnetic resonance images of 419 cases, with prostate and lesion masks, as well as diagnostic descriptions for each patient. We evaluated our proposed method through extensive experiments using the proposed and the benchmark datasets. Experimental results show that our model achieves better segmentation and generalization performance compared to state-of-the-art approaches.
Soft sensor with deep feature extraction for a sugarcane milling system

Abstract Extraction rate and energy consumption are two important indicators of the sugarcane milling system, which are calculated by offline experiments and inspection methods. Soft sensors are inferential models, which are enabling online prediction of these indicators. Selection of input variables is one of the most critical issues in soft sensors design. So a deep feature extraction method is proposed by combining mutual information theory and hybrid chicken swamp algorithm to determine the input and parameters of model. Then, input the obtained variables into the data‐driven model of deep kernal extreme learning machine (DK‐ELM) with optimized parameters to realize the prediction of extraction and hourly energy consumption. It combines the kernel method with the multi‐layer ELM. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Comparing the proposed framework with the four experiments designed on a sugarcane milling system, the proposed framework for predicting extraction and energy consumption with better feasibility and effectiveness, and provided a good reference for end‐point control and judgment of quick direct tapping. Practical Applications Extraction and energy consumption are two important indicators of the milling process and whether they meet standards will affect the smooth operation and economic benefits of sugar production. This study presents a novel modeling method to predict these indicators based on the deep feature extraction and deep kernal extreme learning machine. These methods provided a good reference for end‐point control and judgment of quick direct tapping. In addition, it a new concept for the design of soft sensors for such complex industrial processes.
Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding

Current bundle generation studies focus on generating a combination of items to improve user experience. In real-world applications, there is also a great need to produce bundle creatives that consist of mixture types of objects (e.g., items, slogans and templates) for achieving better promotion effect. We study a new problem named bundle creative generation: for given users, the goal is to generate personalized bundle creatives that the users will be interested in. To take both quality and efficiency into account, we propose a contrastive non-autoregressive model that captures user preferences with ingenious decoding objective. Experiments on large-scale real-world datasets verify that our proposed model shows significant advantages in terms of creative quality and generation speed.
Quantum error correction in the noisy intermediate-scale quantum regime for sequential quantum computing

We use density-matrix simulations to study the performance of three distance three quantum error correction (QEC) codes in the context of the rare-earth (RE) ion-doped crystal platform for quantum computing. We analyze pseudothresholds for these codes when parallel operations are not available, and examine the behavior both with and without resting errors. In RE systems, resting errors can be mitigated by extending the system's ground-state coherence time. For the codes we study, we find that if the ground-state coherence time is roughly 100 times larger than the excited-state coherence time, resting errors become small enough to be negligible compared to other error sources. This leads us to the conclusion that beneficial QEC could be achieved in the RE system with the expected gate fidelities available in the noisy intermediate-scale quantum regime. However, for codes using more qubits and operations, a factor of more than 100 would be required. Furthermore, we investigate how often QEC should be performed in a circuit. We find that for early experiments in RE systems, the minimal $[[5,1,3]]$ would be most suitable as it has a high threshold error and uses few qubits. However, when more qubits are available the $[[9,1,3]]$ surface code might be a better option due to its higher circuit performance. Our findings are important for steering experiments to an efficient path for realizing beneficial quantum error correcting codes in early RE systems where resources are limited.
Does the motivation and parents involvement affected by distance learning media during pandemic covid 19?

The COVID-19 pandemic has been increasing the demand for creative and innovative strategies for early childhood education in achieving adequate educational goals. The present study aimed to empirically find out the effect of the use of tutorial video and children worksheet on preschool children’s motivation and parents’ involvement in accompanying children during study from home period. This research is experimental with a one-group pre-test-post-test experimental design. The population of this study were parents of grade A kindergarten children consisting of 8 kindergarten schools through purposive. Each class consists of 12-20 parents. The research sample consisting of two classes was divided into class A as an experimental group and class B as control group. Data collection using parental involvement and motivation questionnaires, observation, interviews, and documentation. Data analysis used used T-test and analysis of variance (ANAVA) paths. The results showed that there was a significant difference both in parents' motivation and involvement when using children's worksheets and tutorial videos obtained from the results of analysis with ANAVA by the sig. value of 0.01 0.05, which means the using children's worksheets and tutorial videos was enhancing parents' motivation and involvement in assisting children's learning process.
TPACK Tried and Tested: Experiences of Post-Secondary Educators During COVID-19 Pandemic

This article explores educators’ experiences during the rapid shift from face-to-face to emergency virtual remote teaching and learning as a result of the COVID-19 pandemic. One hundred and forty educators from a Canadian province completed a survey with Likert scale and open-ended questions designed to capture their application of technological pedagogical content knowledge (TPACK) during the pandemic. Data was collected during fall of 2020. More than 50% of the study participants reported that the quality of their practice declined as they shifted from face-to-face to remote teaching as a result of the pandemic. Educators’ descriptions of their virtual remote experiences were examined using Hutchings’ (2000) taxonomy of scholarship and inquiry questions as an analytic lens. The findings suggest that educators who were more comfortable with their TPACK had an easier transition to virtual remote teaching. Institutional support, students’ digital literacy/access, and overall wellbeing were also identified as factors that influenced educators’ overall experiences.
Assessment of Trace Metal Content in Soils of Automobile Workshops around Bori Urban Area, Rivers State, Nigeria

This study was designed to assess the concentration of trace metals (Cr, Cd, Mn, Ni, Pb, Zn) in soils of automobile workshops around Bori Urban, Rivers State, Nigeria. Samples were collected from three auto mechanic workshops within the Bori urban area, Rivers State, Nigeria. The soil samples were prepared by standard procedures, and analyzed using the Atomic Absorption Spectrophotometer, AAS (Solaar Thermo Elemental Atomic Absorption Spectrometer-Model SE 71906). Detectable concentrations (mg/kg) of the metals, Zn, Mn, Pb, Cd, Ni and Cr were 4.05 ± 1.02, 9.64 ± 0.71, 13.39 ± 2.28, 2.46 ± 0.42, 2.86 ± 0.79 and 2.76 ± 0.99 respectively. The concentrations and contamination factors (CFs) across the three auto mechanic garages were of the order, Pb &gt; Mn &gt; Zn &gt; Cd &gt; Ni &gt; Cr and Cd &gt; Cr &gt; Ni &gt; Mn &gt; Pb &gt; Zn respectively while the pollution load index (PLI) values for the three auto mechanic garages were all greater than 1, indicating a decline in the soil quality for the three studied sites following the order station 3 &gt; station 1 &gt; station 2. The Geo accumulation index (Igeo) revealed that the soils were extremely contaminated by Cd and were all contaminated with trace metals when compared with regulatory standards such as WHO and world average values in shale. Therefore, regular monitoring of the soil in the studied sites be carried out to mitigate dangers implicit in the continued presence of the metals determined. &nbsp
aRTIC GAN: A Recursive Text-Image-Conditioned GAN

Generative Adversarial Networks have recently demonstrated the capability to synthesize photo-realistic real-world images. However, they still struggle to offer high controllability of the output image, even if several constraints are provided as input. In this work, we present a Recursive Text-Image-Conditioned GAN (aRTIC GAN), a novel approach for multi-conditional image generation under concurrent spatial and text constraints. It employs few line drawings and short descriptions to provide informative yet human-friendly conditioning. The proposed scenario is based on accessible constraints with high degrees of freedom: sketches are easy to draw and add strong restrictions on the generated objects, such as their orientation or main physical characteristics. Text on its side is so common and expressive that easily enforces information otherwise impossible to provide with minimal illustrations, such as objects components color, color shades, etc. Our aRTIC GAN is suitable for the sequential generation of multiple objects due to its compact design. In fact, the algorithm exploits the previously generated image in conjunction with the sketch and the text caption, resulting in a recurrent approach. We developed three network blocks to tackle the fundamental problems of catching captions’ semantic meanings and of handling the trade-off between smoothing grid-pattern artifacts and visual detail preservation. Furthermore, a compact three-task discriminator (covering global, local and textual aspects) was developed to preserve a lightweight and robust architecture. Extensive experiments proved the validity of aRTIC GAN and show that the combined use of sketch and description allows us to avoid explicit object labeling.
Rigorous calibration of homodyne detection efficiency for continuous-variable quantum key distribution

We propose a rigorous calibration method for homodyne detection efficiency, which combines all the factors that affect detection efficiency to calibrate together through the actual homodyne detection. With this method, the transmittance converted from electronic noise in the one-time calibration method of the shot noise can be attributed to the detection inefficiency. Thus, a trusted detection noise-free model for continuous-variable quantum key distribution (CV-QKD) can be established, which simplifies the calibration of shot noise while having the same performance as the trusted detection noise model. We demonstrate this calibration method with a balanced detector based on a transimpedance amplifier. Experimental results show that detection efficiency will be overestimated if the integration factor of the detector is overlooked. The overestimation of the detection efficiency leads to an underestimation of modulation variance and excess noise when the modulation variance is monitored by the balanced detector, which opens security loopholes. Our method may prove a necessary method in the calibration of detection efficiency for CV-QKD.
A quantum partially blind signature scheme without entanglement

In a quantum partially blind signature (QPBS) scheme, the contradiction between anonymity and controllability of completely blind signature (BS) schemes was better solved by adding common negotiated public information between the signer and the requester (the message holder). After reviewing the proposed scheme, we give an analysis about it and then propose a novel QPBS scheme without entanglement. The security without preconditions of this signature scheme is ensured by the secure quantum key distribution (QKD) protocol and quantum one-time pad (QOTP). Furthermore, in the process of security analysis, it proves that our scheme satisfies the foundational attribution of partially blind signature (PBS). Moreover, the scheme can solve the problem we put forward so as to allow it to have a wider application in the future.
DevOps Education: An Interview Study of Challenges and Recommendations

Over the last years, the software industry has adopted several Dev-Ops technologies related to practices such as continuous integration and continuous delivery. The high demand for DevOps practitioners requires non-trivial adjustments in traditional software engineering courses and educational methodologies. This work presents an interview study with 14 DevOps educators from different universities and countries, aiming to identify the main challenges and recommendations for DevOps teaching. Our study identified 83 challenges, 185 recommendations, and several association links and conflicts between them. Our findings can help educators plan, execute and evaluate DevOps courses. They also highlight several opportunities for researchers to propose new methods and tools for teaching DevOps.
Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster

Due to the low contrast, blurred boundary and intensity inhomogeneity of the images, accurate segmentation of breast cancer lesions with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) still has great challenges. This paper proposed an improved active contour model (ACM) for segmenting breast cancer lesions in DCE-MRI images. First, based on the extreme learning machine (ELM) method, a robust function is proposed that combines image intensities and time-domain features to enhance the difference between the lesions and other tissues. Second, an edge-stop function (ESF) is introduced by combining the image intensity, time-domain feature, and Hessian shape index to detect the irregular and blurred boundaries. At the boundary of breast cancer lesions, the energy function of ACM is minimized and the evolution of the contour curve completes, so the accurate lesion region of breast cancer can be segmented. The mean Dice similar coefficient (DICE), Jaccard similarity (JC) and Hausdorff distance (HD) of the segmentation of the proposed model in 50 samples are 85.88±6.62%, 75.72±9.68% and 11.62±4.72 mm, respectively. The results segmented by the proposed ACM are more similar to the manual segmentation than the compared models.