Reviewerly/exHarmony huggingface dataset:
Linking Legacies: Realising the Potential of the Rothamsted Long-Term Agricultural Experiments

Abstract Long-term agricultural experiments are used to test the effects of different farm management practices on agricultural systems over time. The time-series data from these experiments is well suited to understanding factors affecting soil health and sustainable crop production and can play an important role for addressing the food security and environmental challenges facing society from climate change. The data from these experiments is unique and irreplaceable. We know from the Rothamsted experience that the datasets available are valued assets that can be used to address multiple scientific questions, and the reuse and impact of the data can be increased by making the data accessible to the wider community. However, to do this requires active data stewardship. Long-term experiments are also available as research infrastructures, meaning external researchers can generate new datasets, additional to the routine data collected for an experiment. The publication of the FAIR data principles has provided an opportunity for us to re-evaluate what active data stewardship means for realising the potential of the data from our long-term experiments. In this paper we discuss our approach to FAIR data adoption, and the challenges for refactoring and describing existing legacy data and defining meaningful linkages between datasets.
Governing Agricultural Data: Challenges and Recommendations

Abstract The biomedical domain has shown that in silico analyses over vast data pools enhances the speed and scale of scientific innovation. This can hold true in agricultural research and guide similar multi-stakeholder action in service of global food security as well (Streich et al. Curr Opin Biotechnol 61:217–225. Retrieved from https://doi.org/10.1016/j.copbio.2020.01.010 , 2020). However, entrenched research culture and data and standards governance issues to enable data interoperability and ease of reuse continue to be roadblocks in the agricultural research for development sector. Effective operationalization of the FAIR Data Principles towards Findable, Accessible, Interoperable, and Reusable data requires that agricultural researchers accept that their responsibilities in a digital age include the stewardship of data assets to assure long-term preservation, access and reuse. The development and adoption of common agricultural data standards are key to assuring good stewardship, but face several challenges, including limited awareness about standards compliance; lagging data science capacity; emphasis on data collection rather than reuse; and limited fund allocation for data and standards management. Community-based hurdles around the development and governance of standards and fostering their adoption also abound. This chapter discusses challenges and possible solutions to making FAIR agricultural data assets the norm rather than the exception to catalyze a much-needed revolution towards “translational agriculture”.
Ethical and Legal Considerations in Smart Farming: A Farmer’s Perspective

Abstract Smart farming contributes to exponential income growth, enhanced decision making, better services and products, as well as productivity and profitability. Nowadays, numerous agricultural technology providers are entering the market, focusing on aggregating farmers’ data. But many farmers, especially smallholders, do not benefit from the sharing and exchange of this data, which leaves them feeling disempowered. Until today, ethical considerations were often side-lined because gathering more data was seen as necessary, and concerns about how data might be abused or misused were only subsequently considered. However, with the increase of big data in smart farming, it is more essential than ever to focus on the ethical aspects of data governance (access, control, consent) and practices. Therefore, these ethical questions will provide valuable insights into how data is being collected and used, for what purposes, how to bridge the digital divide, and how to create transparency and build trust between stakeholders. This chapter will focus on farmers’ perspectives and how they could actively participate in a more equitable data sharing and exchange in the agri-food value chain by contributing to the design of a fairer data governance framework. The adoption of agricultural codes of conduct is the example that will be explored.
Multi-Angle Fast Neural Tangent Kernel Classifier

Multi-kernel learning methods are essential kernel learning methods. Still, the base kernel functions in most multi-kernel learning methods only with select kernel functions with shallow structures, which are weak for large-scale uneven data. We propose two types of acceleration models from a multidimensional perspective of the data: the neural tangent kernel (NTK)-based multi-kernel learning method is proposed, where the NTK kernel regressor is shown to be equivalent to an infinitely wide neural network predictor, and the NTK with deep structure is used as the base kernel function to enhance the learning ability of multi-kernel models; and a parallel computing kernel model based on data partitioning techniques. An RBF, POLY-based multi-kernel model is also proposed. All models use historical memory-based PSO (HMPSO) for efficient search of parameters within the model. Since NTK has a multi-layer structure and thus has a significant computational complexity, the use of a Monotone Disjunctive Kernel (MDK) to store and train Boolean features in binary achieves a 15–60% training time compression of NTK models in different datasets while obtaining a 1–25% accuracy improvement.
Extended Informative Local Binary Patterns (EILBP): A Model for Image Feature Extraction

In the area of Image Processing extraction of feature from an image done by most preferred model of many researches named Local Binary Patterns (LBP) which is widely used local texture descriptors. In the context of feature extraction the global invariant features also applicable for texture classification. Classical LBP has a major drawback which is unable to process the global spatial information in context of little local texture information more global features preserve. In this paper we proposed a model to match the global rotation invariant to local texture information to preserve the more information to classify in better way named as Extended Informative Local Binary patterns (EILBP). The Proposed method will reduce the loss of global special information. In this process instead of computing the joint histogram globally the EILBP evaluate the VAR from local regions accurate them with LBP bins. This process greatly reduced the large training data sets. In our model the classification have done with SVM and normalized feature forwarded as input to the SVM to provide accurate result for match. The experimental results of proposed model achieved significantly improved compared to classical LBP.
OMSST Approach for Unit Selection from Speech Corpus for Telugu TTS

Design of a text-to-speech system requires fine-tuning to achieve human-like utterance. For Dravidian languages like Telugu that are morphologically rich, this problem can be addressed by minimizing the number of concatenations. In order to reduce the number of concatenations, this research proposes an approach named OMSST to select speech units which minimize the concatenation cost and thus results in more naturally sounding speech. In this approach, most frequent speech units of different length syllables and frequently co-occurring bi-, tri-, and quad-syllables are identified and stored in the corpus. The OMSST algorithm generates the state space tree using the available speech units in corpus for the given word and explores the state space to select the units that result in optimum concatenations. The performance of the algorithm is evaluated by giving the generated utterance to a Telugu speech recognition system, and precision of 97% and recall of 81% are achieved.
Spatial-temporal alignment of time series with different sampling rates based on cellular multi-objective whale optimization

• Transform the multi-rate time series alignment to a spatial-temporal multi-objective optimization problem. • Propose a novel Cell-MOWOA integrating the principles of cellular automata and whale optimization algorithm to find the Pareto optimal alignment solutions. • Design innovative multi-variate non-linear cell state evolution rules to improve the convergence and diversity of Pareto solutions, and design new whale population updating mechanism to accelerate convergence to Pareto front. • Comparing with six state-of-the-art baselines, our proposal has the greatest alignment accuracy, smallest singularity on 85 gold-standard UCR datasets, and achieved outstanding runtime efficiency, especially on long time series. Aligning time series of different sampling rates is an important but challenging task. Current commonly used dynamic time warping methods usually suffer from pathological temporal singularity problem. In order to overcome this, we transform the alignment task to a spatial-temporal multi-objective optimization (MOO) problem. Existing MOO algorithms are always inefficient in finding Pareto optimal alignment solutions due to their insufficiency in maintaining convergence and diversity among the obtained Pareto solutions. In light of this, we propose a novel and efficient MOO algorithm Cell-MOWOA which integrates Cellular automata with the rising Whale Optimization Algorithm to find Pareto optimal alignment solutions. Innovative multi-variate non-linear cell state evolutionary rules are designed within Pareto solution external archive to improve the convergence and diversity of the Pareto solutions, and novel whale population updating mechanism is designed to accelerate the convergence to the Pareto front. Besides, new integer whale updating mechanism is presented to transform real-number whale solutions to integer whale solutions. Experimental results on 85 gold-standard UCR time series datasets showed that Cell-MOWOA outperformed six state-of-the-art baselines by 24.53% in average in increasing alignment accuracy and 42.66% in average in reducing singularity. Besides, it achieved outstanding runtime efficiency, especially on long time series datasets.
Polarity Identification from Micro Blogs Using Multimodal Sentiment Analysis

Now-a-days, people are freely expressing their opinions in multiple ways through micro blogs usage like Twitter and Facebook in the Web. These channels are rich social media repositories, which provide an emerging channel for web users to express their sentiments in multimodal way, which is composed of image, video, short text, and emoticons. Such multimodal social media has significant application ranging from event monitoring, social network analytics, to commercial recommendations, etc. The existing unimodal sentiment analysis is purely on textual modality or emoticon based alone. Predicting sentiment of multimodal micro blogs in the current scenario by incorporating two types of data for analysis like texts and images. To analyze the images, Haar feature classifier was used which uses the rectangle integral to calculate the value of a feature and for the text analysis the Artificial neural networks algorithm was used which is trained with a large set of data consisting of different texts and their emotion. A few sample images and texts are used to test this work and sentiment was correctly predicted.
A Span-based Target-aware Relation Model for Frame-semantic Parsing

Frame-semantic Parsing (FSP) is a challenging and critical task in Natural Language Processing (NLP). Most of the existing studies decompose the FSP task into frame identification (FI) and frame semantic role labeling (FSRL) subtasks, and adopt a pipeline model architecture that clearly causes error propagation problem. However, recent jointly learning models aim to address the above problem and generally treat FSP as a span-level structured prediction task, which, unfortunately, leads to cascading error propagation problem between roles and less-efficient solutions due to huge search space of roles. To address these problems, we reformulate the FSRL task into a target-aware relation classification task and propose a novel and lightweight jointly learning framework that simultaneously processes three subtasks of FSP, including frame identification, argument identification, and role classification. The novel task formulation and jointly learning with interaction mechanisms among subtasks can help improve the overall system performance and reduce the search space and time complexity, compared with existing methods. Extensive experimental results demonstrate that our proposed model significantly outperforms 10 state-of-the-art models in terms of F1 score across two benchmark datasets.
Automatic posture capture of sports movement based on sensor information fusion

Aiming at the problems of low accuracy and long time consumption of motion attitude information collection in the existing methods, a new automatic attitude capture method based on sensor information fusion is proposed. The line builds a motion attitude information acquisition architecture through five functional modules, including data acquisition and processing, laser sensor, data storage, voltage conversion and serial communication, to obtain motion attitude data. Then, the sensor image information collected by median filtering is processed, and the target image is obtained by fusing the binary image obtained by optical flow segmentation with the original image. Finally, the Mean-Shfit algorithm and particle filter are used to track the human moving target, and the self-similar matrix is used to automatically capture the motion posture. Experiments show that the method has high recall and accuracy for the collection results of motion attitude information, the accuracy rate of automatic motion attitude capture is as high as 95.5%, the maximum capture time is 79 ms, the time consumption is short, and the practical application effect is good.
Flow distribution optimization of parallel pumps based on improved mayfly algorithm

An improved mayfly algorithm is proposed for the energy saving optimization of parallel chilled water pumps in central air conditioning system, with the minimum energy consumption of parallel pump units as the optimization objective and the speed ratio of each pump as the optimization variable for the solution. For the problem of uneven random initialization of mayflies, the variable definition method of Circle chaotic mapping is used to make the initial position of the population uniformly distributed in the solution space, and the mayfly fitness value and the optimal fitness value are incorporated into the calculation of the weight coefficient, which better balances the global exploration and local exploitation of the algorithm. For the problem that the algorithm is easy to fall into the local optimum at the later stage, a multi-subpopulation cooperative strategy is proposed to improve the global search ability of the algorithm. Finally, the performance of the improved mayfly algorithm is tested with two parallel pumping system cases, and the stability and time complexity of the algorithm are verified. The experiments show that the algorithm can get a better operation strategy in solving the parallel water pump energy saving optimization problem, and can achieve energy saving effect of 0.72% 8.68% compared with other optimization algorithms, and the convergence speed and stability of the algorithm have been significantly improved, which can be better applied to practical needs.
Fuzzy line enhancement scheme of computer art image based on DRL algorithm

In this study, a new blurred line enhancement scheme of computer art image based on Deep Reinforcement Learning (DRL) algorithm was proposed. The hard threshold method is adopted to remove the noise of computer art images and the blurred lines are extracted by texture separation method. Based on the line extraction results, the Deep Q-Network (DQN) model was built with DRL algorithm, and the sample images were input into the model, and the fuzzy line enhancement results of computer art images were obtained in the output layer. The proposed method exhibits excellent noise reduction effect, and the fuzzy line enhancement quality of computer art image is good. The average enhancement time is 0.58 s, and the practical application effect is good.
Time domain speech scrambler based on particle swarm optimization

Abstract Speech scrambler is used to transform clear speech into an unintelligible signal to prevent eavesdropping. The speech scrambling algorithm involves the permutation of speech segments depending on a specific permutation matrix which may be fixed or dynamic during encryption. A fixed permutation matrix is easy to break and has given high residual intelligibility in the scrambled signal. The proposed scheme used a particle swarm optimization algorithm to generate a dynamic permutation array that can attain a high degree of security. The outcome of the scrambled speech signal does not have any residual intelligibility, and the quality of the descrambled speech is extremely satisfying, with zero mean squared error.
Temporal Dynamic Concept Modeling Network for Explainable Video Event Recognition

Recently, with the vigorous development of deep learning and multimedia technology, intelligent urban computing has received more and more extensive attention from academia and industry. Unfortunately, most of the related technologies are black-box paradigms that lack interpretability. Among them, video event recognition is a basic technology. Event contains multiple concepts and their rich interactions, which can assist us to construct explainable event recognition methods. However, the crucial concepts needed to recognize events have various temporal existing patterns, and the relationship between events and the temporal characteristics of concepts has not been fully exploited. This brings great challenges for concept-based event categorization. To address the above issues, we introduce the temporal concept receptive field, which is the length of the temporal window size required to capture key concepts for concept-based event recognition methods. Accordingly, we introduce the temporal dynamic convolution (TDC) to model the temporal concept receptive field dynamically according to different events. Its core idea is to combine the results of multiple convolution layers with the learned coefficients from two complementary perspectives. These convolution layers contain a variety of kernel sizes, which can provide temporal concept receptive fields of different lengths. Similarly, we also propose the cross-domain temporal dynamic convolution (CrTDC) with the help of the rich relationship between different concepts. Different coefficients can help us to capture suitable temporal concept receptive field sizes and highlight crucial concepts to obtain accurate and complete concept representations for event analysis. Based on the TDC and CrTDC, we introduce the temporal dynamic concept modeling network (TDCMN) for explainable video event recognition. We evaluate TDCMN on large-scale and challenging datasets FCVID, ActivityNet, and CCV. Experimental results show that TDCMN significantly improves the event recognition performance of concept-based methods, and the explainability of our method inspires us to construct more explainable models from the perspective of the temporal concept receptive field.
Certificateless multi-source signcryption with lattice

Network coding can improve network transmission performance and reduce data transmission delay. However, the nodes in network are vulnerable to pollution attacks. Previous network coding schemes are based on difficult problems in number theory and cannot resist the quantum computing attacks. Hence, certificateless multi-source signcryption with lattice (LCL-MSSC) is devised for network coding, it uses common public key to verify the combination ciphertext from different source node users. Based on learning with errors and inhomogeneous small integer solution problems, LCL-MSSC is unforgeable under the adaptive chosen-message attacks and indistinguishable under the adaptive chosen-ciphertext attacks; in addition, it can resist the pollution attacks and quantum computing attacks. Efficient LCL-MSSC has broad application prospect in UAV wireless communication network, mobile wireless network, wireless sensor network or cyberspace security.
Increasing-Dimension Evolution: Make the evolutionary design of passive filters more efficient

Evolutionary circuit design is a promising way to achieve the automatic synthesis of analog circuits, and passive filters are essential circuits that must be considered. For now, in the evolutionary design of passive filters, the size control of filters under practical difficulty is still an important issue to be studied. This paper proposed a search strategy called Increasing-Dimension Evolution (IDE). The strategy introduces the concepts of exon and intron to control gene expression levels in evolutionary search. Through this strategy, even if the length of the chromosome is fixed, the search engine can effectively search up multiple design spaces and find a small circuit size at low computing costs. We built an effective evolutionary filter design model using the IDE strategy. Experimental results show that the proposed strategy can obtain filter sizes comparable to advanced conventional design methods for several passive filter design tasks with practical difficulties. In addition, the strategy makes the search engine more effective in a given complex task, and this task is difficult to be directly solved using conventional design methods.
CalcGraph: taming the high costs of deep learning using models

Abstract Models based on differential programming, like deep neural networks, are well established in research and able to outperform manually coded counterparts in many applications. Today, there is a rising interest to introduce this flexible modeling to solve real-world problems. A major challenge when moving from research to application is the strict constraints on computational resources (memory and time) . It is difficult to determine and contain the resource requirements of differential models, especially during the early training and hyperparameter exploration stages. In this article, we address this challenge by introducing C alc G raph, a model abstraction of differentiable programming layers. C alc G raph allows to model the computational resources that should be used and then C alc G raph’s model interpreter can automatically schedule the execution respecting the specifications made. We propose a novel way to efficiently switch models from storage to preallocated memory zones and vice versa to maximize the number of model executions given the available resources. We demonstrate the efficiency of our approach by showing that it consumes less resources than state-of-the-art frameworks like TensorFlow and PyTorch for single-model and multi-model execution.