Reviewerly/exHarmony huggingface dataset:
Mathematical Modeling to Estimate Photosynthesis: A State of the Art

Photosynthesis is a process that indicates the productivity of crops. The estimation of this variable can be achieved through methods based on mathematical models. Mathematical models are usually classified as empirical, mechanistic, and hybrid. To mathematically model photosynthesis, it is essential to know: the input/output variables and their units; the modeling to be used based on its classification (empirical, mechanistic, or hybrid); existing measurement methods and their invasiveness; the validation shapes and the plant species required for experimentation. Until now, a collection of such information in a single reference has not been found in the literature, so the objective of this manuscript is to analyze the most relevant mathematical models for the photosynthesis estimation and discuss their formulation, complexity, validation, number of samples, units of the input/output variables, and invasiveness in the estimation method. According to the state of the art reviewed here, 67% of the photosynthesis measurement models are mechanistic, 13% are empirical and 20% hybrid. These models estimate gross photosynthesis, net photosynthesis, photosynthesis rate, biomass, or carbon assimilation. Therefore, this review provides an update on the state of research and mathematical modeling of photosynthesis.
Automatic Search of Rectangle Attacks on Feistel Ciphers: Application to WARP

In this paper we present a boomerang analysis of WARP, a recently proposed Generalized Feistel Network with extremely compact hardware implementations. We start by looking for boomerang characteristics that directly take into account the boomerang switch effects by showing how to adapt Delaune et al. automated tool to the case of Feistel ciphers, and discuss several improvements to keep the execution time reasonable. This technique returns a 23-round distinguisher of probability 2−124, which becomes the best distinguisher presented on WARP so far. We then look for an attack by adding the key recovery phase to our model and we obtain a 26-round rectangle attack with time and data complexities of 2115.9 and 2120.6 respectively, again resulting in the best result presented so far. Incidentally, our analysis discloses how an attacker can take advantage of the position of the key addition (put after the S-box application to avoid complementation properties), which in our case offers an improvement of a factor of 275 of the time complexity in comparison to a variant with the key addition positioned before. Note that our findings do not threaten the security of the cipher which iterates 41 rounds.
Artificial Potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot Path Prediction

Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a state action function in mobile robots suited to their environment.During trial-and-error interaction with its surroundings, it helps a robot to find an ideal behavior on its own.The Deep Q Network (DQN) algorithm is used in TurtleBot 3 (TB3) to achieve the goal by successfully avoiding the obstacles.But it requires a large number of training iterations.This research mainly focuses on a mobility robot's best path prediction utilizing DQN and the Artificial Potential Field (APF) algorithms.First, a TB3 Waffle Pi DQN is built and trained to reach the goal.Then the APF shortest path algorithm is incorporated into the DQN algorithm.The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System (ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algorithm.The performance improvement rate of the proposed DQN + APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN + APF in comparison with DQN in terms of average time is achieved by 0.331 s.The performance of the proposed DQN + APF in comparison with DQN average rewards in which the positive goal is attained by 85% and the negative goal is attained by -90%.
Deep alternating non-negative matrix factorisation

Non-negative matrix factorisation (NMF) is a promising data-mining technique for non-negative data. NMF achieves feature extraction by factorising the original data matrix into a basis matrix and coding matrix both with non-negative entries. Recently, multi-layer or deep NMF has been studied because of its ability to extract deep representative features which can help profoundly understand the original data. The existing deep NMF approaches are implemented by cascading the factorisation of the coding matrix at each layer. This paper proposes a novel scheme, in which the factorisations of the basis and coding matrices are alternated along layers. Based on this scheme, several deep NMF models to address various data scenarios are developed. Extensive experimental results on several machine learning tasks, including collaborative filtering, image inpainting, and community detection, show that the proposed alternating deep factorisation algorithms perform significantly better than existing state-of-the-art algorithms in most instances of these tasks.
Research on the SDIF Failure Principle for RF Stealth Radar Signal Design

Radio frequency (RF) stealth is one of the essential research hotspots in the radar field. The anti-sorting signal is an important direction of the RF stealth signal. Theoretically speaking, the anti-sorting signal design is based on the failure principle of the radar signal sorting algorithm, and the SDIF algorithm is a core sorting algorithm widely used in engineering. Thus, in this paper, the SDIF algorithm is first analyzed in detail. It is pointed out that the threshold function of the SDIF algorithm will fail when the signal pulse repetition interval (PRI) value obeys the interval distribution whose length is 20 times larger than the minimum interval of PRI. Secondly, the correctness of the failure principle of SDIF threshold separation is proved by the formula. Finally, the correctness is further verified by the signal design case. The principle of SDIF sorting threshold failure provides theoretical support for anti-sorting RF stealth signal design. It also complements the shortcoming of the casual design for the anti-sorting signal. Furthermore, the principle of SDIF sorting threshold failure helps improve anti-sorting signal design efficiency. Compared with the Dwell &amp; Switch (D&amp;S) signal and jitter signal, the anti-sorting ability of the signal designed by using the sorting failure principle is notably enhanced through simulation and experimentation.
Road scene classification based on street-level images and spatial data

Understanding the context of the scene is one of the most important aspects for new generation of autonomous vehicles. It is a very trivial task for a human to recognize the scene context by only a single look at the picture, however, for a computer, it is still a challenging task. This problem can be solved by automatic data labeling using deep-learning models for scene classification. Relying on scene type labels we can select relevant scenes to prepare a balanced dataset to train more advanced instance detection models using data from a specific road condition. This study presents a novel framework based on a deep convolutional neural network (CNN) for the automatic road scene classification of street-level automotive images. For the evaluation of our approach, we use a well-known autonomous benchmark dataset, from which we extract geo-position data and combine them with predictions from the scene classification model to get ground truth labels to train and evaluate a ResNet-50 model for scene classification. The results and comparison with state-of-the-art methods are presented.
Software Test Architecture Definition by Analogy with Software Architecture

A term, "software test architecture" has been discussed and defined. The discussion about analogy with software architecture is still remaining. Software architecture has been already defined and become a general term. In this paper, we discuss and define software test architecture as analogy with software architecture. Software architecture provides a well-developed framework for the structuring of specifications for large-scale, distributed systems. We define objective of software test architecture as helping for testing large-scale complex soft-ware by applying practices of software architecture to software testing.
A Method for Detecting Dynamic Objects Using 2D LiDAR Based on Scan Matching

The autonomous movement of the mobile robotic system is a complex problem. If there are dynamic objects in the space when performing this task, the complexity of the solution increases. To avoid collisions, it is necessary to implement a suitable detection algorithm and adjust the trajectory of the robotic system. This work deals with the design of a method for the detection of dynamic objects; based on the outputs of this method, the moving trajectory of the robotic system is modified. The method is based on the SegMatch algorithm, which is based on the scan matching, while the main sensor of the environment is a 2D LiDAR. This method is successfully implemented in an autonomous mobile robotic system, the aim of which is to perform active simultaneous localization and mapping. The result is a collision-free transition through a mapped environment. Matlab is used as the main software tool.
TM-BERT: A Twitter Modified BERT for Sentiment Analysis on Covid-19 Vaccination Tweets

In transfer learning a model is pre-trained on a large unsupervised dataset and then fine-tuned on domain-specific downstream tasks. BERT is the first true-natured deep bidirectional language model which reads the input from both sides of input to better understand the context of a sentence by solely relying on the Attention mechanism. This study presents a Twitter Modified BERT (TM-BERT) based upon Transformer architecture. It has also developed a new Covid-19 Vaccination Sentiment Analysis Task (CV-SAT) and a COVID-19 unsupervised pre-training dataset containing (70K) tweets. BERT achieved (0.70) and (0.76) accuracy when fine-tuned on CV-SAT, whereas TM-BERT achieved (0.89), a (19%) and (13%) accuracy over BERT. Another enhancement introduced is in terms of time efficiency as BERT takes (64) hours of pre-training while TM-BERT takes only (17) hours and still produces (19%) improvement even after pre-trained on four (4) times fewer data.
Multimodal motivation modelling and computing towards motivationally intelligent E-learning systems

Abstract Motivation to engage in learning is essential for learning performance. Learners’ motivation is traditionally assessed using self-reported data, which is time-consuming, subjective, and interruptive to their learning process. To address this issue, this paper proposes a novel framework for multimodal assessment of learners’ motivation in e-learning environments with the ultimate purpose of supporting intelligent e-learning systems to facilitate dynamic, context-aware, and personalized services or interventions, thus sustaining learners’ motivation for learning engagement. We investigated the performance of the machine learning classifier and the most and least accurately predicted motivational factors. We also assessed the contribution of different electroencephalogram (EEG) and eye gaze features to motivation assessment. The applicability of the framework was evaluated in an empirical study in which we combined eye tracking and EEG sensors to produce a multimodal dataset. The dataset was then processed and used to develop a machine learning classifier for motivation assessment by predicting the levels of a range of motivational factors, which represented the multiple dimensions of motivation. We also proposed a novel approach to feature selection combining data-driven and knowledge-driven methods to train the machine learning classifier for motivation assessment, which has been proved effective in our empirical study at selecting predictors from a large number of extracted features from EEG and eye tracking data. Our study has revealed valuable insights for the role played by brain activities and eye movements on predicting the levels of different motivational factors. Initial results using logistic regression classifier have achieved significant predictive power for all the motivational factors studied, with accuracy of between 68.1% and 92.8%. The present work has demonstrated the applicability of the proposed framework for multimodal motivation assessment which will inspire future research towards motivationally intelligent e-learning systems.
Robust deep ensemble method for real-world image denoising

Recently, deep learning-based image denoising methods have achieved promising performance. However, when the distribution of real-world noisy images is unknown, the denoising performance is still limited due to the domain gap between the training set and the testing set. Nonetheless, the unknown noise distribution usually can be modeled as proper combination of existing noise distributions. In this paper, we propose a simple yet effective Bayesian deep ensemble (BDE) method for real-world image denoising, where several representative deep denoisers pre-trained with various training data settings can be fused to improve robustness. The foundation of BDE is that real-world image noises are highly signal-dependent, and heterogeneous noises in a real-world noisy image can be separately handled by different denoisers. In particular, we take well-trained CBDNet, NBNet, HINet, Uformer and GMSNet into denoiser pool, and a U-Net is adopted to predict pixel-wise weighting maps to fuse these denoisers. Instead of solely learning pixel-wise weighting maps, Bayesian deep learning strategy is introduced to predict weighting uncertainty as well as weighting map, by which prediction variance can be modeled for improving robustness on real-world noisy images. Extensive experiments have shown that real-world noises can be better removed by fusing existing denoisers instead of training a big denoiser with expensive cost. Furthermore, our BDE can be extended to other image restoration tasks, i.e. image deblurring, image deraining and single image super-resolution, and also achieves significance gain on benchmark datasets.
Performance Comparison of Ensemble Learning and Supervised Algorithms in Classifying Multi-label Network Traffic Flow

Network traffic classification is of significant importance. It helps identify network anomalies and assists in taking measures to avoid them. However, classifying network traffic correctly is a challenging task. This study aims to compare ensemble learning methods with normal supervised classification to come up with improved classification methods. Three types of network traffic were classified (Benign, Malicious, and Outliers). The data were collected experimentally by using Paessler Router Traffic Grapher software and online and were analyzed by R software. The datasets were used to train five supervised models (k-nearest neighbors, mixture discriminant analysis, Naïve Bayes, C5.0 classification model, and regularized discriminant analysis). The models were trained by 70% of the samples and the rest 30% were used for validation. The same samples were used separately in predicting individual accuracy. The results were compared to the ensemble learning models which were built with the use of the same datasets. Among the five supervised classifiers, k-nearest neighbors and C5.0 classification scored the highest accuracy of 0.868 and 0.761. The ensemble learning classifiers Bagging (Random Forest) and Boosting (eXtreme Gradient Boosting) had accuracy of 0.904 and 0.902 respectively. The results show that the ensemble learning method has higher accuracy compared to the normal supervised classifiers. Therefore, it can be used to detect malicious activities in network traffic as well as anomalies with improved accuracy.
A Survey on Mobile Payment Request Verification over Cloud using Key Distribution

Payment request examination of some of the cryptography's most widely used encryption algorithms, with the goal of first identifying as many flaws and vulnerabilities as possible, and then identifying the elements of these flaws that could have been avoided with proper implementation. The number of attacks on the Internet is flourishing. An encrypted communications algorithm was necessary to overcome these flaws. In this survey paper, we looked at the importance of existing mobile user payment encryption solutions and literature analysis of these algorithms. In cryptography, a mobile payment encryption technique is used. The algorithm converts data from a readable format into an unreadable, secure format.
Minimum Rainfall for Applying the Curve-Number Method

In engineering practice the curve-number method is sometimes inappropriately applied to low-rainfall events, thereby ignoring the dependence of the curve number on the rainfall amount. Lack of adjustment of the curve number for low rainfall can lead to substantial underestimation of runoff, and is particularly problematic in the design of water-quality components of stormwater-management systems. An expression is derived for the minimum rainfall required for the applicable curve number to be within 10% of its commonly cited handbook value, and a procedure is presented for adjusting the handbook curve number to account for low-design rainfall. A realistic example is presented to illustrate the procedure.
Helmholtz: A Verifier for Tezos Smart Contracts Based on Refinement Types

Abstract A smart contract is a program executed on a blockchain, based on which many cryptocurrencies are implemented, and is being used for automating transactions. Due to the large amount of money that smart contracts deal with, there is a surging demand for a method that can statically and formally verify them. This article describes our type-based static verification tool Helmholtz for Michelson, which is a statically typed stack-based language for writing smart contracts that are executed on the blockchain platform Tezos. Helmholtz is designed on top of our extension of Michelson’s type system with refinement types. Helmholtz takes a Michelson program annotated with a user-defined specification written in the form of a refinement type as input; it then typechecks the program against the specification based on the refinement type system, discharging the generated verification conditions with the SMT solver Z3. We briefly introduce our refinement type system for the core calculus Mini-Michelson of Michelson, which incorporates the characteristic features such as compound datatypes (e.g., lists and pairs), higher-order functions, and invocation of another contract. Helmholtz successfully verifies several practical Michelson programs, including one that transfers money to an account and that checks a digital signature.
Adapt-Flow: A Flexible DNN Accelerator Architecture for Heterogeneous Dataflow Implementation

Deep neural networks (DNNs) have been widely applied to various application domains. DNN computation is memory and compute-intensive requiring excessive memory access and a large number of computations. To efficiently implement these applications, several data reuse and parallelism exploitation strategies, called dataflows, have been proposed. Studies have shown that many DNN applications benefit from a heterogeneous dataflow strategy where the dataflow type changes from layer to layer. Unfortunately, very few existing DNN architectures can simultaneously accommodate multiple dataflows due to their limited hardware flexibility. In this paper, we propose a flexible DNN accelerator architecture, called Adapt-Flow, which has the capability of supporting multiple dataflow selections for each DNN layer at runtime. Specifically, the proposed Adapt-Flow architecture consists of (1) a flexible interconnect, (2) a dataflow selection algorithm, and (3) a dataflow mapping technique. The flexible interconnect provides dynamic support for various traffic patterns required by different dataflows. The proposed dataflow selection algorithm selects the optimal dataflow strategy for a given DNN layer with the aim of much improved performance. And the dataflow mapping technique efficiently maps the dataflow amenable to the flexible interconnect. Simulation studies show that the proposed Adapt-Flow architecture reduces execution time by 46%, 78%, 26%, and energy consumption by 45%, 80%, 25% as compared to NVDLA, ShiDianNao, and Eyeriss respectively.
Stateless forward-secure key-insulated linkable ring signature scheme in ID-based setting

Forward-security and key-insulation have been the mainstream antidotes to the key exposure problem in conventional and ring signatures primarily in the setting of public key infrastructure. However, all extant schemes require keeping state of time periods. Moreover, time-bound keys are stored for retrieval during key update and more importantly signature-related dispute resolution which may extend beyond stipulated time periods. Spurred on by the gravity of key exposure in the setting of identity-based cryptography, we introduce a stateless forward-secure key-insulated linkable ring signature accomplished partly with on-demand ephemeral secret keys consequently eliminating the need for state-keeping and long-term storage. We prove security of our construction in the Random Oracle Model based on intractable problems of Computational Diffie–Hellman (CDH) and Discrete Logarithm (DL). Subsequently, we present evaluation results from a comparative analysis of extant identity-based linkable ring signature schemes to attest to the uniqueness, convenience and efficiency of our scheme which incidentally compared to state-of-the-art achieves a running time of 22.113ms faster at verification phase making it the fastest compared to extant schemes aside the statelessness and constant linkability attributes.
Mission‐centric decision support in cybersecurity via Bayesian Privilege Attack Graph

Abstract We present an approach to decision support in cybersecurity with respect to cyber threats and stakeholders' requirements. We approach situations in which cybersecurity experts need to take actions to mitigate the risks, such as temporarily putting an IT system out of operation, but need to consult them with other stakeholders. We propose a decision support system that uses a mission decomposition model representing the organization's functional and security requirements on its IT infrastructure. Based on the cybersecurity state assessment, that is, discovery of vulnerabilities and attacker's position, the decision support system calculates the resilience metrics for each IT infrastructure's configuration, that is, how likely are they to not be disrupted. The calculation is enabled by two novel formal models, Privilege‐Exploit Attack Graph and Bayesian Privilege Attack Graph, which reduce complex attack graphs into a comprehensible bipartite graph. Moreover, they illustrate the impact of exploiting the vulnerabilities and attackers gaining the privileges. The system recommends the most resilient mission configurations that are comprehensible to both cybersecurity experts and non‐technical stakeholders, who may then choose which configuration to apply. Our approach is illustrated in a case study of a real‐world medical information system.