Reviewerly/exHarmony huggingface dataset:
Single-shot phase-shifting interferometry using deep learning

Multiple equal phase-shifted interferograms are the key for accurate phase measurement in phase-shifting interferometry (PSI). Here, we present single-shot PSI technique assisted with deep learning to extract the phase map of biological specimens.
Recasting Canons of Interpretation and Construction into "Canonical" Queries

This Article advocates recasting the canons of construction into neutral queries rather than presumptions or directives of meaning. Such an approach would not only rectify problems with the canons discussed in this Article. It would also provide lawyers with highly useful "checklists" of semantic questions lawyers might otherwise overlook when interpreting and construing meaning in contexts of both private law (e.g., contracts) and public law (e.g., constitutional provisions and statutes). As a part of such advocacy, this Article explores in detail the following "canonical" queries and sub-queries (and the canons of construction they replace where applicable): the applicable text query, the plain meaning query, the ambiguity sub-query, the vagueness sub-query, the indeterminacy sub-query, the ordinary meaning query, the technical and term of art query, the grammar query, the punctuation query, the further meaning query, and the irony/non-literal meaning query. This Article also includes a detailed Appendix outlining further needed queries to be addressed in future articles. These include the ejusdem generis query, the noscitur a sociis query, the expressio unius query, the antecedent/subsequent query (rejecting the rule of the last antecedent), the anaphora query, the whole text query, the surplusage query, the absurdity query, the exercise of power query (rejecting general construction against the drafter), and queries of meaning through time. Additionally, to help direct proper application of the queries, this Article also explores the distinction between interpretation and construction.
Using a Loyalty Reward Token Program to Reduce the Cost of Solar Desalinated Water.

Desalinated water is often created using fuels that contribute to atmospheric CO2. These decisions of fuel and technical methodology are made using a cost benefit analysis which often ignores externalities. In such situations, Solar and clean solutions may lose out.Loyalty and reward mechanisms using tokens or credits for consumers are often used to create value for marketing consumer goods, etc.A lesser known fact is that the economics of loyalty or reward mechanisms can function like currencies with similar economic value properties emerging.Currencies function like social protocols for the transfer of value among participants in an economic network. As such, the value of a currency emerges as a direct function of the number of participants. This emergent value effect has been observed in 50 of the world’s largest currencies, gold, and even Bitcoin. The cumulative economic effect of this currency phenomenon is valued at $16 trillion USD. The effect is emergent and ranges from $500-$5,000 per participant using a value transfer protocol be it currency, token or credit.The potential to apply the positive economic externality effects of currency networks to help offset the negative externalities associated with carbon pollution are worth studying and applying.The users of desalinated water are a collective economic network. As such they can create economic value which can be applied to reduce the cost of solar desalinated water, making solar a more attractive option than the use of fossil fuels in the desalination process. The proposed mechanism for this is a Token or reward scheme which distributes a portion of tokens to the users of said water and the purchasing agent or utility of said water.
Parameter Filtering Method with Circular Update for Deep Neural Network Parallel Training

This paper proposes a parameter filtering circular update algorithm, which provides an efficient solution for parallel training of deep neural networks as the scale of deep neural networks continues to expand. The parameter filtering circular update algorithm filters a large number of redundant unimportant parameters in the deep neural network training process by setting the importance threshold, and in an iteration process, the parameters of each node are synchronized through the circular update algorithm. The parameter filtering circular update algorithm compresses the training parameters of the DNN, and greatly reduces the overhead caused by the communication between each node and the parameter server in the synchronous parallel training. Experiments show that the parameter filtering circular update algorithm can improve the parallel training speed of the DNNs without losing the prediction accuracy, and the GPU utilization rate on each computing node is higher than that of the general synchronous training method.
A Logic and Computation for Popper’s Conditional Probabilities

A Popper function is one that gives a conditional probability of propositional formulae. This paper gives a finite set of axioms that defines the set of Popper functions in many-sorted monadic second-order logic, and proves the decidability of the validity problem for a practically important first-order fragment of the second-order language with respect to a set of Popper functions. Upon these logical foundations, we propose, with results on their time complexity, two algorithms that compute the range of values that designated conditional probabilities can take under given constraints.
A Doubly Graduated Method for Inference in Markov Random Field

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 2 December 2020Accepted: 17 June 2021Published online: 20 September 2021Keywordsmaximum a posteriori, Markov random field, Gaussian smoothing, continuous relaxationAMS Subject Headings62M40, 68U10Publication DataISSN (online): 1936-4954Publisher: Society for Industrial and Applied MathematicsCODEN: sjisbi
Robust and Efficient Bio-Inspired Data-Sampling Prototype for Time-Series Analysis

Data acquisition is crucial for efficient AI systems. We present a bio-inspired prototype implementation of discrepancy-based adaptive threshold-based sampling on a low-cost microcontroller. We show measurement results demonstrating that an adaptive threshold-based sampling approach can be performed only using onboard components of the microcontroller. To measure the sampling precision, we used sinusoidal signals output by a waveform generator and compared the signals after reconstruction to exact signals with the set parameters. These measurements show that, even with such low-cost components, discrepancy-based adaptive threshold-based sampling can be performed with high precision.
Energy Conservation in Infinitely Wide Neural-Networks

A three-layered neural-network (NN), which consists of an input layer, a wide hidden layer and an output layer, has three types of parameters. Two of them are pre-neuronal, namely, thresholds and weights to be applied to input data. The rest is post-neuronal weights to be applied after activation. The current paper consists of the following two parts. First, we consider three types of stochastic processes. They are constructed by summing up each of parameters over all neurons at each epoch, respectively. The neuron number will be regarded as another time different to epochs. In the wide neural-network with a neural-tangent-kernel- (NTK-) parametrization, it is well known that these parameters are hardly varied from their initial values during learning. We show that, however, the stochastic process associated with the post-neuronal parameters is actually varied during the learning while the stochastic processes associated with the pre-neuronal parameters are not. By our result, we can distinguish the type of parameters by focusing on those stochastic processes. Second, we show that the variance (sort of “energy”) of the parameters in the infinitely wide neural-network is conserved during the learning, and thus it gives a conserved quantity in learning.
A Systematic Approach for a Secure Authentication System

Authentication is a process of verifying the credibility of a user who is trying to access classified or confidential information. There is a vast unfold in the number of internet users, and the demand for IoT devices, cloud services has been increasing; it is now essential more than ever to protect the data hosted on the internet. So, the authentication process cannot be relied on single-factor static authentication methods to verify the user credentials. All devices in the market are not equipped with biometric systems, so a form of multi-factor authentication which is independent of biometrics needs to be adopted for a secure authentication system. This paper portraits a systematic architecture to verify user credentials using specific parameters, trying to unfold patterns using machine learning algorithms based on user's past login records, thus trying to provide a safer and secure authentication process for the users.
Construction of Multiple Linear Regression Prediction Model of PRETCO-A Scores and Its Positive Backwash Effect on Teaching

PRETCO-A is a standardized English proficiency test set up to evaluate the English application ability of the students in higher vocational college. In order to improve the passing rate, a multiple linear regression prediction model is constructed in this paper. A significance test was first performed on the regression model and the regression coefficient to verify a high correlation among the variables. The confirmed model was then put into application to predict the students' scores and identify the students who may fail the exam, leading to targeted tutoring assistance given to those students in advance. Finally, 60 students with predicted scores lower than 60 points were selected as research samples, and randomly divided into the control group and the experimental group, 30 students in each group. Finally, the experimental group students were given 40 teaching hours of precision assistance and targeted training, while the control group did not engage in any teaching intervention. The experimental results indicate that the pass rate of experimental group is 20% higher than the control group, which means the backwash effect of the test prediction is positive. The prediction model is proved to be scientific and reliable for teaching.
How Missile Engineering is Taking Product Line Engineering to the Extreme at Raytheon

Abstract This paper describes a new approach to designing and building missiles by Raytheon Missile and Defense Systems. Key aspects of the approach include (1) modular common components connected through identified standards, (2) modular open systems approaches and standards based interfaces, (3) Feature‐based Product Line Engineering (FbPLE) practices for identifying commonality and managing variation, and (4) implementing digital transformation through digital engineering capabilities to begin a missile's digital twin. Much more ambitious than simply reusing existing component designs from previously built missiles, this approach involves automatic generation, exploration, and pruning of an automatically generated trade space of possible missile designs that satisfy a given set of requirements. The goal is to radically lower development and production costs by rapidly settling on a viable design that can be taken to design validation, then complete design and production, all in a digital ecosystem. The paper focuses on the FbPLE aspect of the engineering approach and shows how its technology is used to create and manage the trade space, and then create a digital twin of a chosen design.
Webpage Categorization Using Deep Learning

The Web is the main source of information for almost all human activities. Webpages are growing exponentially and becoming more sophisticated, presenting a serious challenge to search engines, recommender systems and Web directories. To organize this complex and vast content, webpage classification is the basic technique, usually based on textual analysis of HTML code. However, the visual appearance of a webpage plays an important role and can be a new alternative to distinguish between categories. In a previous paper, we implemented a Deep Learning model to classify webpages into certain categories using only screenshots, where the results demonstrated a very hard multi-class problem. In the present work, we address the same problem under a new approach, applying One vs. Rest strategy, using regularization techniques, redefining the prediction mechanism, and generating a more reliable dataset. Thus, we have obtained interesting improvements in accuracy and reduction of overfitting. Also, we have developed an online system for classifying webpages that can help designers, website owners, webmasters, and users in general.
Performance Evaluation of Entropy Based LBP for Face Recognition

To improve the accuracy rate of face recognition, a local entropy-based adaptive-weight Local Binary Pattern (LBP) is suggested. A comparative analysis of elliptical and rectangular cropping has been integrated into the methodology for analyzing the recognition rate in different cropping scenarios. Local Binary Pattern is applied on each block to construct local histogram and given as the input K-Nearest Neighbor (K-NN) classifier. A local entropy principle is used here to assign weights to classifier outputs from individual sub-blocks to give more weightage to relevant areas in the face image. Maximum accuracy rates up to 84.1% is achieved in FERET database using rectangular cropping and 76% accuracy is achieved using elliptical cropping with K-NN classifier.
Design Thinking in the Improvement of Usability of Physics Mobile Educational Application

This article describes the creation of a mobile application following the design thinking (DT) approach and its assessment of usability. DT is thought of as a methodology to create innovative solutions to the needs of end-users through well-established five stages. We worked with high school students, a pedagogical team, and a group of undergraduate students in software engineering. In each of the DT stages, diagrams, maps, matrices, groupings, prototypes, and simulations were elaborated that allowed us to empathize with the user, define, create, and test the creative ideas generated throughout the process (development process). To evaluate the usability of the application, the USE questionnaire (Usefulness, Satisfaction, and Ease of use) was applied to a group of students from the education career. The results indicate that it has been possible to develop an educational mobile application with a good level of usability that meets the needs of users.
The Golden Snitch: A Byzantine Fault Tolerant Protocol with Activity

The increasing popularity of blockchain-based cryptocurrencies has revitalized the search for efficient Byzantine fault-tolerant (BFT) protocols. Many existing BFT protocols can achieve good performance in fault-free cases but suffer severe performance degradation when faults occur. This is also a problem with DiemBFT. To mitigate performance attacks in DiemBFT, we present an improved BFT protocol with optimal liveness called the Golden Snitch. The core idea is to introduce unbiased randomness in leader selection and improve the voting mechanism to protect honest leaders from being dragged down by the previous leader. The performance of the Golden Snitch is evaluated through experiments, turning out it outperforms DiemBFT in the presence of faults.
Development a new conjugate gradient algorithm for improvement feedforward artificial neural networks for predicting the numbers of the Iraqi

Abstract a conjugate gradient method has been proposed for use in the training of Artificial Neural Networks with the feedforward technique. The proposed method demonstrated that the proposed CG method is accurate in predicting the numbers of the Iraqi population, the Our numerical results where very close to the exact solution of Tomas Malthose Model.
Research on College English Online Teaching Design Based on Self-built Corpus

Based on the self-developed English language corpus and a series of College English micro courses, this study constructs College English online teaching platform, focus on a innovative design of college English online courses and try to test its teaching effectiveness. The results show that the corpus-based College English online curriculum design changes the traditional classroom teaching mode and teaching evaluation mode, promoting students’ English level and autonomous learning ability, providing practical basis for the exploration of College English online teaching mode.
On Local Convergence of Stochastic Global Optimization Algorithms

Abstract In engineering optimization with continuous variables, the use of Stochastic Global Optimization (SGO) algorithms is popular due to the easy availability of codes. All algorithms have a global and local search character, where the global behaviour tries to avoid getting trapped in local optima and the local behaviour intends to reach the lowest objective function values. As the algorithm parameter set includes a final convergence criterion, the algorithm might be running for a while around a reached minimum point. Our question deals with the local search behaviour after the algorithm reached the final stage. How fast do practical SGO algorithms actually converge to the minimum point? To investigate this question, we run implementations of well known SGO algorithms in a final local phase stage.
Saliency Detection in a Virtual Driving Environment for Autonomous Vehicle Behavior Improvement

To make the best decisions in real-world situations, autonomous vehicles require learning algorithms that process a large number of labeled images. This paper aims to compare the automatically generated saliency maps with attention maps obtained with an eye-tracking device in order to provide automated labeling of images for the learning algorithm. To simulate traffic scenarios, we are using a virtual driving environment with a motion platform and an eye-tracking device for identifying the driver’s attention. The saliency maps are generated by post-processing the driver’s view provided by the front camera.
Feature Fusion Network for Low-Light Image Enhancement

Abstract With the popularity of electronic cameras, we can get pictures under various lighting conditions in our daily life. The images which obtained in low illumination environment will lose contrast, colour, and content details which will bring obstacles for understanding the image. To tackle this problem, a method is proposed in this paper to tackle low-light image enhancement. This method fuses the high level and the low level feature, uses an attention module to model the context information and selects the critical feature to supplemented the high level feature to reconstruct the enhanced image. A number of experiments demonstrate that our method can get images with improved and reduced noise.
Human Activity Recognition with Capsule Networks

Human activity recognition is a challenging problem, where deep learning methods are showing to be very efficient. In this paper we propose the use of capsule networks. This type of networks have proved to generalize better to novel viewpoints than convolutional neural networks. We show that the use of capsule networks into a straightforward architecture, between a convolutional preprocessing stage to extract visual features and a header for carrying out the task, is able to attain competitive results with spatio-temporal data without the use of any kind of recurrent neural network. Moreover, an analysis of the obtained results shows that our architecture is capable of learning the properties that encode the spatio-temporal dynamics of the movements that characterize each activity.
Shared Memory Based Parallel Enactment of a Hybrid Authentication Technique

The paper deals with a hybrid authentication through shared memory based parallel algorithm. Recursive prime non-prime substitution algorithm (RPSP) encrypts the secret data and converts it into a cipher text. The cipher secret is then embedded into the cover image using a chaotic LSB steganographic technique. The shared memory based parallel execution of the technique ensured low time complexity. Parallel execution in a multi-core environment has been implemented and the scalability of the technique is demonstrated with enhanced efficiency for authentication.
Photo-Voltaic Power Daily Statistical Predictions Using PDE Models of Stepwise Evolved Polynomial Networks with the Sum PDE Partition and L-transform Substitution

Computational methods based on Artificial Intelligence (AI) can convert or post-process data produced by Numerical Weather Prediction (NWP) systems to predict Photo-Voltaic (PV) power in consideration of a plant specific situation. Their statistical models, developed with historical data series, are more precise if they rely on the latest weather observations and PV measurements. NWP models are usually run every 6 h with the prognoses delayed a few hours. Moreover, their accuracy is mostly inadequate for PV plant actual operation. Differential Polynomial Neural Network (D-PNN) is a novel biologically inspired neuro-computing technique which can model complex patterns without reducing significantly the data dimensionality as standard regression or soft-computing does. D-PNN combines appropriate 2-inputs to decompose the n-variable Partial Differential Equation (PDE), being able to describe the atmospheric dynamics, into a set of particular sub-PDEs in its nodes. The selected 2-variable composed PDEs are converted using adapted procedures of Operational Calculus (OC) to obtain the Laplace images of unknown node functions, which are inverse L-transformed to obtain the originals. D-PNN produces applicable sum PDE components in its nodes to extend step by step its composite models towards the optima. The compared AI models are developed with spatial historical data from the estimated optimal daily training periods to process the last day input data series and predict Clear Sky Index (CSI) at the corresponding 24-h horizon.
A Study of Integration of Information Technology and Teaching in Aviation Physical Education

As well-rounded education has been extensively promoted, the physical constitution receives an enormous amount of attention. In aviation colleges, students are expected to have a strong constitution so that they can serve the country in the spirit of "aeronautical patriotism". In the new era, physical education (PE) instructors should focus on the integration of information technology and teaching in aviation physical education. In view of this, this study investigates measures that can strengthen the integration of information technology and teaching in aviation physical education based on the description of the characteristics of the integration of information technology and teaching in aviation physical education and the analysis of the status quo of the integration of information technology and teaching in aviation physical education.