Reviewerly/exHarmony huggingface dataset:
Research on animal image classification based on transfer learning

Training a convolutional neural network requires a large number of sample data and a large amount of computing power. In some practical application scenarios, there may be difficulties in sample data collection and complex network model construction. To improve the classification accuracy and fitting speed of the convolutional neural network, a transfer learning classification method for an animal image is proposed. The fully connected layer of the pre-trained ResNet18 network is modified, and the eight animals in the animal-10 dataset on Kaggle are used to fine-tune the network model. The best classification accuracy of the obtained network model for a single animal is 97%, and the classification accuracy for all animals is 92%. Compared with the model that did not use transfer learning training, this kind of transfer learning network model has a great improvement in accuracy and fitting speed.
Absolute phase correction for phase-measuring profilometry based on color-coded fringes

A phase correction method that utilizes fringe color-coding in phase-measuring profilometry is proposed. Original and additional fringes are encoded into the R and B channels, respectively. The R + B channel patterns are projected onto an object, captured using a camera, where the R and B channels are extracted. Then, phase shifts obtained from the R and B channels are utilized for absolute phase calculation and correction. Simulations and experiments demonstrate that this method can correct edge order errors and avoid increasing the fringe number. Experiments performed using the R + G, G + B, and R + B channels demonstrate that R + B exhibits the least color crosstalk.
SQL Query from Portuguese Language Using Natural Language Processing

Nowadays, there is a great challenge to make information technology transparent so the end-user can have a good user experience. We are proposing a Natural Language Interface Model in which the user introduces the phrase through a text box in the local natural language, preferably in the Portuguese language. The system will read these words and separate them into a list of tokens, which will be analyzed, and if unnecessary words are found, they will be removed. The system will make a combination of the user’s request. In the end, it will arrive until the generation of SQL Query and basics MongoDB Query as well ready to be executed directly in the Database. The model has an average accuracy of 70.27%. A morphological analysis will understand the meaning of the user’s sentence ending in a friendly interface.
Visible Feature Guidance for Crowd Pedestrian Detection

Heavy occlusion and dense gathering in crowd scene make pedestrian detection become a challenging problem, because it’s difficult to guess a precise full bounding box according to the invisible human part. To crack this nut, we propose a mechanism called Visible Feature Guidance (VFG) for both training and inference. During training, we adopt visible feature to regress the simultaneous outputs of visible bounding box and full bounding box. Then we perform NMS only on visible bounding boxes to achieve the best fitting full box in inference. This manner can alleviate the incapable influence brought by NMS in crowd scene and make full bounding box more precisely. Furthermore, in order to ease feature association in the post application process, such as pedestrian tracking, we apply Hungarian algorithm to associate parts for a human instance. Our proposed method can stably bring about 2–3% improvements in mAP and $$\text {AP}_{50}$$ for both two-stage and one-stage detector. It’s also more effective for $$\text {MR}^{-2}$$ especially with the stricter IoU. Experiments on Crowdhuman, Cityperson, Caltech and KITTI datasets show that visible feature guidance can help detector achieve promisingly better performances. Moreover, parts association produces a strong benchmark on Crowdhuman for the vision community.
Constructing Clustering Transformations

Clustering is one of the fundamental tasks in data analytics and machine learning. In many situations, different clusterings of the same data set become relevant. For example, different algorithms for the same clustering task may return dramatically different solutions. We are interested in applications in which one clustering has to be transformed into another, e.g., when a gradual transition from an old solution to a new one is required. In this paper, we devise methods for constructing such a transition based on linear programming and network theory. We use a so-called clustering-difference graph to model the desired transformation and provide methods for decomposing the graph into a sequence of elementary moves that accomplishes the transformation. These moves are equivalent to the edge directions, or circuits, of the underlying partition polytopes. Therefore, in addition to a conceptually new metric for measuring the distance between clusterings, we provide new bounds on the circuit diameter of these partition polytopes.
Efficient computation of counterfactual explanations of LVQ models

The increasing use of machine learning in practice and legal regulations like EU's GDPR cause the necessity to be able to explain the prediction and behavior of machine learning models. A prominent example of particularly intuitive explanations of AI models in the context of decision making are counterfactual explanations. Yet, it is still an open research problem how to efficiently compute counterfactual explanations for many models. We investigate how to efficiently compute counterfactual explanations for an important class of models, prototype-based classifiers such as learning vector quantization models. In particular, we derive specific convex and non-convex programs depending on the used metric.
What they do when in doubt: a study of inductive biases in seq2seq learners

Sequence-to-sequence (seq2seq) learners are widely used, but we still have only limited knowledge about what inductive biases shape the way they generalize. We address that by investigating how popular seq2seq learners generalize in tasks that have high ambiguity in the training data. We use four new tasks to study learners' preferences for memorization, arithmetic, hierarchical, and compositional reasoning. Further, we connect to Solomonoff's theory of induction and propose to use description length as a principled and sensitive measure of inductive biases. In our experimental study, we find that LSTM-based learners can learn to perform counting, addition, and multiplication by a constant from a single training example. Furthermore, Transformer and LSTM-based learners show a bias toward the hierarchical induction over the linear one, while CNN-based learners prefer the opposite. The latter also show a bias toward a compositional generalization over memorization. Finally, across all our experiments, description length proved to be a sensitive measure of inductive biases.
DNI and slant path transmittance for the solar resource of tower thermal solar plants: The validation of the ASoRA method and impact in exploiting a global data set

The ASoRA method consists in estimating the solar resource in tower concentrated solar plants by exploiting global data sets. The solar resource parameters are not only the Direct Normal Irradiance (DNI) but also the slant path transmittance (Tsp). The column to surface-level conversion process was validated by comparing the retrieved aerosol extinction coefficient (AEC) to independent measurements performed by a ground-based nephelometer. The Aerosol Robotic NETwork (AERONET) provided column-integrated aerosol extinction properties. The European Centre for Medium-Range Weather Forecasts Reanalysis Interim boundary layer height was used to reproduce the seasonal dependence of the aerosol vertical profile. At the daily resolution, ASoRA reproduced observed AEC with a relative root mean square difference (rRMSD) of 33% and a correlation coefficient of 0.90. The spectral extrapolation from monochromatic measurements to the solar broadband was validated by comparing computations and observations of DNI. ASoRA was also able to reproduce the hourly variability of DNI observed by a pyrheliometer with a RMSD of 38 W/m2, a rRMSD of 4.6%, and a correlation coefficient larger than 0.98. Despite larger uncertainties caused by using Modern-Era Retrospective analysis for Research and Applications (version 2) instead of AERONET, the annual average of Tsp was underestimated by only 0.01, and the annual average of the irradiance loss in the slant path by only 4 W/m2. Largest impact on Tsp comes from the parameterisation of the aerosol vertical profile, and the annual average of Tsp was 0.945 at Ouarzazate (Morocco) in 2014, with an estimated uncertainty of 0.030.
Evolution and current development trends of the Russian Federation’s higher education system

espanolEl objetivo del estudio es sustanciar las etapas de desarrollo evolutivo y las tendencias actuales en el sistema de educacion superior en la Federacion de Rusia a traves de metodos cientificos generales de cognicion, evaluacion cuantitativa y cualitativa, metodos de analisis comparativo y factorial, analisis y sintesis, agrupacion y comparacion, El metodo de generalizacion logica. Como resultado, la creciente globalizacion cambia el enfoque de la educacion. Los autores llegaron a la conclusion de que es necesario tener en cuenta el impacto de los factores externos e intersistemicos en el desarrollo de las organizaciones de educacion superior en la Federacion de Rusia. EnglishThe aim of the study is to substantiate evolutionary development stages and current trends in the higher education system in the Russian Federation via general scientific methods of cognition, quantitative and qualitative assessment, methods of comparative and factor analysis, analysis and synthesis, grouping and comparison, the method of logical generalization. As a result, increasing globalization changes the approach to education. The authors came to the conclusion that it is necessary to take into account the impact of external and inter systemic factors on the development of higher education organizations in the Russian Federation.
Clustering by quantum annealing on the three-level quantum elements qutrits

Clustering is grouping of data by the proximity of some properties. We report on the possibility of increasing the efficiency of clustering of points in a plane using artificial quantum neural networks after the replacement of the two-level neurons called qubits represented by the spins S = 1/2 by the three-level neurons called qutrits represented by the spins S = 1. The problem has been solved by the slow adiabatic change of the Hamiltonian in time. The methods for controlling a qutrit system using projection operators have been developed and the numerical simulation has been performed. The Hamiltonians for two well-known clustering methods, one-hot encoding and k-means, have been built. The first method has been used to partition a set of six points into three or two clusters and the second method, to partition a set of nine points into three clusters and seven points into four clusters. The simulation has shown that the clustering problem can be effectively solved on qutrits represented by the spins S = 1. The advantages of clustering on qutrits over that on qubits have been demonstrated. In particular, the number of qutrits required to represent $$N$$ data points is smaller than the number of qubits by a factor of $$\log_{2} N/\log_{3} N$$ . For qutrits, the simplest is to partition the data points into three clusters rather than two ones. At the data partition into more than three clusters, it has been proposed to number the clusters by the numbers of states of the corresponding multi-spin subsystems, instead of using the numbers of individual spins. This reduces even more the number of qutrits ( $$N\log_{3} K$$ instead of $$NK$$ ) required to implement the algorithm.
Design and Development of a Carbon Footprint Calculation Model for Universiti Tenaga Nasional

Carbon footprint is the amount of a greenhouse gas (GHG) produced as a result of human activities, usually expressed in equivalent kilograms of carbon dioxide (CO2). As the amount of greenhouse gases emission increases, the global temperature increases. In the context of a global awareness of the climate change, carbon footprint has recently become extensively calculated and ways to reduce it are proposed. The purpose of this research is to calculate the amount of carbon footprint discharged by students in College of Engineering (COE), Universiti Tenaga Nasional (UNITEN). From this research a model to calculate the carbon dioxide emission released from the activities done by the COE students particularly, is developed. Literature reviews are done leading to an explanation of what emission categories should be presented in a CO2 calculation for a university. This includes emission sources in each buildings, activities and services in COE particularly. Conducting survey is one of the methods to get the data from the students and staffs directly. From this data, the calculation is done and tabulated in the Excel template. From this template model, the carbon footprint for each or total students of COE can be known. Referring to this data, CO2 reduction strategy in COE particularly and UNITEN generally can be proposed.
Exploring single-path Architecture Search ranking correlations

Recently presented benchmarks for Neural Architecture Search (NAS) provide the results of training thousands of different architectures in a specific search space, thus enabling the fair and rapid comparison of different methods. Based on these results, we quantify the ranking correlations of single-path architecture search methods in different search space subsets and under several training variations; studying their impact on the expected search results. The experiments support the few-shot approach and Linear Transformers, provide evidence against disabling cell topology sharing during the training phase or using strong regularization in the NAS-Bench-201 search space, and show the necessity of further research regarding super-network size and path sampling strategies.
Exploration &amp; Research on Distance Education System Based on Blockchain Technology

Abstract In this paper, it mainly explores and studies the distance education system based on blockchain technology according to the characteristics of blockchain technology, such as “decentralization, de-trust, non-tampering and traceability”, etc. First of all, it designed a multi-layer logical structure; Second, it proposed an improved DPoS consensus mechanism; Third, it analyzed the generation steps of the blockchain in the system and the functional process of the consensus mechanism. As a result, by using the distance education system based on blockchain technology, the distance education centers distributed all over China can not only strengthen the sharing of teaching resources and improve the management efficiency, but also better improve the safety and transparency of their teaching, scientific research and management.
An Efficient CRT-Based Bit-Parallel Multiplier for Special Pentanomials

The Chinese remainder theorem (CRT)-based multiplier is a new type of hybrid bit-parallel multiplier, which can achieve nearly the same time complexity compared with the fastest multiplier known to date with reduced space complexity. However, the current CRT-based multipliers are only applicable to trinomials. In this article, we propose an efficient CRT-based bit-parallel multiplier for a special type of pentanomial <inline-formula><tex-math notation="LaTeX">$x^m+x^{m-k}+x^{m-2k}+x^{m-3k}+1, 5k+1&lt;m\leq 11k$</tex-math></inline-formula> . Through transforming the non-constant part <inline-formula><tex-math notation="LaTeX">$x^m+x^{m-k}+x^{m-2k}+x^{m-3k}$</tex-math></inline-formula> into a binomial, we can obtain relatively simpler quotient and remainder computations, which lead to faster implementation with reduced space complexity compared with classic quadratic multipliers for the same pentanomials. Moreover, for some <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> , our proposal can match the fastest multipliers for irreducible Type I, Type II, and Type C.1 pentanomials of the same degree, but space complexities are roughly reduced by 8 percent.
Bayesian Evidential Deep Learning with PAC Regularization

We propose a novel method for closed-form predictive distribution modeling with neural nets. In quantifying prediction uncertainty, we build on Evidential Deep Learning, which has been impactful as being both simple to implement and giving closed-form access to predictive uncertainty. We employ it to model aleatoric uncertainty and extend it to account also for epistemic uncertainty by converting it to a Bayesian Neural Net. While extending its uncertainty quantification capabilities, we maintain its analytically accessible predictive distribution model by performing progressive moment matching for the first time for approximate weight marginalization. The eventual model introduces a prohibitively large number of hyperparameters for stable training. We overcome this drawback by deriving a vacuous PAC bound that comprises the marginal likelihood of the predictor and a complexity penalty. We observe on regression, classification, and out-of-domain detection benchmarks that our method improves model fit and uncertainty quantification.
A SURVEY OF VARIANTS OF ROUND ROBIN CPU SCHEDULING ALGORITHMS

Quite a number of scheduling algorithms have been implemented in the past, including First Come First Served (FCFS), Shortest Job First (SJF), Priority and Round Robin (RR). However, RR seems better than others because of its impartiality during the usage of its quantum time. Despite this, there is a big challenge with respect to the quantum time to use. This is because when the quantum time is too large, it leads to FCFS, and if the quantum time is too short, it increases the number of switches from the processes. As a result of this, this paper provides a descriptive review of various algorithms that have been implemented in the past 10 years, for various quantum time in order to optimize the performance of CPU utilization. This attempt will open more research areas for researchers, serve as a reference source and articulate various algorithms that have been used in the previous years – and as such, the paper will serve as a guide for future work. This research work further suggests novel hybridization and ensemble of two or more techniques so as to improve CPU performance by decreasing the number of context switch, turnaround time, waiting time and response time and in overall increasing the throughput and CPU utilization.
A method of constructing syllable level Tibetan text classification corpus

Corpus serves as an indispensable ingredient for statistical NLP research and real-world applications, therefore corpus construction method has a direct impact on various downstream tasks. This paper proposes a method to construct Tibetan text classification corpus based on a syllable-level processing technique which we refer as TC_TCCNL. Empirical evidence indicates that the algorithm is able to produce a promising performance, which may lay a starting point for research on Tibetan text classification in the future.
Analysis of the Optimal Application of Blockchain-Based Smart Lockers in the Logistics Industry Based on FFD-SAGA and Grey Decision-Making

Blockchain technology has been applied to logistics tracking, but it is not cost-effective. The development of smart lockers has solved the problem of repeated distribution to improve logistics efficiency, thereby becoming a solution with convenience and privacy compared to the in-store purchase and pickup alternative. This study prioritized the key factors of smart lockers using a simulated annealing–genetic algorithm by fractional factorial design (FFD-SAGA) and grey relational analysis, and investigated the main users of smart lockers by grey multiple attribute decision analysis. The results show that the Web application programming interface (API) concatenation and money flow provider are the key success factors of smart lockers, and office workers are the main users of the lockers. Hence, how to better meet the needs of office workers will be an issue of concern for service providers.
Simple, Scalable, and Stable Variational Deep Clustering

Deep clustering (DC) has become the state-of-the-art for unsupervised clustering. In principle, DC represents a variety of unsupervised methods that jointly learn the underlying clusters and the latent representation directly from unstructured datasets. However, DC methods are generally poorly applied due to high operational costs, low scalability, and unstable results. In this paper, we first evaluate several popular DC variants in the context of industrial applicability using eight empirical criteria. We then choose to focus on variational deep clustering (VDC) methods, since they mostly meet those criteria except for simplicity, scalability, and stability. To address these three unmet criteria, we introduce four generic algorithmic improvements: initial \(\gamma \)-training, periodic \(\beta \)-annealing, mini-batch GMM (Gaussian mixture model) initialization, and inverse min-max transform. We also propose a novel clustering algorithm S3VDC (simple, scalable, and stable VDC) that incorporates all those improvements. Our experiments show that S3VDC outperforms the state-of-the-art on both benchmark tasks and a large unstructured industrial dataset without any ground truth label. In addition, we analytically evaluate the usability and interpretability of S3VDC.
Analysis of the Application of Virtual Simulation Software in Food Testing Technology Course with Atomic Absorption Spectrum<sup>1</sup>-- Taking the Example of Determining Calcium in Drinking Water by Atomic Absorption Spectrometry

Abstract Atomic absorption spectrometry is the core content of instrumental analysis and also one of the most commonly used skills in modern detection positions. It can improve the teaching effect to train students on this skill with virtual simulation software. In this project, the experimental group (virtual simulation teaching) and the control group (traditional teaching) were adopted to make a check test in the teaching project of determining calcium in drinking water by atomic absorption spectroscopy, with the purpose of introducing information teaching means and improving teaching quality with simulation software.
Comparative analysis on CNN and wavelet features based technology for medical image classification

The paper below introduces a comparative analysis between two image classification technologies for the purposes of medical breast cancer. The first technology is based on the concept of convolutional neural networks, and the second one on the use of feature vectors generated by wavelet transform and the use of Euclidean distance to determine similarity. Experiments are performed using both technologies and a specialized test database. Based on the obtained results, the classification accuracy, precision and recall measures are determined. The obtained values are compared, analyzed and summarized, as the data show comparable values of classification in the technologies presented in the report.
Challenges for the Future of Education brought by the Pandemic: The Coppead Case

ABSTRACT This teaching case describes the process experienced by Coppead, one of the main graduate schools in business in Brazil, to adapt to e-learning. The change is driven by the social isolation adopted in the country due to the COVID-19 pandemic. The case reports on the decisions that the faculty collegiate, led by the director Elaine Tavares, has needed to take to circumvent the quarantine and ensure teaching continuity at Master, Doctoral, and Executive MBA levels. The case discusses the main challenges during the transition, but most importantly highlights the strategic opportunities that this shift has offered for innovation in the coming years.