Reviewerly/exHarmony huggingface dataset:
Recognition of Facial Patterns Using Surface Electromyography—A Preliminary Study

Facial expressions are considered as a universal language method with non-verbal communication between humans. The surface Electromyography (sEMG) is a technique to acquire and processing signals obtained from the electrical activity of the contraction of voluntary muscles. From that, this work presents a preliminary study of recognition of facial expression obtained from sEMG signals. Signals from 4 subjects were acquired for six basic facial expressions that expresses the following emotions: happiness, surprise, sadness, angry, disgust, and fear. The sEMG signals were acquired from two muscles: zygomaticus major and corrugator of the eyebrow. Three feature sets for sEMG and four different classifiers were evaluated. The best combination was found with the TD9 feature set and SVM with radial basis function kernel classifier, reaching 79.8% of accuracy. Among the expressions, the best recognition was happiness, with individual accuracy of 99.2%. The most difficult expression to classify was the expression of disgust.
Maximum Projection Distance Classifier for High Dimension and Low Sample Size Problems

To collect and label sample data is costly and time-consuming in data science, while features used to characterize sample data are relatively accessible to us with advances in information technology. The high dimension and low sample size (HDLSS) problems are therefore very common in data mining and machine and learning. Since sample points in high-dimensional space have almost identical distances between them, some traditional classifiers, for instance, support vector classifier (SVC), often give poor classification accuracy. In this paper, we propose a maximum projection distance classifier (MPDC) in the SVC framework to effectively deal with aforementioned problems. The constraints of maximizing the projection distances of between-class and within-class means are integrated into the SVC model in order to obtained the optimal weight vector. Compared with SVC and population-guided large margin classifier (PGLMC) on four real HDLSS microarray datasets, the MPDC model achieves the better predictive accuracy and lower classification errors than other classifiers.
Prediction of Ecotourism Population Based on Exponential Smoothing and ARIMA Mixed Model

While material and spiritual wealth is greatly en-riched, a series of existential crises such as resource, environmental and ecological problems have awakened human environmental awareness, and the green movement and green consumption have swept the world. Thus, ecotourism has also become a hot topic in academia and industry, attracting the attention of many scholars. In this paper, we will propose a hybrid model based on Exponential smoothing and ARIMA, and use the ARIMA model with the proposed model to predict the number of tourists and the total amount of tourism consumption in Guangxi. Finally, the performance of each model will be evaluated using regression predictors such as ME, RSME, and MAE. Through the results of this paper, we will make our suggestions for the future development of eco-tourism in Guangxi and further promote the healthy development of eco-tourism in Guangxi.
3D Visualization of the 2400-Year-Old Garments of Salt Man 4 from Chehrābād, Iran

This article presents a digital reconstruction of Salt man 4 (405–380 BCE), a salt miner’s mummy found in the Douzlākh salt mine (Chehrābād, northern Iran). The mummy and its costume are relatively well preserved because the salt mine offers good preservation conditions for organic finds. Salt man 4 is unique in that his garments are a complete set of worker’s gear. This offers an exceptional opportunity to study garments worn during heavy physical work in ancient times. An international research team has been conducting an in-depth study of the mummy since 2004. The results include measurements, photographs, MRI (magnetic resonance imaging) scans and descriptions, which taken together provide a good basis for a digital reconstruction. This article describes technical features of the reconstruction process and challenges that were solved to reproduce the garments. The reconstruction opens new ways of studying the costume in virtual reality using computer graphics and computer-aided design tools to obtain further insights into ancient mining clothes within the broader area of digital humanities.
Factoring Gaussian Integers: A Quantum Algorithm

In this paper, we developed a quantum algorithm for factoring Gaussian integers based on Shor's Algorithm. It is one of the problems that are intractable for classical computers but are manageable for quantum computers. An efficient quantum algorithm for factoring is influential in the application of cryptography because a large portion of the current cryptographic schemes rely on the assumption that factoring is difficult. We also discuss potential generalizations of our algorithm to other algebraic integers.
Qualidade de vida de mulheres submetidas à intervenção miofuncional estética da face - influência da escolaridade

ABSTRACT Objective: to analyze the influence of educational status on the quality of life of women submitted to facial aesthetic myofunctional therapy. Methods: a self-controlled experimental clinical trial conducted at the Universidade Aberta à Terceira Idade (Open University for Seniors - UNATI) of the Pontífica Universidade Católica (Pontifical Catholic University - PUC) of Goiás, Brazil. The 44 participating women, aged 50 to 65 years, were divided into group 1 (having completed middle or high school) and group 2 (with a higher education degree) and were submitted to aesthetic myofunctional therapy. Data analysis was made with the paired t-test, Levene test, t-test to compare the means of independent groups, and Spearman correlation, at the 5% significance level. Results: after the aesthetic myofunctional therapy, the wrinkles and orofacial myofunctional balance improved significantly in groups 1 and 2. The physical aspects and overall health status mean scores increased significantly in group 1, after the intervention. There was no statistically significant difference in the women’s quality of life between groups. The correlation between educational status and the quality-of-life domains was predominantly low. Conclusion: the educational status did not interfere with the quality of life of women submitted to aesthetic myofunctional therapy.
Design Guidance for Lightweight Object Detection Models

The lightweight target detection model is deployed in an environment with limited computing power and power consumption, which is widely used in many fields. Most of the current lightweight technologies only focus on a few steps of the model implementation and lack a global perspective. Therefore, this paper proposes a general lightweight model implementation framework, including network construction indicators, lightweight backbone network design, and model optimization. By analyzing the complexity indicator of network structure, the factors that affect network performance such as depth and width are summarized. On this basis, combine the One-Shot Aggregation (OSA) idea and Cross-Stage Partial Network (CSPNet) transformation to construct a general lightweight detection network CSPOSA. Further specific optimization strategies are proposed to prune the network structure and training process. For the network structure, the width and depth of the network are adjusted, the amount of parameters of the model is compressed. The training process is divided into the first, middle and last three stages to improve the detection performance of the model without adding extra computation. Taking embedded platform helmet detection as the experimental scene, the parameter amount of the realized model is 1/10 of the mainstream models YOLOv3 and YOLOv4, and the detection accuracy is similar, so it is more suitable for deployment on devices with limited computing power.
A New Hybrid Image Encryption Technique Using Lorenz Chaotic System and Simulated Kalman Filter (SKF) Algorithm

Nowadays, encryption is one of the most popular and effective security methods used by company and organizations. A new hybrid technique, Lorenz chaotic system and an optimization algorithm, Simulated Kalman Filter (SKF) had been proposed to solve image encryption problem. The objectives of the hybrid technique are to improve the security and add noise from the optimization algorithm and generate chaotic secret key. To achieve that, Lorenz chaotic system is implemented to this method and produce secret key sequence. SKF is one of the optimization methods that had been proved to have great performance in engineering applications from prediction, measurement, and estimation process. Thus, the proposed method is outperformed the results and analysis compared to literature as benchmarks. In short, the proposed hybrid approach is agile and efficient to apply in image encryption problem.
Improving CSE-F 1.0 Prover with Importance Measure Based Literal Selection Strategy

Based on multi-clause standard contradiction separation (S-CS) calculation for first-order logic, some firstorder automated theorem provers are developing. But only some basic heuristic strategies have been implemented. The development and effective use of heuristic strategies will be the important research work. So we propose a novel measurement method in this paper. We apply the measurement method to CSE-F 1.0 prover, experimental results show that the method improves the capabilities and efficiency of CSE-F 1.0.
Unsupervised Machine Scoring of Free Response Answers—Validated Against Law School Final Exams

This paper presents a novel method for unsupervised machine scoring of short answer and essay question responses, relying solely on a sufficiently large set of responses to a common prompt, absent the need for pre-labeled sample answers—given said prompt is of a particular character. That is, for questions where “good” answers look similar, “wrong” answers are likely to be “wrong” in different ways. Consequently, when a collection of text embeddings for responses to a common prompt are placed in an appropriate feature space, the centroid of their placements can stand in for a model answer, providing a lodestar against which to measure individual responses. This paper examines the efficacy of this method and discusses potential applications.
Behavior Inference based on Joint Node Motion under the Low Quality and Small-Scale Sample Size

Behavior inference based on joint node motion is a relatively small field. In this field, the information density is barren and usually accompanied with low quality, because the data acquisition equipment will not use RGB images, but the use of structured light and TOF technology will introduce series problem, like overlap or drift of key nodes. This paper propose a lightweight model combining matrix convolution and continuous frame feature analysis to realize the motion behavior classification of key nodes. The size of our model is controlled within 70MB, and the calculation time on CPU and GPU is satisfactory. At the same time, our model shows linear attenuation in the process of gradually reducing the training sample size, which means that users can predict the model performance according to the sample distribution.
Seeking Specialization Through Novelty in Distributed Online Collective Robotics

Online Embodied Evolution is a distributed learning method for collective heterogeneous robotic swarms, in which evolution is carried out in a decentralized manner. In this work, we address the problem of promoting reproductive isolation, a feature that has been identified as crucial in situations where behavioral specialization is desired. We hypothesize that one way to allow a swarm of robots to specialize on different tasks is through the promotion of diversity. Our contribution is twofold, we describe a method that allows a swarm of heterogeneous agents evolving online to maintain a high degree of diversity in behavioral space in which selection is based on originality. We also introduce a behavioral distance measure that compares behaviors in the same conditions to provide reliable measurements in online distributed situations. We test the hypothesis on a concurrent foraging task and the experiments show that diversity is indeed preserved and, that different behaviors emerge in the swarm; suggesting the emergence of reproductive isolation. Finally, we employ different analysis tools from computational biology that further support this claim.
The Third Intelligence Layer—Cognitive Computing

This chapter explains the third intelligence layer of the machine brain—cognitive computing. We employ facial features detection as a practical example and utilize the meta-learning (learn-to-learn) processes for a further interpretation, including meta-learning processes with logistic regression and entropy method. The cognitive computing mechanisms for the meta-learning process with logistic regression are to use the classifier that based on logistic regression as the base learner, which is embedded with prototype and residual networks and has the property of fast convergence to improve the robustness and efficiency. The cognitive mechanisms of meta-learning process with entropy weight are to improve two competitive meta-learning systems—the model-agnostic meta-learning and prototypical networks by integrating the entropy weight method with the CACTUs method (clustering to automatically construct tasks for unsupervised meta-learning). At the end of this chapter, we also develop the theory in Chap. 2 by introducing a deep multi-view active learning framework which combines multi-view active learning and deep learning to reduce the annotation efforts in the framework of the third intelligence layer.
Waiting Time Based Combinative Double Auction Scheme in Cloud Computing

In IT-related services Cloud computing is the main challenging platform for business. The intention of cloud suppliers is to increase their utilization of services, whereas cloud user’s intension is to gain access to resources at a reasonable cost. Formerly, the users might bid for each and every resource type, which turned into a problem of cloud resource allotment. To remedy those troubles, investigators have concentrated on combinatorial auction wherein offer resources in bundles by suppliers and client bids for his or her required package deal. However, some drawbacks in this allotment scheme must be addressed, such as the clients being dropped from auction mechanism due to lower average bid price. To answer this issue, we proposed "Waiting time based Combinative Double Auction Mechanism for Resource Allotment (WT-CDARA) in cloud computing". Our technique makes a deal with dropped clients and calculates waiting time of those users. Lower average bid rate clients are asked to increase their bids by our proposed technique as the auctioneer will drop them based on the quoted bids. The majority of users who are on the way to winning agree the offer and raise their bid amounts. In our mechanism, we consider the waiting time of dropped users, If the user having longer waiting time with low bid price will get the chance to win an auction. A CloudSim simulation toolkit is used to implement the proposed mechanism. When the outcomes are compared to most recent technique, the performance analysis shows that our technique more clients winning as well as receiving their required resources, and also the provided service utilization rate was up to 32.6 percent higher than remaining mechanisms.
Multimachine Collaborative Path Planning Method Based on A* Mechanism Connection Depth Neural Network Model

The naval battlefield is one of the main positions for future conflicts between major powers. The powerful naval battlefield target search capability is the last barrier to carry out maritime training and operations. At the same time, because of its complex and changeable environment and important strategic position, it has become the most difficult battlefield in joint search and rescue. the most central part. In order to reduce the time of target search in maritime battlefield, a real-time path planning method in maritime battlefield based on deep reinforcement learning is proposed. The method proposed in this paper has advantages for path planning in multi-machine collaborative and can meet the requirements of real-time performance. First, a mathematical planning model for target search in the naval battlefield is constructed and mapped into a reinforcement learning model; then, based on the Rainbow deep reinforcement learning algorithm, the state vector, neural network structure and algorithm framework for target search path planning in the naval battlefield are designed with process. Finally, the feasibility and effectiveness of the proposed method are verified by experiments. The sequential exchange strategy and sequential insertion strategy of the A* algorithm are added to improve the optimal path obtained by the algorithm in each iteration. The improved algorithm is applied to the target path planning problem with time window. Through experiments on the Solomon data set, the optimal solution of the path before and after the improved algorithm is compared, and it is proved that the improved algorithm has better performance. In this way, the cooperative behavior of multi-agents can be realized, and the stability of reinforcement learning can be improved. The simulation results show that the length of the path searched by the proposed algorithm and the inflection point of the path are shortened by 19% and 58% respectively compared with the benchmark algorithm, which verifies the superiority of the proposed algorithm.
Knowledge map reasoning algorithm based on Weight

Auxiliary triage is an important direction for the application of medical knowledge graphs. Entity reasoning is the technical difficulty to realize auxiliary triage. The existing methods mainly rely on entity reasoning algorithms based on subgraph extraction, which have the disadvantages of large calculation amounts and low accuracy. This paper proposes a weight-based entity reasoning algorithm. The method uses Naive Bayes to calculate the weight of the relationship between entities, and quantifies the local structural characteristics of the entity by calculating the purity of the entity and the correlation between the entities, and based on this to improve the subgraph extraction process to reduce the complexity of the algorithm. The experiment is based on the open-source medical knowledge map of the Chinese Academy of Sciences and the patient data set provided by the partner hospital. The average time consumption is 0.252s, the additional storage consumption is 950KB, and hits@5 is 91.34%. At the cost of occupying a small part of the storage space, inference speed and accuracy have been improved.
Quasi Character-Level Transformers to Improve Neural Machine Translation on Small Datasets

In the Neural Machine Translation community, it is a common practice to use some form of subword segmentation to encode words as a sequence of subword units. This allows practitioners to represent their entire dataset using the least amount of tokens, thus avoiding memory and performance-related problems derived from the full wordor purely character-level representations. Even though there is strong evidence that each dataset has an optimal vocabulary size, in practice it is common to use as many “words” as possible. In this work, we show how this standard approach might be counter-productive for small datasets or low-resource environments, where models trained with quasi character-level vocabularies seem to con-sistently outperform models with large subword vocabularies. Nonetheless, these improvements come at the expense of requiring a neural architecture capable of dealing with long sequences and long-term dependencies.
Tokengrid: Toward More Efficient Data Extraction From Unstructured Documents

Key information extraction from unstructured documents is a practical problem in many industries.Machine learning models aimed at solving this problem should efficiently utilize textual, visual, and 2D spatial layout information of the document.Grid based approaches achieve this by representing the document as a 2D grid and feeding it to a fully convolutional encoder-decoder network that solves a semantic instance segmentation problem.We propose a new method for the instance detection branch of that network for the task of automatic information extraction from invoices.Our approach reduces this problem to 1D region detection.The proposed network has fewer parameters and a shorter inference times.Additionally, we suggest a new metric for evaluating the results.
Obstacle Avoidance Strategy of Improved APF Method in C-space

As human-robot integration problem attracts attentions, obstacle avoidance algorithm has become a concern for robots which achieves safety. This paper proposed an improved artificial potential field (APF) method which applies APF in C-space (C-APF). C-APF is brief and robust. Simulation results show the effectiveness of C-APF and its advantages with the traditional APF method was compared.
Operating Model for the Optimization of an Electronic Service of Group Learning Rooms at a University Library

This paper is concerned with analyzing the service time of the provision of group learning rooms at a university library. The study is based on a real life situation at a faculty library of the University of Vienna. It examines arrival rate, system utilization, waiting time, number of students in the system as well as in a queue in a queuing model. A distinction is made between different days of the week as well as different months of a semester. The results show that the implementation of an electronic service system would significantly improve service time and benefit students and library staff alike.
BBCT: A Smart Blockchain-Based Bulk Commodity Trade System

The bulk commodity is a major strategic resource of the country, and related trade markets are booming. However, due to channel isolation and information barriers, the trade platforms also have risks and industry chaos, which make the bulk commodity trade process suffer from information asymmetry, difficulty in choosing a suitable commodity, difficulty in commodity pricing, and lack of trust in the trade platform. To cope with these challenges, we propose BBCT, a Blockchain-based Bulk Commodity Trade system, to achieve credible and fair bulk commodity trade. The process of multi-party trade is divided into four stages: matching, negotiation based on time-constrained Bayesian learning, decision-making based on the technique for order preference by similarity to ideal solution and signing. We design a “negotiation-signing” dual-channel blockchain architecture, so that buyers and sellers can complete the negotiation and signing process coordination on the blockchain. We finally verify the efficiency and reliability of BBCT through experiments.