Reviewerly/exHarmony huggingface dataset:
Temperature Recognition of Infrared Temperature Map Based on Improved BP Neural Network

Aiming at the problem of low efficiency and low accuracy of manual recognition of infrared temperature map, a temperature recognition method based on improved BP neural network was proposed. Firstly, de-noising, image enhancement processing and image segmentation processing were performed on the temperature image of infrared thermometry map. Secondly, the normalized Zernike invariant moment was used for feature extraction. Furthermore, the corresponding improvement was carried out on the BP neural network. Finally, the improved neural network was used to classify and recognize infrared images. Experimental results showed that using the improved BP neural network algorithm for training, the accuracy and efficiency of the training results are higher. The improved BP algorithm is used for temperature recognition of infrared images, and its accuracy reaches 99%. Therefore, the improved BP algorithm has a higher recognition accuracy.
An Improved Method for Making CNN Immune to Backdoor Attack by Activating Clustering

When a neural network is trained with a data set from an untrusted source, an attacker can insert poisoned data with a backdoor trigger into the data set to make the neural network make wrong decisions. By using Activation Clustering over convolutional neural networks, we propose an improved method for defensing backdoor attacks in the process of data collection and preparation. Experimental results show that this method can reliably protect neural networks from the interference of malicious data during training. The essence of this method is making a neural network to learn the feature of the trigger and classify the toxic data into a separate class. The structure of the existing model is also optimized to make the size of the model lightweight.
Quality assessment of cross-topic article features based on improved CTS model

Assessing article quality is an important task in the field of education. To improve the semantic understanding of the assessment model and improve the generalizability of the article quality assessment system at the same time, an improved cross-topic article features quality assessment method based on CTS (Cross-prompt Trait Scorer) is proposed. In the shared low-level layer part, a new depth-separable convolution with residual structure is used to extract more semantic feature information; in the feature enhancement part, multi-headed attention is added to strengthen the effective features and improve the learning ability of lexicality; finally, a Log-Cosh loss function is used to make the model more robust to outliers and improve the model's QWK (Quadratic Weighted Kappa) evaluation metric of the model. The improved method experimented on the ASAP (Automated Student Assessment Prize) dataset, and the average QWK score reached 0.571 under different topics and 0.566 under different features. The experimental results showed that the improved model obtained superior results in the cross-topic article feature quality assessment task compared to the CTS and PAES (Prompt Agnostic Essay Scorer) models.
GAME-BC: A Graph Attention Model for Exploring Bitcoin Crime

Blockchain technology has been invented as a fundamental technique to the cryptocurrency Bitcoin in 2008, which is decentralized, consensus and cryptographic leger. However, due to the anonymity of the Blockchain, Bitcoin has been becoming one critical finance platform applied to transfer or hidden criminal income by offenders. Bitcoin crime refers to criminal activities which use Bitcoin as a criminal tools, criminal object or criminal settlement. Typical bitcoin crimes include online gambling, money laundering, fraud and more. To address these issues, our work aims to propose an efficient method to find transactions related with Bitcoin crime in the Bitcoin network. Which will efficiently support regulators to combat Bitcoin crimes. Through collecting and concluding kinds of Bitcoin crimes, we find several typical relation patterns among Bitcoin transactions with respect to crimes, and then construct and analysis a bitcoin criminal transaction network. At last, we study a graph neural network model with attention mechanisms to detect illegal transactions. Experimental results show that our method can achieve better classification accuracy, and has the ability to efficiently detect criminal clues and locate related illegal transactions.
Learning Spatiotemporal Features for Video Semantic Segmentation Using 3D Convolutional Neural Networks

In recent years, significant progress has been made in still image segmentation. However, applying these advanced algorithms to each video frame requires extensive calculation. In this paper, we made two main contributions. The first contribution is a new dataset, we made a human semantic segmentation video dataset based on the Refer-YubeVOS dataset. It provides a benchmark for evaluating video semantic segmentation models. The second contribution is to propose a video semantic segmentation architecture suitable for spatiotemporal feature learning and a method for modifying 2D networks into 3D networks. The trials showed that the 3D network outperforms the 2D network on our dataset. And it is concluded that 3D HRNetV2 has the best performance, with an mIoU <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</inf> of 61.72%, 14.89% higher than 2D HRNetV2.
Evaluation of University Teaching Quality Based on Hadoop and Deep Mining

In order to further improve the evaluation level of university teaching quality, a university teaching quality evaluation method based on Hadoop and deep mining was proposed. SVM was selected as the classification evaluation method for teaching quality. PSO was selected to optimize SVM data classification. At the same time, considering the problems of PSO algorithm, PSO was improved by weight and random parameter optimization. The results showed that the improved PSO-SVM has a good evaluation effect when evaluating the university teaching quality, and performs better in F1, recall and accuracy. Compared with other improved algorithms, the proposed PSO-SVM algorithm has better convergence performance and shorter distributed running time, which verifies the feasibility of the evaluation method selected in this paper and provides a new qualitative analysis method for the objective evaluation of university teaching.
Disentangling word order and function assignment preferences in Modern Greek

Construction alternations are influenced by several conceptual properties of referents, including animacy or definiteness, known as "prominence features" (Aissen 1999, Haspelmath 2020).However, it remains unclear whether these features influence construction alternations through word order or function assignment.To disentangle these effects, we conducted a quantitative corpus study in Greek, comparing a word order alternation (SOV/OVS) and a function assignment alternation (active/passive).We annotated a random sample of 600 sentences from the Corpus of Modern Greek (Arkhangelskiy, Kisilier 2018) and fitted a Bayesian categorical model to construction (SVO/OVS/passive).We show that animacy and definiteness features have an effect on function assignment, not on worder order, but we found a short-before-long length effect.
Synergistic Effect Research of K-modes Algorithm in the Evaluation of Physical Education Teaching

In order to improve the efficiency and accuracy of evaluation of physical education teaching, a physical education teaching evaluation method based on improved K-modes was proposed. Firstly, the minimum sum of the squared errors method and similarity calculation method were used to improve the number of cluster families, the initial cluster center and the distance measure method, so as to improve the traditional K-modes clustering. Then, the ratio of intra-class average distance and inter-class average distance was used as the evaluation index to evaluate the proposed algorithm. The results showed that compared with other algorithms, the proposed improved K-modes algorithm can best cluster teachers into three categories. In the actual evaluation test, the ratio of K-modes improved algorithm is 0.2302, the ratio of "random+0-1 matching method" and "SSE+ similarity calculation method" is 0.7241 and 0.3497, respectively. It shows that the improved K-modes algorithm has better clustering effect and more effective effect on physical education teaching evaluation.
Research on personalized resource recommendation algorithm for parent education

Because of the single teaching mode, the extreme imbalance of educational resources and the low efficiency of education, the traditional parent education can no longer meet the requirements of educational informationization. Therefore, in order to improve the learning efficiency of learners, the research introduces collaborative filtering algorithm based on the construction of personalized recommendation model, and verifies its effectiveness while improving it. The experimental results show that, compared with Random algorithm, the accuracy of User based Collaborative Filtering (UBCF) algorithm is 65.53%, which is much better than Random algorithm; Among the three different methods, the accuracy of collaborative filtering algorithm is the highest, reaching 67.92%, and the performance of this method is the best under the same data. In a word, in parent education, collaborative filtering algorithm can effectively improve the accuracy of recommendation results and help parents find deficiencies in the teaching process.
A Real time Decision Making Method of Electronic Warfare Based on the Protection of Key Points

Electronic warfare decision-making involves many kinds of targets, different performances, complex environments and other issues, which makes it difficult to make real-time decisions adaptively. It is generally used to simulate the equipment of all parties under the scenario settings to realize the simulation of the battlefield environment of electronic warfare. In order to better realize the real-time combat decision-making of electronic warfare, a real-time decision tree scheme of electronic warfare equipment based on protection key points is proposed here. Through the decision tree, the real-time decision-making of enemy targets in the scene is carried out to realize the battle simulation of electronic warfare. The battle command decision-making of electronic warfare is improved according to the scenario of electronic warfare, and better electronic warfare decision-making is obtained in certain special combat scenarios.
MFSCD* Lite: A situation-based path planning algorithm for agent in wargame

Path planning plays a vitally important role for agents in adversarial strategy games, and even directly determines the final success or failure at times. However, most previous studies of path planning focus on minimizing path length or cost, ignoring the growing complexity resulting from dynamic changing situations. In this paper, we propose a multi-factor situational coupling D* lite (MFSCD* lite) to address the issues above. First, this paper models important dynamic situational factors and integrates them into MFSCD* lite, allowing the planning path to dynamically fit with the changing of situation. Secondly, the buffer and extended buffer of factor-values are established to reduce the repeated calculation of situational factors in MFSCD* lite. And the update strategy is optimized to improve the efficiency of path planning. To validate the effectiveness of our method, we conduct comprehensive experiments on wargame, a typical real-time agent adversarial game. The experimental results show that MFSCD* lite can meet the needs of high-quality and fast path planning, which is better than ordinary path planning algorithms. Further, it has achieved excellent performance in major wargame competitions.
Summary Model based on Semantic Similarity Attention Focus

For the current summary model, it is difficult to obtain context information related to the semantics of the original text, which leads to the problem of inaccurate summary generation. This paper proposes a summary model based on semantic similar attention mechanism. First, a semantic similarity attention mechanism is established. By calculating the cosine distance between the encoder and decoder hidden layer states, and introducing the distance into the attention mechanism to assist the mechanism to obtain context semantics with high semantic similarity to the original text information. Secondly, a fusion loss method for learning semantic similarity features is proposed, and the weighted fusion of the cosine distance and the original loss function is fed back to the semantic similarity attention mechanism, and then the context information is updated iteratively. Finally, the decoder uses the semantic information closest to the original text to generate a more accurate summary. From the experimental results, the model has some advantages over the previous model.
An Analysis of Current Continual Learning Algorithms in an Image Classification Context

Artificial Intelligence aims to mimic natural intelligent learning by using lifelong machine learning, which allows an AI to train and learn over its lifetime. Various algorithms have been suggested and developed to allow lifelong learning, these algorithms require deeper analysis, to evaluate and highlight performance benefits. In this research, we will study three state-of-the-art algorithms for lifelong learning: Rehearsal, elastic weight consolidation and synaptic intelligence. We do an analysis and evaluation of their performance in a multiple-task experiment, using different amounts of data, and measuring several performance metrics. We found that these algorithms are similar in performance, but some algorithms perform better than others with less data, or show good performance in task one, but not subsequent tasks. These algorithms could be built upon and improved in future research. The evaluation demonstrated in this research is in the image classification context.
Research on the Construction and Usage of University History Image Data Database Based on Cloud Storage

In order to further strengthen the storage and sharing level of school history image data, a massive data sharing method based on cloud storage was proposed. In order to improve the utilization efficiency of cloud storage space, the optimization algorithm based on the residual space was used to optimize the cloud storage space, and the intermediate value fitting effect optimal heuristic algorithm was used to solve the virtual machine placement problem in the cloud storage process of school history image data. Experimental results showed that compared with the Cinder algorithm, the optimization algorithm based on residual space used in this paper has higher computational efficiency on the second dataset of the school history image database, which is improved by 35%. Compared with MBFD algorithm and PABFG algorithm, the intermediate value fitting effect optimal heuristic algorithm consumes less power. It is concluded that the spatial optimization algorithm and the virtual machine placement algorithm can further improve the storage and sharing level of the school history image data database.
FAQ question Answering method based on semantic similarity matching

Frequently asked questions (FAQ) is a typical limited domain-oriented retrieval question answering system, and semantic similarity matching is the key technology of FAQ question answering. Aiming at the FAQ problems of insufficient deep semantic representation and extraction and synonyms and polysemy, this paper proposes a cascade matching model combining coarse-grained recall and fine-grained sorting. Firstly, an improved Okapi BM25 algorithm based on inverse word frequency is proposed. This paper proposes a new solution to FAQ answer based on the traditional text matching and retrieval pipeline, and gives a new semantic similarity matching model. Additionally, we further analyze the shortcomings of semantic matching and the advantages of data-driven semantic similarity matching model. Finally, the proposed hybrid matching model is evaluated. Experimental results show that the proposed method not only ensures the retrieval speed of question and answer, but improves its performance
A Soft Computing Approach for Predicting and Categorizing Learner’s Performance Using Fuzzy Model

COVID-19: a pandemic situation in the world gives a turning point to education system and it becomes e-education. E-learning is emerging trend in digital era and empowerment of this trend is necessary. Traditional education system trying to adopt this new method of teaching and learning. But only teaching and learning is not sufficient in education system. We have to focus on learner and the environmental impact on them. Traditional education system unable to resolve all the issues arises due to obstacles such as understanding ability, thinking, mood and concentration. Proposed research work focusing on designing, developing and modelling of soft computing decision-making model for solving real-life problems and learners capability in education system. This research work uses fuzzy inference system (FIS) which is one of the applications in MATLAB software, for analysing learners result from obtained score and other factors related to environment. It also predicts the learner which is helpful in e-learning.
Stress Level Analysis Using Bipolar Picture Fuzzy Set

The concept of bipolar picture fuzzy set is introduced, and few of its basic properties were provided. Also, presented the modal operators and arithmetic operators of picture fuzzy set. The relations and product on picture fuzzy set and its associated outcomes are explored. Further, the application is carried out in the stress level of the people who are working by using online platforms.
Interval Type-3 Fuzzy Decision-Making in Material Surface Quality Control

This article is presenting a proposal for the mathematical definition of interval type-3 fuzzy sets, as a basis for later constructing all the theory needed for building interval type-3 fuzzy systems for different areas of application. It is well known that most used form of fuzzy logic is the one called type-1, which later evolved into interval and general type-2. Recently, we have seen that type-2 fuzzy has outperformed type-1 in many real-world problems when there is more uncertainty, dynamic situations or nonlinearity. For these reasons, it is interesting to explore the new area of type-3 fuzzy theory, and in this paper, we are dealing with the application side of this theory. We illustrate the potential of interval type-3 in a decision-making application in surface quality control. The proposed method uses interval type-3 fuzzy to automate quality control in material manufacturing. Surface roughness and porosity of the materials are utilized to estimate the quality of the material with a type-3 fuzzy approach. Material manufacturing requires strict quality control, and a type-3 fuzzy approach is able to outperform type-2 and type-1 in surface quality control.
The Ensemble of Ant Colony Optimization and Gradient Descent Technique for Efficient Feature Selection and Data Classification

Feature selection is of the paramount importance in the domain of data classification task especially when the number of features is huge. Further, gradient descent is an important technique for training the perceptron network and to find the hyper-plane that can classify the nonlinearly classifiable data sets with least error. However, modifying the synaptic weights of each input neuron is cumbersome and also time taking. In this work, an ensemble approach of ant colony optimization (ACO) and gradient descent technique is used. In this ensemble model, ACO will first select the important features, and subsequently, the gradient descent technique will modify the synaptic weights of these input features. This combined approach renders reduced training time of the perceptron network with enhanced classification accuracy of the test data sets. Experimental analysis and validation of proposed ensemble technique has also been incorporated for standard data sets. Over and above, the proposed technique have also been implemented for the purpose of identifying different commonly used mechanical parts.
Quadratically Sound Proof-of-Sequential-Work

Proof-of-sequential-work (PoSW) is a protocol which ensures that a prover must spend a specified number of sequential steps to evaluate a proof against some given statement, but can be efficiently verified. A crucial criterion for PoSW, known as soundness, is that a prover even with reasonable parallelism should not be able to compute the proof in steps much less than the specified amount. In particular, if a malicious prover skips $$\alpha $$ (known as soundness gap) fraction of computations to produce a proof, then the verifier should accept this proof with the probability $$\le (1-\alpha )^t$$ using t number of random challenges. While all the existing PoSWs [1, 4, 5] achieve soundness of $$(1-\alpha )^t$$ , our proposed scheme gives a quadratic improvement of $$(1-\alpha )^{2t}$$ . Our construction is based on linear hybrid cellular automata (LHCA), a widely used primitive in symmetric-key cryptography. Additionally, we show that our scheme is proven to be secure in the random oracle model.
Quantum Simulation of Perfect State Transfer on Weighted Cubelike Graphs

A continuous-time quantum walk on a graph evolves according to the unitary operator $$e^{-iAt}$$ , where A is the adjacency matrix of the graph. Perfect state transfer (PST) in a quantum walk is the transfer of a quantum state from one node of a graph to another node with $$100\%$$ fidelity. It can be shown that the adjacency matrix of a cubelike graph is a finite sum of tensor products of Pauli X operators. We use this fact to construct an efficient quantum circuit for the quantum walk on cubelike graphs. In [5, 15], a characterization of integer weighted cubelike graphs is given that exhibit periodicity or PST at time $$t=\pi /2$$ . We use our circuits to demonstrate PST or periodicity in these graphs on IBM’s quantum computing platform [1, 10].
The openness of corporate law in supervising commodity futures trading in emerging markets

Regulations regarding the prohibition of giving full power of attorney from customers to brokers still cannot be fully implemented by stock futures brokerage companies in emerging economies. This then becomes a problem for brokerage firms in the future. The purpose of this study is to analyze the legal protection of cryptocurrency investors reviewed based on Indonesia’s Commodity Futures Trading Authority Regulation No. 5 of 2019 and analyze the legal remedies taken in the event of a dispute over cryptocurrency investment. This research is sociological legal research and uses a positive law enforcement approach to the community. The results highlighted when the customer agrees to transact in futures trading through the facilities provided by the brokerage company, the customer must follow the procedure set by Commodity Futures Trading Authority (CoFTRA) or Badan Pengawas Perdagangan Berjangka Komoditi (BAPPEBTI), where the customer must fill out an agreement book whose contents are also regulated in CoFTRA Regulation No. 8 of 2019 concerning Technical Provisions for Futures Trading and Futures Broker Behavior.