Reviewerly/exHarmony huggingface dataset:
Efficient exploration by switching agents according to degree of convergence of learning on Heterogeneous Multi-Agent Reinforcement Learning in Single Robot

In recent years, a robot is required to perform autonomously in complex environment. Some researchers use reinforcement learning that learns actions autonomously according to environment. Reinforcement learning requires exploratory actions, but in conventional reinforcement learning it was random. Random exploratory actions are inefficient and takes a lot of time to learn. To prevent inefficient exploratory actions, we proposed a method that uses Heterogeneous Multi-Agent Reinforcement Learning system (HMARL) in previous research. HMARL enables efficient exploratory actions by using multiple agents with heterogeneous learning spaces. HMARL system is a system that performs exploratory actions using the learning of multiple agents. In addition, HMARL needs an index that autonomously selects an agent from among all the agents inside heterogeneous learning space. We propose a method to select an agent using the degree of convergence of the learning of the agents in HMARL based on the TD errors. As a result, efficient exploratory actions by multiple agents with different learning spaces was achieved. Then, experiment to compare the proposed method and the method of previous research was conducted. From experimental results, the usefulness of the proposed method has been demonstrated.
Structured precision skipping: Accelerating convolutional neural networks with budget-aware dynamic precision selection

Despite the remarkable advancement in various intelligence tasks achieved by Convolutional Neural Networks, the massive computation and storage consumption limit applications on resource-constrained devices. Existing works explore to reduce computation cost by leveraging the input-dependent redundancy at runtime. The irregular dynamic sparsity distribution, however, limits the real speedup for dynamic models deployed in traditional neural network accelerators. To solve this problem, we propose an algorithm-architecture co-design, named structured precision skipping (SPS), to exploit the dynamic precision redundancy in statically quantized models. SPS computes most neurons in a lower precision and only a small portion of important neurons in a higher precision to preserve performance. Specifically, we first propose the structured dynamic block to exploit the dynamic sparsity in a structured manner. Based on the block, we then apply a budget-aware training method by inducing a budget regularization to learn the precision skipping under a target resource constraint. Finally, we present an architecture design based on the bit-serial architecture with support for SPS models, where only a predict controller module with small overhead is introduced. Extensive evaluation results demonstrate that SPS can achieve up to 1.5× speedup and 1.4× energy saving on various models and datasets with marginal accuracy loss.
Semi-Supervised Graph Attention Networks for Event Representation Learning

Event analysis from news and social networks is very useful for a wide range of social studies and real-world applications. Recently, event graphs have been explored to model event datasets and their complex relationships, where events are vertices connected to other vertices representing locations, people’s names, dates, and various other event metadata. Graph representation learning methods are promising for extracting latent features from event graphs to enable the use of different classification algorithms. However, existing methods fail to meet essential requirements for event graphs, such as (i) dealing with semi-supervised graph embedding to take advantage of some labeled events, (ii) automatically determining the importance of the relationships between event vertices and their metadata vertices, as well as (iii) dealing with the graph heterogeneity. This paper presents GNEE (GAT Neural Event Embeddings), a method that combines Graph Attention Networks and Graph Regularization. First, an event graph regularization is proposed to ensure that all graph vertices receive event features, thereby mitigating the graph heterogeneity drawback. Second, semi-supervised graph embedding with self-attention mechanism considers existing labeled events, as well as learns the importance of relationships in the event graph during the representation learning process. A statistical analysis of experimental results with five real-world event graphs and six graph embedding methods shows that our GNEE outperforms state-of-the-art semi-supervised graph embedding methods.
Knowledge Tracing With Learning Memory and Sequence Dependence

Knowledge tracking (KT) can dynamically track the student's knowledge state based on the student's past test data, and evaluate the student's knowledge level. In this paper, a KT model based on learning ability and learning experience (LALEKT) is proposed, which fully considers the difference characteristics of students, and tracks students' knowledge changes through their learning ability and learning experience. The LALEKT model combines GRU's data modeling ability and MANN's memory ability, which can more realistically simulate the human learning process and improve the model's predictive ability. Through experiments on two public data sets, it has been verified that LALEKT is advanced and effective.
Predicting QoS for Web Service Recommendations Based on Reputation and Location Clustering with Collaborative Filtering

Nowadays, numerous web services with equivalent functionality have become available on the Internet. The Quality of Service (QoS) for web services seems to have an essential role when it comes to selecting the best web services. However, evaluating the user-side efficiently in terms of quality of web services has become a critical research topic. Predicting the QoS values of web services and the credibility of the values published by different users are major challenges in this area. A commonly used technique to predict QoS values of web services is collaborative filtering (CF). To address these critical challenges, a personalized QoS predicting technique is proposed for web services which depends on the reputation and location-based CF approach. Firstly, a set of untrusted users is identified through the Dirichlet probability distribution on the basis of the user's reputation, followed by processing the unreliable data contributed by untrusted users. Secondly, the users are clustered based on their geographic information to improve the neighborhood similarity computation. Finally, the similarity weights of neighboring users are used to predict unknown QoS values in each cluster. It has been observed that the proposed model realized a more favorable performance in terms of accuracy and efficiency as compared to other existing approaches. According to the matrix densities from 10% to 90%, the measures of MAE and RMSE for the response time attribute range from 0.47 to 0.30 and from 1.26 to 0.95, respectively, and the measures of MAE and RMSE for the throughput attribute range from 15.64 to 7.58 and from 50.50 to 34.15, respectively.
A Study on EFL Learners’ Perception of College General English Class Type in the COVID-19 Non-contact Era

This study investigated 301 college students who attended mandatory online general English classes due to the COVID-19 pandemic for the purpose of examining their preferred online instruction type, as well as their perception regarding post-pandemic instruction type for general English. The order of the preferred online instruction is as follows: 1) fully pre-recorded videos, 2) blended classes, and 3) fully real-time videos. However, their preferences showed significant difference depending on their interest in English learning and their majors. With regard to the instruction type of post-pandemic general English, the order of the positive response is fully in-person instruction, blended classes, and fully on-line instruction, which also showed a significant difference depending on gender, year, major, and interest in English learning. Of course, since the data was gathered from a single instructor’s students, and thus are not generalizable, we might fairly expect that these results could be utilized as a basic reference point when it comes to the planning and designing of general English languages programs during and after the pandemic.
Credit Card Fraud Detection in Financial Transactions Using Data Mining Techniques

Every year, fraudulent credit card transactions result in the loss of billions of dollars. The development of effective fraud detection algorithms is critical for lowering this loss, and more algorithms are turning to advanced data mining approaches to help in fraud detection. Due to the unstable distribution of the data, the design of fraud detection algorithms is very difficult, and the distribution of the categories is highly unbalanced, yet there are many transactions that are categorized by fraud detection system. This paper proposes a system for detection fraud in financial transactions by using some types of data mining models which are logistic regression, random forest, naïve bayes and support vector machine. This is done through suggested basic steps: the first step is to use European cardholder dataset which contains 284.807 transactions that split into two groups. First one contains 199.3649 transactions which is used for training the models, while 85.4421 transactions remained for testing the models. This dataset is highly imbalanced, therefore by using SMOTE technique it will transform to a balanced one. The Second step is preparing the data and apply the Correlation function on training dataset, then implementing the used models on it. The results are compared by evaluation metrics to show which model is the best for detecting fraud. From these results, it is concluded that the Random Forest classifier is the best for fraud detection, which achieved accuracy with 99.15% in testing data.
Fingerprint Defender: Defense Against Browser-Based User Tracking

It is difficult to be anonymous online with user activities always under the scanner. Multiple identifiers and their combinatories are used for user identification. While browsing, trackers keep a record of artifacts such as OS version, screen resolution, and fonts enabled. Browser fingerprinting tries to identify a user’s browser uniquely, without using cookies or other stateful signatures. We propose a browser fingerprint defender tool to anonymize user browsers. It creates captures current user attributes and anonymizes them before sending a request to the server. It also gives current browser fingerprint attributes.
Procedural Content Generation of Levels with Increased Connectedness using Complex String Generators

This study refines and extends an earlier effort that demonstrated the utility of the do-whats-possible representation for laying out large numbers of rooms with connected adjacency for a level map. In this study, we continue development of the technique as well as adding doors to establish the connectedness of the rooms on a level, yielding a graph of the rooms and the connections between said rooms. Using complex string generators and an evolutionary algorithm, the diameter of three starting level maps is decreased to the best possible diameter using the least amount of doors. This produces maps which have a variety of features useful to developers and enjoyed by the players.
Sentiment Analysis of User Feedback on Business Processes

Business Process Management (BPM) is an established discipline that uses business processes for organizing the operations of an enterprise. The enterprises that embrace BPM continuously analyze their processes and improve them to achieve competitive edge. Consequently, a plethora of studies have developed contrasting approaches to analyze business processes. These approach vary from examining event logs of process-aware information systems to employing the data warehousing technology for analyzing the execution logs of business processes. In contrast to these classical approaches, this work proposes to combine two prominent domains, BPM and Natural Language Processing, for analyzing business processes. In particular, this study has proposed to perform sentiment analysis of end-user feedback on business processes to assess the satisfaction level of end-users. More specifically, firstly, a structured approach is used to develop a corpus of over 7000 user-feedback sentences. Secondly, these feedback sentences are annotated at three levels of classification, where, the first-level classification determines the relevance of a sentence to the process. Whereas, the second-level classifies the relevant sentences across four process performance dimensions, time, cost, quality and flexibility, and the third-level classifies the sentences into positive, negative, or neutral sentiments. Finally, 78 experiments are performed to determine the effectiveness of six supervised learning techniques and one state-of-the-art deep learning technique for the automatic classification of user feedback sentences at three levels of classifications. The results show that deep learning technique is most effective for the classification tasks.
Machine Learning for Single and Complex 3D Head Gestures: Classification in Human-Computer Interaction

This paper presents a new Hidden Markov Model based approach for fast and automatic detection and classification of head movements in real time dynamic videos. The model has been developed to utilize human-computer interaction applications by using only the laptop webcam. The proposed model has the ability to predict single head and combined simultaneously in fast responses. Other models paid more attention to classify head nod and shake only, but our model contribute the role of other head movements. The model proposed here doesn’t need any user intervention or previous knowledge of its environment. In addition, there is no limitation on illumination changes and occlusions, as well as no restrictions on head movements ranges. The model achieved significant results and efficient performances when tested on unseen data. As the model accuracies were 94%, 99%, 83%, 87%, 93%, 96% for all head gestures (rest, nod, turn, shake, tilt and tilting) respectively. On the other hand, the model accuracy was 99% and 88% for combined and single cues respectively. The aim of this model is to provide a fast application to infer and predict human emotions and affective states in real time through head gestures.
Detection of Business Email Compromise Attacks with Writing Style Analysis

Phishing scams have long been used to obtain sensitive information via email. Recently, scammers have increasingly been using spear-phishing and targeting corporate employees, this type of attack is called Business Email Compromise (BEC-attacks). BEC-attacks problem is highly relevant to mobile networks, as mobile users are much more vulnerable to such types of attacks than regular users. The main methods of detecting BEC-attacks are considered and their comparative analysis is made. It is demonstrated that the most promising approach for detecting BEC-attacks is a complex analysis of email headers, content analysis, and authors writing-style analysis with machine learning algorithms. BEC-attacks detection method is proposed based on the above-mentioned analysis and its decomposed functional model is presented. A feature space includes writing-style features (words 3-grams); day of the week and time of sending the email; email’s urgency features; email headers features. To evaluate the BEC-attacks detection accuracy, the experiments on datasets, containing emails in Russian and English, were carried out. The experiments showed that the best accuracy is achieved with word n-grams and LSVC with a feature scaling method for emails in Russian and English.
A vectorial minimized surface regularizer based image registration model and its numerical algorithm

In this paper, we propose a vectorial minimized surface regularizer based image registration model which is suitable for smooth and non-smooth registration. In order to avoid the mesh folding phenomenon, inequality constraint on transformed Jacobian matrix determinant is imposed. In addition, we use Lagrange multipliers combining Gauss-Newton method with Armijo line search with the multilevel method to solve the corresponding model. And guided filter is utilized on the displacement field before and after the registration of each level to avoid noise and preserve the edge information of the image. Furthermore, the convergence analysis of the algorithm is given. Finally, numerical experiments using both synthetic and realistic images are carried out to show the robustness of the proposed model and the effectiveness of the algorithm.
A Pattern for Proof of Work Consensus Algorithm in Blockchain

Blockchain technology has gained immense popularity over the last decade. It has transformed several industries by enabling processes to work in a secure and decentralized manner. Blockchain consists of blocks that are linked together through cryptography. Blockchain is a distributed ledger that contains a set of sequenced blocks of data. Each block records transaction data in a transparent, immutable, and secure fashion. Consensus algorithm is the key part of this technology as it is used for the decision-making process among nodes in the blockchain network. Nodes in the verifying network use consensus algorithm to reach mutual agreement about state of the blockchain whenever a new block is added. This makes it possible to accept any transaction in the blockchain. Many consensus algorithms have been proposed in the literature each having its own performance and security characteristics. In this paper, we present a pattern for one of the most widely used blockchain consensus algorithms, which is the Proof of Work (PoW) algorithm. This paper is a part of our project on blockchain architecture and patterns.
Efficient person search via learning-to-normalize deep representation

The task of person search is to search for the target pedestrian in the scene images with the given query image, which means to perform detection and re-identification at the same time. In this paper, we propose a one-step method to jointly handle the two subtasks of person search using an end-to-end framework. The proposed framework contains the feature learning module and the multi-task learning module. We explore the performance of learning-to-normalize deep representation on the feature learning module, which allows the network to achieve normalizer selection on each normalization layer. To solve the imbalance problem of hard-to-classify examples, we introduce the focal loss in the multi-task learning module. Besides, we adopt the gradient generalization centralization technique, which makes the training process more stable and enhances the generalization ability of the model. We present extensive experiments on two benchmark datasets, i.e., CUHK-SYSU and PRW. Experimental results prove that our method outperforms the state-of-the-art one-step methods and has a competitive performance compared with two-step methods.
Introduction to China’s Earthquake Emergency Response System

Following the MS6.4 earthquake that occurred on May 21, 2021 in Yangbi, Yunnan, China, the earthquake emergency response system (EERS) responded immediately. The real-time software delivered many seismic parameters that provided a preliminary assessment of the earthquake. The 24-hour on-duty staff and scientific researchers revised these parameters and produced more detailed reports to understand the cause of the earthquake and the potential damage, which provided valuable information for emergency rescue operations and earthquake situation assessment. Emergency personnel were dispatched immediately to the earthquake site to observe the aftershocks, investigate the damage, and guide and assist in the relief efforts. This paper describes the EERS response to the Yangbi earthquake to demonstrate the characteristics of the system and discuss the potential for further improvement.
K-means for Evolving Data Streams

Nowadays, streaming data analysis has become a relevant area of research in machine learning. Most of the data streams available are unlabeled, and thus it is necessary to develop specific clustering techniques that take into account the particularities of the streaming data. In streaming data scenarios, the data is composed of an increasing sequence of batches of samples where the concept drift phenomenon may occur. In this work, we formally define the streaming K -means (SKM) problem, which implies a restart of the error function when a concept drift occurs. An approximated error function that does not rely on concept drift detection is proposed. We prove that such a surrogate is a good approximation of the SKM error. Then, we introduce an algorithm to deal with SKM problem by minimizing the surrogate error function each time a new batch arrives. Alternative initialization criteria are presented and theoretically analyzed for streaming data scenarios. Among them, we develop and analyze theoretically two initialization methods that search for the best trade-off between the importance that is given to the past and the current batches. The experiments show that the proposed algorithm with, the proposed initialization criteria, obtain the best results when dealing with the SKM problem without requiring to detect when concept drift takes place.
ROSE-NER: Robust Semi-supervised Named Entity Recognition on Insufficient Labeled Data

With the wide application of deep learning technology in recent years, the effect of named entity recognition tasks has been significantly improved when there is sufficient labeled data. However, obtaining a large amount of labeled data is quite difficult in many domains, for example in the domain of medicine. Noisy data can also negatively affect the robustness of NER models. In this paper, we propose a robust semi-supervised NER approach ROSE-NER to tackle these challenges. A two-step semi-supervised model is introduced to expand a handful of labeled data with a large amount of predicted pseudo-labeled data. The combination of these data will be exploited in the model training. In addition, we introduce an adversarial training method to improve the robustness of the model by eliminating the impact of noise samples. Experiments on medical datasets show that our approach reduces dependency on massive labeled data, and it outperforms other State-of-the-Art approaches.
Self-Sovereign Identity for the Financial Sector: A Case Study of PayString Service

PayString is an initiative to make payment identifiers global and human-readable, facilitating the exchange of payment information. However, the reference implementation lacks privacy and security features, making it possible for anyone to access the payment information as long as the PayString identifier is known. In this paper, we propose an innovative solution for this issue by integrating a privacy layer based on Self-Sovereign Identity (SSI), Decentralized Identifier (DID) and Verifiable Credential (VC) to the PayString protocol. We also present the first performance evaluation of PayString. Via a large-scale testbed, our experimental results show an overhead which, given the privacy and security advantages offered, is acceptable in practice, thus making the proposed solution feasible.