Reviewerly/exHarmony huggingface dataset:
Hypersonic Target Track Initiation Method Based on Interactive Multiple Gate

With regards to the problem that the near space hypersonic target flight speed and maneuverability, traditional methods can not initiate target track effectively, a kind of near space hypersonic target track initiation method based on interactive multiple gate (IMG) was proposed. Based on the near space hypersonic characteristics of target motion velocity and the set-up of the target's maneuver ability of the fan gate, oval gate and part of the annular gate, by using data processing method, the initial tracking of the target can be calculated through the input interaction, trace detection, weight updating and output interaction of the interactive computing of multiple gates. The simulation results show that, the new method can initiate near space hypersonic target track effectively and the correct track initiation probability is high with wide mobile coverage. This method has a certain applicability and adoptability.
Web-Based Sentiment Analysis Application of Hotel Reviews in Indonesia

For many nations, including Indonesia, tourism is crucial. Following the oil and gas sector, tourism is Indonesia’s second-largest source of foreign currency earnings. A comfortable operation of tourism activities depends on the presence of amenities like hotels and tourist attractions. To ensure that tourism operates smoothly, it is also crucial to understand the meaning behind customer feedback to maintain good facilities and services and improve inadequate or poor facilities. The data used in this work is based on reviews of 150 hotels in the “Best Value Hotels” category in Indonesia from the Tripadvisor website. The reviews contained in the data are reviews in the form of positive and negative. The data is then processed for machine learning model training to classify sentiments as positive or negative reviews. We then applied feature extraction methods such as Term Frequency - Inverse Document Frequency (TF-IDF) and Synthetic Minority Oversampling Technique (SMOTE) sampling method and compared the three models’ performance with and without using them. This work also implements the trained model into web-based application, so the user can perform sentiment analysis on hotel reviews independently. The result is the overall performance of the three models in performing sentiment analysis on hotel reviews are great. SMOTE and TF-IDF also play a vital role in improving the three models’ with an overall F1-Score above 80%.
Action Guidance-Based Deep Interactive Reinforcement Learning for AUV Path Planning

Autonomous underwater vehicle (AUV) is playing an increasingly important role in marine scientific research and resource exploration due to its autonomy and flexibility. As the core technology to improve AUV's autonomy, path planning facilitates AUV to complete its mission safely by avoiding obstacles in the route. In this paper, we proposed an action guidance-based interactive deep deterministic policy gradient (IDDPG) method for AUV path planning. The human trainer can provide a suggested action based on the real-time state of AUV and indirectly assign reward values by comparing the suggested action with the one selected by control policy. We tested IDDPG and compared to the original DDPG algorithm in two path planning tasks: obstacle boundary detour and local obstacle avoidance, on the Gazebo simulation platform. The simulation results show that IDDPG can alleviate low sample efficiency problem of traditional deep reinforcement learning and significantly improve the learning speed of AUV. In addition, IDDPG is shown to generalize better to untrained new environments than DDPG.
Online Deep Knowledge Tracing

This study focuses on solving the problem of knowledge tracing in a practical situation, where the responses from students come in a stream. Most current works of deep knowledge tracing are pursuing to integrate of more side information or data structure, but they often fail to make self-update in the dynamic learning situation. Towards this end, we here proposed an online deep knowledge tracing model, dubbed ODKT, by utilizing the online gradient descent algorithm to develop the traditional deep knowledge tracing (DKT) into online learning. Rather than learning a perfect model, the ODKT aims to train DKT in its using process step by step. Experiments were conducted on four public datasets for knowledge tracing. The results demonstrate that the ODKT model is effective and more suitable for practical applications.
TPE-AutoClust: A Tree-based Pipline Ensemble Framework for Automated Clustering

Novel technologies in automated machine learning ease the complexity of building well-performed machine learning pipelines. However, these are usually restricted to supervised learning tasks such as classification and regression, while unsu-pervised learning, particularly clustering, remains a largely un-explored problem due to the ambiguity involved when evaluating the clustering solutions. Motivated by this shortcoming, in this paper, we introduce TPE-AutoClust, a genetic programming-based automated machine learning framework for clustering. TPE-AutoCl ust optimizes a series of feature preprocessors and machine learning models to optimize the performance on an unsupervised clustering task. TPE-AutoClust mainly consists of three main phases: meta-learning phase, optimization phase and clustering ensemble construction phase. The meta-learning phase suggests some instantiations of pipelines that are likely to perform well on a new dataset. These pipelines are used to warmstart the optimization phase that adopts a multi-objective optimization technique to select pipelines based on the Pareto front of the trade-off between the pipeline length and performance. The ensemble construction phase develops a collaborative mechanism based on a clustering ensemble to combine optimized pipelines based on different internal cluster validity indices and construct a well-performing solution for a new dataset. The proposed framework is based on scikit-learn with 4 preprocessors and 6 clustering algorithms. Extensive experiments are conducted on 27 real and synthetic benchmark datasets to validate the superiority of TPE-AutoCl ust. The results show that TPE-AutoClust outperforms the state-of-the-art techniques for building automated clustering solutions.
Knowledge-Driving Language in Text-Based Learning: Integrated Thematic Learning Material Design

Based on the results of research on government-issued textbooks using the 2013 Curriculum for elementary school education levels, it can be seen that the materials in the book do not describe the integration of one material with other materials from various competencies in different fields of science, although under the same theme or sub-theme.In fact, on the cover page it is labelled "Thematic Books for Integrated Curriculum 2013".The lack of integration of these materials is due to the absence of competency material from certain fields that can become the binding thread and act as a medium of transmission from one competency to another when there is a change of discussion material.On that basis, this article provides an overview of how Bahasa materials can be used as a binder and transmission medium between various materials of competence in different fields of science or subjects.In other words, this article presents the conceptual design of an integrated thematic learning material design model based on the role of language as a fetcher of knowledge.
ENiD: An Encrypted Web Pages Traffic Identification Based on Web Visiting Behavior

With the development of network encryption tech-nologies, more web sites use encrypted web pages to protect users' data security. Despite this, attackers use encrypted web pages to hide real web content, such as phishing pages, malware, bots, etc. Detecting such vulnerable web pages and malicious phishing web sites can be accomplished by identifying encrypted web pages. In recent years, using traffic features and machine learning to identify encrypted web pages is one of the most important research directions in cyber security. In this paper, we propose ENiD, an encrypted web page traffic identification approach. This method uses upload-only blocks and accumulation response size to describe the web page visiting process. Based on a large number of encrypted web page traffic case studies, we evaluated the contributions of different features and selected those features that contributed the most. We first capture and publish the encrypted web pages traffic dataset, which contains 8,480 web pages traffic. We evaluate our method's effectiveness by four machine learning algorithms, which shows that our approach achieved accuracy and an Fl score of 0.97 on 50 web pages. Moreover, we evaluate the effectiveness of ENiD on different numbers of web pages, and the results demonstrate that our methods are still effective on more than 400 web pages.
Overall Rating Prediction from Review Texts using Category-oriented Japanese Sentiment Polarity Dictionary

Hotel booking sites provide evaluations, including textual reviews and numerical ratings by hotel guests. However, some evaluations are without numerical ratings, and there are some inconsistent evaluations between textual reviews and numerical ratings (e.g., a positive review text is posted along with low ratings). Such evaluations may confuse site users. To resolve such problems, we propose a high-accuracy method to predict an overall numerical rating from a textual review. Our new idea is to use Category-oriented Sentiment Polarity Dictionaries (CSPD), which are automatically compiled for each category using a Rakuten Travel review database. The CSPD gives the sentiment polarity value (i.e., the positivity/negativity value) for each sentiment word for each category. Our proposed method first predicts category ratings from its BERT vector and the CSPD. After that, based on the predicted category ratings and the BERT vector, our method predicts the overall rating. Our experimental results show that our method attains higher accuracy than using only BERT vectors.
BASS: Blockchain-Based Asynchronous SignSGD for Robust Collaborative Data Mining

Federated learning (FL) is a machine learning framework for collaborative data mining in many scenarios (e.g. Internet of Things) due to its privacy-preserving feature. However, various attacks arise security concerns of FL, such as poisoning, backdoor, and DDoS attacks. Several blockchain-based FL schemes strengthen credibility and security without considering the increased communication overhead. Some existing work compresses local updated gradients to sign vectors to lower communication overhead at the expense of model accuracy. To address the above concerns, this paper offers a blockchain-based asynchronous SignSGD (BASS) scheme. A novel asynchronous sign aggregation algorithm is introduced to ensure model accuracy even if the local updated gradients are compressed to sign vectors. Considering the unstable network connection on IoT, a consensus algorithm that elects multiple leader nodes enables reliable global model aggregation. The introduced blockchain improves credibility and security without downgrading efficiency. Empirical studies show that BASS outperforms other schemes in efficiency, model accuracy, and security.
Edge Detection with the Cross-layer Fusion Feature Based on RCF

To solve the problems such as chaotic and fuzzy edge lines caused by current edge detection based on deep-learning, inspired by RCF (Richer Convolutional Features), an end-to-end Cross-layer Fusion Feature for edge detection (CFF) model was presented. In it, RCF was used as a baseline, CBAM (Convolutional Block Attention Module) was appended to the backbone network, translation-invariant downsampling technology was adopted, and some downsampling operations in the backbone network were deleted to preserve the image details information, dilated convolution technique was adopted to increase the model receptive field simultaneously. Additionally, the cross-layer fusion feature map is used to fully fuse high level and low-level features together. To balance the relationship between each stage loss and the fusion loss, and to avoid the phenomenon of excessive loss of low-level details after multi-scale feature fusion, the weight parameters were added to the losses. The model was trained on the PASCAL VOL Context and Berkeley Segmentation (BSDS500) Set, and the image pyramid technology was used in testing to improve the edge images quality. Experimental results verified that the contour extracted by CFF model is clearer than that extracted by the baseline network and can improv the edge blurring problem.
Multi-Sensor Control for Distributed Multi-Target Tracking with Limited Sensor Fields-of-view

This paper addresses the multi-sensor control problem for distributed multi-target tracking (DMTT) with limited sensor fields-of-view (FoVs). Existing control methods cannot be directly used when the FoVs of sensors are very limited. To solve this problem, we propose a new multi-sensor control strategy that contains two key points. One is to generate the FoV-based pseudo measurement set, which is also used to sift through control commands. The other one is to propose a new reward function composed of the Cauchy-Schwarz (CS) divergence between the fused predicted density and the fused posterior density and a penalty function related to the reduction of cardinality estimate. Based on it, the optimal control scheme is obtained by maximizing the reward function. Moreover, we employ the clustering algorithm generalized covariance intersection (CA-GCI) method to fuse the local densities for dealing with the different FoVs cases. Simulation results in a challenging multi-object tracking scenario with three radar-type sensors demonstrate effectiveness of the proposed solution.
cSmartML-Glassbox: Increasing Transparency and Controllability in Automated Clustering

Machine learning algorithms have been widely employed in various applications and fields. Novel technologies in automated machine learning (AutoML) ease algorithm selection and hyperparameter optimization complexity. AutoML frame-works have achieved notable success in hyperparameter tuning and surpassed the performance of human experts. However, depending on such frameworks as black-box can leave machine learning practitioners without insights into the inner working of the AutoML process and hence influence their trust in the models produced. In addition, excluding humans from the loop creates several limitations. For example, most of the current AutoML frameworks ignore the user preferences on defining or controlling the search space, which consequently can impact the performance of the models produced and the acceptance of these models by the end-users. The research in the area of transparency and controllability of AutoML has attracted much interest lately, both in academia and industry. However, existing tools are usually restricted to supervised learning tasks such as classification and regression, while unsupervised learning, particularly clustering, remains a largely unexplored problem. Motivated by these shortcomings, we design and implement cSmartML-GlassBox, an interactive visualization tool that en-ables users to refine the search space of AutoML and analyze the results. cSmartML-GlassBox is equipped with a recommendation engine to recommend a time budget that is likely adequate for a new dataset to obtain well-performing pipeline. In addition, the tool supports multi-granularity visualization to enable machine learning practitioners to monitor the AutoML process, analyze the explored configurations and refine/control the search space. Furthermore, cSmartML-GlassBox is equipped with a logging mechanism such that repeated runs on the same dataset can be more effective by avoiding evaluating the same previously considered configurations. We demonstrate the effectiveness and usability of the cSmartML-GlassBox through a user evaluation study with 23 participants and an expert-based usability study based on four experts. We find that the proposed tool increases users' understanding and trust in the AutoML frameworks.
Domain Adaptation Through Cluster Integration and Correlation

Domain shift is a common problem in many real-world applications using machine learning models. Most of the existing solutions are based on supervised and deep-learning models. This paper proposes a novel clustering algorithm capable of producing an adapted and/or integrated clustering model for the considered domains. Source and target domains are represented by clustering models such that each cluster of a domain models a specific scenario of the studied phenomenon by defining a range of allowable values for each attribute in a given data vector. The proposed domain integration algorithm works in two steps: (i) cross-labeling and (ii) integration. Initially, each clustering model is crossly applied to label the cluster representatives of the other model. These labels are used to determine the correlations between the two models to identify the common clusters for both domains, which must be integrated within the second step. Different features of the proposed algorithm are studied and evaluated on a publicly available human activity recognition (HAR) data set and real-world data from a smart logistics use case provided by an industrial partner. The experiment's goal on the HAR data set is to showcase the algorithm's potential in automatic data labeling. While the conducted experiments on the smart logistics use case evaluate and compare the performance of the integrated and two adapted models in different domains.
Precise Instance Segmentation Network for High-Resolution SAR Images

With the development of synthetic aperture radar (SAR) system technology and the wide application of deep learning technology, ship detection on SAR images has rapidly developed. Benefit from the strong generalization ability and end-to-end training capabilities, convolution neural network (CNN) based ship detection methods have the proprietary advantage in high-performance SAR ship detection. However, relevant SAR ship detection methods adopt rectangular bounding boxes to locate the ships which are unable to extract the contours feature of the ships. To solve this problem, we proposed a precise instance segmentation network for high-resolution SAR images. The method combines the bottom-up path augmentation module, global context module, and soft non-maximum suppression to improve Mask R-CNN for segmenting high-resolution SAR ships in pixel-wise. The network is trained and tested on the high-resolution SAR images dataset (HRSID), and the ablation experiments are conducted with Microsoft Common Objects in Context (MS COCO) evaluation metrics to verify the effects of each module. Quantitatively, the experimental results show that the method exceeds vanilla Mask R-CNN 2% AP in instance segmentation of high-resolution SAR images. Meanwhile, the visualized instance segmentation results indicate that our method fits the practical application, and it possesses the ability to extract the contour of the ship, which is more conducive to the instance segmentation of SAR images.
The FawAI ASR System for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge

This paper describes the FawAI automatic speech recognition systems submitted to the Intelligent Cockpit Speech Recognition Challenge Track 1 by our team, named as T002. For this speech recognition system, connectionist temporal classification/attention-based encoder-decoder architecture is used as the backbone model and a 3-gram language model based on a weighted finite state transducer is used to decode the model. To mitigate the gap between train set and test set, a two-stage training strategy with forced alignment objective is used to improve the noise robustness and accuracy on track1 test set. Finally, the submitted system achieves a character error rate of 12.67% for track1 on the test set of the challenge and ranked second place among the submitted systems in the challenge.
Improving Black-box Adversarial Attacks on HRRP-based Radar Automatic Target Recognition

In the field of high-resolution range profile (HRRP)-based radar automatic target recognition(RATR), deep learning has gradually become the mainstream. However, crafted adversarial samples will cause misclassification of deep neural network (DNN), which has not attracted extensive attention in the RATR community. In this paper, we first systematically study the robustness of several state-of-the-art neural networks in the face of adversarial attack. Experiments show that the existing methods can not deceive some robustness neural network with high probability in the black-box setting. To improve cross-model transferability, we introduce a data augmentation strategy, which performs a simple transformation on the input data. Extensive experiments on HRRP dataset show that our proposed method can greatly enhance the attack success rates.
Three-Level Password Approach in Granting User Access to Shared Folders

An established file sharing infrastructure can be a huge convenience for organizations to allow members of all levels within an organization to collaborate and share data. While allowing multiple access to files and folders, the privacy and secrecy of files that reside in the shared space become something of a tradeoff among users. It has now become a major challenge to set folder exclusive access to certain users when shared media is accessible to all. Thus, this study proposed a method to lock a specific shared folder that can only be accessed by utilizing a three-level password approach. The proposed method required users to provide textual password at the first level, then employing a color combination picker at the second level and finally utilizing a picture password in third level. The knowledge of the passwords for each level can only be known by exclusive users in order to acquire access to the protected folder. This enhanced method will ensure the access right would only be granted to users with exclusive rights. Consequently, hard to guess password challenge has the added benefit of hindering illicit folder access attempt.
A Low-complexity Hand Gesture Recognition Method Based on 2D-Trajectory Feature Using Bistatic Radars

Radar-based hand gesture recognition is robust to the line-of-sight and lighting environments, and has the advantages of non-contact, strong penetrability and protection of user privacy. It is becoming one of the hottest topics in the area of gesture recognition. Most of the existing radar-based hand gesture recognition methods are based on images such as time-distance map and time-frequency map, and have the problems of large model and high computational complexity. To handle these problems, this paper proposes a low-complexity hand gesture recognition method based on two-dimensional trajectory feature using bistatic radars. First, signal preprocessing and target detection are carried out on the radar data. Then, the adaptive order polynomial fitting method is used to fit the centroid and two-dimensional trajectory is then synthesized. After that, a lightweight variable-length long short term memory (LSTM) is designed for identifying hand gestures. The experimental results show that the average recognition rate of the trained people and the untrained people are 98% and 92.0% respectively.