Reviewerly/exHarmony huggingface dataset:
Large-Scale Sequential Utility Pattern Mining in Uncertain Environments

High utility sequential pattern mining (HUSPM) considers timestamp, internal quantization, and external utility factors to mine high utility sequential patterns (HUSP), which has taken an essential place in data mining. The data collection may be uncertain in real life due to environmental factors, equipment limitations, privacy issues, etc. With the rapid increase of uncertain data volume, the efficiency of traditional mining algorithms decreases seriously. When the data volume is large, the conventional stand-alone algorithm will generate more candidate sequences, occupy a lot of memory, and significantly affect the execution speed. This paper designs a high utility probability sequence pattern mining algorithm based on MapReduce. The algorithm utilizes the MapReduce framework to solve the bottleneck of single-computer operation when the data volume is too large. The algorithm adopts an effective pruning strategy, which can effectively handle and reduce the number of candidate itemsets generated, thus the performance of the designed model can be greatly improved. The performance of the proposed algorithm is verified experimentally, and the correctness and completeness of the proposed algorithm are demonstrated and discussed to show the great achievement of the designed model.
ALTRec: Adversarial Learning for Autoencoder-based Tail Recommendation

Autoencoder-based methods have achieved significant performance on item recommendation. However, they may not perform well on tail items due to the ignorance of the items’ popularity bias. As a response, in this paper, we focus on tail items and propose a novel adversarial learning method for tail recommendation (ALTRec). In our ALTRec, the generator (i.e., AutoRec) not only reconstructs the input well, but also minimizes the (any two-user) similarity difference between the input stage and the output stage to keep users’ interaction relationships unchanged. And the discriminator maps the inputs and outputs of the generator to a same semantic space for scoring the similarity and maximizes the similarity difference as the target, and will identify some unsatisfactory predictions, especially on tail items. In order to preserve the similarity, the generator will pay more attention to the tail items compared with the previous autoencoder-based methods. An ablation study validates the effectiveness of preserving the two-user similarity, as well as the adversarial learning strategy in our ALTRec. Extensive experiments on three real-world datasets show that our ALTRec significantly boosts the performance on tail items compared with several state-of-the-art methods.
K-means without K in multi-view SAR clustering

K-means algorithm realizes clustering through the correlation between data, which is a typical unsupervised algorithm. But the K-means algorithm needs to set the corresponding number of clusters K first, and the accuracy of K greatly affects the performance of the algorithm. In multi-view SAR images, the similarity value of adjacent azimuth images of the same category is higher than the similarity of adjacent azimuth images of the different category. Therefore, the K can be estimated through the relationship between the azimuth angles, and the negative impact of clustering caused by the inaccurate setting of K can be avoided. Therefore, in this paper, we propose a method for estimating the number of clusters K in SAR images based on the similarity relationship of multi-view SAR images. This method simplifies the use of K-means in multi-view SAR images.
An acoustic profile of right-dislocations in French

This study provides an acoustic profile of the prosody of right-dislocations in French, using the computer hardware and software package known as CECIL. Twenty-eight right-dislocations occurring in a corpus of natural French discourse provide the data for the acoustic analysis. While right-dislocations appear to fulfil various functional roles in discourse, no correlation appears between functional type and the acoustic properties studied (pause, frequency, amplitude).
Foreground and attention guided Center Net for SAR Ship Detection

In recent years, with the vigorous improvement of synthetic aperture radar (SAR) imaging technology, many deep learning-based methods are developed to detect ships in SAR images with higher accuracy under real-time applications. However, some challenges still exist: (1) Ship targets are difficult to distinguish from complicated background. (2) The densely distributed targets around the land area are prone to be missed. In this paper, we present a center-point based ship predictor, called FG-Att CenterNet for SAR ship detection under complex scenarios. First, the foreground information is extracted by foreground region proposal network to eliminate the complex inshore and inland interferences. This information serves for regression branch in detection framework. Then, for more accurate center point location, a coordinate attention module (CAM) is integrated to strengthen the feature maps for classification branch. The experimental results conducted on HRSID SAR ship dataset demonstrate the effectiveness of our method. As an anchor free detector, the proposed detector can well detect targets under inshore and offshore scenario, which outperforms several mainstream detectors in speed and maintains high accuracy.
A RLSN-based Current Statistical Model for Target Range Tracking

In classical “Current” statistical model, maneuvering frequency is normally set as typical value, and an acceleration limit has to be set. In order to break this limitation, this paper utilizes recursive linear smoothed Newton method to predict acceleration. An analytical solution of target maneuvering frequency is then achieved, by which the need of setting maneuvering acceleration limit is avoided. Meanwhile, adaptive calculation of process noise covariance matrix is accomplished. Moreover, usage of simplified Sage-Husa filter to realize an adaptive update of measuring noise covariance matrix is established. Simulation demonstrates that the proposed algorithm is with a better range tracking performance.
Multi-scale Broad Learning System for SAR Automatic Target Recognition

Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), as a frontier topic in the domain of artificial intelligence regarding anthropology, has witnessed some good results as deep learning technology progressively develops. However, it is still limited by the complexity of the deep structure network in need of a large number of parameters and high dimensions, easily leading to a great consumption of time. To solve this problem, this paper proposes an effective and efficient method, which is called multi-scale Broad Learning System (BLS). First, the SAR images is down-sampled to obtain images of different sizes and feature extraction. Then, the multi-scale images are concatenated. Finally, it is input to the broad learning network for training to obtain the final prediction results. Extensive experiments with the proposed method on SAR datasets can be conducted within 11.35s while sustaining an accuracy of 97.55 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , impressively outstripping other state-of-the-art methods both in speed and accuracy.
Speaking style compensation on synthetic audio for robust keyword spotting

With the rise of intelligent speech processing applications, quickly producing keyword spotting (KWS) models with low resource has gained particular importance in recent years. Multi-speaker text-to-speech (TTS) has been proved to be an effective data augmentation technique for KWS to help complement inadequacies in the training data. However, in previous works, KWS system built with TTS augmented data couldn’t obtain considerable performance of that trained with real recordings as synthetic speeches could not fully represent target speaker’s speaking style. This work focuses on how to make synthesized speeches to be more similar to reference speaker’s speaking style under a specific metric. Speaker classification accuracy of synthesized keyword data on a speaker recognition model trained with real common recordings is used as the objective metrics, and the deep complex convolution recurrent network (DCCRN) is used to optimize it. Experimental results show that TTS augmentation helps improve the KWS system’s robustness. Moreover, by compensating speaking style of the synthetic data, we achieve a significant further improvement.
The cost of passing – using deep learning AIs to expand our understanding of the ancient game of Go

AI engines utilizing deep learning neural networks provide excellent tools for analyzing traditional board games. Here we are interested in gaining new insights into the ancient game of Go. For that purpose, we need to define new numerical measures based on the raw output of the engines. In this paper, we develop a numerical tool for automated move-by-move performance evaluation in a context-sensitive manner and for recognizing game features. We measure the urgency of a move by the cost of passing, which is the score value difference between the current configuration of stones and after a hypothetical pass in the same board position. Here we investigate the properties of this measure and describe some applications.
A Maritime Targets Tracking Algorithm

In order to solve the problem of tracking for maritime targets, this paper proposes Probability Hypothesis Density algorithm with amplitude information(APHD). Traditional radars only use position information for target tracking, however, the amplitude measurement is introduced to track the target with traditional position information measurement in this paper. First, this work assumes that the background is K-distribution, the amplitude measurement is introduced into traditional probability hypothesis density algorithm to establish the amplitude measurement likelihood function between the target and clutter. Second, the amplitude measurement likelihood function is introduced into the update stage, and the updated posterior intensity of Gaussian mixture is used for the estimation of the state of the next frame. The simulation results verify the effectiveness of the proposed algorithm.
Machine Learning–Based Profiling in Test Cheating Detection

Abstract In recent years, machine learning (ML) techniques have received more attention in detecting aberrant test‐taking behaviors due to advantages when compared to traditional data forensics methods. However, defining “True Test Cheaters” is challenging—different than other fraud detection tasks such as flagging forged bank checks or credit card frauds, testing organizations are often lack of physical evidences to identify “True Test Cheaters” to train ML models. This study proposed a statistically defensible method of labeling “True Test Cheaters” in the data, demonstrated the effectiveness of using ML approaches to identify irregular statistical patterns in exam data, and established an analytical framework for evaluating and conducting real‐time ML‐based test data forensics. Classification accuracy and false negative/positive results are evaluated across different supervised‐ML techniques. The reliability and feasibility of operationally using this approach for an IT certification exam are evaluated using real data.
TsEDM: A two-steps event detection model for Chinese event detection

Event detection (ED) consists of two phases – trigger identification (TI) and trigger classification (TC). Traditional ED adopts a unified model to process the above two-stage tasks at once. We argue that there are certain differences in the contextual semantics required and the goals of these two phases in ED. In which, TI remains suffers from the word-trigger mismatch problems in languages without natural word delimiters such as Chinese. And the TC is facing challenging problems of trigger ambiguity and multiple triggers in a sentence. In this article, we propose a brand-new two-steps event detection model (TsEDM), which attempts to alleviate above-mentioned problems. Specifically, a novel ‘head-tail dual-pointer’ (HT-DP) labelling strategy is developed to obtain more candidate triggers to overcome the problems of continuous labelling, nested labelling and independent labelling in the first step (TI). Besides, an ‘entity–topic–candidate–trigger’ interaction graph (E2T-IG) is constructed in the second step (TC) to consider the interaction relationship between candidate triggers and core information inter or in all event sentences, which enhance the representation of each candidate trigger. Last but not least, a shake-gated and residual-based atrous convolution neural network (SGR-ACNN) is proposed as the common framework of these two steps, which dynamically integrates various representations as model inputs. Experiments on the ACE2005-CN show that TsEDM significantly outperforms state-of-the-art (SOTA) methods.
Exploring the Attitudes of Kuwait’s Residents Toward the Role of Corruption and Anti-Corruption Entities (Nazaha) in Kuwait

AbstractThis paper explored the attitudes of Kuwaiti residents toward corruption practices in Kuwait and the role of the Nazaha Kuwait Anti-Corruption Authority in tackling such actions. To achieve the aim of this study, a survey questionnaire, which was based on a survey introduced by the United Nations Office on Drugs and Crime in 2018, was distributed to 535 participants in Kuwait. The questionnaire covered areas related to problems facing Kuwait, behaviors of public officers, opinions on practices of public sector employees, corruption in Kuwait by industry, the role of Nazaha in Kuwait, the correlation between bribery and access to government services, and the reporting of corruption in Kuwait. The collected data were analyzed with SPSS. The findings indicate that 374 respondents noted corruption as the most serious problem in Kuwait, which is practiced systematically in Kuwait due to the prevalence of nepotism and other cultural reasons. In addition, it was found that there is dissatisfaction with the role of Nazaha, with 46.9 and 32.3% of participants reporting that Nazaha did not react in time and did not catch offenders, respectively. This study suggests that Nazaha should be empowered with further legal means to tackle corruption in the future.Keywords: Anti-corruptioncorruptiongovernmentKuwaitNazaha
Eigen value based loss function for training attractors in iterated autoencoders

The way that the human brain handles the input variations has been one of the most interesting areas of research for neuroscientists. There are some evidences that the human brain acts like an attractor when trying to memorize or retrieve some information. Based on this fact, in this research, a new method is presented for creating attractors during training of an iterated autoencoder. In this method a new loss function is presented which decreases the absolute real of Eigen values while preserving the reconstruction error during training. A fully connected structure is chosen for constructing the iterated autoencoder in this research which mostly faces with local minima especially when they are deep. For getting through this issue, a layer-by-layer pre-training approach is taken to train the network. Using the evaluation on MNIST dataset, it is shown that the proposed model can retrieve 59.98% of test samples which shows a considerable improvement over Dense Associative Memory (DAM) when trained on 100 similar MNIST test samples. The performance of the proposed model is compared to overparameterized autoencoder (OAE) model which was recently presented and showed promising results in constructing associative memories. The results show that the proposed model outperforms OAE in terms of the number of attractors learned by the network in a similar number of network parameters. Finally, the performance of the proposed model is evaluated with corrupted version of training samples, revealing significant robustness when compared to the baseline autoencoder.
A Searchable Encryption with Forward/Backward Security and Constant Storage

Dynamic searchable encryption satisfies users’ needs for ciphertext retrieval on semi-trusted servers, while allowing users to update server-side data. However, cloud servers with dynamically updatable data are vulnerable to information abuse and file injection attacks, and current public key-based dynamic searchable encryption algorithms are often complicated in construction and high in computational overhead, which is not efficient for practical applications. In addition, the client’s storage costs grow linearly with the number of keywords in the database, creating a new bottleneck when the size of the keyword set is large. To solve the above problems, a dynamic searchable encryption scheme that uses a double-layer structure, while satisfying forward and backward security, is proposed. The double-layer structure maintains a constant client-side storage cost while guaranteeing forward and backward security and further reduces the algorithm overhead by avoiding bilinear pairings in the encryption and decryption operations. The analysis results show that the scheme is more advantageous in terms of security and computational efficiency than the existing dynamic searchable encryption scheme under the public key cryptosystem. It is also suitable for the big data communication environment.
Rethinking 1D convolution for lightweight semantic segmentation

Lightweight semantic segmentation promotes the application of semantic segmentation in tiny devices. The existing lightweight semantic segmentation network (LSNet) has the problems of low precision and a large number of parameters. In response to the above problems, we designed a full 1D convolutional LSNet. The tremendous success of this network is attributed to the following three modules: 1D multi-layer space module (1D-MS), 1D multi-layer channel module (1D-MC), and flow alignment module (FA). The 1D-MS and the 1D-MC add global feature extraction operations based on the multi-layer perceptron (MLP) idea. This module uses 1D convolutional coding, which is more flexible than MLP. It increases the global information operation, improving features' coding ability. The FA module fuses high-level and low-level semantic information, which solves the problem of precision loss caused by the misalignment of features. We designed a 1D-mixer encoder based on the transformer structure. It performed fusion encoding of the feature space information extracted by the 1D-MS module and the channel information extracted by the 1D-MC module. 1D-mixer obtains high-quality encoded features with very few parameters, which is the key to the network's success. The attention pyramid with FA (AP-FA) uses an AP to decode features and adds a FA module to solve the problem of feature misalignment. Our network requires no pre-training and only needs a 1080Ti GPU for training. It achieved 72.6 mIoU and 95.6 FPS on the Cityscapes dataset and 70.5 mIoU and 122 FPS on the CamVid dataset. We ported the network trained on the ADE2K dataset to mobile devices, and the latency of 224 ms proves the application value of the network on mobile devices. The results on the three datasets prove that the network generalization ability we designed is powerful. Compared to state-of-the-art lightweight semantic segmentation algorithms, our designed network achieves the best balance between segmentation accuracy and parameters. The parameters of LSNet are only 0.62 M, which is currently the network with the highest segmentation accuracy within 1 M parameters.
Investigating Trace Equivalences in Information Networks

Equivalences are widely used and have achieved much success in concurrent systems. Meanwhile, information networks are ubiquitous for representing many complex systems and have similar characteristics and properties to concurrent systems such that they both can be described by graphs. In order to simplify information networks, we introduce equivalence to information networks, specifically leveraging the trace equivalence to reduce the complexity of these networks. In this paper, we first define the concept of trace and trace equivalence in information networks, drawing on the similar concept of concurrent systems. We then propose a computational method for determining whether two nodes are trace equivalent in an information network. With the help of this method, we derive trace-equivalent networks from original networks. Experiments show that we are able to reduce the number of nodes in the ACM and DBLP datasets by at most 65.21% and 46.68%, respectively. Running the PathSim algorithm on the original and derived networks, the mean error is 0.0728 in ACM and 0.0446 in DBLP. Overall, the results indicate that the derived networks have fewer nodes and edges than the original networks, yet still capture the same or similar information. By using trace equivalence, we are able to simplify information networks and improve their efficiency while preserving most of their informational content.
The context of Songdok: Two purposes of traditional Korean education

AbstractThis study explores the educational meaning of Songdok in traditional Korean education. Songdok refers to the act of memorizing text completely while reading it aloud; however, in traditional Korean education, it used to symbolize ‘learning’ itself. Historically, Songdok was regarded in extreme terms: being criticized as low-level memorization or encouraged as a religious ritual. In the Goryeo Dynasty, when civil service exams were introduced, Songdok was performed to memorize Confucian textbooks solely for passing the exam. However, its status changed in the mid-1600s because of an educational reform movement by Neo-Confucian fundamentalists. Given the widespread perception in South Korea that ‘imitating a saint is a way of learning’, Songdok has been elevated as part of moral consciousness. Here, we discuss how Songdok takes on two opposing educational objectives: One is passing the exam, and the other is imitating a saint. An examination of Songdok provides insightful perspectives on Korean education, especially regarding sluggish moral education.Keywords: Purpose of educationNeo-ConfucianismSongdokcivil service examination Notes1 Shin (Citation2011) noted that Songdok was established as a representative study method, and it went beyond simple memorization. However, this study was limited in that it did not fully reveal the meaning of Songdok in traditional Korean education.2 Passing the exam was the main way to enter the bureaucracy, as more than 40% of those from the ancestors’ virtue system had to retake the exam (Kim, Citation1991).Additional informationNotes on contributorsSujin SongSujin Song is researcher at the Institute of Humanities Studies, Kyungpook National University. Her research interests include the origin, formation, and historical development of traditional Korean and East asian education.Sanghyun KimSanghyun Kim is professor of Department of Education at Kyungpook National University. His research interest is in the History and Philosophy of Education, Ethics of Educational Policy and Practice, Social and Political Philosophy, and Korean Studies.
DR-CIML: Few-shot Object Detection via Base Data Resampling and Cross-iteration Metric Learning

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