Miracle-dz/UniFAR-FTUs huggingface dataset:
Whales, Dolphins, or Minnows? Towards the Player Clustering in Free Online Games Based on Purchasing Behavior via Data Mining Technique

The free online game market is one of the fastest growing and most profitable markets in the electronic game industry. In order to improve players’ gaming experience and increase revenue, game data analytics is usually applied to support game development, in-game event design, and game operation. In particular, the game companies strive to understand and characterize the players’ buying power and design marketing strategies or incentives accordingly to increase their revenues. In this paper, we propose a novel algorithm to perform player clustering into the groups of high spenders (whales), moderate spenders (dolphins), and low spenders (minnows) based on the players’ purchase records via data mining technique. Evaluations including an A/B test with a typical popular free online game show that our proposed algorithm can effectively cluster the players in terms of their purchasing behavior and the game companies can benefit from our algorithm.
3-D Inverse Synthetic Aperture Ladar Imaging and Scaling of Space Debris Based on the Fractional Fourier Transform

The inverse synthetic aperture ladar (ISAL) is an important method for observation and imaging of space targets. Here, a 3-D ISAL imaging algorithm is proposed for spinning targets such as space debris. Since laser wavelength is 4–5 orders of magnitude smaller than that of microwave, the Doppler frequency caused by target motion is more pronounced in ISAL. Doppler frequency modulation rates can be estimated by the fractional Fourier transform with respect to azimuth slow time even when the rotation angle is small such that scattering centers do not migrate through a range cell. Then, slant range, Doppler frequency, and Doppler frequency modulation rates form a 3-D space. The angular velocity and the incident angle can be estimated by the position relationship between the scattering centers in two observations. After image scaling, the 3-D shape and size of the target can be obtained. The 3-D structure of the target in the simulation experiment is accurately reconstructed. Monte Carlo experiments are conducted to discuss the effect of the signal-to-noise ratio and observation time on the algorithm. Finally, the effectiveness and robustness of the algorithm are verified.
Mining Semantic Variation in Time Series for Rumor Detection Via Recurrent Neural Networks

Social media has promoted the dissemination of information, which becomes an ideal platform for rumor diffusing. Due to debunking rumors automatically is crucial to minimize the adverse effects of rumors, numerous hand-crafted features based approaches have been proposed. However, as the information spreading in social media is usually large scale, feature engineering is time-consuming and labor-intensive. Besides, social media is dynamic and complicated which makes features difficult to cover the potential features in new states. In this paper, we propose a novel method which learns the variation of semantics over the entire life-cycle of events for rumor detecting. In this model, a bidirectional recurrent neural network (RNN) model with attention mechanism is utilized to learn high-level semantic representations automatically. And another RNN model captures the variation of semantics. Experimental results on datasets from two real-world microblog platforms show that (1) our model outperforms other state-of-the-art rumor detection models; (2) by using hierarchical attention mechanism, our method can select informative posts and distinctive words as features; (3) our model can detect rumors more accurately at an early stage.
Scheduling Large-scale Distributed Training via Reinforcement Learning

Scheduling the training procedure of deep neural networks (DNNs) such as tuning the learning rates is crucial to the successes of deep learning. Previous strategies such as piecewise and exponential learning rate schedulers have different arguments (hyper-parameters) that need to be tuned manually. With the expanding of data scale and model computation, searching for these arguments spends lots of empirical efforts. To address this issue, this work proposes policy schedular that determines the arguments of learning rate (lr) by reinforcement learning, significantly reducing costs to tune them. The policy schedular possesses several appealing benefits. First, instead of manually defining the values of initial lr and ultimate lr, it autonomously determines these values in training. Second, rather than using predefined functions to update lr, it adaptively oscillates lr by monitoring learning curves without human intervention. Third, it is able to select lr for each block or layer of a DNN. Experiments show that the DNNs trained with policy schedular achieve superior performances, outperforming previous work on various tasks and benchmarks such as ImageNet, COCO, and learning-to-learn.
Throughput, Coverage and Scalability of LoRa LPWAN for Internet of Things

LoRa is a leading Low-Power Wide-Area Network (LPWAN) technology for Internet of Things (IoT). While LoRa networks are rapidly being deployed around the world, it is important to understand the capabilities and limitations of this technology in terms of its throughput, coverage and scalability. Using a combination of real-world measurements and high fidelity simulations, this paper aims at characterizing the performance of LoRa. Specifically, we present and analyze measurement data collected from a city-wide LoRa deployment in order to characterize the throughput and coverage of LoRa. Moreover, using a custom-built simulator tuned based on our measurement data, we present extensive simulation results in order to characterize the scalability of LoRa under a variety of traffic and network settings. Our measurement results show that as few as three gateways are sufficient to cover a dense urban area within an approximately 15 Km radius. Also, a single gateway can support as many as 105 end devices, each sending 50 bytes of data every hour with negligible packet drops. On the negative side, while a throughput of up to 5.5 Kbps can be achieved over a single 125 KHz channel at the physical layer, the throughput achieved at the application layer is substantially lower, less than 1 Kbps, due to the network protocols overhead.
A Matlab Tool for Subjective Assessment of Image Quality

Image quality assessment (IQA) is to evaluate the performance of image processing techniques, and acts as an important way to promote their further developments. In objective evaluation, the image quality of a to-be-evaluated image is related to its difference with the ground-truth image which is estimated by using various metrics, such as the average error and the mean square error. However, an objective evaluation is often impossible to exactly match the perception of human vision system. Therefore, a reliable subjective assessment of image quality is always necessary. In this paper, a Matlab tool is designed to facilitate this evaluation task from the following four aspects. First, every to-be-evaluated image is presented to viewers with a random order and without any label; that is, a blind evaluation is conducted. Second, after a round of evaluation, the order of the to-be-evaluated images will be randomly changed and a second round of blind evaluation will be required; that is, a double-confirmed evaluation is achieved. Third, during the evaluation, the current image (either the ground truth or a to-be-compared one) can be enlarged by the viewers for a close watching. In particular, when this image is enlarged, other images in the same group will be automatically enlarged at the same portion for fascinating the comparison; that is, a synchronous magnification is realized. The final evaluation result is taken to be a mean of the scores of the two evaluation rounds, provided that their difference is below a given threshold; that is, a automatic analysis is achieved. The tool is publicly available at https://github.com/hdddx/SubjectiveEvaluation.
Accelerated Super-resolution MR Image Reconstruction via a 3D Densely Connected Deep Convolutional Neural Network

Magnetic resonance (MR) images are often acquired with low resolution (LR) due to hardware limitations, practical limitations on acquisition time, and patient comfort. In this study, we propose a novel method to reconstruct high resolution (HR) MR images through efficiently learning the LR to HR nonlinear mapping by a densely connected 3D deep convolutional neural network (CNN) that uses learnable deconvolution on multi-level features for upsampling. Different from the current CNN-based MR image super-resolution (SR) methods that take interpolated patches as input, the proposed method directly takes LR MR images (or LR patches) as input to reduce computational complexity and accelerate SR reconstruction. Improved dense blocks in this architecture are adopted to extract multi-level features from the LR image, and carry the information forward with dense connections. The final deconvolution layer automatically learns a filter to fuse and upscale all feature maps to generate HR MR images. The experimental results on three benchmark datasets demonstrate that the proposed method achieves state-of-the-art MR image SR reconstruction performance with less computational load and memory usage.
On the Authentication of Certificateless RSA Public Key

Public key certifications are formal and secure in user authentication. The infrastructure of certificate authorities (CA) is necessary in public key certifications. In many secure and non-restrictive applications, such as authentication in the secure wireless access points (AP), the password authentication is not secure enough, and the cost of public key certification is too high. In this article, we focus on the certificateless RSA public key authentication: the technology, limitation, content, and security of the predetermined portion in RSA public key. In order to authenticate RSA public key, we can predetermine portion in RSA key generation by using multiplication properties, multiplicative inverse with suitable authenticated content. In comparison with traditional RSA, the key generation must be modified. On the other side, the encryption, decryption, signature generation and verification are all the same in comparison with traditional RSA cryptosystem that is secure and convenient to many applications.
Saturated Output Feedback Control for Flexible-joint Robot Manipulators*

Considering the limitation of the joint driving torque and the unavailability of partial state measurements, a saturated output feedback control method with flexibility compensation is proposed for flexible-joint robot manipulators. Firstly, using the singular perturbation theory, the dynamics model of flexible-joint robot manipulator is decoupled into fast and slow subsystems. The saturation function is subsequently involved to design the bounded control law of each subsystem and the flexibility compensation term, so as to ensure the boundedness of the system control inputs. Then, considering the unmeasurements of speed on both the link side and the joint motor side in the system, a high-gain observer is designed to achieve the output feedback control that only needs position measurements. At the same time, combining with strict system stability analysis and control law boundedness discussion, the physical domain of control gains of each subsystem is obtained, and online self-tuning of control gains is realized by fuzzy logic. Finally, the effectiveness and superiority of the proposed control method is verified by simulation illustration.
BigSR: real-time expressive RDF stream reasoning on modern Big Data platforms

Shifting from Big Data to Big Knowledge requires systems that are able to cope with the large volume and high-velocity dimensions in a scalable and inference-enabled manner. In this work, we are focusing on stream processing and reasoning using the graph-based RDF data model. We are aiming to explore the ability of modern distributed computing frameworks to process highly expressive knowledge inference queries over Big Data streams. To do so, we consider queries expressed as a positive fragment of a temporal logic framework based on Answer Set Programming and propose solutions to process such queries, based on the two main execution models adopted by major parallel and distributed execution frameworks: Bulk Synchronous Parallel (BSP) and Recordat-A-Time (RAT). We implement our solution named BigSR and conduct a series of experiments with 15 queries from 4 different datasets. Our experiments show that BigSR achieves high throughput beyond million-triples per second using a rather small cluster of machines.
Markov-Based Image Splicing Detection in the DCT High Frequency Region

Image splicing, which is open to malicious uses, has become widespread in recent years. Therefore detection of forged images becomes a popular topic in recent years. Image splicing is one of the image forgery methods. In this study, a Markov based method in Discrete Cosine Transform domain is proposed to detect image splicing forgery. Firstly, with applying DCT to the image high frequency coefficiensts are obtained, later Markov features are extracted from the high frequency matrix. Support vector machines are used to classify images. Three commonly used data sets in the literature have been used to evaluate the performance of the method [9,10] and to compare it with similar studies. In the light of the experiments performed, authentication performance of the method gives better results when compared to others.
Internet of Agriculture-Based Low Cost Smart Greenhouse Remote Monitor System

In this article, reference will be made to the concept of architecture of Internet of Things (IoT) to design a complete remote monitoring system on the basis of Agricultural IoT and to improve energy efficiency, reliability, safety and customer experience. In addition, design for perception recognition layer has been optimized in terms of low power consumption, reliability, easy-to-install and easy maintenance. Low cost single chip machine series STM32F103 will be adopted for network transmission layer to form complete gateway equipment. This will reduce cost while realizing the management service layer for encrypted TLS link and ensuring safe data transmission through Internet of Agriculture. The management service layer adopts the combination of cloud services to achieve data forwarding, storage and analysis. At comprehensive application layer, remote monitor terminals at customers will be designed for desktop and mobile terminals respectively, thereby improving customer experience. It is shown from the long-term operations of this smart IoT system for greenhouse at a test base that the system can operate reliably and accurately respond to customer control requests in a quick and accurate manner.
Research on Obstacle Surmounting Performance of All-terrain Eight Wheel Drive Robot

A new type of eight-wheel drive robot with high obstacle performance is developed by using the variable structure design by using variable centroid method. The motion mechanism of robot crossing vertical step is studied through D'Alembert's principle. The control strategy of active adjustment in obstacle surmounting is revealed. The ADAMS simulation model of the robot is established, and the obstacle surmounting process of the vertical step is simulated, and the kinematic characteristic curve of the robot is obtained. Based on the motion control for the obstacle, the obstacle surmounting experiment of robot prototype is carried out. The theoretical model calculation, simulation analysis and obstacle surmounting performance of robot are verified through experiment.
Representation Learning for Question Classification via Topic Sparse Autoencoder and Entity Embedding

Deep learning models have achieved great successes these days. There are intensive studies of word representation learning for question classification. As questions are typically short texts, existing techniques are often not effective for extracting discriminative representations of questions just from a limited number of words. This motivates us to exploit additional information beyond words in order to improve the representation learning of questions. On one hand, topic modeling often captures meaningful semantic structures from the question corpus. Such global topical information should be helpful for question representations. On the other hand, entities extracted from question themselves provide more auxiliary information for short texts from a local viewpoint. Together with words, topics and entities, question representations can be substantially improved.In this paper, we propose a unified neural network framework by integrating Topic modeling, Word embedding and Entity Embedding (TWEE) for question representation learning. Concretely, we introduce a novel topic sparse autoencoder to incorporate discriminative topics into the representation learning of questions. In addition, both words and entity related information are embedded into the network to help learn a more comprehensive question representation. Empirical experiments show that the proposed TWEE framework outperforms the state-of-the-art methods on different datasets.
Thingspeak Based Monitoring IoT System for Counting People in A Library

In this study, a real-time system which counts the number of people with the help of a camera was demonstrated. The system can send the number of people to a mobile application via Internet of Things (IoT) and monitor simultaneously. This work is carried out in the main library of Inonu University. Background subtraction method was used to recognize moving humans on the visual field of the camera. According to motion information of humans, a counter was used to count the number of people in the saloon by determining whether going inside or outside. The counter will inform the users about what percentage of the saloon is empty. Matlab and Thingspeak combination help to send counter information to internet environment. A mobile application was used to track the counter information from Android and iOS smartphones. The results were presented in Matlab environment and mobile application simultaneously. Thanks to this work, students will not have to look for a place to work when the library is crowded and will not bother other working students. It is believed that this project will serve all students.
Device-Agnostic Log Anomaly Classification with Partial Labels

Anomaly classification, i.e., detecting whether a network device is anomalous and determining its anomaly category if yes, plays a crucial role in troubleshooting. Compared to KPI curves, device logs contain too much more valuable information for anomaly classification. However, the regular expression based anomaly classification techniques cannot tackle the challenges lying in log anomaly classification. We propose LogClass, a data-driven framework to detect and classify anomalies based on device logs. LogClass combines a word representation method and the PU learning model to construct device-agnostic vocabulary with partial labels. We evaluate LogClass on tens of millions of switch logs collected from several real-world datacenters owned by a top global search engine. Our results show that LogClass achieves 99.515% F1 score in anomalous log detection, 95.32% Macro-F1 and 99.74% Micro-F1 in anomalous log classification in a computationally efficient manner.
Tuning Optimization of Extended State Observer for Two Mass System with Elastic Joint and Backlash

Extended State Observer (ESO) is an essential part of the Active Disturbance Rejection Control. The paper presents research over optimization of the tuning process of the ESO utilized in motor speed controller for two mass system with variable backlash. Authors presented two approaches to the observer deriving and tuning. The quality of the tuning process was measured with proper performance indexes presented as plots and tables. Conclusions and future work were determinated.
Speed Accuracy Trade-off in Pedestrian and Vehicle Detection Using Localized Big Data

This paper aims to obtain a guide of several experimental object detection architectures for pedestrian detection / vehicle detection (PD/VD) in the South Korean driving environment and to present the trade-off relationship between the mean average precision (mAP) and the frame per second (FPS) values. For these purposes, we generated a Korean vehicle black box front view dataset (KVD) to consider the actual driving environments in South Korea. We then experimented with various configurations of object detection architectures using the KVD. Next, the trade-off relationship for each architecture was summarized and analyzed. This paper presents a guide for choosing PD/VD architectures to achieve a suitable mAP–FPS balance for advanced driver assistance systems (ADAS) and autonomous navigation technologies.
Triplet-Based Semantic Relation Learning for Aerial Remote Sensing Image Change Detection

This letter presents a novel supervised change detection method based on a deep siamese semantic network framework, which is trained by using improved triplet loss function for optical aerial images. The proposed framework can not only extract features directly from image pairs which include multiscale information and are more abstract as well as robust, but also enhance the interclass separability and the intraclass inseparability by learning semantic relation. The feature vectors of the pixels pair with the same label are closer, and at the same time, the feature vectors of the pixels with different labels are farther from each other. Moreover, we use the distance of the feature map to detect the changes on the difference map between the image pair. Binarized change map can be obtained by a simple threshold. Experiments on optical aerial image data set validate that the proposed approach produces comparable, even better results, favorably to the state-of-the-art methods in terms of F-measure.
Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

Cardiovascular diseases currently pose the highest threat to human health around the world. Proper investigation of the abnormalities in heart sounds is known to provide vital clinical information that can assist in the diagnosis and management of cardiac conditions. However, despite significant advances in the development of algorithms for automated classification and analysis of heart sounds, the validity of different approaches has not been systematically reviewed. This paper provides an in-depth systematic review and critical analysis of all the existing approaches for automatic identification and classification of the heart sounds. All statements on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 Checklist were followed and addressed thoroughly to maintain the quality of the accounted systematic review. Out of 1347 research articles available in the academic databases from 1963 to 2018, 117 peer-reviewed articles were found to fall under the search and selection criteria of this paper. Amongst them: 53 articles are focused on segmentation, 72 of the studies are related to the feature extraction approaches and 88 to classification, and 56 reported on the databases and heart sounds acquisition. From this review, it is clear that, although a lot of research has been done in the field of automated analysis, there is still some work to be done to develop robust methods for identification and classification of various events in the cardiac cycle so that this could be effectively used to improve the diagnosis and management of cardiovascular diseases in combination with the wearable mobile technologies.