Miracle-dz/UniFAR-FTUs huggingface dataset:
ST-RNet: A Time-aware Point-of-interest Recommendation Method based on Neural Network

Point-of-interest (POI) recommendation is one of the most important services in the rapid growing location-based social networks (LBSNs). Good POI recommendation can help people explore the locations they haven’t visited but are interested in, and help merchants find their target users. Time-aware POI recommendation aims to recommend unvisited POIs for a given user at a specified time in a day. However, previous methods, such as user-based collaborative filtering, lack the mining of the features of POIs and the learning of abstract spatio-temporal interactions. In this paper, we propose a novel time-aware POI recommendation method named ST-RNet (Spatio-Temporal Recommender Network) to address these shortages. ST-RNet works in the following fashion. Firstly, we analyze the crucial features in LBSNs to alleviate data sparsity problem and further measure the similarities between POIs. For subsequent network training, we then construct the embedding matrices with same dimension for users and POIs by POI-based Collaborative Filtering (PCF). Furthermore, the positive and negative check-in records are fed into a novel recommender neural network (RNet) to learn the embedding matrix of times and the abstract interactions between users, POIs and times. Finally, ST-RNet recommends the unvisited POIs most likely to be visited to a given user at a given time. The experimental results on Foursquare real-world dataset show that ST-RNet is effective on time-aware POI recommendation task and is capable of analyzing the hidden patterns behind spatio-temporal interactions.
Stability Analysis of the Proportional-Resonant Controller in Single Phase Converters

The proportional-resonant control (PR-control) is a simple and efficient control, which has the ability to eliminate error in linear time varying signals. This type of control is typically used in applications such as the integration of solar panels, energy storage and electric vehicles among others. Despite having non-linear dynamics, the analysis of the PR-control is usually performed using linear systems theory. This paper proposes a non-linear stability analysis based on Lyapunov theory which formalizes the main stability results on these controls that are standard in industrial applications. The proposed approach is general and independent of the structure of the phase-locked loop which is considered as a vanishing perturbation. This approach allows a better understanding of the control from a non-linear perspective. In addition, it gives an exact criteria for tuning the parameters of the controller. Numerical simulation results in a wide range of scenarios complement the analysis and validate them.
Sequencing the musical sections with deep learning

Deep learning has become increasingly popular for sequence modeling in various domains. In this work, we address the musical order verification as a sequential pattern learning task. We present a more advanced self-supervised learning model, dubbed as the triple-wise similarity with recurrent neural networks (TSN-R) which is on the basis of state-of-the-art Similarity Embedding Network. We take triple-wise musical sections as the input instance, and leverage the temporal coherence as a supervisory signal. As a first step, we use the triplet Siamese network as the input layer, and assess the similarity of the triplet feature maps. After that, we choose the bidirectional LSTM to extract features along the time dimension. Finally, the subsequent fully-connected layers are used for order verification. The experiments show that our approach outperforms other competing models in terms of prediction accuracy. Additionally, this paper reveals the reasons for the good performance of our model through the 2d-visualization of features. All the source code, pre-trained models and the experiment results are available in our project page. https://github.com/ISCASTEAM/Sequencing-the-musical-sections
Nonlinear Transformation for Multiple Auxiliary Information in Music Recommendation

Online music recommender systems are becoming increasingly prevalent because of the popularity of digital music and music recommendation generally caters to users by discovering songs that match their preferences. However, these systems have to face a challenge: how to recommend new songs in a situation where prior knowledge is scarce. Some researches take auxiliary information into consideration in new recommendation approaches to deal with this problem. Nevertheless, they rarely pay attention to complex relationships among different feature spaces when they map those information to a latent space. To this end, this paper proposes an approach that uses non-linear transformation to integrate different auxiliary information into the songs latent representations. Unlike other studies which directly map auxiliary information to the feature space, the proposed music recommendation model (NeuTrans) maps different information features to low-dimensional vector representation by non-linear neural networks. Specifically, the NeuTrans separately employs matrix factorization and attribute network embedding to extract auxiliary information (historical interaction, network structure and attributes of songs). The feature space of different information is obtained by nonlinearly mapping the feature space of the songs. Experimental analysis on two real-world datasets shows that our framework outperforms the state-of-the- art approaches on Top-N music recommendation.
Hybrid Model for Cavitation Noise Spectra Prediction

In the latest years, models combining physical knowledge of a phenomenon and statistical inference are becoming of much interest in many real world applications. In this context, ship propeller underwater radiated noise is an interesting field of application for these so-called hybrid models, especially when the propeller cavitates. Nowadays, model scale tests are considered the state-of-the-art technique to predict the cavitation noise spectra. Unfortunately, they are negatively affected by scale effects which could alter the onset of some interesting cavitating phenomena respect to the full scale propeller; as a consequence, for some ship operational conditions it is not trivial to correctly reproduce the cavitation pattern in model scale tests. Moreover, model scale tests are quite expensive and time-consuming; it is not feasible to include them in the early stage of the design. Nevertheless, data collected during these tests can be adopted in order to tune a data-driven model while the physical equation describing the occurring phenomenon can be used to refine the prediction. In this work, the authors propose a hybrid model for the prediction of ships propeller underwater radiated noise, able to exploit both the physical knowledge of the problem and the real data obtained from cavitation tunnel experiments performed on different propellers in different working conditions. Results on real data will support the validity and the effectiveness of the proposal.
RASE: Relationship Aware Social Embedding

This paper studies the problem of learning latent representations or embeddings for users in social networks, by leveraging relationship semantics associated with each link. User embeddings are low-dimensional vector-space representations designed to preserve structural proximity indicated by the pairwise relationships. In social networks, the closeness (or proximity) between pairs of users is very different w.r. t. multiple social relationships and thus cannot be represented accurately using a single embedding space. Furthermore, social networks pose a unique challenge of relationship label sparsity that precludes the application of knowledge-graph embedding techniques.In this paper, we associate each observed link with multiple relationship types through relationship weights and learn projection matrices for each relationship type to model the social distance (or proximity) between users specific to each relationship. We propose a novel two-step mutual enhancement framework to iteratively (a) learn user embeddings preserving relationship-specific proximity, and (b) link-relationship weights capturing the role of each link in multiple relationship types. The first step learns user embeddings optimizing relationship-specific proximity, while fixing the relationship weights (or roles) for each link. In the second step, the user embeddings and corresponding projection matrices are assumed to be fixed, while the link-relationship weights are learned. We demonstrate that the relationship-aware user embeddings learned through this mutual enhancement framework, are more effective in representing the users and outperform representative baseline techniques in multi-label classification and relationship prediction tasks.
Intrusion detection method based on information gain and ReliefF feature selection

Traditional random forest has slow convergence in network intrusion detection and its learning performance is not perfect. In order to eliminate the redundant information in the original intrusion detection data, this paper proposes a random forest intrusion detection method based on the combination of information gain and ReliefF algorithm. The proposed method first uses the information gain to calculate the information gain value of each feature. Then, the ReliefF algorithm is used to calculate the weight of each feature. According to the information gain and the feature weight, the final feature subset is obtained. Finally, this paper uses a random forest classifier for classification. The experiment compares the three feature selection methods, including the proposed method, information gain based method and ReliefF-based method. The experimental results show that the precision, recall rate, and false positive rate of the proposed method are superior to those of the other two methods.
The Conditional Boundary Equilibrium Generative Adversarial Network and its Application to Facial Attributes

We propose an extension of the Boundary Equilibrium GAN (BEGAN) neural network, named Conditional BEGAN (CBEGAN), as a general generative and transformational approach for data processing. As a novelty, the system is able of both data generation and transformation under conditional input. We evaluate our approach for conditional image generation and editing using five controllable attributes for images of faces from the CelebA dataset: age, smiling, cheekbones, eyeglasses and gender. We perform a set of objective quantitative experiments to evaluate the model’s performance and a qualitative user study to evaluate how humans assess the generated and edited images. Both evaluations yield coinciding results which show that the generated facial attributes are recognizable in more than 80% of all new testing samples.
End-to-end Learning Method for Self-Driving Cars with Trajectory Recovery Using a Path-following Function

We propose an end-to-end learning method for autonomous driving systems in this article. End-to-end model estimates an appropriate motor command from raw sensory signals. End-to-end model for autonomous driving systems has recently been based on neural networks, which are popular for their good recognition ability. A common problem is how to return a car to the driving lane when the car goes off the track. In our research, we collect recovery data based on the distance from a desired track (the nearest waypoint link) during a road test with a simulator. To train the recovery behavior, instead of collecting human driving data, we use a path-following module (which means the car automatically drives on a pre-decided route using the car’s current position). Our proposed method is divided into three phases. In phase 1, we collect data only using a path-following module during 100 laps of driving. In phase 2, we generate driving behavior using a neural driving module trained by the data collected in phase 1. This includes switching between the accelerator, brake and steering based on a threshold. We collect further data on the recovery behavior using the path-following module during 100 laps of driving. In phase 3, we generate driving behavior using the neural driving module trained by the data collected in phases 1 and 2. To assess the proposed method, we compared the average distance from the nearest waypoint link and the average distance traveled per lap for datasets with no recovery, for datasets with random recovery, and for datasets for the proposed method with recovery. A model based on the proposed method drove well and paid more attention to the road rather than the sky and other unrelated objects automatically for both untrained and trained courses and weather.
Generalized Alignment for Multimodal Physiological Signal Learning

Revealing the correspondences and relationships between physiological signals is attractive for bioinformatics and human-computer interaction. Time alignment is a straightforward way to figure out correspondences between time sequential data. However, alignment between multimodal physiological signals is hard to achieve because the similarity metrics are difficult to define if the two physiological signals being investigated are non-linearly correlated, misaligned or quite different in morphology. In this paper, we propose a generalized time alignment method for multimodal physiological signals which (i) learns the feature extractions on physiological signals in a generalized way, and (ii) enables learned features to be in a coordinated space where the similarity between sub-components from two signals can be defined. Furthermore, we applied our alignment based multimodal feature fusion on an evaluation model to perform emotion recognition tasks on the DEAP multimodal physiological signal dataset. The experimental results show that the alignment based feature fusion outperforms the non-aligned feature fusion in most cases.
An LSTM based Encoder-Decoder Model for MultiStep Traffic Flow Prediction

Traffic flow prediction has been regarded as a key research problem in the intelligent transportation system. In this paper, we propose an encoder-decoder model with temporal attention mechanism for multi-step forward traffic flow prediction task, which uses LSTM as the encoder and decoder to learn the long dependencies features and nonlinear characteristics of multivariate traffic flow related time series data, and also introduces a temporal attention mechanism for more accurately traffic flow prediction. Through the real traffic flow dataset experiments, it has shown that the proposed model has better prediction ability than classic shallow learning and baseline deep learning models. And the predicted traffic flow value can be well matched with the ground truth value not only under short step forward prediction condition but also under longer step forward prediction condition, which validates that the proposed model is a good option for dealing with the realtime and forward-looking problems of traffic flow prediction task.
Experimental Magnetization Curve of a Transformer Considering Harmonic Distortion

Transformers are essential devices in power and distribution systems, since they reduce energy losses produced in the transmission stage. The magnetic system is the most-important part to its operation because energy conversion process will always be defined and bounded by the electromagnetic characteristics of its ferromagnetic core. This paper proposes an experimental method based on electromagnetic principles to obtain an approximated magnetization curve of an electric transformer, which can be used as a reference to analyze magnetic-system-behavior evolution through time. Moreover, the obtained magnetization curve considers voltage and current harmonic components produced during the test. Therefore, a detailed division of input transformer power is done, assuming the total active power are magnetic losses and the total non-active power corresponds to magnetization power. Obtained results showed the proposed method relevance, after comparing it with a magnetization curve which rejects harmonic content of signals and a magnetization curve released by a ferromagnetic material manufacturer.
LTL Model Checking Based on Binary Classification of Machine Learning

Linear Temporal Logic (LTL) Model Checking (MC) has been applied to many fields. However, the state explosion problem and the exponentially computational complexity restrict the further applications of LTL model checking. A lot of approaches have been presented to address these problems. And they work well. However, the essential issue has not been resolved due to the limitation of inherent complexity of the problem. As a result, the running time of LTL model checking algorithms will be inacceptable if a LTL formula is too long. To this end, this study tries to seek an acceptable approximate solution for LTL model checking by introducing the Machine Learning (ML) technique. And a method for predicting LTL model checking results is proposed, using the several ML algorithms including Boosted Tree (BT), Random Forest (RF), Decision tree (DT) or Logistic Regression (LR), respectively. First, for a number of Kripke structures and LTL formulas, a data set A containing model checking results is obtained, using one of the existing LTL model checking algorithm. Second, the LTL model checking problem can be induced to a binary classification problem of machine learning. In other words, some records in A form a training set for the given machine learning algorithm, where formulas and kripke structures are the two features, and model checking results are the one label. On the basis of it, a ML model M is obtained to predict the results of LTL model checking. As a result, an approximate LTL model checking technique occurs. The experiments show that the new method has the similar max accuracy with the state of the art algorithm in the classical LTL model checking technique, while the average efficiency of the former method is at most 6.3 million times higher than that of the latter algorithms, if the length of each of LTL formulas equals to 500. These results indicate that the new method can quickly and accurately determine LTL model checking result for a given Kripke structure and a given long LTL formula, since the new method avoids the famous state explosion problem.
Attentive Dual Embedding for Understanding Medical Concepts in Electronic Health Records

Electronic health records contain a wealth of information on a patients healthcare over many visits, such as diagnoses, treatments, drugs administered, and so on. The untapped potential of these data in healthcare analytics is vast. However, given that much of medical information is a cause and effect science, new embedding methods are required to ensure the learning representations reflect the comprehensive interplays between medical concepts and their relationships over time. Unlike one-hot encoding, a distributed representation should preserve these complex interactions as high-quality inputs for machine learning-based healthcare analytics tasks. Therefore, we propose a novel attentive dual embedding method called MC2Vec. MC2Vec captures the proximity relationships between medical concepts through a two-step optimization framework that recursively refines the embedding for superior output. The framework comprises a Skip-gram model to generate the initial embedding and an attentive CBOW model to fine-tune the embedding with temporal information gleaned from sequences of patient visits. Experiments with two public datasets demonstrate that MC2Vecs produces embeddings of higher quality than five state-of-the-art methods.
VN-GAN: Identity-preserved Variation Normalizing GAN for Gait Recognition

Gait is recognized as a unique biometric characteristic to identify a walking person remotely across surveillance networks. However, the performance of gait recognition severely suffers challenges from view angle diversity. To address the problem, an identity-preserved Variation Normalizing Generative Adversarial Network (VN-GAN) is proposed for learning purely identity-related representations. It adopts a coarse-to-fine manner which firstly generates initial coarse images by normalizing view to an identical one and then refines the coarse images by injecting identity-related information. In specific, Siamese structure with discriminators for both camera view angles and human identities is utilized to achieve variation normalization and identity preservation of two stages, respectively. In addition to discriminators, reconstruction loss and identity-preserving loss are integrated, which forces the generated images to be the same in view and to be discriminative in identity. This ensures to generate identity-related images in an identical view of good visual effect for gait recognition. Extensive experiments on benchmark datasets demonstrate that the proposed VN-GAN can generate visually interpretable results and achieve promising performance for gait recognition.
Combining convolutional side-outputs for road image segmentation

Image segmentation consists in creating partitions within an image into meaningful areas and objects. It can be used in scene understanding and recognition, in fields like biology, medicine, robotics, satellite imaging, amongst others. In this work we take advantage of the learned model in a deep architecture, by extracting side-outputs at different layers of the network for the task of image segmentation. We study the impact of the amount of side-outputs and evaluate strategies to combine them. A post-processing filtering based on mathematical morphology idempotent functions is also used in order to remove some undesirable noises. Experiments were performed on the publicly available KITTI Road Dataset for image segmentation. Our comparison shows that the use of multiples side outputs can increase the overall performance of the network, making it easier to train and more stable when compared with a single output in the end of the network. Also, for a small number of training epochs (500), we achieved a competitive performance when compared to the best algorithm in KITTI Evaluation Server.
Design and FPGA-implementation of Asynchronous Circuits Using Two-Phase Handshaking

This paper addresses the design and FPGA-prototyping of asynchronous circuits using static data-flow handshake components implemented using the two-phase bundled-data protocol. The contributions are partly tutorial and partly scientific. The paper introduces the design process, including initialization and design of coupled rings with any number of tokens. Following this, the paper presents gate-level implementations of the full set of handshake components as well as some peephole optimizations that merge the implementation of several components. The components are implemented using the click-template. The handshake register implementation is extended with circuitry that decouples the phase of the handshake signals on the input and output ports. Such decoupling is needed to facilitate implementation of rings with one token (or in the general case, rings with any number of tokens). Finally, the paper illustrates the design process using two circuits: one that outputs the sequence of Fibonacci numbers, and one that computes the greatest common divisor of two positive integers. All components are described in VHDL, and all code is available as open source. All components and the two circuits mentioned have been tested on a Xilinx Nexys4DDR FPGA board.