Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Constrained Shortest Path Query Answering with Forest Hop Labeling

The Constrained Shortest Path (CSP) problem aims to find the shortest path between two nodes in a road network subject to a given constraint on another attribute. It is typically processed as a skyline path problem on the two attributes, resulting in very high computational cost which can be prohibitive for large road networks. The main bottleneck is to deal with a large amount of partial skyline paths, which further makes the existing index-based methods incapable to obtain the complete exact skyline paths. In this paper, we propose a novel skyline path concatenation approach to avoid the expensive skyline path search, which is then used to efficiently construct a 2-hop labeling index for the CSP queries. Specifically, a rectangle-based technique is designed to prune the concatenation space from multiple hops, and a constraint pruning method is used to further speed up the CSP query processing. To further scale up to larger networks, we propose a novel forest hop labeling that constructs labels from different partitions in parallel. Our approach is the first method that can achieve both accuracy and efficiency for CSP query answering. Extensive experiments on real-life road networks demonstrate that our method outperforms the state-of-the-art CSP solutions by several orders of magnitude.
Graph Based Approach to Real-Time Metro Passenger Flow Anomaly Detection

Real-time anomaly detection of passenger flows in the metro system is very important to maintain the URT system’s normal operation and ensure passengers’ safety. This paper proposes a novel abnormal passenger flow detection method based on smart card data. The method constructs a graphic model whose topological structure can capture the spatial distribution of anomalous passenger flow. It further incorporates external information (e.g. geographical information) to depict the latent passenger flow’s spatial dependence embedded in URT system. Considering abnormal flows may only exist in local regions of the metro system, a detection statistic is constructed by using graph community detection. The statistic also incorporates an adaptive sampling strategy for further signal selection and noise filter. It can be efficiently solved via a Min-Cut-based algorithm and can provide real-time solutions to anomaly detection and diagnosis. Preliminary experimental results demonstrate the efficiency of our method.
Design of Miniaturized Single Bit MEMS Phase Shifter using MEMS Switches

This article proposes a novel miniaturization single bit MEMS phase offset (shifter) using triangular Micro-electro mechanical system (MEMS) switches. The primary goal in the design of a single-bit switched delay line digital phase offset is to reduce the area of phase offset bits and the minimize the number of switches, which aids in the reduction of mechanical switching errors during the turn ON and OFF operation. The proposed phase shifter is simply based on a switched delay route and reference route configuration, which is connected with a gold connection sequence switch. The phase offset is built on 50 Ohm coplanar waveguide (CPW) configuration, which would generally be used in radio frequency (RF) MEMS systems. The RF and mechanical characteristics of the proposed switches were analyzed using Intellisuite software, and the finite element method was carried out in the HFSS software. The performance of the S11 (return loss) and S21 (insertion loss) of the miniaturized single bit phase shifter (22.5°) are 23 dB and 0.55 dB, respectively. The miniaturization phase offset is reduced by 30% by using three triangular SPST switches.
Kalman Filter for Predictive Maintenance and Anomaly Detection

There are various strategies in optimization of anomaly detection problem on sensor data. This paper describes a Gaussian Mixture Model (GMM) and Kalman filter to detect outliers within the sensor data of wire bonding. With limitation on data samples and high dimensional parameters, Principal Component Analysis (PCA) helped to reduce dimensionality without losing important information. The Expectation-Maximization algorithm for estimating Gaussian distribution parameters of GMM provided us a clustering model to fit our data. A weighted distance from the cluster center in the employed GMM model is applied to tune noise variances on measurement errors. The proposed method is validated using real measurements in the context of a manufacturing system.
Probing zeta potential of glass in electrolyte solutions by colloidal probe technique

On the microfluidic chip, the zeta potential is an important parameter dominating the direction and magnitude of the electroosmotic flow, electrophoresis and other electrokinetic transport phenomenon. However, the traditional methods for zeta potential measurement mainly work based on the indirect methods, which would introduce many errors. In this paper, a novel direct method based on AFM colloidal probe technique was developed to measure the zeta potential. Firstly, to simplify the theoretical model, the approaching speed of the colloidal probe, at which the hydrodynamic force could be neglected, was determined. Then by fitting the experimental data with the theoretical model, the zeta potential of glass in electrolyte solutions was directly calculated.
An Integrated Stretchable Sensing Patch for Simultaneously Monitoring of Physiological and Biochemical Parameters

Application of wearable sensors for sports monitoring provides real-time insight into an athlete's physical condition. At present, most wearable sensing devices measure only a small number of physical or electrophysiological parameters, but neglect the rich chemical information available from biomarkers in human body fluid. This work developed a multi-parameter monitoring device based on flexible patch, which integrated a variety of sensors, and is capable of continuous, real-time and synchronous monitoring of physiological parameters (e.g. ECG, PPG) and biochemical parameters (e.g. sodium, potassium, glucose in sweat). The flexible patch was designed with an island-bridge structure, making itself miniaturized and stretchable. In addition, an Android application was developed to receive the Bluetooth low energy packets of the monitoring device. And the monitoring device was tested in signal acquisition of all sensors. Finally, on-body experiments were carried out, to successfully verify the function of multi-parameter monitoring. The results show that our multi-parameter monitoring device provided in this work is suitable for sports scenes, which allows the feedback of body status through the comprehensive evaluation of individuals, and has a good application prospect for athletes to monitor physiological load during training.
Reliable Recommendation with Review-level Explanations

The quality of user-generated reviews is significant for users to understand recommendation results and make online purchasing decisions correctly. However, the reliability of a review, which captures the likelihood that a review is benign, is ignored by many studies. The low reliability reviews cause a recommendation system’s unsatisfying performance. Especially the fake reviews written by fraudulent users mislead the system into generating error recommendation results and explanations, which confuse customers and deprive customers of confidence in the system. In this paper, we propose a model, Reliable Recommendation with Review-level Explanations (RRRE), which detects reliable reviews and improves the performance of the explainable recommendation system as well. Recognizing the textual content of reviews, user-item interactions are valuable features for both rating prediction and reliability prediction. RRRE builds a uniform framework to predict rating scores and reliability scores simultaneously. Firstly, RRRE embeds user preferences and item profiles, which are extracted from textual and interactive features, into the representation of the review. Secondly, the supervised information of two subtasks is jointly combined. It makes the optimization of RRRE faster and better. Finally, the reviews with both high reliability scores and rating scores are given to customers as reliable explanations. To the best of our knowledge, we are the first to consider the reliability of reviews for improving explainable recommender system. And the experimental results confirm this idea and show that our model outperforms other baseline methods on Yelp and Amazon datasets.
Volumetric Convolutional Neural Network for Alzheimer Detection

Alzheimer's disease is a progressive brain disorder, which over a period leads to loss of memory due to the formation of mainly two types of lesions being senile plaques and neurofibrillary tangles. Alzheimer's detection at an early stage thus becomes of paramount importance to lessen the loss of cognitive, other memory since this disease cannot be reversed, and no cure is available until now. This study has put forward a 3-Dimensional Convolutional neural network (3D-CNN) framework for binary classification of Alzheimer disease as Healthy Control (HC) and Alzheimer Disease Control (AD) using the pre-processed volumetric T1 weighted Magnetic Resonance Images obtained from the MIRIAD dataset. The pre-processing pipeline applied on the MRI Images obtained from the MIRIAD dataset is bias correction, skull stripping, and registration. This research also highlights the broad areas for future research on multimodal and multiclass Alzheimer detection.
IntelliTag: An Intelligent Cloud Customer Service System Based on Tag Recommendation

To reduce the customer service pressure of small and medium-sized enterprises, we propose an intelligent cloud customer service system, called IntelliTag. Unlike traditional customer service, a cloud service based system has difficulty in collecting user personal information. Therefore, we add a tag recommendation function to quickly capture the user’s question intent by clicking on the tags. Specifically, IntelliTag is elaborately designed with the consideration of the following three aspects. First, how to mine high-quality tags is a challenging problem. Second, in the tag recommendation tasks, we have multifarious data types and relations that are used to build a sequential recommendation model. Finally, system implementation and deployment also need to be carefully designed to satisfy online service requirements. In this paper, we show the details of data construction, model designs, system implementation and deployment, and the empirical results compared with several state-of-the-art methods. Nowadays, our IntelliTag has already supported hundreds of thousands of enterprises and millions of users in our industrial production environment.
Real-Time Thought Controlled Action for Paralytic Patient using Electroencephalogram

Brain computer interface is an assistive technology that tries to create a direct link between the brain and the environment. This possibly make one to aid paralytic patient which pose as a major challenge in fast technology growing world. This paper describes the method to capture the thoughts of a person using EEG and to perform automated actions according to the thoughts.
Process Variation Sensitivity of Spin-Orbit Torque Perpendicular Nanomagnets in DBNs

Neuromorphic architectures with low energy barrier nanomagnetic devices have been attracting increasing interest over the past few years. More recently, a low barrier nanomagnet (LBNM)-based probabilistic device (p-bit) has been shown to be the basis of neuronal nodes in deep belief networks (DBNs). The LBNMs with perpendicular magnetic anisotropy (PMA) are analyzed and optimized in the interest of achieving stochasticity present in the learning system. In p-bit-based DBNs, several defects, such as variation of the nanomagnet diameter (<inline-formula> <tex-math notation="LaTeX">$\sigma d$ </tex-math></inline-formula>), thickness (<inline-formula> <tex-math notation="LaTeX">$\sigma t_{f}$ </tex-math></inline-formula>), and anisotropy field (<inline-formula> <tex-math notation="LaTeX">$\sigma H_{K}$ </tex-math></inline-formula>), which results in alteration of the fluctuation speed of the p-bit’s nanomagnet can impair functionality. In this article, the accuracy of p-bit-based DBNs is examined under variation of nanomagnet diameter, thickness, and anisotropy field for various tilt angles and temperatures. As evaluated for the MNIST data set for temperature and tilt angle of 300 K and 25°, accordingly, it is shown that the process variation (PV) of <inline-formula> <tex-math notation="LaTeX">$\sigma d$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sigma t_{f}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\sigma H_{K}$ </tex-math></inline-formula> can be tolerated up to 8%, 23%, and 25%, respectively. A new method is developed to control the fluctuation frequency of the output of a p-bit device by employing a feedback mechanism. The feedback can alleviate this PV sensitivity of p-bit-based DBNs. The compact and low complexity method, which is presented by introducing the self-compensating circuit, can alleviate the influences of PV in fabrication and practical implementation.
Research on BP Neural Network Recommendation Model Fusing User Reviews and Ratings

Sparsity of rating data is a severe problem to be solved in modern recommendation researches. The fusion recommendation method is an effective solution for the problem. The method combines rating data and other types of user feedback data, such as reviews and image, to improve performance of the traditional recommendation algorithms. Some researchers have proposed fusion recommendation algorithms based on BP (Back Propagation) neural network and achieved better results. However, some existing fusion recommendation algorithms based on BP neural network still have some shortcomings. They rely on the assistance of the traditional recommendation algorithms. Moreover, the high complexity of the fusion processes of these algorithms possibly has negative impacts on the fusion effects. In this paper, we modify the fusion recommendation algorithm and propose the NNFR (neural networks fusion recommendation) model. This model improves the structure of BP neural network by specially designing the structure of network layers. User reviews and ratings can be processed in two separate sub-networks respectively and further fused in the fusion layer. The fusion features of user reviews and ratings are directly applied to perform recommendation, in order to avoid the assistance of the traditional recommendation algorithms and improve the fusing efficiency and quality. Experimental results indicate that the outstanding performance of NNFR model than comparative recommendation algorithms on rating predictions and top-k recommendations. Moreover, NNFR model can still produce high-quality recommendation results in the scenarios of sparse data.
Rapid Fabrication of 2-D Magnetic Microstructures by Laser Direct Writing (LDW)

A 2-D permalloy (Ni81Fe19) microstructures have been patterned using laser direct writing (LDW). The magnetic structures were designed and fabricated in a single-step process using scanning stage system based on A3200 software from a thin film. This process was implemented under atmospheric conditions and room temperature by removing unwanted areas of thin film by laser ablation. The 2-D magnetic structures included: arrays of squares, rectangles with a range of aspect ratios, and rhombic elements. These elements exhibited shape-sensitive magnetic behavior with the increase in shape aspect ratio. The average fabrication time per element was 0.47 ms at a scanning speed of 7.5 mm/s and a laser repetition rate of 6 kHz. This demonstration of flexibility and speed of laser direct write processing being applied to magnetic microstructures may assist in prototyping and rapid manufacture of devices such as sensors and magnetic wires for interactions with biological cells.
Complexity-Based Convolutional Neural Network for Malware Classification

Malware classification remains at the forefront of ongoing research as the prevalence of metamorphic malware introduces new challenges to anti-virus vendors and firms alike. One approach to malware classification is Static Analysis - a form of analysis which does not require malware to be executed before classification can be performed. For this reason, a lightweight classifier based on the features of a malware binary is preferred, with relatively low computational overhead. In this work a modified convolutional neural network (CNN) architecture was deployed which integrated a complexity-based evaluation based on box-counting. This was implemented by setting up max-pooling layers in parallel, and then extracting the fractal dimension using a polyscalar relationship based on the resolution of the measurement scale and the number of elements of a malware image covered in the measurement under consideration. To test the robustness and efficacy of our approach we trained and tested on over 9300 malware binaries from 25 unique malware families. This work was compared to other award-winning image recognition models, and results showed categorical accuracy in excess of 96.54%.
Construction and Analysis on College EFL Learners’ Autonomous Learning System Based on Mobile-aided Language Learning Pattern

With the rapid development of big data, cloud computing, artificial intelligence, and other technologies, the "Internet+" era has arrived. In this era, mobile device (also called as "handheld device" or "handheld computer") is widely used. Therefore, mobile-aided language learning (MALL) is extensively made use of in college EFL learners’ autonomous learning. But the research in this area is still in its infancy period. The paper makes a survey of this area through questionnaire, analyzes the statistics through SPSS software and applies the research findings to construct a MALL pattern in college EFL learners’ autonomous learning, and in hope of integrate the learning pattern with college English education.
Optimization of Thermostatically Controlled Appliances to Minimize Energy Consumption Cost Based on Real-Time Pricing

An optimization model for residential users to minimize power consumption cost using real-time pricing is proposed in this paper. This model chooses the thermostatically controlled appliances (TCA) which consume the most power in the residential area. This scheme targets the residential area rather than the industrial area because the latter has interdependent tasks difficult to isolate. The proposed model automatically adjusts the TCA according to dynamic pricing and customer demand. Constrains like customer preferences, power consumption, dynamics of appliances, real-time pricing is taken into consideration. Simulation of the optimization scheme is done in the MATLAB platform. The model can successfully optimize the power consumption cost keeping customer comfort in mind.
Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator Based on Volterra Series

Magnetic shape memory alloy-based actuator (MSMA-BA) has the capability to generate the micro–nano-scale precision positioning, which has unparalleled virtue in contrast with the traditional motion driving mechanism. Nevertheless, the output displacement of the MSMA-BA exhibits complex hysteresis nonlinearity, which hinders its utility in the high-precision positioning field. In this article, an online Volterra series model based on wavelet neural network (VSM-WNN) is proposed to describe the rate-dependent hysteresis of the MSMA-BA. The kernel function of the VSM is extracted with a WNN, in which the feasibility of this extraction is proved in theory via Taylor expansion of the wavelet function. Finally, the validity of the proposed model is confirmed by means of experiments. Experimental results indicate that the VSM-WNN has a remarkable performance in describing the traits of hysteresis behavior of the MSMA-BA.
Collaborative-Set Measurement for ECG-Based Human Identification

Electrocardiogram (ECG) has attracted intense research interests and contests due to its great value in biometric applications, especially with the technological progress of smart instrumentation and artificial intelligence nowadays. For the issue of ECG-based human identification, distance measure in a suitable data space can be an effective solution. Almost all conventional distance measures rely on the independent data samples for classification. However, such measuring mechanism is vulnerable to the variation and bias of data distribution, which are easily caused by the noisy artifacts in this issue. To tackle this problem, we suggest doing distance measurement at the level of multiple-set bundle that consists of multiple sample sets obtained under different conditions. More specifically, we propose a novel method, “Collaborative-Set Measurement (CSM),” which creatively extends distance measure from the sample level through the set level to the bundle level. CSM not only captures the representative information from within-set distribution but also exploits the discriminative information from between-set collaboration, and meanwhile incorporates the robust information from in-bundle fusion. Attributing to these information, this method can largely enhance the identification performance in this issue despite the serious noisy artifacts influencing the data samples. The experimental results have demonstrated the capability of CSM for ECG-based human identification, by means of the public challenging database, DREAMER, on which the proposed method produces an accuracy of 91.30%. In summary, this proposal has initially brought forward a new thought of “collaborative sets. ” This thought may provide inspirations for further researches on biometric recognition and general signal classification topics.
Fully Unsupervised Spike-Rate-Dependent Plasticity Learning With Oxide- Based Memory Devices

Artificial synapses are fundamental building blocks for neuromorphic computing hardware. In this work, we demonstrate fully unsupervised neuromorphic learning using analog weight change properties of bilayer nonfilamentry oxide-based resistive memory (OxRAM) devices as synaptic elements. Essential functions of a biological synapse, such as potentiation, depression, and spike-rate-dependent plasticity (SRDP), are experimentally demonstrated. Furthermore, we show the tune-ability of SRDP with preneuron/postneuron spike-train parameters (frequency and amplitude). Through simulations, we show a two-layer fully connected neuromorphic network powered by SRDP and OxRAM synapses. Our network achieves state-of-the-art best classification accuracy results compared to the literature (for SRDP-based purely unsupervised spiking neural networks (SNNs), without the use of any convolutional layer) on full Modified National Institute of Standards and Technology (MNIST) dataset: 92.07% (training) and 90.76% (test). Detailed impact of device properties, such as variability, retention, and endurance, is analyzed. The impact of introducing sparsity during training and weight pruning during inference was also investigated.