Miracle-dz/UniFAR-FTUs huggingface dataset:
AMDet: An Efficient Infrared Small Object Detection Model Based on Visual Attention and Multi-dilation Feature

Infrared small object detection had made many breakthroughs in early warning systems, precision-guided weapons, and maritime surveillance systems. However, infrared small object occupies less pixels and lacks color, shape and texture features, which makes infrared small object detection a challenging subject. In order to improve the detection performance of infrared small object, a novel algorithm (i.e., AMDet) based on visual attention and multi-dilation feature is proposed in this paper. First, Darknet53 is employed as the backbone of the AMDet. In addition, the attention refining module is proposed to enhance the ability of fine-grained feature extraction by utilizing the global context information. Furthermore, the multi-scale dilated convolution module is designed by expanding the receptive field of feature map, to characterize the multi-scale information, thus can further improve the feature expression ability of infrared small object. Experiment results on infrared small object detection dataset demonstrate that our method is superior to the other methods in detection accuracy, and is more applicable for infrared small object detection in sophisticated background.
FAtiMA Toolkit: Toward an Accessible Tool for the Development of Socio-emotional Agents

More than a decade has passed since the development of FearNot! , an application designed to help children deal with bullying through role-playing with virtual characters. It was also the application that led to the creation of FAtiMA, an affective agent architecture for creating autonomous characters that can evoke empathic responses. In this article, we describe the FAtiMA Toolkit, a collection of open-source tools that is designed to help researchers, game developers, and roboticists incorporate a computational model of emotion and decision-making in their work. The toolkit was developed with the goal of making FAtiMA more accessible, easier to incorporate into different projects, and more flexible in its capabilities for human-agent interaction, based upon the experience gathered over the years across different virtual environments and human-robot interaction scenarios. As a result, this work makes several different contributions to the field of Agent-Based Architectures. More precisely, the FAtiMA Toolkit’s library-based design allows developers to easily integrate it with other frameworks, its meta-cognitive model affords different internal reasoners and affective components, and its explicit dialogue structure gives control to the author even within highly complex scenarios. To demonstrate the use of the FAtiMA Toolkit, several different use cases where the toolkit was successfully applied are described and discussed.
FINet: Feature Interactions Across Dimensions and Hierarchies for Camera Localization

Current absolute camera pose regression with neural networks has made impressive progress, but estimating camera poses from a single RGB image in dynamic environments is still a challenging task. Although alleviating the lack of robustness by temporal and geometric constraints supplement, this class of methods only extracts a global latent vector to regress camera poses. In this paper, we construct a feature hierarchy from the shallow and deep layers of deep convolutional networks to accumulate more local appearance features. Through the feature interaction of internal interchange and external blend, our introduced fusion module following attention lays emphasis on temporal consistent and geometrically robust objects but neglects foreground interferences during both training and testing. Experimental results on indoor and outdoor datasets demonstrate that our approach can effectively be applied to improve the accuracy of camera localization even with only one image as input. The source code of our implementation is released at https://github.com/lirodndra/FINet.
Graph Neural News Recommendation with User Existing and Potential Interest Modeling

Personalized news recommendations can alleviate the information overload problem. To enable personalized recommendation, one critical step is to learn a comprehensive user representation to model her/his interests. Many existing works learn user representations from the historical clicked news articles, which reflect their existing interests. However, these approaches ignore users’ potential interests and pay less attention to news that may interest the users in the future. To address this problem, we propose a novel Graph neural news Recommendation model with user Existing and Potential interest modeling, named GREP. Different from existing works, GREP introduces three modules to jointly model users’ existing and potential interests: (1) Existing Interest Encoding module mines user historical clicked news and applies the multi-head self-attention mechanism to capture the relatedness among the news; (2) Potential Interest Encoding module leverages the graph neural network to explore the user potential interests on the knowledge graph; and (3) Bi-directional Interaction module dynamically builds a news-entity bipartite graph to further enrich two interest representations. Finally, GREP combines the existing and potential interest representations to represent the user and leverages a prediction layer to estimate the clicking probability of the candidate news. Experiments on two real-world large-scale datasets demonstrate the state-of-the-art performance of GREP.
MEMS-based Tunable Optical Filter Arrays for Nano-Spectrometer in the Visible Spectral Range

Tunable Fabry Perot filter arrays in a visible spectral range for the implementation in a nanospectrometer are investigated. The aim of this work is to fabricate tunable filter arrays using low cost technologies. Materials and technological processes which are adequate for the filter arrays fabrication are evaluated. In addition, a numerical model simulation to select the optimal design of filter structures is presented, wherein an investigation on the displacement of the filter membranes due to the stress in the materials is performed. Multilayers of dielectric DBRs and UV-NIL polymers as filters cavity material are introduced. Lateral structuring of the DBRs in RIE process, and underetching of the polymers in an oxidation plasma to obtain the filter’s air gap cavity, are attained. The tunable Si3N4/SiO2 DBRs-based filter arrays are succesfully fabricated. Optimization on structuring of high index contrast of TiO2/SiO2 DBRs-based filters are accomplished.
Fog-supported Low-latency Monitoring of System Disruptions in Industry 4.0: A Federated Learning Approach

Industry 4.0 is based on machine learning and advanced digital technologies, such as Industrial-Internet-of-Things and Cyber-Physical-Production-Systems, to collect and process data coming from manufacturing systems. Thus, several industrial issues may be further investigated including, flows disruptions, machines’ breakdowns, quality crisis, and so on. In this context, traditional machine learning techniques require the data to be stored and processed in a central entity, e.g., a cloud server. However, these techniques are not suitable for all manufacturing use cases, due to the inaccessibility of private data such as resources’ localization in real time, which cannot be shared at the cloud level as they contain personal and sensitive information. Therefore, there is a critical need to go toward decentralized learning solutions to handle efficiently distributed private sub-datasets of manufacturing systems. In this article, we design a new monitoring tool for system disruption related to the localization of mobile resources. Our tool may identify mobile resources (human operators) that are in unexpected locations, and hence has a high probability to disturb production planning. To do so, we use federated deep learning, as distributed learning technique, to build a prediction model of resources locations in manufacturing systems. Our prediction model is generated based on resources locations defined in the initial tasks schedule. Thus, system disruptions are detected, in real time, when comparing predicted locations to the real ones, that is collected through the IoT network. In addition, our monitoring tool is deployed at Fog computing level that provides local data processing support with low latency. Furthermore, once a system disruption is detected, we develop a dynamic rescheduling module that assigns each task to the nearest available resource while improving the execution accuracy and reducing the execution delay. Therefore, we formulate an optimization problem of tasks rescheduling, before solving it using the meta-heuristic Tabu search. The numerical results show the efficiency of our schemes in terms of prediction accuracy when compared to other machine learning algorithms, in addition to their ability to detect and resolve system disruption in real time.
Workload Based Model of Large Scale 1:N Biometrics Multi-Step Narrowing Down Process

The use of 1:N identification, which can be easily used with free of hands, for large-scale is expanding. For example, 1:N identification where N=100,000 or more is needed for large companies or nationwide payment systems. To realize a large-scale 1:N identification, the processing time is a key factor. Multi-step narrowing down is an effective method for speeding up. The multi-step narrowing achieves high speed by combining high-precision features (low speed) and low precision features (high speed), appropriately. However, a major disadvantage of multi-step narrowing down is that it is difficult to find the optimal setting. The possible combinations of features and the narrowing down rate is enormous, and usually the brute-force search was necessary in the past. In this paper, we propose a model that represents the multi-step narrowing down and report the optimal multi-step narrowing down setting based on the model. As experiments, we evaluated the validity of the model using face data. As a result, our proposed model was confirmed valid as an overall tendency. The evaluation of two-step narrowing down shows the optimum total processing time was realized when the processing time of each step was equal or close, as the model predicted.
Reliability Calculation Method Based on General Data of Small Sample Equipment in Launch Site

Based on the launch site ground equipment in mission important guarantee function and high reliability requirements, in order to better solve the equipment failure regularity of distribution with time, the reliability calculation problems when general data for small sample equipment, puts forward the poisson distribution approximately transform the binomial distribution, optimized the launch site equipment under the condition of small sample and operation without failure, The method of reliability calculation based on objective data provides data support for further study of probabilistic safety analysis of ground system in space launch site.
Optimal Design of Multi-stress Accelerated Degradation Tests Based on Bootstrap Method

Step-stress accelerated degradation tests (SSADTs) have been applied to obtain products’ reliability information when the available test time is limited. As the products performance is generally affected by multiple stresses, optimal design for multiple SSADT is very necessary to guarantee the assessment accuracy and improve the test efficiency. In the current study, a multi-stress step-stress accelerated degradation model is first proposed, which can reasonably depict the comprehensive influence of accelerated stress levels on degradation regulation Then an optimal design method involving a bootstrap-based objective function is constructed, where multiple decision variables involving sample allocation and test time arrangement for each stress level can be simultaneously optimized based on genetic algorithm (GA) and Monte Carlo simulation. Finally, a simulation example is implemented, and a series of conventional methods are compared to illustrate the superiority of the proposed method. The comparative results and sensitivity analysis results both demonstrate the effectiveness and rationality of the proposed method.
Data Augmentation Based on Frequency Warping for Recognition of Cleft Palate Speech

In this paper, we present an automatic speech recognition (ASR) system for the speech of a person with a cleft lip and palate (CLP). The accuracy of speech recognition for a person with CLP is lower than that of a physically-unimpaired (PU) person because the CLP speech has characteristics that differ from those of a PU person; moreover, the amount of available training data is quite limited. In the field of ASR for PU people, data augmentation and self-supervised learning have been studied to tackle this problem of data scarcity. In this paper, we evaluate the effectiveness of those approaches on CLP speech recognition, and propose a data augmentation technique based on frequency warping. The formant of CLP speech tends to fluctuate compared to that of PU people. In order to compensate for the large variety of formant components, our data augmentation method stretches or contracts the spectrogram through the frequency axis. The experimental results on an ASR task with two CLP subjects showed that both data augmentation and self-supervised learning were effective for CLP speech recognition, and our proposed method further improved the performance of those two approaches based on conventional SpecAugment techniques.
Mask-based Beamforming Using Complex-valued Neural Network for Recognition of Spatial Target Speech

We propose a novel method of mask-based beam-forming using a complex-valued neural network (CVNN) for recognition of spatial target speech. Conventional neural network-supported mask-based beamformers enhance speech signals regardless of direction of arrival. In contrast, our method enhances target sounds (i.e., speech signals from a set of specified target directions) and suppresses non-target sounds (i.e., speech signals from other directions and noise signals). The CVNN for mask estimation is trained to distinguish between target and non-target sounds. Our method is evaluated in simulated environments and on real-recorded signals. Experimental results show that our method can enhance target sounds and suppress non-target sounds. Our method can configure a beamformer with two target directions using a two-channel microphone array. This demonstrates that the number of target directions is free from the constraints of conventional generalized sidelobe canceller methods, in which the number of directions must be less than the number of microphones.
Time-Dependent Reliability Analysis of System Based on Dynamic Bayesian Fault Network

Prior and sample data information are two key information for Bayesian model to analyze and predict accurately. In order to make the time-dependent analysis results accurately describe the trend of reliability degradation with time, a Dynamic Bayesian fault network model (DBFN) is constructed. Firstly, the prior information is combined with exponential, uniform and normal probability density distribution functions to calculate the failure rate of the system based on curve fitting. Secondly, the backwards reasoning of Bayesian network is used to realize reliability analysis, which can trace the fault phenomenon to the fault cause. Finally, the time-dependent sensitivity analysis is carried out and the trend with time is given. Using the Cabin Door indication system failure as a case, the results show that the failure rate calculated by the dynamic method is closer to the time-varying state of the system than the static value. The method provides an objective means for system time-dependent reliability analysis.
Artificial Reef Detection and Recognition Based on Faster-RCNN

Aiming at the problem of target detection of artificial reefs in the sea, the deep learning network Faster-RCNN and SSD are used to detect artificial reefs in the seabed images generated by multi-beam sonar grid data. Aiming at the problem of insufficient training samples, an AR21 data set was constructed. Based on this data set, the Faster-RCNN model and SSD model were trained using transfer learning, and verified on the test set. Experimental results show that the Faster-RCNN model has a detection accuracy of 95.53% and a recall rate of 98.83%. The SSD target detection model has an accuracy of 73.31% and a recall rate of 76.63%. The deep learning algorithm can identify artificial reefs more accurately and efficiently on multi-beam sonar images, and the Faster-RCNN model has higher accuracy, robustness and generalization capabilities.
Deep Reinforcement Learning for NPDCCH Period Adjustment in NB-IoT Networks

In narrowband Internet of Things (NB-IoT) networks, a base station can provide multiple coverage enhancement (CE) levels to accommodate devices with diverse channel qualities for achieving massive connections. The base station can determine a narrowband physical downlink control channel (NPDCCH) period (NP) for each CE level. The base station should allocate uplink and downlink radio resources on an NP-basis for each device in a CE level. The NP length of each CE level significantly affects the efficiency of uplink and downlink radio resource allocation. However, the key challenge in determining an NP length for each CE level is that the NP length should be decided before the uplink and downlink resource allocation and cannot be frequently changed. Therefore, this paper studies the NPDCCH period adjustment problem considering uplink and downlink traffic. The objective is to minimize the radio resource consumption while each device can transmit or receive its data. To overcome the above issue, we propose an algorithm based on deep reinforcement learning to solve the target problem. Compared with the two baselines, the simulation results show the efficacy of the proposed algorithm and useful insights into the design of the NPDCCH period adjustment algorithm for NB-IoT networks.
Explainable Deep Learning Models on the Diagnosis of Pneumonia

Pneumonia is a lung infection caused by pathogens such as bacteria and viruses. If the diagnosis is not timely and the treatment cannot be performed in time, it may threaten the life of the patient. Currently, the most common method for the diagnosis of pneumonia is to take chest X-rays and perform manual diagnosis. However, manual diagnosis is less efficient and highly subjective. In this paper, we present a deep learning approach for X-ray image identification of pneumonia and conducts the corresponding explainability. The essential idea is to use the existing pre-trained deep learning model and the Grad-CAM method to construct an explainable deep learning model of pneumonia X-ray image recognition. In the proposed deep learning model, ResNet50 and VGG16 models can achieve higher accuracy after data augmentation. The proposed deep learning model could be applied to the computer-aided diagnosis of pneumonia to achieve efficient detection and diagnosis of pneumonia.
Energy-Efficient UAV Communication With Trajectory Optimization

With the development of the 5G network, many emerging technologies which highly depend on the performance of communication have appeared and their performance is improved significantly. UAVs applications are becoming popular in many fields. They create new opportunities and lifestyles but also new challenges. UAVs assist the communication and transmit throughput as well as consuming the energy. In this paper, we study the energy efficiency of uav given certain trajectories. We derive two models on the communication scenarios dominated by LOS and NLOS communications respectively. They share the same propulsion energy consumption model where is based on UAV’s mass and velocity. For the NLOS communication scenario, we investigate how the altitude impacts the throughput. For the case of the LOS scenario, we suppose UAVs flying in a circular trajectory and study the impact of the radius. Because of the obstructions like terrains, altitude is investigated in the NLOS communication. Meanwhile, the coverage area of all users is considered in both scenarios. We deliver the experiments in investigating flight radius and altitude respectively. The results meet our expectation that there will be an optimal radius and altitude to have the optimal energy efficiency. Meanwhile, we have discussed some future directions which can be extended from our works.
Target Speaker Extraction for Customizable Query-by-Example Keyword Spotting

To achieve a better user experience, it is desirable to have a customizable keyword spotting (KWS) system. Query-by-Example (QbE) is a promising way to achieve customization. In order to reduce the false alarms caused by interfering speech and ambient noise, we propose a speech enhancement frontend based on VoiceFilter for QbE based KWS system. VoiceFilter is a speaker extraction model, which extracts the voice of a target speaker from multi-speaker speech signals, with a reference signal from the target speaker. In this paper, we improve VoiceFilter substantially to better fit the KWS scenario, enhancing the voice of the target speaker, suppressing the voice of non-target speakers, and reducing ambient noise as well. To further reduce false rejections of the system with a VoiceFilter frontend, we apply exemplar augmentation to add reverberation to enrollment templates. Our proposed method leads to improved performance according to our experiments. Comparing with a DTW-based QbE system, our best system achieves a 39.0% relative reduction in false reject rate, at a false alarm rate of 0.5 times per hour.