Miracle-dz/UniFAR-FTUs huggingface dataset:
A method of network traffic anomaly detection based on Packet Window Transformer

As Web 2.0 applications grow in popularity, Internet is playing an increasingly significant role in our lives. People exchange information and share resources anytime and anywhere through the Internet. In other words, the Internet is the driving force behind new technologies like 5G, blockchain, Internet of Things, etc. At the same time, Internet attacks based on vulnerabilities and security flaws have gradually increased. Attackers hide their real IP addresses to find and analyze target hosts, send some fake malicious packets to the system vulnerabilities. With the emergence and popularity of new Internet applications, advanced persistent network attacks are also increasing, which brings new challenges to network security. At present, most intrusion detections only focus on multi-dimensional traffic features but ignore the spatio-temporal features of network traffic. In this paper, a packet window based is proposed to transform network flow data into multiple window blocks. The feature matrix formed by window blocks can better show the local sequence relationship. Then we propose an improved Transformer sequence analysis model on these feature matrix to network traffic anomaly detection. Our method can exploit the multi-dimensional traffic features and model the time series relationship between network traffic. Empirical results on public IDS2017 dataset show that our approach achieves leading accuracy though integrating spatio-temporal features. It has 96.1% accuracy and 95.6% F1 score. Compared to other state-of-the-art deep learning models, Our model achieves excellent results.
Thermo-Mechanical Constraints for Packaging of Diamond Components

In this work a Finite Element Simulation thermal and mechanical study of a power SOT-227 package with a diamond die is performed in order to assess the impact of its mechanical properties when compared to the standard silicon dies. Furthermore, the simulations are carried using silver sintering as die-attach material, and different conditions and parameters that affect its mechanical behaviour are explained and quantified in order to illustrate the variation in results and the need for reliable modelisation and material parameters identification. Mechanical behavioural data is also extracted for the sintered silver simulating a lap shear test, which is used to validate a non-linear identification method of the Anand model parameters. The results show that the inclusion of a diamond die increases significantly the amount of stress and plastic deformation in the die-attach layer, which will lead to reliability problems and shorter life cycle. In addition, when using silver sintering as die-attach, the model parameters should be extracted for each particular material, owing to the fact that the mechanical properties of the die-attach layer strongly depend on its processing conditions. Finally, the Anand model parameter identification routine was validated, obtaining minimum deviation from the original values used to simulate the pseudo-experimental data.
Calibrated Probabilistic QoT Regression for Unestablished Lightpaths in Optical Networks

Quality-of-Transmission (QoT) regression of unestablished lightpaths is a fundamental problem in Machine Learning applied to optical networks. Even though this problem is well-investigated in current literature, many state-of-the-art approaches either predict point-estimates of the QoT or make simplifying assumptions about the QoT distribution. Because of this, during lightpath deployment, an operator might take either overly-aggressive or overly-conservative decisions due to biased predictions. In this paper, we leverage state-of-the-art Gradient Boosting Decision Tree (GBDT) models and recent advances in uncertainty calibration to perform QoT probabilistic regression for unestablished lightpaths. Calibration of a regression model allows for an accurate modeling of the QoT Cumulative Distribution Function (CDF) without any prior assumption on the QoT distribution. In our illustrative experimental results, we show that our calibrated GBDT model’s predictions provide accurate confidence interval estimates, even when only few samples per lightpath configuration are available at training time.
An enhanced UNet variant for Effective Lung Cancer Detection

Lung cancer is one of the diseases with the highest mortality rate and early detection is key to making the disease as treatable as possible. The most common and useful method for recognizing pulmonary nodules is computed tomography, which allows them to be located and monitored. The disadvantage of this technique is that the scans have to be interpreted by doctors, who could make mistakes. The use of pulmonary CAD is therefore becoming increasingly widespread, a system capable of automatically analyzing CT images and providing information on possible suspicious regions found in the images. These systems, by offering radiologists a list of already marked regions of interest to view with particular attention, increase the efficiency of detection of small nodules and reduce reporting times by physicians. This study aims to accurately detect the location of pulmonary nodules through a Deep Learning approach with the use of computed tomography scans. In particular, it proposes the use of a new variant of the UNet architecture, called GUNet3++, which has been compared with the other types of this network. To validate the approach, the public LIDC-IDRI dataset was used, which collects pulmonary CT images of about a thousand patients with different types of cancer. The results obtained are very promising, showing a performance improvement compared to other UNet networks.
Optimal Grasping Pose Selection Method for Dual-arm Robot Based on Improved Genetic Algorithm

Dual-arm robot has been widely used in industry and service trades. It increases the degree of freedom within the workspace, while leading to more complex task planning problems. When setting goals for the dual-arm, it is a key issue to consider the impact of the setting of the goals on the complexity of the task. In this paper, an optimal grasping pose selection method has been proposed in order to select the optimal grasping pose of the dual-arm robot. This method uses an improved genetic algorithm. Facing the task of multi-objective optimization, the fitness function and gene reservation strategy can be adjusted automatically according to the iterative depth. Thereby, the coordinates and grasping pose of the arms on the object are obtained. The simulation experiment of dual-arm robot grasping slender objects was carried out. The results show that it has a better performance in symmetry of grasping points, position variation and synchronization of dual-arm robot.
ADV-ResNet: Residual Network with Controlled Adversarial Regularization for Effective Classification of Practical Time Series Under Training Data Scarcity Problem

Practical time series datasets in classification tasks often suffer from scarcity in number of training instances owing to the expenses associated with the annotation exercise. Deep learning algorithms generally demand sufficiency in the seen examples for its learning purposes. In this paper, we consider adversarial perturbation as the set of invariants such that a robust model can be constructed under the practical constraints of training sample scarcity. We propose ADV-ResNet that augments the learning ability of a Residual Network (ResNet) through adversarial regularization, where adversarial training is transformed to robust form of data augmentation. We propose novel algorithm for adversarial regularization factor computation that estimates the amount of regularization for controlled perturbation in the learning process. The intentional yet measured introduction of perturbations in the ResNet training process enables it to learn better under crafted and controlled perturbations that constitute unseen but important input space. Our empirical investigation on publicly available time series classification datasets (UCR time series archive) demonstrates the utility of ADV-ResNet through ablation study. The empirical evidence clearly indicates the superiority of ADV-ResNet over the baseline ResNet, as well as ADV-ResNet significantly outperforms the state-of-the-art time series classification models.
Visual Recognition of Artificial Intelligence Open Source Comparison Study: Focusing on the Situation of People

For human-centered design, which has been focused on the development of artificial intelligence since the beginning, it is important to recognize unstructured data through cameras and microphones. There is still a limit to motion recognition done through specific actions such as text input and gesture input. To provide more appropriate service to users in services such as chatbots, which have recently been receiving the focus of advertisement and public attention, it is also important to understand the current status of users in front of cameras and microphones. In this study, the limitations and future direction of improvement for each service are discussed by comparing and studying the results of human emotion analysis for human-centered design, which is suggested as the future direction of development of artificial intelligence for visual recognition open source currently in service.
An Edge Computing based Defect Detection Algorithm for Malleable Iron

With the annual increase of malleable iron (MI) production, MI factories have to employ almost three-quarters of workers to inspect unqualified products. To promote the automation of the quality inspection process of MI products, many convolution neural networks have been proposed to address such defect detection tasks. However, they sometimes fail to achieve the expected detection results due to the different defect presentation patterns on MI surface. In this work, we proposed a lightweight neural network model ES-MobileNet* fused with the attention mechanism, specifically designed for the MI defect detection on edge computing devices. Our ES-MobileNet* has two main parts: the expansion and shrinking bottleneck (ESNet) and the selective kernel (SK). Functionally, ESNet includes two feature extraction branches, one of which is responsible for extracting the features of the internal defects of MI, and the other one is responsible for extracting the features of the edge contours of MI. Then, SK will recalibrate the output features of two branches before feature fusion that suppress interference information in the channel domain of the original feature map. Experiment result shows that compared with MobileNetV2, the computational complexity and parameter of the proposed model are reduced by 28.6% and 20.2% respectively. It has a mAP of 62.45% on dataset IIDD and a FPS of 78.7 at Nvidia Jetson NANO, which can more than enough provide technical support for the surface defect detection of MI at edge computing devices.
Electromagnetic Pigging System Based on Sandwich Differential Planar Coil

In-pipeline inspection is an important precontrol method to ensure the safety of oil and gas pipeline transportation. This article proposes an electromagnetic in-pipe detector based on passive resonance-enhanced differential planar coils to detect defects on the inner surface of pipes. Both qualitative and quantitative analyses of pipeline defects and damage are developed. The introduction of passive resonant coils is shown to significantly improve the detection capability of the sensor. This is coupled with the establishment of a theoretical derivation model of the proposed structure. The hardware platform of the laboratory system has been built, and an eddy current internal detector suitable for 8-in-diameter pipes is developed and integrated into the system. Numerical simulations and experimental verifications on flat defects and pipe defects have been undertaken. The obtained results have shown that the real defects have been correctly detected, and the system is effective, reliable, and efficient.
Robots with Theory of Mind for Humans: A Survey*

Theory of Mind (ToM) is a psychological construct that captures the ability to ascribe mental states to others and then use those representations for explaining and predicting behavior. We review recent progress in endowing artificially intelligent robots with ToM. A broad array of modeling, experimental, and benchmarking approaches and methods are present in the extant literature. Unlike other domains of human cognition for which research has achieved super-human capabilities, ToM for robots lacks a unified construct and is not consistently benchmarked or validated—realities which possibly hinder progress in this domain. We argue that this is, at least in part, due to inconsistent defining of ToM, no presence of a unifying modeling construct, and the absence of a shared data resource. We believe these would improve the ability of the research community to compare the ToM abilities of different systems. We suggest that establishing a shared definition of ToM, creating a shared data resource that supports consistent benchmarking & validation, and developing a generalized modeling tool are critical steps towards giving robots ToM capabilities that lay observers will recognize as such.
E-EmotiConNet: EEG-based Emotion Recognition with Context Information

Electroencephalogram (EEG) based emotion recognition has received considerable research attention for brain-computer interface (BCI) and health care. The main challenge is to find an effective representation that is invariant to within and inter-subject differences associated with raw EEG data. In this paper, we propose a novel deep learning architecture for subject-independent emotion recognition, which is named E-EmotiConNet. The proposed model is a two-stream CNN architecture that effectively incorporates multi-channel EEG-time series information and spatial interactions. It comprises EEG-Emotion and EEG-Context networks, which can find emotion related features and subject oriented ones from raw EEG data, respectively. By fusing the interaction model with emotional features, the proposed model can improve emotion recognition accuracy. Experiments were performed on two publicly available datasets, DEAP and SEED, and verified the proposed E-EmotiConNet achieved classification accuracy of 93.39% and 93.69% on DEAP and SEED, respectively, compared with current state-of-the-art approach.
Evaporative Cooling of High Power Density Motors: Design and Analysis

This study presents the design and thermal analysis of a 25kW high-power-density electric motor prototype. The design employs a capillary flow-assisted evaporative cooling system, resulting in a lightweight and power-dense architecture suitable for high-performance electric automobiles and electric aviation applications. The cooling system proposed in this study significantly reduces motor mass by eliminating the need for an external cooling jacket. This design differs from traditional internally cooled motors since a highly wetting dielectric coolant is used to facilitate evaporation within the stator core windings. Furthermore, in place of the usual slot liner material, wicking structures are used to aid coolant flow into the stator core slots. In this way, two-phase flow can be maintained around the stator windings. Novec-7200 dielectric coolant with a saturation temperature of 76 °C at atmospheric pressure is used as the working fluid within the electric motor. The motor is semi-flooded with coolant; therefore, the end windings are also cooled via evaporation. A thin composite sleeve separating the rotor and stator cores of the electric motor was used to prevent coolant from entering the air gap, thus preventing inefficiencies due to friction against the rotor as well as damage to the permanent magnets.
Overleaf Example

©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Abstract— Gaze cues play an important role in human communication and are used to coordinate turn-taking and joint attention, as well as to regulate intimacy. In order to have fluent conversations with people, social robots need to exhibit humanlike gaze behavior. Previous Gaze Control Systems (GCS) in HRI have automated robot gaze using data-driven or heuristic approaches. However, these systems tend to be mainly reactive in nature. Planning the robot gaze ahead of time could help in achieving more realistic gaze behavior and better eye-head coordination. In this paper, we propose and implement a novel planning-based GCS. We evaluate our system in a comparative within-subjects user study (N=26) between a reactive system and our proposed system. The results show that the users preferred the proposed system and that it was significantly more interpretable and better at regulating intimacy.
Copper Trace Failures in Ball Grid Array (BGA) Packages under Sequential Harmonic Vibration and Temperature Cycling

Solder interconnects in ball grid array (BGA) packages can present substantial fatigue degradation risk under combinations of mechanical (vibration) and thermo-mechanical cyclic loading. Durability tests to investigate such risks typically use either sequential or simultaneous combinations of vibration loading and temperature cycling. The interconnect system consists of not just the solder joint itself but also the associated features, such as the interfacial IMC layer, the copper pads on the substrates and the copper traces and vias connected to the copper pads. The copper traces are known to be particularly vulnerable to mechanical flexural loads, such as those experienced due to vibration loading. Recent studies have shown a combined failure mode consisting of cracks in both the solder and copper pad. However, there is limited information in the literature on the vulnerability of copper traces under sequential vibration and temperature cycling loads. Therefore, this work examines the influence of loading sequence (harmonic vibration followed by temperature cycling and vice versa) on copper trace cracks in a Printed Wiring Assemble (PWA) that consists of a centrally located BGA component on a multilayered organic printed wiring board (PWB). In addition, the width of Copper traces connected to the copper pad was varied (45µm, 100µm and 200µm) to determine how the fatigue degradation risks scale with trace width.In order to understand the loading conditions that lead to copper trace cracks, the test matrix consists of (i) room-temperature harmonic vibration tests close to the fundamental mode of the PWA at 150g’s and 175g’s; (ii) temperature cycling (TC) tests (-40°C to 125°C) and (iii) sequential application of items (i) and (ii). Failure analysis conducted on the tested specimens revealed that copper trace cracks were highly likely to occur during the harmonic vibration segment of individual and sequential tests. In contrast, temperature cycling loads did not lead to copper trace failures, although it did show evidence of accelerating the propagation and growth of cracks that had already initiated during any prior vibration exposure.
Fully-automatic segmentation of the ciliary muscle using anterior segment optical coherence tomography images

The study of the ciliary muscle represents a fundamental step in the diagnosis and treatment of many high-incidence diseases, such as glaucoma or myopia. Currently, Anterior Segment Optical Coherence Tomography (AS-OCT) is widely used by clinicians to analyse the morphological changes that affect this important ocular structure. AS-OCT is a non-invasive imaging technique that produces high-resolution cross-sectional images, allowing a precise visualization of the main ocular tissues of the anterior segment of the eye. In this work, we propose a novel methodology for the ciliary muscle segmentation using AS-OCT images, an emerging ophthalmic imaging technology with great potential to support early diagnosis of relevant ocular conditions. For this purpose, we have analysed the performance of the U-Net architecture with two different encoders (ResNet-18 and ResNet-34) combined with a transfer learning-based approach. The validation of the proposed system was performed through different and representative experiments, using an AS-OCT dataset that was specifically designed for this work. The results demonstrated that the proposed system is robust and reliable, achieving an average Precision of 0.8902 ± 0.0815, an average Recall of 0.8237 ± 0.1239, an average Accuracy of 0.9961 ± 0.0021, an average Jaccard of 0.7431 ± 0.1116 and an average Dice of 0.8445 ± 0.0870. These results demonstrate that the proposed method has a satisfactory performance that can help the clinicians to make a more accurate diagnosis and proceed with appropriate treatments of different diseases of interest.
Investigation of Performance of Visual Attention Mechanisms for Environmental Sound Classification: A Comparative Study

Classification of environmental sound is an equally challenging task to classification of human speech or music. In recent years, attention mechanism has gained considerable attention in learning representative and prototypical features to resolve various research challenges from domains such as computer vision, text mining and sound classification. Advancements in the computer vision can be used for improving the performance of deep models designed to perform environmental sound classification as well. In this paper, we investigated the performance of deep network when twelve SOTA visual attention mechanisms are incorporated in the training of the deep network. The performance of the deep model is evaluated on two benchmark environmental sound classification datasets, viz. ESC-10 and DCASE-2019 task-1(A) datasets.
Performance Characterization of a Novel Low-Cost Additively Manufactured PCM-Air Polymer Composite Thermal Energy Storage

To meet the increasing building cooling requirements, thermal energy storage (TES) systems are suggested to provide daytime peak-load shifting. Phase-Change Materials (PCMs) can store large amounts of energy with reasonably low-temperature gradients due to their high specific latent energy. However, PCMs need encapsulation and augmented heat transfer before they can be widely applied to Heating ventilation and air conditioning (HVAC) systems. Encapsulation and heat transfer augmentation of PCMs is expensive and usually reduces the energy density of the system, thus requiring special designs to overcome the associated shortcomings.The present study focuses on the development of a novel low-cost additively manufactured polymer composite TES with PCM encapsulated inside the polymer channels. It is fabricated using a customized additive manufacturing technique which involves laying continuous metal wires across the polymer channels. The metal wires provide a low resistance thermal path between the air and the PCM, independent of the conductivity of the polymer. An organic PCM (n-hexadecane) with a phase transition temperature of around 18°C, ideal for building cooling applications, is used. An 0.29 m × 0.26 m × 0.12 m prototype TES unit was designed and additively manufactured. An experimental investigation was performed on the TES heat exchanger capable of storing about 0.75 MJ of energy during the day at typical airflow rates and temperatures utilized in equipment cooling applications. The paper discusses and presents in detail the fabrication process, post-processing technique to obtain a leak-proof construction, experimentally obtained thermal and hydraulic performance results, comparison against analytical-based numerical simulation results. A good agreement between the experimental results and numerical predictions is observed.