Miracle-dz/UniFAR-FTUs huggingface dataset:
GaN Power Integration for High Frequency and High Efficiency Power Applications: A Review

High frequency and high efficiency operation is one of the premier interests in the signal and energy conversion applications. The wide bandgap GaN based devices possess superior properties and have demonstrated exceeding performance than Si or GaAs devices. In order to further exploit the potential of GaN electronics, monolithic power integration is proposed. Firstly, this paper discusses the structure and properties of GaN power devices to explain the choice of lateral integration in the view of GaN power ICs. Then the state-of-the-art performance of GaN power integration in two major application areas is reviewed, which are the microwave power amplification and DC-DC power conversion. The GaN power integration technologies in MMIC platforms are summarized in terms of the gate length, operation frequency and power added efficiency of ICs. On the other hand, the smart GaN power IC platforms have boosted the development of DC-DC power converters. Demonstrations of high frequency (>1 MHz) and high efficiency (>95 %) converters with various kinds of integration technology and topology are reviewed. Lastly novel integration schemes and methods are introduced to stimulate new thoughts on GaN power integration road.
Transfer Learning Algorithm for Enhancing the Unlabeled Speech

To improve the generalization ability of speech enhancement algorithms for unlabeled noisy speech, a speech enhancement transfer learning model based on the feature-attention multi-kernel maximum mean discrepancy (FA-MK-MMD) is proposed. To obtain a representation of the shared subspace (the part related with clean speech in the feature extracted by shared encoder) between source domain (speech with known noise and labels) and target domain (speech with unknown noise and no labels), the algorithm takes MK-MMD as loss function for reducing distribution differences between these two domains, which could improve the adaptability to the unknown noise. Furthermore, considering that different noise have different influence on the representation of shared subspace, the attention mechanism is applied to feature dimension to screen out the information less polluted by noise, which is helpful for reconstructing the clean speech. In the term of speech with unknown noise and no labels, the experiments demonstrate that the proposed algorithm has improved the frequency-weighed segmental signal-to-noise ratio (fwsegSNR), the perceptual evaluation of the speech quality (PESQ) and the short time objective intelligibility (STOI) compared with the baseline algorithm.
Polarization Transmission Matrix for Completely Polarization Control of Focal Spots in Speckle Field of Multimode Fiber

The propagation of light transmitting through a scattering medium generates random speckle. Transmission matrix (TM) is introduced to describe this transmission process. In general, only scalar TM is considered. Meanwhile, for a non-polarization maintaining multimode fiber (MMF), depolarization effect is extremely strong. Therefore, the field at the exit end of a MMF is a speckle with random distribution not only in terms of intensity but also polarization. In addition to traditional TM, we propose polarization TM (PTM) to describe the depolarization effect of a MMF. Based on PTM, the speckle pattern formed by a coherent light transmitting through a MMF can be modulated into one focal spot or multiple focal spots with desired polarization.
Brutus: Refuting the Security Claims of the Cache Timing Randomization Countermeasure Proposed in CEASER

Cache timing attacks are a serious threat to the security of computing systems. It permits sensitive information, such as cryptographic keys, to leak across virtual machines and even to remote servers. Encrypted Address Cache, proposed by CEASER – a best paper candidate at MICRO 2018 – is a promising countermeasure that stymies the timing channel by employing cryptography to randomize the cache address space. The author claims strong security guarantees by providing randomization both spatially (randomizing every address) and temporally (changing the encryption key periodically). In this letter, we point out a serious flaw in their encryption approach that undermines the proposed security guarantees. Specifically, we show that the proposed Low-Latency Block Cipher, used for encryption in CEASER, is composed of only linear functions which neutralizes the spatial and temporal randomization. Thus, we show that the complexity of a cache timing attack remains unaltered even with the presence of CEASER. Further, we compare the encryption overheads if CEASER is implemented with a stronger encryption algorithm.
MACoMal: A Multi-Agent Based Collaborative Mechanism for Anti-Malware Assistance

Anti-malware tools remain the primary line of defense against malicious software. There is a wide variety of commercial anti-malware tools in the IT security market. However, no single tool is able to provide a full protection against the overwhelming number of daily released malware. Hence, collaboration among malware detection tools is of paramount importance. In this paper, we propose MACoMal, a multi-agent based decision mechanism, which assists heterogeneous anti-malware tools to collaborate with each other in order to reach a consensual decision about the maliciousness of a suspicious file. MACoMal consists of two main elements: (1) an executable file identification model, and (2) a collaborative decision-making scheme. MACoMal is analyzed with respect to network connectivity and global decision correctness. By leveraging a multi-agent simulation tool and a set of real malware samples, we present a simulation methodology to assess its effectiveness and efficiency. Experimental results show that MACoMal is able to immunize a network against a malware threat within a time that ranges from a few seconds to a few minutes after the threat detection.
Modeling and Simulation of an Aerodrome Electrical Power Source Based on Fuel Cells

The current challenge in aviation is to reduce environmental impact by reducing fuel consumption and emissions released, especially NOx. An open research direction, used to achieve these goals, is to develop new power sources based on unpolluting fuel, a solution being constituted by the use of H2 fuel cells. The article presents the modeling and simulation of a possible configuration for a hybrid aerodrome power source based on the fuel cell. The proposed architecture is composed of fuel cell stack, DC to DC boost converter, supercapacitor and buck-boost converter. The supercapacitor is connected to a buck-boost converter and the fuel cell is connected to a boost converter. The DC bus voltage is equal to 28.5 V. The proposed configuration is simulated in Matlab/ Simulink/ Simscape Power Systems. The main purpose of these simulations is to verify the simulation scheme behavior in order to use it in future detailed studies.
5G Vehicle-to-Everything Services: Gearing Up for Security and Privacy

5G is emerging to serve as a platform to support networking connections for sensors and vehicles on roads and provide vehicle-to-everything (V2X) services to drivers and pedestrians. 5G V2X communication brings tremendous benefits to us, including improved safety, high reliability, large communication coverage, and low service latency. On the other hand, due to ubiquitous network connectivity, it also presents serious trust, security, and privacy issues toward vehicles, which may impede the success of 5G V2X. In this article, we present a comprehensive survey on the security of 5G V2X services. Specifically, we first review the architecture and the use cases of 5G V2X. We also study a series of trust, security, and privacy issues in 5G V2X services and discuss the potential attacks on trust, security, and privacy in 5G V2X. Then, we offer an in-depth analysis of the state-of-the-art strategies for securing 5G V2X services and elaborate on how to achieve the trust, security, or privacy protection in each strategy. Finally, by pointing out several future research directions, it is expected to draw more attention and efforts into the emerging 5G V2X services.
On Modeling the Effects of Auditory Annoyance on Driving Style and Passenger Comfort

Despite the impressive progress being made in autonomous vehicles, human drivers will remain ubiquitous in the imminent years. Therefore, intelligent hybrid vehicular systems must be aware of the interactions between humans and the environment (e.g., sound, vibration, speed, etc.). In this paper, we evaluate the effect of acoustic annoyance on drivers in a real-world driving study. We found significant differences in driving styles elicited by annoying acoustics and present an online classifier that uses onboard inertial measurement unit measurements to distinguish whether a driver is annoyed with 77% accuracy. Moreover, we directly measured the forces applied on the passenger with a pressure mat lined on the car seat, and empirically confirm that our proposed passenger dynamics model is reasonable. However, due to our acoustically induced driving styles not being polarizing enough, we were unable to show that passengers’ self-reported ride comfort changed with acoustic annoyance.
TapSnoop: Leveraging Tap Sounds to Infer Tapstrokes on Touchscreen Devices

We propose a novel tapstroke inference attack method, called TapSnoop, that precisely recovers what user types on touchscreen devices. Inferring tapstrokes is challenging owing to 1) low tapstroke intensity and 2) dynamically-changing noise. We address these challenges by revealing the unique characteristics of tapstrokes from audio recordings exploited by TapSnoop as a side channel of tapstrokes. In particular, we develop tapstroke detection and localization algorithms that collectively leverage audio features obtained from multiple microphones, which are designed to reflect the core properties of tapstrokes. Furthermore, we improve its robustness against environmental changes, by developing environment-adaptive classification and noise subtraction algorithms. Extensive experiments with ten real-world users on both number and QWERTY keyboards show that TapSnoop can achieve an inference accuracy of 85.4% and 75.6% (96.2% and 90.8% in best case scenarios) in stable environments, respectively. TapSnoop can also achieve a reasonable accuracy even with varying noise. For example, it shows an inference accuracy of 84.8% and 72.7% in a numeric keyboard when the noise level is varied from 37.9 to 51.2 dBA and 46.7 to 60.0 dBA, respectively.
Plasticity in Collective Decision-Making for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lock-ins

Swarm robots operate as autonomous agents and a swarm as a whole gets autonomous by its capability of collective decision-making. Despite intensive research on models of collective decision-making, the implementation in multi-robot systems is still challenging. Here, we advance the state of the art by introducing more plasticity to the decision-making process and by increasing the scenario difficulty. Most studies on large-scale multi-robot decision-making are limited to one instance of an iterated exploration-dissemination phase followed by successful and permanent convergence. We investigate a dynamic environment that requires constant collective monitoring of option qualities. Once a significant change in qualities is detected by the swarm, it has to collectively reconsider its previous decision accordingly. This is only possible by preventing lock-ins, a global consensus state of no return (i.e., a dominant majority of robots prevents the swarm from switching to another, possibly better option). In addition, we introduce a scenario of increased difficulty as the robots must locate themselves to assess the quality of an option. Using local communication, swarm robots propagate hop-count information throughout the swarm to form a global reference frame. We successfully validate our implementation in many swarm robot experiments concerning robustness to disruptions of the reference frame, scalability, and adaptivity to a dynamic environment.
Docker Image Sharing in Distributed Fog Infrastructures

Fog computing platforms offer virtualized resources located in the vicinity of their end users. Their broad geographical distribution force them to split physical resources in large numbers of relatively weak machines. The limited available disk space per fog node however creates problems for Docker-based systems which locally cache a copy of every container image they execute: first, caches may fill very quickly, whereas standard Docker never automatically evicts any image from its cache. This motivates us to implement automatic cache replacement in Docker. Second, splitting cache space in multiple disjoint partitions negatively impacts the hit rates. This motivates us to propose allowing multiple co-located Docker servers to share their caches. Our trace-based evaluations show that the proposed design achieves significant cache hit improvements, leading to reductions of average container deployment times between 37% and 78% depending on the scenarios.
Programmable Multilayer INT: An Enabler for AI-Assisted Network Automation

Recently, the fast development of backbone networks has made the traffic, services, and infrastructure of packet-over-optical networks increasingly complicated. This stimulates research and development on fine-grained and real-time performance monitoring and troubleshooting. In this article, we propose a ProML-INT system to oversee packet-over-optical networks in real time and enable customized performance monitoring and troubleshooting. We introduce the system design in detail, and explain how to control the overhead of multilayer INT ML-INT by inserting INT fields in packets selectively. Experiments demonstrate the ProML-INT system, a small-scale packet-over-optical network testbed. The experimental results confirm that our proposal can monitor packet and optical layers jointly in real time, and the homemade data analyzer in it can leverage artificial intelligence to identify the root causes of exceptions in packet-over-optical networks correctly and promptly.
Fault Localization With Weighted Test Model in Model Transformations

Model transformations and model-driven engineering (MDE) have been applied widely in service-oriented architecture based information systems. To support the development of such a service-oriented information system, it is necessary to guarantee the quality of model transformations. With the increasing complexity and scale of model transformations, debugging of model transformations becomes more and more time-consuming and difficult, there is an increasing need to rely on efficient and accurate fault localization approaches to help with debugging. Among the existing fault localization approaches, the spectrum-based fault localization (SBFL), as a dynamic analysis method, mainly used the coverage information and execution results of the rules of model transformation to estimate the probability of each rule may be faulty. However, there are many false-positive and false-negative results in the rule coverage information, the accuracy of the SBFL is not ideal, so we consider mining the impact of covered range in different test models to further improve the effectiveness of fault localization. In this paper, we propose an optimized strategy of fault localization based on the impact of the test model, according to the covered range of test models, weight values are assigned to different test models, and the statistical coverage information of rules are weighted and adjusted accordingly. We compare the proposed approach with the SBFL, the experimental results show that under the same techniques for computing the suspiciousness, our approach can help locate around 26% more faulty rules in the ranking Top-1 of the suspicious list than the SBFL, the effectiveness of fault localization techniques can be improved by 50.42% in the best case and 8.9% in the average case.
Pain Detection from Facial Videos Using Two-Stage Deep Learning

A new method to objectively measure pain using computer vision and machine learning technologies is presented. Our method seeks to capture facial expressions of pain to detect pain, especially when a patients cannot communicate pain verbally. This approach relies on using Facial muscle-based Action Units (AUs), defined by the Facial Action Coding System (FACS), that are associated with pain. It is impractical to use human FACS coding experts in clinical settings to perform this task as it is too labor-intensive and recent research has sought computer-based solutions to the problem. An effective automated system for performing the task is proposed here in which we develop an end-to-end deep learning-based Automated Facial Expression Recognition (AFER) that jointly detects the complete set of pain-related AUs. The facial video clip is processed frame by frame to estimate a vector of AU likelihood values for each frame using a deep convolutional neural network. The AU vectors are concatenated to form a table of AU values for a given video clip. Our results show significantly improved performance compared with those obtained with other known methods.
Hybrid Attention-Based Prototypical Network for Unfamiliar Restaurant Food Image Few-Shot Recognition

As eating-out became an indispensable part of our daily lives, demand for the food recognition of unfamiliar restaurant increased significantly due to health-care. Although there are many researches on generic food recognition, there are relatively fewer studies on restaurant food image recognition. Meanwhile, it becomes extremely challenging for restaurant food image recognition due to insufficient food image. Prototypical network is common utilized to address the such a task in recent years. Although the methods based on prototypical network achieve impressive results in capturing similarities feature of the same food category, it fails to highlight important information on feature and instance level. Toward this end, we propose an effective food image recognition scheme by incorporating hybrid attention mechanism into prototypical network in this paper. Specifically, the image feature is first captured by convolutional neural network (CNN). Then the image attention weights yielded by instance-based attention mechanism are used to modulate the image feature of CNN for constructing class prototypes. And feature-based attention mechanism is employed to grasp important information of image for enriching image representation. Extensive experimental results on the large food image dataset verify that the performance of our proposed classification scheme outperforms the state-of-the-art ones.
Automatic recognition of the characteristic melody of bird vocalizations

We present a method for the automated recognition of birdsong syllables type. For that purpose, we automatically segment the birdsong to acoustic evens, and subsequently each segment is modeled by a GMM-based interpolation of the short-term energy of the dominant frequency component. Next, the parameters of the GMM model are fed to a classifier in order to recognize the birdsong syllables type. We evaluated the practical worth of this approach using publicly available field recordings of species Myrmotherula multostriata which were recorded in natural habitats. The experimental protocol was based on seventy-eight acoustic events, which were obtained after the automatic segmentation of the audio signal. We report recognition accuracy of up to 98%, depending on the classification method and the particular syllable type. Summarizing the experimental results obtained in this study, we concluded that the proposed method has good potential for achieving higher recognition accuracy, however additional work is needed for satisfying the needs of practical applications.
Evaluation of a Link Quality Estimator in an Outdoor WSN Using a Dedicated Node

Industrial Wireless Sensor Networks (IWSN) demand deterministic and reliable communication. Adaptive mechanisms, such as channel adaptation and channel hopping, can be applied to deal with the temporal and spatial variations in link quality that can appear in an IWSN. The first step to effectively use such mechanisms is to estimate the quality of the links in the network, which can provide useful information for the adaptive mechanisms, in order to improve important metrics, such as the packet reception rate, and energy consumption. In this context, this paper evaluates experimentally a link quality estimator, which uses dedicated nodes to estimate the link quality in real-time, without imposing a high overhead. A long-term experiment was performed in a fuel loading plant and the results showed that the estimator is accurate and reactive for the scenario considered in the paper. From the results obtained in the experiment, it was also possible to observe the quality of the 16 channels defined by the IEEE 802.15. 4 standard in the 2.4 GHz band, for different nodes, and discuss how the adaptive mechanisms could be used to increase the network quality of service, by picking the appropriate channels for each link during the network operation.
Blind Recognition of Channel Codes via Deep Learning

This paper considers the blind recognition of channel codes from noisy received signal. Specifically, based on the residual neural network (ResNet), the recurrent neural network (RNN), and the attention mechanism, three types of recognizers are proposed to recognize the type of a channel code through a classification process. Other code parameters (e.g., code rate and length) can be recognized by similar ways. Numerical experiments show that the proposed recognizers perform well even when the training set is a very small subset of all possible codewords.
Multi-Hand Direct Manipulation of Complex Constrained Virtual Objects

We present a method to manipulate virtual objects which are constrained complexly and reconfigurable as if they would exist in a real world by using multiple hands simultaneously. A complexly constrained, and reconfigurable object, such as a Rubik’s cube, is hard to describe its physical motion constraints, mainly because they are determined by the grasping situation and dynamically changeable. Rather than describing the physical motion constraints in a general form, we more focus on the multiple hand interaction of a complex object. A complex object is divided into multiple subparts which are grasped by each hand, and the constraints between the subparts are optimized for inducing natural and continuous movement. For this, we propose a dynamically adjustable data structure for representing object parts grasped by multiple hands, and an optimization-based pose estimation of the constrained subparts along with their grasped hands. The experiments show that human subjects can manipulate a complexly constrained object such as a Rubik’s cube without any difficulty as if it exists in the real-world.