Miracle-dz/UniFAR-FTUs huggingface dataset:
Service Migration with High-Order MDP in Mobile Edge Computing

Mobile Edge Computing (MEC) has gained a lot of popularity for supporting newly emerging services with low latency by deploying servers in base stations (BSs). For a moving user, the Quality of Service (QoS) needs to be guaranteed through service migration among edge servers. To avoid service interruption, determining when and where to migrate services is critical and challenging for users with unknown future trajectories. Besides, frequent service migration incurs unexpected high network resource consumption, resulting in a trade-off between QoS and resource cost. In this paper, we focus on the problem of service migration in MEC networks aiming at minimizing the total resource cost while guaranteeing the QoS of moving users. We innovatively model the service migration of a user using k-order Markov Decision Process (MDP), where the correlation of user’s historical locations is taken in account to help better decision. The optimal correlation coefficient k is obtained through analyzing the real-world dataset of taxi trajectories. An online algorithm based on Deep Q-Network (DQN) is proposed to solve the service migration problem to minimize the long-term communication and migration costs. Compared with the widely-used benchmarks, our algorithm shows a better performance in reducing communication and migration costs under different parameter settings.
Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling

Alzheimer’s dementia is a progressive neurodegenerative disease that causes cognitive and physical impairment. It severely deteriorates the quality of life in affected individuals. An early diagnosis can assist immensely in better management of their healthcare needs. In recent years, there has been a renewed impetus in development of automated methods for recognition of various disorders by leveraging advancements in artificial intelligence. Here, we propose a multimodal system that can identify linguistic and paralinguistic traits of dementia using an automated screening tool. We show that bag-of-deep-neural-embeddings and ensemble learning offer a viable approach to objective assessment of dementia. The developed system is tested on the Alzheimer’s Dementia Recognition Challenge dataset, where it achieved a new state-of-the-art (SOTA) performance for the classification task and matched the current SOTA for the regression task. These results highlight the efficacy of our proposed system for facilitating an early diagnosis of dementia.
A Photovoltaic MPPT Method Based on Mnemonic Enhancement Optimization with the Use of Past Experience Data

The output characteristics of photovoltaic cells are easily affected by the external environment, so the study of maximum power point tracking (MPPT) technology is of great significance for improving the energy conversion efficiency of photovoltaic cells. Aiming at the problem that the traditional perturbation observation based MPPT method cannot consider the tracking speed and vibration amplitude at the same time, a mnemonic enhancement optimization (MEO) based MPPT method is proposed. This method preserves the past experience data of MPPT, and uses the experience data to estimate a good starting point for MPPT when the external environment changes. BP neural network and interpolation can be applied for the estimation of a good starting point, which can speed up the procedure of MPPT. Compared with other methods through simulations, the results demonstrate that the proposed method can significantly improve the tracking speed and adapt to severe weather.
A Novel Sparse Subspace Correlation Analysis-Based Domain Adaptation Method for Sensor Drift Suppression in E-nose

Sensor drift caused by the sensor aging and environmental factors is an urgent problem that seriously affects the detection performance and service life of electronic nose (E-nose). It is necessary to research the sensor drift suppression methods to realize the long-term and stable detection of E-nose. In this paper, a highly efficient sparse subspace correlation analysis-based domain adaptation(SSCA-DA) method is proposed to suppress the sensor drift. This method is to find the optimal subspace for each dataset, and the transformed data after transforming to the optimal subspace is sparsely reconstructed, which can realize the knowledge transfer in the data domains with and without drift information. From the experiment results, it can be found that the sensor drift can be satisfactorily solved by the proposed method.
Analysis of transaction flooding attacks against Monero

Monero was one of the first cryptocurrencies to address the problem of providing privacy-preserving digital asset trading. Currently, it has a market capitalization of over 2.5 billion US dollars and is among the 15 most valuable cryptocurrencies. This digital currency aims to protect users’ identities and hide transaction information by using obfuscation mechanisms such as stealth addresses and ring signatures. However, in spite of the efforts to protect Monero’s users’ privacy, researchers have found ways to identify true payment keys within a ring signature in the past, making attacks against transaction privacy feasible. Since then, the system has received updates and adopted improved measures to provide privacy. This work presents an analysis on how an attacker can take advantage of the system’s current settings to conduct both a high-profile transaction flooding attack and a stealthier version. Our results show that after flooding the network for 12 months, the attacker can identify the true spend of 46.24% of newly created transaction inputs by conducting the strongest attack and 14.47% by using the low-profile strategy.
mm-Wave Single-Pole Double-Throw switches: HBT- vs MOSFET-based designs

This paper aims to compare the performance of HBT-based and MOSFET-based mm-Wave SPDT switches in a single BiCMOS technology. To the best of authors’ knowledge, a direct comparison of this function in the same integrated process has never been reported before. Measurement results on two 50-GHz integrated SPDTs reveal that the HBT-based SPDT switch yields 1.7 dB of insertion loss and 14 dB of isolation in its central frequency, with a bandwidth covering the 30-80 GHz frequency range when considering a return loss greater than 10 dB. On the other hand, the MOSFET-based SPDT switch yields 2.1 dB of insertion loss and 12 dB of isolation at center frequency and a bandwidth covering the 33-80 GHz frequency range.
Adaptive Event-triggered Fuzzy Control for DC Motor Servo Systems

In this paper, an adaptive event-triggered fuzzy tracking control problem is studied for direct current (DC) motor servo systems. Fuzzy logic system (FLS) is introduced to deal with the problem of unknown nonlinear functions. Then, an adaptive event-triggered tracking control scheme is proposed by using backstepping design and event-triggered strategy. The proposed event-triggered tracking controller guarantees that the tracking error converges to an arbitrarily small neighborhood of zero and all the signals in the closed-loop system remain bounded. Finally, the effectiveness of the proposed control scheme is proved by a numerical example.
An Intelligent Recommendation System for Performance Equipment Operation and Maintenance via Deep Neural Network and Attention Mechanism

The theater has a huge number and variety of performing arts equipment, so the artificial maintenance will take a great time and energy. How to reduce the cost of manual maintenance is particularly important. This paper proposes an intelligent recommendation system for performance equipment operation and maintenance based on deep learning network and attention mechanism. First, according to the research main goal, the performance equipment is used to construct dataset and a preprocess scheme is adopted for the raw data, then we use the deep neural network to train the feature data, and integrate the attention mechanism into the recommendation system. Attention mechanism can increase attention to important information. Finally, through the cosine similarity between the calculated features, the recommended results are generated. An illustrative example is given to show the effectiveness of main results.
Kinematic Analysis of Mobile Robot with Klann Walking Mechanism

There have been important developments in the field of robotics until today. Since the areas where the leg touch the ground in legged mobile robots are small, the usage area is quite wide. They can adapt to all kinds of terrain conditions and can be used in places unsuitable for human health. Examples of these include areas containing radioactive, closed areas that do not contain sufficient oxygen, areas with explosives and caves. Considering that the robot will be used on rough terrain, the Klann walking mechanism, which is superior to other walking mechanisms, has been chosen. In this paper, solid modeling of a mobile robot with four legs has been done in SolidWorks program. In the Klann walking mechanism, which consists of six link, the coupler curve of the end point where the leg touches the ground has been drawn in the Cinderella program. Then, by performing kinematic analysis of the mechanism, velocity and acceleration graphs were obtained.
An 8 Channel Transceiver ASIC to Interface a CMUT Array

This paper presents an 8 channel interface ASIC for a 2D Capacitive Micromachined Ultrasonic Transducer (CMUT) array. We propose a novel interfacing scheme to eliminate the need for a high voltage (HV) external DC biasing. A two-level HV driver circuit switches between two HV levels to produce a 47 V peak to peak output pulse with rise and fall times of 78 ns and 67 ns at 1.8 MHz. A resistive feedback transimpedance amplifier (TIA) with 4 adjustable gain settings that converts a time-varying current input into an output voltage is used in the receive path. The TIA has a measured −3 dB bandwidth of 4 MHz at the gain setting employing a 300 kΩ resistor in the feedback network. The input referred noise current is measured to be $2.6{\text{pA}}/\sqrt {Hz} $. A pulse-echo measurement on a CMUT sample demonstrates successful transmit-receive operation of the system. The chip is fabricated in a 0.18 µm HV SOI CMOS process with an active area of 6 mm2.
Large Graph Sampling Algorithm for Frequent Subgraph Mining

Large graph networks frequently appear in the latest applications. Their graph structures are very large, and the interaction among the vertices makes it difficult to split the structures into separate multiple structures, thus increasing the difficulty of frequent subgraph mining. The process of calculating subgraph isomorphism often requires many calculations. Reducing the unessential structure of the graph is an effective method to improve the efficiency. Therefore, we propose a large graph sampling algorithm (RASI) based on random areas selection sampling and incorporate graph induction techniques to reduce the structure of the original graph. In addition, we find that constraining the weight of the number of vertices in the entire graph is essential to reduce the calculation of subgraph isomorphisms. This parameter is constrained in the sampling process to improve the efficiency of frequent subgraph mining. Experimental results show that RASI has more stable performance and performs better than other sampling algorithms in non-connected graphs. Mining frequent subgraphs by graph sampling can significantly improve the efficiency of mining, and the number of subgraphs before and after sampling is very similar.
Correlated Wasserstein Autoencoder for Implicit Data Recommendation

Recommender systems for implicit data, e.g., browsing data, have attracted more and more research efforts. Most existing approaches assume the implicit data is i.i. d. which ignores the fact that the real-world data is generally correlated with each other. To cope with this issue, this paper proposes the correlated Wasserstein autoencoders (CWAEs) model to capture data correlation to enhance recommendation peformance. Particularly in the proposed approach, we first formulate correlated data via an undirected acyclic graph and then generalize the undirected acyclic graph to an acyclic graph by averaging all its’ maximum acyclic subgraphs. To further enhance model performance, we introduce negative sampling strategy. Experiments are evaluated on Epinions dataset. The widely adopted evaluation criteria, i.e., CRR and NCRR, are adopted to evaluate both baseline models and our proposed approach. Experimental results have demonstrated the superiority of the proposed models.
Development of an Experimental Setup for Intraoperative Angiography System

The article is devoted to technological and program features of developing and creating an experimental setup for intraoperative imaging. Clinical problem of the necessity of vessel imaging has been established. Current state of intraoperative vessel imaging has been analyzed. Structure of the setup has been determined. The requirements for parts of the setup have been formulated. Usage of infrared radiation for vessel imaging has been justified. A chicken breast tissue as an ex-vivo model has been chosen. Usage of the chlorophyll as a new liquid for blood substitution has been experimentally justified. Strobing as an approach to the vessel imaging has been used. A MATLAB-based model for quality evaluation of functions of the setup has been developed. A research for verification of functionality of the setup has been undertaken. Peak signal-to-noise ratio as the main criteria for the setup verification has been used. A prediction of further usage of the results obtained has been made.
A Flexible Research-Oriented Framework for Distributed Training of Deep Neural Networks

We present PyDTNN, a framework for training deep neural networks (DNNs) on clusters of computers that has been designed as a research-oriented tool with a low learning curve. Our parallel training framework offers a set of functionalities that cover several must-have features for advanced deep learning (DL) software: 1) it is developed in Python in order to expose an accessible entry point for the newcomer; 2) it is extensible, allowing users to prototype new research ideas without requiring them to deal with complex software-stacks; and 3) it delivers high parallel performance, exploiting MPI via mpi4py/NCCL for communication; and NumPy, cuDNN, and cuBLAS for computation.This paper provides practical evidence that PyDTNN attains similar accuracy and parallel performance to those exhibited by Google’s TensorFlow (TF), though we recognize that PyDTNN cannot compete with a production-level framework such as TF or PyTorch in terms of maturity and functionality. Instead, PyDTNN is designed as an accessible and customizable tool for prototyping ideas related to distributed training of DNN models on clusters.
A Regulatable Blockchain Transaction Model with Privacy Protection

Blockchain is a decentralized distributed ledger technology. The public chain represented by Bitcoin and Ethereum only realizes the limited anonymity of user identity, and the transaction amount is open to the whole network, resulting in user privacy leakage. Based on the existing anonymous technology, the concealment of the sender, receiver, amount of the transaction, and does not disclose any information, which makes the supervision difficult. Therefore, the design of blockchain scheme with privacy protection and supervision functions is of great significance. In this paper, a blockchain transaction model with both privacy and supervision function is proposed. It uses probability encryption to realize the hiding of the true identity of the blockchaintransaction,andusesthecommitmentschemeandzero-knowledgeprooftechnologytorealizetheprivacyprotection andguaranteelegitimacyverificationofthetransaction.Withtheuseofencryptiontechnology,theregulatorscansupervise blockchaintransactionswithoutstoringtheusers’information,whichgreatlyreducesthepressureonstorage,computingandkeymanagement.Inaddition,itdoesnotrelyonspecificconsensusmechanismandcanbeusedasanindependentmodule.The securityperformanceanalysisshowsthattheproposedschemehasgreatpracticabilityandhaspotentialapplicationinmany
Adaptive exponentially asymptotic tracking control for a one-link manipulator

This article addresses the asymptotic tracking issues of a one-link manipulator system. To realize the exponentially asymptotic tracking performance, the exponential term has been introduced into the Lyapunov function and the bounds estimation method and the smooth modification function are used to guarantee the zero-error tracking. In addition, the neural networks (NNs) is devised to cope with the uncertain disturbance and unknown nonlinearlities. At last, a simulation example has been shown to verify the raised scheme.
Wood Product Tracking Using an Improved AKAZE Method in Wood Traceability System

Tracking of the wood product is an important technology in the trade activity of rare plants. Normally, the factories use Quick Response (QR) and Radio-Frequency Identification (RFID) to identify the individual wood product, but these technologies are not safe enough because they can be easily falsified. It can be seen that traditional methods are hard to catch the detail of the slim wood texture from the wood product. In this study, a novel method is employed to resolve these problems using a biometric feature on the surface of the real wood product to distinguish the individual wood product. AKAZE is used to extract the key-point of wood texture. A sub-area detection technique along with a serialization method is then developed to improve the rate of identification. The sub-area detection technique deals with picking out a sub-region in which there are enough AKAZE points as small as possible. The serialization method is also utilized to reduce the redundant process of feature extraction. The experimental results demonstrate that the values of accuracy, recall, and $F1$ reach 0.98, 0.96, and 0.96, respectively. The match time that uses serialized function is reduced to 1/3 of which has no application in the original image. The validated results also reveal that our proposed methodology improves the robustness of the wood product identification, and it can be used in Wood Traceability System (WTS) with the blockchain to resolve the digital trust problem and the fast distinction issues of the real wood product.
Identifying and Characterizing Silently-Evolved Methods in the Android API

With over 500,000 commits and more than 700 contributors, the Android platform is undoubtedly one of the largest industrial-scale software projects. This project provides the widely known Android API, which facilitates the development of Android apps. Unfortunately, because the Android platform and its API evolve at an extremely rapid pace, app developers need to continually monitor API changes to avoid compatibility issues in their apps (i.e their apps do not work as expected when running on the new version of the API). Despite a large number of studies on compatibility issues in the Android API, the research community has not yet investigated issues related to silently-evolved methods (SEMs). These methods are functions whose behavior might have changed but the corresponding documentation did not change accordingly. Because app developers rely on the provided documentation to evolve their apps, changes to methods that are not suitably documented may lead to unexpected runtime issues in the apps using these methods. To fill this gap, we conducted a large-scale empirical study in which we identified and characterized SEMs across 10 versions of the Android API. In the study, we identified SEMs, characterized the nature of the changes, and analyzed the impact of SEMs on a set of 1,000 real-world Android apps. Our experimental results show that SEMs do exist in the Android framework, and that 957 of the apps we considered use at least one SEM. Based on these results, we argue that the Android platform developers should take actions to avoid introducing silently-evolved methods, especially those involving semantic changes. This situation highlights the need for automated techniques and tools to help Android practitioners in this task.
Coil Optimization in a Fluxgate Magnetometer With Co₆₈.₂Fe₄.₃Si₁₂.₅B₁₅ Amorphous Wire Cores for Geomagnetic Station Observation

The coils in a three-axis fluxgate magnetometer have been investigated to enhance the noise and power performance for the use in geomagnetic stations. The turn number of the excitation and pickup coils in the fluxgate sensors is carefully optimized by measuring the equivalent magnetic noise spectral density of the magnetometers. Correspondingly, the values of the tuning and demodulation capacitors are delicately designed to match the optimized coils. The noise of the fluxgate magnetometer is 6 pT/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz at 1 Hz with a power consumption lower than 360 mW. The size of the packaged magnetometer is <inline-formula> <tex-math notation="LaTeX">$30 \times 30 \times 113$ </tex-math></inline-formula> mm<sup>3</sup>, and its weight is less than 80 g (without cable). The features of the fluxgate magnetometer make it a suitable instrument for geomagnetic stations and other applications.
Signal transmission for an isolated gate driver using capacitive coupling technique

Resonant converter is widely used due to its high efficiency when carefully designed, including the tank circuit. Tuning the tank circuit is design challenging. In this paper signal isolation in the AC switch driver is presented to implement an auto-tuning system that uses synchronous switched capacitor to control the resonant frequency of the tank circuit of a half-bridge voltage fed resonant converter to power HIFU. Capacitive coupling is proposed as signal isolation instead of classical technique due to compatibility with MRI regulations used in its vicinity, avoiding bulky magnetic components. The proposed circuit works under 5V voltage and consumes 1.45mW power. With input signal frequency of 1MHz and a carrier frequency of 100MHz, the observed output signal delay is only 10ns, i.e. 1% of the signal period. The proposed circuit is designed and simulated in a CMOS 0. 35μm technology. Performances such as signal delay and power consumption were studied under varying carrier frequencies to evaluate the proposed circuit.
Ada-LLD: Adaptive Node Similarity Using Multi-Scale Local Label Distributions

In many applications, data is represented as a network connecting nodes of various types. While types might be known for some nodes in the network, the type of a newly added node is typically unknown. In this paper, we focus on predicting the types of these new nodes based on their connectivity to the already labeled nodes. To tackle this problem, we propose Adaptive Node Similarity Using Multi-Scale Local Label Distributions (Ada-LLD) which learns the dependency of a node’s class label from the distribution of class labels in this node’s local neighborhood. In contrast to previous approaches, our approach is able to learn how class labels correlate with labels in variously sized neighborhoods. We propose a neural network architecture that combines information from differently sized neighborhoods allowing for the detection of correlations on multiple scales. Our evaluations demonstrate that our method significantly improves prediction quality on real world data sets. In the spirit of reproducible research we make our code available 1.1https://github.com/adalld/wi2020