Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Fault-tolerant VNF Chain Placement: A Deep Reinforcement Learning Approach

Since Network Function Virtualization (NFV) decouples network functions (NFs) from the underlying dedicated hardware and realizes them in the form of software called Virtual Network Functions (VNFs), they are enabled to run in any resource-sufficient virtual machines (VMs) and offer diverse network services by service function chains (SFCs). Given the complexity and unpredictability of the network state, we propose a deep reinforcement learning (DRL) based online SFC placement method named DDQP (Double Deep Q-networks Placement). Meanwhile, VNFs are vulnerable to various faults such as software failures. Thus, we backup standby instances to enhance the fault tolerance of our model, and DDQP automatically deploys both active and standby instances in real-time. Specifically, we use DNN (Deep Neural Networks) to deal with large continuous network state space. In the case of stateful VNFs, we offer constant generated state updates from active instances to standby instances to guarantee seamless redirection after failures. With the goal of balancing the waste of resources and ensuring service reliability, we introduce five progressive schemes of resource reservations to meet different customer needs. Our experimental results demonstrate that DDQP responds rapidly to arriving requests and reaches near-optimal performance. Specifically, DDQP outweighs the state-of-the-art method by 16.30% higher acceptance ratio with 82x speedup on average.
A New Strong Adversary Model for RFID Authentication Protocols

Radio Frequency Identification (RFID) systems represent a key technology for ubiquitous computing and for the deployment of the Internet of Things (IoT). In RFID technology, authentication protocols are often necessary in order to confirm the identity of the parties involved (i.e. RFID readers, RFID tags and/or database servers). In this article, we analyze the security of a mutual authentication protocol proposed by Wang and Ma. Our security analysis clearly shows major security pitfalls in this protocol. Firstly, we show two approaches that an adversary may use to mislead an honest reader into thinking that it is communicating with a legitimate database. Secondly, we show how an adversary that has compromised some tags can impersonate an RFID reader to a legitimate database. Furthermore, we present a new adversary model, which pays heed on cases missed by previous proposals. In contrast to previous models where the communication between an RFID reader and a back-end server is through a secure channel, our model facilitates the security analysis of more general schemes where this communication channel (RFID reader-to-server) is insecure. This model determines whether the compromise of RFID tags has any impact on the security of the reader-to-server communication or vice versa. In a secure protocol, the possible compromise of RFID tags should not affect the RFID reader-server communication. In this paper, we show that compromising of RFID tags in Wang and Ma protocol has a direct impact on the reader-server security. Finally, we propose a new authentication protocol that offers an adequate security level and is resistant against the mentioned security risks. The security proofs of the proposed protocol are supported with Gong-Needham-Yahalom (GNY) logic and Scyther tool, which are formal methods to evaluate the security of a cryptographic protocol.
Transactive Energy Systems: The Market-Based Coordination of Distributed Energy Resources

Due to pressing environmental concerns, there is a global consensus to commit to a sustainable energy future. Germany has embraced Energiewende, a bold sustainable energy policy of no operational nuclear plants by 2022. California has set an ambitious goal that mandates 50% renewable penetration by 2025, 60% by 2030, and 100% by 2045 [1]. The vast integration of renewable energy into the power grid imposes daunting challenges on the conventional supply-side control paradigm. First, renewable energy is intermittent and uncertain. Integrating renewable energy will reduce the system inertia, inject undesirable variability, and substantially increase the need for system reserves. These reserves are typically provided by conventional generators. However, conventional generators have limited capacity and ramping rate, and they produce carbon emissions that defeat the carbon benefit of renewable integration. On the other hand, the increasing penetration of renewable energy also squeezes the response time needed to balance the power grid. That is, the time required to make critical operating decisions is decreasing from minutes to seconds and, in some cases, even subseconds due to the increasing variability of supply and demand. The shorter response time for decision making places significant challenges on the conventional power grid, which requires human interaction.
Targeted Speech Adversarial Example Generation With Generative Adversarial Network

Although neural network-based speech recognition models have enjoyed significant success in many acoustic systems, they are susceptible to be attacked by the adversarial examples. In this work, we make first step towards using generative adversarial network (GAN) for constructing the targeted speech adversarial examples. Specifically, we integrate the target speech recognition network with GAN framework, which can then be formulated as a three-party game. The generator in GAN aims at generating perturbation that could make the target network misclassified to a specific target, while simultaneously fooling the discriminator treating the adversarial example as a beguine one. The discriminator is to distinguish the crafted examples from the geniue samples. The classification error of the target network is back-propagated via gradient flow to the generator for updating. The target network is responsible for back-propagating the classification error via gradients to the generator for updating, but the target network itself is freezed. With the carefully designed network architecture, loss function and training strategy, we successfully train a generator that could generate the adversarial perturbation for a given speech clip and a target label. Experiential results show that the generated adversarial examples could effectively fool the state-of-the-art speech classification networks, while attaining an acceptable auditory perception quality. In addition, our proposed method runs much faster than the prevalent optimization-based schemes. To facilitate reproducible research, codes, models and data are publicly available at https://github.com/winterwindwang/SpeechAdvGan.
Home Automation System Based Mobile Application

The idea of home automation or smart home system derives from the ideal of doing things inside a home to be easily controlled and managed. Also, they can automatically work as the resident’s demand and increase the living standard in different aspects for life. This paper presents the work of using mobile application as an user interface that provides convenience for user(s) to be able to control the home appliance remotely by two alternative tasks; voice command and graphical user interface. The user interface employs the Google assistance for voice command environment while the graphical user interface is developed Blynk App. The experiment has been demonstrated with the environment which is programming commands to the Node MCU V3 ESP8266 to control the home appliance devices through Wi-Fi and the Internet. The test results to user(s) can control the On/Off switch in the connected devices by both methods of command. The effective work benefits firstly the user(s) of family, elderly, and may effective for disable people to empower themselves to live with least burden to the family member. Secondly, the ordinary people can develop the system for their home by extending the basic communication equipment they have with the effective cost of implementation.
Seasonal changes in the water status of three woody legumes from the Atlantic forest, Caratinga, Brazil

The Atlantic forest of Brazil is considered to be a high priority for conservation in South America. Despite its importance, few phenological and ecophysiological data are available for plants of this area. In this study the seasonal changes in the water potentials (Ψ) and the phenological behaviour of three woody legumes are related to environmental conditions, particularly air vapour pressure deficit (VPD). The values of Ψ at predawn were greater than −0.75 MPa for all species even during the dry season (May–September). The minimal daily value of Ψ was −3.8 MPa. The maximum daily amplitude of Ψ (Δ parameter) was correlated with the air vapour pressure deficit, showing high correlation coefficients in linear equations. A high recovery rate of Ψ after rain (greater than 0.80) was registered for all species, suggesting a high hydraulic conductivity. The high values of Ψ at predawn during the year and the strong relationship between the daily amplitude of Ψ and VPD suggested that the changes in the canopy cover are related more to atmospheric drought than to soil water deficit.
Developing a sense of agency scale for heavy machinery operation

Sense of agency (SoA) has long been a focus area in the field of human-machine interactions, because it is thought to relate to progress and improvements in developing interface systems that users can control with intention. However, there is currently no systematic SoA evaluation method within the context of heavy machinery operation. With our research, we intended to develop a SoA scale for heavy machinery operation (SoAS-HMO). The scale evaluated the psychological experiences of operators while they voluntarily operated heavy machinery. To build this scale, we conducted two surveys: a semi-structured interview survey to initially prepare the scale items, and a questionnaire survey to explore and confirm the scale structure. Subsequent factor analysis revealed one factor and twenty-one items that were related to the SoA experienced during operation, with sufficient reliability and validity, specifically with goodness of fit index (GFI) = 0.933, adjusted GFI (AGFI) = 0.911, comparative fit index (CFI) = 0.909. , and Cronbach's a = 0.97. Therefore. , this scale can successfully measure the subjective SoA of the operator and be applied to evaluate machinery efficiency. Our study provides progress towards improving heavy machinery systems that can be intentionally controlled by operators.
A Complex Network-Based Critical Node Identification Method for Industrial Control Systems Using Range of Failure Impacts

Critical node identification of Industrial Control System (ICS) can theoretically identify key components and help to allocate security protection resources reasonably. However, traditional critical node identification algorithms are difficult to adapt to the current security defense requirements. In this paper, we propose a critical node identification method adapted to the safety requirements of an ICS. In simulations, we use the Tennessee Eastman Process (TEP) as an example to test and analyze our critical node identification algorithm. The simulation results show that our algorithm can obtain criticality ranking results that meet the theoretical expectations. Moreover, the attack results on the TEP testbed show that the criticality ranking results obtained by our algorithm is 25% higher than that by the contrast algorithm.
Deep Learning based Recommedation System: A Review of Recent Works

The research upsurge of recommendation system and deep learning makes the deep learning based recommendation system become an inevitable trend. Through the introduction of the latest research of deep learning based recommendation, we present the current popular research directions in the recommendation field and provide a comprehensive review of various advanced technologies. Finally, we propose a new viewpoint on the future development of deep learning based recommendation system.
Shot Boundaries Detection Based Video Summary Using Dynamic Time Warping and Mean Shift

Recently, an increasing in videos amount presents in cyberspace in conjunction with growth of multimedia communications and technologies, and low-cost storage devices; it has become obligatory to have the capability to rapidly browse and search through huge multimedia databases. In this context, video summarization-based video retrieval has become a critical issue needs to be addressed. The main objective of shots boundary detection SBD methods pursues to detect the transitions between consecutive frames along video sequences in term of shots boundary detection. Hence, shots with abundant information are employed in the content-based video retrieval. This paper presents a new approach for shot boundary detection with two main challenges; static and dynamic objects with static and dynamic camera motion. The proposed method employed the Dynamic Time Warping DTW method for identifying the differences between sequenced frames. The abrupt and gradual changes are captured along frame’s sequences within and between shots based on mean shift technique. From the experiments, the proposed shot boundary detection based key frame extraction method exhibits an efficient performance represented by more informative frames with less redundancy.
Semantic Relevance Feedback Based on Local Context

Semantic similarity is a measure based on the meaning of the word and represented by the type of semantic relationships between the meanings of two words. The semantic similarity between words or sentences can be calculated by following the method, WordNet-based similarity. In our suggested method, the proposed solution depends on the two-step to return the target document collection. Once the top d1, d2…, dn documents have been retrieved using the initial query. The second compares these retrieved documents with the query, this step takes the most frequent senses of sentences in d1, d2…, dn with the senses of the query sentences. Query enrichment is then carried out adding terms semantically related to these senses the aim is to resolve the issue of incompatibility and the ambiguity problem affecting the results of information retrieval through the use of techniques to Word sense disambiguation (WSD) the word and the results be more semantically related with the query. Our experiment, carried out using part of the fire2011 data set.
A STUDY ON DYNAMIC VOCAL TRACT SHAPING FOR DIPHTHONG SIMULATION USING A 2D DIGITAL WAVEGUIDE MESH FINLAND, FRANCE

This paper presents a study of an articulatory-based speech synthesis based on a 2D-Digital Waveguide Mesh (2D-DWM) to model acoustic wave propagation in the oral tract. It is employed to study the effects of changing oral tract area, and in particular, of moving the articulators during the production of diphthongs. The operation of the synthesizer including details of how diphthongs are produced are discussed. The results support earlier findings that the wall reflection coefficient is inversely proportional to the formant bandwidth.
Predicting Neonatal Condition at Birth through Ensemble Learning Methods in Pregnancy Care

Prematurity represents the determinant cause of infant mortality. This serious public health problem is directly related to the assistance provided during pregnancy and childbirth. Hence, this paper proposes the use of leading machine learning techniques capable of supporting health experts in pattern recognition in the prediction of high-risk situations for the fetus. The proposed model creates an ensemble of nearest-neighbor classifiers using the random subspace algorithm, reaching an overall accuracy of 0.937 and area under the curve of 0.721, in predicting the Apgar score, and 0.829 and 0.669 in predicting if the newborn will be small for gestational age, respectively. These results show the model effectiveness in reducing severe pregnancy related-problems.
Wireless Controlled Robotic Spider for data retrieval

This project is a demonstration of a legged robot called “WICORS” which has four legs for movement. Legged robots have also been very useful in the military in performing the remote operations from a distant place. Legged robots are the robots that imitate the walking patterns of various animals. It is used for the data retrieval for the specific applications in different areas. This project is made for the purpose to collect data in the regions where it is dangerous for humans to enter such as the mining areas containing the toxic gases. It also serves the purpose of collecting the information about the injured and casualties in the disaster affected regions. The sole purpose of this project is to increase the real-time data transmission wirelessly by reducing the live interaction by humans with the surrounding environment.
Quality Detection of Laser Welding Based on One-Dimensional Convolutional Neural Network

During the laser welding process, the occurrence of abnormal conditions can cause workpiece defects and make the products fail. Therefore, a real-time monitoring system required to detect the laser welding process for ensuring the product quality. In this paper, we develop a real-time laser welding data acquisition equipment that can measure the plasma density, laser light intensity, and melt pool temperature. In addition, a convolutional neural network is built to achieve rapid detection of laser welding quality. The experimental results show that the proposed method can quickly detect the quality of the laser welding process, and the average accuracy for the welding defects has reached 97%.
Research on dynamic division of trunk sub areas based on key junctions

Saturated traffic has become a common road traffic phenomenon in large and medium-sized cities. Traffic jams and the problem of oversaturated traffic pose new challenges to the traditional traffic control theories. The dynamic division is needed to divide the urban trunk line into several sub-sections. , so as to realize the coordinated control of each section in the subsection. , to balance the saturation of the sub area road segment. , and to improve the efficiency of the cooperative control of the trunk line. Based on the analysis and characterization of key road sections. , this paper studies the dynamic sub-region division method based on key road sections. , and analyzes its feasibility with examples.
Transformer-based power system energy prediction model

With the rapid development of smart grid construction, building energy consumption prediction is gaining more and more attention in energy planning, management, and conservation. Improving the accuracy of energy consumption judgment is a key factor to ensure efficient operation of the energy system. In addition, the model needs to be able to quickly adapt to changes in energy consumption and respond to various emergencies. This paper thus proposes a modified Transformer model based on Multi-head attention and position encoding mechanism. This model makes Transformer focus on the important information which influence the prediction performance by using self-attention mechanism. Compared with LSTM, it achieves the higher prediction accuracy with less training time by the combination of Transformer and position encoding. The final experiment results demonstrate that the proposed method has a good performance in the electronic load prediction task, and it is also suitable for the application scenarios in periodic change prediction such as forecasting sales and price prediction.
Prediction of Minor Salivary Gland Carcinoma: A Novel Nomogram and Risk Classification System for Overall Survival and Cancer-Specific Survival

Objective Minor salivary gland carcinoma (MiSGC) is rare, and the understanding of this disease is insufficient. This study aimed to identify independent risk factors and develop a nomogram for evaluating the overall survival (OS) and cancer-specific survival (CSS) of patients with MiSGC. Study Design Retrospective cohort study. Setting SEER database (Surveillance, Epidemiology, and End Results). Subjects and Methods We collected data from patients diagnosed with MiSGC between 2004 and 2015 from the SEER database. According to patient registration, all patients were randomly allocated to training sets and validation sets (2:1). Then, Kaplan-Meier product limit curves and Cox proportional hazard regressions were performed to estimate the prognostic effect of variables. Nomograms based on Cox proportional hazard regressions were established to estimate 3- and 5-year OS and CSS. Finally, the nomogram was developed by the training set, and validation was performed with the concordance index, calibration curves, and decision curve analyses. Results In total, 1787 MiSGC cases were registered in SEER. The concordance index for internal validation of OS and CSS prediction was 0.842 and 0.816; that of external validation was 0.871 and 0.831. The calibration plots showed good consistency between nomogram prediction and actual survival. The decision curve analysis showed substantial net benefits of the new predictive model. Conclusions We constructed nomograms and a corresponding risk classification system predicting the OS and CSS of patients with MiSGC. These tools can generate simple-to-use clinical risk grouping and determine the relationship between adjuvant therapy and active surveillance.
Identification and validation of novel DNA methylation markers for early diagnosis of lung adenocarcinoma

Lung cancer has the highest mortality of all cancers worldwide. Epigenetic alterations have emerged as potential biomarkers for early diagnosis of various cancer tissue types. To identify methylation markers for early diagnosis of lung adenocarcinoma, we aimed to integrate genome‐wide DNA methylation and gene expression data from The Cancer Genome Atlas. To this end, we first examined the global DNA methylation pattern of lung adenocarcinoma and investigated the relationship between DNA methylation subtypes and clinical features. We then extracted differentially methylated and expressed genes, and adopted feature selection techniques to determine the final methylation markers. The performance of the markers in predicting lung adenocarcinoma was evaluated on three independent datasets from Gene Expression Omnibus. Protein levels of marker genes were validated by immunohistochemistry, and their biological function was further verified in vivo. We identified three novel methylation markers in lung adenocarcinoma including cg08032924, cg14823851, and cg19161124, mapping to CMTM2, TBX4, and DPP6, respectively. Validating these results on three independent datasets indicated that the three markers can achieve extremely high sensitivity and specificity in distinguishing lung adenocarcinoma from normal samples. Immunohistochemistry quantification results confirmed that markers are weakly expressed in human lung adenocarcinoma, and CMTM2 decreased tumor growth of mouse Lewis lung carcinoma in vivo. Overall, our study identified three novel methylation markers in lung adenocarcinoma which may contribute toward an improved diagnosis potentially leading to a better outcome for patients with lung adenocarcinoma.
Genome-Wide Association Analysis Identified ANXA1 Associated with Shoulder Impingement Syndrome in UK Biobank Samples

Shoulder impingement syndrome (SIS) is a common shoulder disorder with unclear genetic mechanism. In this study, Genome-wide Association Study (GWAS) was conducted to identify the candidate loci associated with SIS by using the UK Biobank samples (including 3,626 SIS patients and 3,626 control subjects). Based on the GWAS results, gene set enrichment analysis was further performed to detect the candidate gene ontology and pathways associated with SIS. We identified multiple risk loci associated with SIS, such as rs750968 (P = 4.82 × 10−8), rs754832 (P = 4.83 × 10−8) and rs1873119 (P = 6.39 × 10−8) of ANXA1 gene. Some candidate pathways have been identified related to SIS, including those linked to infection response and hypoxia, “ZHOU_INFLAMMATORY_RESPONSE_FIMA_DN” (P = 0.012) and “MANALO_HYPOXIA_UP” (P = 5.00 × 10−5). Our results provide novel clues for understanding the genetic mechanism of SIS.