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2022, Volume 31,  Issue 4

Non-intrusive Load Monitoring Using Gramian Angular Field Color Encoding in Edge Computing
CHEN Junfeng, WANG Xue
2022, 31(4): 595-603. doi: 10.1049/cje.2020.00.268
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Non-intrusive load monitoring (NILM) can infer the status of the appliances in operation and their energy consumption by analyzing the energy data collected from monitoring devices. With the rapid increase of electric loads in amount and type, the traditional way of uploading all energy data to cloud is facing enormous challenges. It becomes increasingly significant to construct distinguishable load signatures and build robust classification models for NILM. In this paper, we propose a load signature construction method for load recognition task in home scenarios. The load signature is based on the Gramian angular field encoding theory, which is convenient to construct and significantly reduces the data transmission volume of the network. Moreover, edge computing architecture can reasonably utilize computing resources and relieve the pressure of the server. The experimental results on NILM datasets demonstrate that the proposed method obtains superior performance in the recognition of household appliances under insufficient resources.
Double-Layer Positional Encoding Embedding Method for Cross-Platform Binary Function Similarity Detection
JIANG Xunzhi, WANG Shen, YU Xiangzhan, GONG Yuxin
2022, 31(4): 604-611. doi: 10.1049/cje.2021.00.139
Abstract(191) HTML (87) PDF(27)
The similarity detection between two cross-platform binary functions has been applied in many fields, such as vulnerability detection, software copyright protection or malware classification. Current advanced methods for binary function similarity detection usually use semantic features, but have certain limitations. For example, practical applications may encounter instructions that have not been seen in training, which may easily cause the out of vocabulary (OOV) problem. In addition, the generalization of the extracted binary semantic features may be poor, resulting in a lower accuracy of the trained model in practical applications. To overcome these limitations, we propose a double-layer positional encoding based transformer model (DP-Transformer). The DP-Transformer’s encoder is used to extract the semantic features of the source instruction set architecture (ISA), which is called the source ISA encoder. Then, the source ISA encoder is fine-tuned by the triplet loss while the target ISA encoder is trained. This process is called DP-MIRROR. When facing the same semantic basic block, the embedding vectors of the source and target ISA encoders are similar. Different from the traditional transformer which uses single-layer positional encoding, the double-layer positional encoding embedding can solve the OOV problem while ensuring the separation between instructions, so it is more suitable for the embedding of assembly instructions. Our comparative experiment results show that DP-MIRROR outperforms the state-of-the-art approach, MIRROR, by about 35% in terms of precision at 1.
A Novel Trustworthiness Measurement Model Based on Weight and User Feedback
ZHOU Wei, MA Yanfang, PAN Haiyu
2022, 31(4): 612-625. doi: 10.1049/cje.2020.00.391
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Software trustworthiness is an essential criterion for evaluating software quality. In component-based software, different components play different roles and different users give different grades of trustworthiness after using the software. The two elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.
Search Algorithm Based on Permutation Group by Quantum Walk on Hypergraphes
JIANG Yaoyao, CHU Pengcheng, MA Yulin, MA Hongyang
2022, 31(4): 626-634. doi: 10.1049/cje.2021.00.125
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Because a significant number of algorithms in computational science include search challenges and a large number of algorithms that can be transformed into search problems have garnered significant attention, especially the time rate and accuracy of search, a quantum walk search algorithm on hypergraphs, whose aim is to reduce time consumption and increase the readiness and controllability of search, is proposed in this paper. First, the data points are divided into groups and then isomorphic to the permutation set. Second, the element coordinates in the permutation set are adopted to mark the position of the data points. Search the target data by the controllable quantum walk with multiparticle on the ring. By controlling the coin operator of quantum walk, it is determined that search algorithm can increase the accuracy and controllability of search. It is determined that search algorithm can reduce time consumption by increasing the number of search particles. It also provides a new direction for the design of quantum walk algorithms, which may eventually lead to entirely new algorithms.
Quantum Wolf Pack Evolutionary Algorithm of Weight Decision-Making Based on Fuzzy Control
LU Na, MA Long
2022, 31(4): 635-646. doi: 10.1049/cje.2021.00.217
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In the traditional quantum wolf pack algorithm, the wolf pack distribution is simplified, and the leader wolf is randomly selected. This leads to the problems that the development and exploration ability of the algorithm is weak and the rate of convergence is slow. Therefore, a quantum wolf pack evolutionary algorithm of weight decision-making based on fuzzy control is proposed in this paper. First, to realize the diversification of wolf pack distribution and the regular selection of the leader wolf, a dual strategy method and sliding mode cross principle are adopted to optimize the selection of the quantum wolf pack initial position and the candidate leader wolf. Second, a new non-linear convergence factor is adopted to improve the leader wolf’s search direction operator to enhance the local search capability of the algorithm. Meanwhile, a weighted decision-making strategy based on fuzzy control and the quantum evolution computation method is used to update the position of the wolf pack and enhance the optimization ability of the algorithm. Then, a functional analysis method is adopted to prove the convergence of the quantum wolf pack algorithm, thus realizing the feasibility of the algorithm’s global convergence. The performance of the quantum wolf pack algorithm of weighted decision-making based on fuzzy control was verified through six standard test functions. The optimization results are compared with the standard wolf pack algorithm and the quantum wolf pack algorithm. Results show that the improved algorithm had a faster rate of convergence, higher convergence precision, and stronger development and exploration ability.
A Novel Plane-Based Control Bus Design with Distributed Registers in 3D NAND Flash Memories
CAO Huamin, WANG Qi, LIU Fei, HUO Zongliang
2022, 31(4): 647-651. doi: 10.1049/cje.2021.00.283
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This work presents a novel plane-based area-saving control bus design with distributed registers in 3D NAND flash memory. 99.47% control signal routing wires are reduced compared to the conventional control circuit design. Independent multi-plane read is compatible with the existing read operations thanks to the register addresses are reasonably assigned. Furthermore, power-saving register group address-based plane gating scheme is proposed which saves about 2.9 mW bus toggling power. A four-plane control bus design with 20K-bits registers has been demonstrated in field programmable gate array tester. The results show that the plane-based control bus design is beneficial to high-performance 3D NAND flash memory design.
A Comprehensive Study on the Theory of Graphene Solution-Gated Field Effect Transistor: Simulations and Experiments
HU Shihui, ZHANG Jizhao, WANG Zhongrong, JIA Yunfang
2022, 31(4): 652-657. doi: 10.1049/cje.2021.00.032
Abstract(228) HTML (100) PDF(41)
Graphene solution-gated field effect transistors (G-SgFETs) have been widely developed in the field of biosensors, but deficiencies in their theories still exist. A theoretical model for G-SgFET, including the three-terminal equivalent circuit model and the numerically calculating method, is proposed by the comprehensive analyses of the graphene-liquid interface and the FET principle. Not only the applied voltages on the electrode-pairs of gate-source and drain-source, but also the nature of graphene and its derivatives are considered by analysing their influences on the Fermi level, the carriers’ concentration and mobility, which may consequently affect the output drain-source current. To verify whether it is available for G-SgFETs based on different method prepared graphene, three kinds of graphene materials which are liquid-phase exfoliated graphene, reduced graphene oxide (rGO), and tetra (4-aminophenyl) porphyrin hybridized rGO are used as examples. The coincidences of calculated output and transfer feature curves with the measured ones are obtained to confirm its adaptivity for simulating the basic G-SgFETs’ electric features, by modulating Fermi level and mobility. Furthermore, the model is exploited to simulate G-SgFETs’ current responding to the biological functionalization with aptamer and the detections for circulating tumor cells, as a proof-of-concept. The calculated current changes are compared with the experimental results, to verify the proposed G-SgFETs’ model is also suitable for mimicking the bio-electronic responding, which may give a preview of some conceived G-SgFETs’ biosensors and improve the design efficiency.
A Unity-Gain Buffer Assisted Noise-Shaping SAR ADC Based on Error-Feedback Structure
YI Pinyun, ZHU Zhangming, XU Nuo, FANG Liang, HAO Yue
2022, 31(4): 658-664. doi: 10.1049/cje.2020.00.286
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The passive noise-shaping successive approximation register (NS-SAR) analog-to-digital converter (ADC) demonstrates high performance in resolution improvement, power reduction, and process scaling, while its charge-sharing loss and limited bandwidth weaken the noise-shaping effect. This paper presents a first-order NS-SAR ADC based on error-feedback (EF) structure to realize high-efficiency noise shaping. It employs a lossless EF path by using a set of ping-pong switching capacitors with passive signal-residue summation technique. The proposed first-order EF NS-SAR prototype can be promoted to multi-order structure with the minor modification. Verified by simulation in 65-nm CMOS process, the proposed 9-bit NS-SAR ADC consumes 183.66 μ W when operating at 20 MS/s with the supply voltage of 1.2 V. At the oversampling ratio of 16, it achieves a peak signal-to-noise-and-distortion ratio of 81 dB, yielding Schreier figure of merit of 176.32 dB.
W-Band High-Efficiency Waveguide Slot Array Antenna with Low Sidelobe Levels Based on Silicon Micromachining Technology
YAO Shisen, CHENG Yujian, BAI Hang, FAN Yong
2022, 31(4): 665-673. doi: 10.1049/cje.2020.00.315
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A high-efficiency waveguide slot array antenna with low sidelobe level (SLL) is investigated for W-band applications. The silicon micromachining technology is utilized to realize multilayer antenna architecture by three key steps of selective etching, gold plating and Au-Au bonding. The radiating slot based on this technique becomes thick with a minimum thickness of 0.2 mm and accompanies with the decrease of slot’s radiation ability. To overcome this weakness, a stepped radiation cavity is loaded on the slot. The characteristic of cavity-loaded slot is investigated to synthesize the low-SLL array antenna. The unequal hybrid corporate feeding network is constructed to achieve sidelobe suppression in the E-plane. A pair of 16 × 8 low-SLL and high-effciency slot arrays is fashioned and confirmed experimentally. The bandwidth with the radiation effciency higher than 80% is 92.3–96.3 GHz. The SLLs in both E- and H-planes are below −19 dB.
Radiation Principle and Spatial Direct Modulation Method of a Low Frequency Antenna Based on Rotating Permanent Magnet
LIU Wenyi, ZHANG Feng, SUN Faxiao, GONG Zhaoqian, LIU Xiaojun, FANG guangyou
2022, 31(4): 674-682. doi: 10.1049/cje.2020.00.130
Abstract(237) HTML (91) PDF(39)
The theory of mechanical antenna is still in its infancy at present, and its radiation mechanism, field distribution, modulation methods and other basic theories need to be explored and improved. The radiation mechanism of a rotating-magnet based mechanical antenna (RMBMA) is explored. An equivalent radiation model of the mechanical antenna is established. The field formula of mechanical antenna is derived using this model and rotation matrix. The spatial direct modulation method of mechanical antenna is also investigated. Two prototype antennas are fabricated using DC/AC servo motors and NdFeB magnets, and experiments are carried out to verify the correctness of the derivation and analysis. The measured and simulated results are in consistent with each other. By precisely controlling the moving parameters of an AC servo motor, signal of binary amplitude shift keying (BASK) is generated, and the original code sequence is recovered by demodulation.
A Design and Comparative Investigation of Graded AlxGa1–xN EBL for W-B0.375GaN/W-B0.45GaN Edge Emitting Laser Diode on AlN Substrate
NIASS Mussaab I., WANG Fang, LIU Yuhuai
2022, 31(4): 683-689. doi: 10.1049/cje.2020.00.178
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In this paper, we numerically demonstrated the possibility of using wurtzite boron gallium nitride (W-BGaN) as active layers (quantum well and quantum barriers) along with aluminum gallium nitride (AlGaN) to achieve lasing at a deep ultraviolet range at 263 nm for edge emitting laser diode. The laser diode structure simulations were conducted by using the Crosslight-LASTIP software with a self-consistency model for varies quantity calculations. Moreover, multiple designed structures such as full and half have been achieved as well as the study of the effect of grading engineering/techniques at the electron blocking layer for linearly-graded-down and linearly-graded-up grading techniques were also emphasized. As a result, a maximum emitted power of 26 W, a minimum threshold current of 308 mA, a slope efficiency of 2.82 W/A, and a minimum p-type resistivity of 0.228 Ω · cm from the different doping concentrations and geometrical distances were thoroughly observed and jotted down.
A 16-bit, ±10-V Input Range SAR ADC with a 5-V Supply Voltage and Mixed-Signal Nonlinearity Calibration
LUO Hongrui, ZHAO Xianlong, JIAO Zihao, ZHANG Jie, WANG Xiaofei, ZHANG Ruizhi, ZHANG Hong
2022, 31(4): 690-697. doi: 10.1049/cje.2021.00.057
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This paper presents a high-precision, successive approximation register (SAR) analog-to-digital converter (ADC) with resistive analog front-end for low-voltage and wide input range applications. To suppress the serious nonlinearity brought by the voltage coefficients of analog front-end without deteriorating differential nonlinearity performance, a mixed-signal calibration scheme based on piecewise-linear method with calibration digital-to-analog converter is proposed. A compensation current is designed to sink or source from the reference to keep it independent of input signal, which greatly improves the linearity performance. Fabricated in a 0.5- μ m CMOS process, the proposed ADC achieves 88-dB signal-to-noise-and-distortion ratio and 103-dB spurious free dynamic range with 5-V supply voltage and 2.5-V reference voltage, and the total power consumption is 37.5 mW.
Statistical Model on CRAFT
WANG Caibing, GUO Hao, YE Dingfeng, WANG Ping
2022, 31(4): 698-712. doi: 10.1049/cje.2021.00.092
Abstract(391) HTML (177) PDF(29)
Many cryptanalytic techniques for symmetric-key primitives rely on specific statistical analysis to extract some secrete key information from a large number of known or chosen plaintext-ciphertext pairs. For example, there is a standard statistical model for differential cryptanalysis that determines the success probability and complexity of the attack given some predefined configurations of the attack. In this work, we investigate the differential attack proposed by Guo et al. at Fast Software Encryption Conference 2020 and find that in this attack, the statistical behavior of the counters for key candidates deviate from standard scenarios, where both the correct key and the correct key xor specific difference are expected to receive the largest number of votes. Based on this bimodal behavior, we give three different statistical models for truncated differential distinguisher on CRAFT (a cryptographic algorithm name) for bimodal phenomena. Then, we provide the formulas about the success probability and data complexity for different models under the condition of a fixed threshold value. Also, we verify the validity of our models for bimodal phenomena by experiments on round-reduced of the versions distinguishers on CRAFT. We find that the success probability of theory and experiment are close when we fix the data complexity and threshold value. Finally, we compare the three models using the mathematical tool Matlab and conclude that Model 3 has better performance.
Rectangle Attack Against Type-I Generalized Feistel Structures
ZHANG Yi, LIU Guoqiang, SHEN Xuan, LI Chao
2022, 31(4): 713-720. doi: 10.1049/cje.2021.00.058
Abstract(126) HTML (51) PDF(25)
Type-I generalized Feistel networks (GFN) are widely used frameworks in symmetric-key primitive designs such as CAST-256 and Lesamnta. Different from the extensive studies focusing on specific block cipher instances, the analysis against Type-I GFN structures gives generic security evaluation of the basic frameworks and concentrates more on the effect of linear transformation. Currently, works in this field mainly evaluate the security against impossible differential attack, zero-correlation linear attack, meet-in-the-middle attack and yoyo game attack, while its security evaluation against rectangle attack is still missing. In this paper, we filled this gap and gave the first structural analytical results of Type-I GFN against rectangle attack. By exploiting its structural properties, we proved there exists a boomerang switch for Type-I GFN for the first time, which is independent of the round functions. Then we turned the boomerang switch into chosen plaintext setting and proposed a new rectangle attack model. By appending 1 more round in the beginning of the boomerang switch, we constructed a rectangle distinguisher and a key recovery attack could be performed.
An Edge-Cloud Collaborative Cross-Domain Identity-Based Authentication Protocol with Privacy Protection
SUN Haipeng, TAN Yu’an, LI Congwu, LEI Lei, ZHANG Qikun, HU Jingjing
2022, 31(4): 721-731. doi: 10.1049/cje.2021.00.269
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Edge-cloud collaborative application scenario is more complex, it involves collaborative operations among different security domains, frequently accessing and exiting application system of mobile terminals. A cross-domain identity authentication protocol based on privacy protection is proposed. The main advantages of the protocol are as follows. 1) Self-certified key generation algorithm: the public/private key pair of the mobile terminal is generated by the terminal members themselves. It avoids security risks caused by third-party key distribution and key escrow; 2) Cross-domain identity authentication: the alliance keys are calculated among edge servers through blockchain technology. Cross-domain identity authentication is realized through the signature authentication of the alliance domain. The cross-domain authentication process is simple and efficient; 3) Revocability of identity authentication: When the mobile terminal has logged off or exited the system, the legal identity of the terminal in the system will also become invalid immediately, so as to ensure the forward and backward security of accessing system resources. Under the hardness assumption of discrete logarithm problem and computational Diffie-Hellman problem, the security of the protocol is proven, and the efficiency of the protocol is verified.
A Fine-Grained Flash-Memory Fragment Recognition Approach for Low-Level Data Recovery
ZHANG Li, HAO Shengang, ZHANG Quanxin
2022, 31(4): 732-740. doi: 10.1049/cje.2020.00.206
Abstract(299) HTML (126) PDF(29)
Data recovery from flash memory in the mobile device can effectively reduce the loss caused by data corruption. Type recognition of data fragment is an essential prerequisite to the low-level data recovery. Previous works in this field classify data fragment based on its file type. Still, the classification efficiency is low, especially when the data fragment is a part of a composite file. We propose a fine-grained approach to classifying data fragment from the low-level flash memory to improve the classification accuracy and efficiency. The proposed method redefines flash-memory-page data recognition problem based on the encoding format of the data segment, and applies a hybrid machine learning algorithm to detect the data type of the flash page. The hybrid algorithm can significantly decompose the given data space and reduce the cost of training. The experimental results show that our method achieves better classification accuracy and higher time performance than the existing methods.
Enhanced Privacy Preserving for Social Networks Relational Data Based on Personalized Differential Privacy
KANG Haiyan, JI Yuanrui, ZHANG Shuxuan
2022, 31(4): 741-751. doi: 10.1049/cje.2021.00.274
Abstract(162) HTML (78) PDF(25)
With the popularization and development of social software, more and more people join the social network, which produces a lot of valuable information, but also contains plenty of sensitive privacy information. To achieve the personalized privacy protection of massive social network relational data, a privacy enhancement method for social networks relational data based on personalized differential privacy is proposed. And a dimensionality reduction segmentation sampling (DRS-S) algorithm is proposed to implement this method. First, in order to solve the problem of inefficiency caused by the excessive amount of data in social networks, dimension reduction and segmentation are carried out to divide the data into groups. According to the privacy protection requirements of different users, we adopt sampling method to protect users with different privacy requirements at different levels, so as to realize personalized different privacy. After that, the noise is added to the protected data to satisfy the privacy budget. Then publish the social network data. Finally, the proposed algorithm is compared with the traditional personalized differential privacy (PDP) algorithm and privacy preserving approach based on clustering and noise (PBCN) in real data set, the experimental results demonstrate that the quality of privacy protection and data availability of DRS-S are better than that of PDP algorithm and PBCN algorithm.
Binary Image Steganalysis Based on Symmetrical Local Residual Patterns
LUO Junwei, YU Mujian, YIN Xiaolin, LU Wei
2022, 31(4): 752-763. doi: 10.1049/cje.2020.00.414
Abstract(395) HTML (153) PDF(27)
Residual computation is an effective method for gray-scale image steganalysis. For binary images, the residual computation calculated by the XOR operation is also employed in the local residual patterns (LRP) model for steganalysis. A binary image steganalytic scheme based on symmetrical local residual patterns (SLRP) is proposed. The symmetrical relationships among residual patterns are introduced that make the features more compact while reducing the dimensionality of the features set. Multi-scale windows are utilized to construct three SLRP submodels which are further merged to construct the final features set instead of a single model. SLRPs with higher probability to be modified after embedding are emphasized and selected to construct the feature sets for training the support vector machine classifier. The experimental results show that the proposed steganalytic scheme is effective for detecting binary image steganography.
MSK-PK: A Public-Key Encryption Cryptosystem with Multiple Secret-Keys
ZHAI Jiaqi, LIU Jian, CHEN Lusheng, WANG Lingyu
2022, 31(4): 764-772. doi: 10.1049/cje.2020.00.049
Abstract(136) HTML (55) PDF(12)
By allowing intermediate nodes to combine multiple packets before forwarding them, the concept of network coding in multi-cast networks can provide maximum possible information flow. However, this also means traditional encryption methods are less applicable, since the different public-keys of receivers imply different ciphertexts which cannot be easily combined by network coding. While network coding itself may provide confidentiality, its effectiveness heavily depends on the underlying network topology and ability of the eavesdroppers. Finally, broadcast encryption and group key agreement techniques both allow a sender to broadcast the same ciphertext to all the receivers, although they rely on the assumptions of trusted key servers or secure channels. In this paper, we propose a novel public-key encryption concept with a single public-key for encryption and multiple secret keys for decryption (MSK-PK), which has limited ciphertext expansion and does not require trusted key servers or secure channels. To demonstrate the feasibility of this concept, we construct a concrete scheme based on a class of lattice-based multi-trapdoor functions. We prove that those functions satisfy the one-wayness property and can resist the nearest plane algorithm.
Android Malware Detection Method Based on Permission Complement and API Calls
YANG Jiyun, TANG Jiang, YAN Ran, XIANG Tao
2022, 31(4): 773-785. doi: 10.1049/cje.2020.00.217
Abstract(177) HTML (77) PDF(16)
The dynamic code loading mechanism of the Android system allows an application to load executable files externally at runtime. This mechanism makes the development of applications more convenient, but it also brings security issues. Applications that hide malicious behavior in the external file by dynamic code loading are becoming a new challenge for Android malware detection. To overcome this challenge, based on dynamic code loading mechanisms, three types of threat models, i.e. Model I, Model II, and Model III are defined. For the Model I type malware, its malicious behavior occurs in DexCode, so the application programming interface (API) classes were used to characterize the behavior of the DexCode file. For the Model II type and Model III type malwares whose malicious behaviors occur in an external file, the permission complement is defined to characterize the behaviors of the external file. Based on permission complement and API calls, an Android malicious application detection method is proposed, of which feature sets are constructed by improving a feature selection method. Five datasets containing 15,581 samples are used to evaluate the performance of the proposed method. The experimental results show that our detection method achieves accuracy of 99.885% on general dataset, and performes the best on all evaluation metrics on all datasets in all comparison methods.
Characterization and Properties of Bent-Negabent Functions
JIANG Niu, ZHAO Min, YANG Zhiyao, ZHUO Zepeng, CHEN Guolong
2022, 31(4): 786-792. doi: 10.1049/cje.2021.00.417
Abstract(147) HTML (74) PDF(29)
A further characterization of the bent-negabent functions is presented. Based on the concept of complete mapping polynomial, we provide a necessary and sufficient condition for a class of quadratic Boolean functions to be bent-negabent. A new characterization of negabent functions can be described by using the parity of Hamming weight. We further generalize the classical convolution theorem and give the nega-Hadamard transform of the composition of a Boolean function and a vectorial Boolean function. The nega-Hadamard transform of a generalized indirect sum is calculated by this composition method.
2022-4 Contents
2022, 31(4): 793-793.
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