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This paper presents a 32 Gb/s transmitter (TX) with non-return-to-zero (NRZ) modulation scheme. Fabricated in the 28nm CMOS technology, the TX incorporates a quarter-rate architecture with a three-tap feed-forward equalizer (FFE) under the consideration of timing requirement and power consumption. Key features of the TX include a low-power data-serializing path, a pulsed pass-gate 4:1 multiplexer (MUX), a pre-driver adopted sub-UI de-emphasis, a current-mode logic (CML) output driver combined common-mode feedback (CMFB), an output network using a T-coil inductor to eliminate the parasitic capacitance of ESD and pad for bandwidth expansion. The critical clock path contains sub-50fs resolution duty cycle detection/correction (DCD/DCC) and quadrature error detection/correction (QED/QEC) circuits. The TX operating at 32Gb/s in NRZ modulation, including the clock path, consumes 98 mW under 1V supply, achieving a 3.06 pJ/b energy efficiency with 0.8 V pp output swing. The TX front end core circuit occupies an area of 0.078 mm2.

In this paper, we conduct a comprehensive study on insect identification and classification. However, the existing insect datasets are made up of several categories, which is far from the demands in reality. To handle this issue, we contribute to a more challenging insect image dataset, which contains 1848 images covering different pests from 118 classes. We further conduct fine-tuning on seven deep convolution neural networks, including VGG16, ResNet50, DenseNet121, Res2Net50_26w_ 4s, SCNet50_ vld, GhostNet, and RegNet. Finally, extensive experiments of the aforementioned networks on our dataset illustrate that the last four state-of-the-art deep convolution neural networks can achieve promising performance on pest identification and classification.

Aiming at the problems of long legal document entities and the lack of annotated data, a cascade model that integrates the characteristics of characters and words is proposed. The traditional NER is decomposed into two cascaded subtasks: the entity recognition and the attribute recognition. The model obtains the vector representation of text character-level and word-level through BERT and Self-attention, respectively. Then the BiLSTM is used to obtain the internal features of the serialized text. Subsequenly, CRF is used to select the entity's optimal tag sequence and the attributed optimal tag sequence. Finally, these two sequences are spliced to obtain the optimal marker sequence. In order to improve the utilization rate of the data, the label linearization is introduced for the data enchancement. The results show that the method is superior to traditional models, can effectively extract named entities of legal documents, and has the vital practical significance.

Aiming at the problems of poor parsing effect and poor generalization ability in Chinese position segmentation using current neural network models, this paper proposes a Chinese position segmentation method based on ALBERT-BiGRU-CRF model. The method first uses the ALBERT pre-training model to pre-train the Chinese location information to obtain the context information in all layers, enhance the semantic representation ability of the Chinese location information, and then extract the feature information of the vector through the BiLSTM model, and finally decode it through the CRF model to obtain the global Optimal labeling sequence. Experimental results show that on the basis of different numbers and regions of Chinese location information data sets, the ALBERT-BiGRU-CRF model has better word segmentation accuracy and F1 value on all test sets than the current commonly used neural network models, and the highest can be achieved. 93.91% and 93.96%. Using the ALBERT-BiGRU-CRF model to segment Chinese location information not only effectively improves the accuracy of Chinese location information analysis and polysemous word analysis, but also has better generalization capabilities.

In view of the difficulty in extracting the key area features of the image and the over-complex training in current street view image retrieval methods, this paper proposes a retrieval method that extracts and integrates multiple global features. First, the Convolutional Neural Network is used to extract the features, and then the new multi-scale pooling layer is used to output multiple global features, and the feature loss is reduced by outputting fixed-dimensional features. Finally, the final feature obtained by concatenating multiple global features is used for retrieval. Experimental results show that this method can effectively extract image features, reduce the complexity of training, and improve the accuracy of retrieval.

Based on the characteristics of live broadcasting platform and fresh agricultural products, combined with SOR theory, two dimensions of social presence and product stimulation were introduced, and the two external stimuli were divided into situational presence, warmth presence, cognitive presence and product quality and logistics service of product stimulation dimension. Combined with the theory of perceived value, this paper introduces perceived trust and perceived risk as mediating variables to construct the model of consumers' purchasing intention of fresh agricultural products under the live broadcast situation. The collected data were collated and analyzed by using the questionnaire, and the reliability and validity of the data were verified by SPSS and AMOS. The results show that situational presence has a positive impact on consumers' perceived trust, the influence of warmth presence and cognitive presence on perceived trust is not supported by empirical studies. Product quality and logistics service have a negative impact on consumers' perceived risk, consumers' perceived trust has a positive effect on their purchase intention. Consumers' perceived risk has a negative impact on their purchase intention.

Pedestrian re-recognition is a very important research direction in video surveillance. With the emphasis on night video surveillance, pedestrian re-recognition is also being studied from a single mode to a cross-mode direction. Since the images taken by the camera at night are generally divided into two types, thermal imaging and infrared images, corresponding to the RegDB data set and the SYSU-MM01 data set respectively. In order to make the trained model have good performance in both data sets, GAN network is used in this article. The visible light image is generated by CycleGAN to generate the corresponding infrared image, and then the infrared image is generated by PTGAN to generate a thermal imaging style image. Then input the image into the single-stream network for training and learning, and finally optimize the network in an end-to-end manner.

Salient object detection methods focus on detecting precise salient objects and acquiring clear boundaries, but many approaches fail to achieve both of them. To solve this problem, we present A Hybrid Salient Object Detection with Global Context Awareness. The proposed model consists primarily of Multi-level Feature Enhancement (MFE) and Top-layer Processing (TP). MFE module is a module that fuses high-level features, low-level features and corresponding global context information and it is used for foreground enhancement. TP module generates two kinds of features, one is the representative top-layer features, and the other is the global context information that acts on the corresponding MFE module. Comparing our method with other 12 state-of-the-art salient object detection methods with different evaluation metrics, the experimental results show that our method is superior to other methods.

Federated learning enables collaborative machine learning among multiple independent participants while preserving data privacy of each participant through model aggregation during training. However, model aggregation still faces potential risks that are associated with indirect leakage, such as parameters. In this paper, we first propose an algorithm called Meta Operation Neural Network (MONN) to perform basic arithmetic operations on encrypted data and generate operation results in a plaintext way. MONN is actually a general neural network composed of an encoder and a meta operation decoder, where both the encryption and meta decryption are lossless. MONN can be applied to federated learning for secure model aggregation. In this way, data privacy can be well preserved not only from malicious attackers but also untrustworthy servers. Experimental results reveal the following three key properties: 1) The proposed MONN based federated aggregation method (denoted as FedMONN) can reach satisfactory performance comparable with non-federated counterparts; 2) FedMONN is more secure than the classic federated averaging and one-time pad aggregation, even the server is not a trustable third party; 3) FedMONN is much efficient than the federated aggregation based on the Paillier homomorphic encryption technique.

The electromagnetic environment of Direct Current (DC) transmission lines has attracted the attention of many experts and scholars at home and abroad, but how to suppress the electromagnetic interference is a brand new topic. Relies on the bipolar DC test lines of Ultra high voltage Engineering (Kunming) National Engineering Laboratory, the electromagnetic interference suppression measures for the UHV DC transmission lines in high altitude has been studyed by erected the shielding wire under the line. First the tower design and suspension point of shield conductor were choosed through the simulation calculation, and then the shield wire was retrofited with different sizes and different layout under the positive wire to field test. From which, the shielding effectiveness of electromagnetic environmental parameters (radio interference (RI), resultant electric field (Es) and ion current density (Js)) was obtained.

SF 6 is listed as a greenhouse gas that limits emissions because of its high GWP. The use of eco-friendly SF 6 alternative gas in the power industry is of great significance for environmental protection. C 6 F 12 O has excellent environmental performance and is a hot research direction of alternative gas. At present, most of the existing research is on the insulation performance and decomposition characteristics of C 6 F 12 O and its gas mixture. Few studies have involved the compatibility of C 6 F 12 O gas mixture. Therefore, this paper builds a compatibility experiment platform to study the compatibility of C 6 F 12 O/CO 2 gas mixture and the conductive metal material copper (Cu) in the equipment at different temperatures, and characterizes the gas decomposition products and the morphology of the Cu surface by GCMS and SEM. Studies have shown that during the interconnection of the C 6 F 12 O/CO 2 gas mixture and the metal Cu, the color of the copper surface changes from purple to golden yellow with the gradual increase of temperature, and then to bright purple and then brown. SEM shows that a large number of crystal particles are generated on the surface of metal Cu due to corrosion, and C 6 F 12 O and metal Cu will also decompose during the interaction process, generating primary decomposition products such as C 3 F 6 , C 3 F 6 O and C 7 F 14 . The results show that the compatibility between C 6 F 12 O gas and metallic Cu materials is poor at high temperature, and anticorrosion treatment should be carried out in engineering applications. The relevant research results have guiding significance for the engineering application and online monitoring of C 6 F 12 O gas mixture.

In this paper, based on the high speed schlieren technology, the test observation platform of long gap discharge process and channel insulation recovery process was set up. The synchronization strategy was put forward too. Based on the applied voltage, discharge current and video image were recorded synchronously, discharge channel and electrode contact point of the spherical shock wave and cylindrical shock wave along the discharge channel were discovered after the long air gap breakdown. Statistics obtained the curve of cylindrical shock wave propagation speed and radius of the shock wave, and the influence law of the applying different voltage type on shock wave velocity.

With strict protections and regulations of data privacy and security, conventional machine learning based on centralized datasets is confronted with significant challenges, making artificial intelligence (AI) impractical in many mission-critical and data-sensitive scenarios, such as finance, government, and health. In the meantime, tremendous datasets are scattered in isolated silos in various industries, organizations, different units of an organization, or different branches of an international organization. These valuable data resources are well underused. To advance AI theories and applications, we propose a comprehensive framework (called Knowledge Federation- KF) to address these challenges by enabling AI while preserving data privacy and ownership. Beyond the concepts of federated learning and secure multi-party computation, KF consists of four levels of federation: (1) information level, low-level statistics and computation of data, meeting the requirements of simple queries, searching and simplistic operators; (2) model level, supporting training, learning, and inference; (3) cognition level, enabling abstract feature representation at various levels of abstractions and contexts; (4) knowledge level, fusing knowledge discovery, representation, and reasoning. We further clarify the relationship and differentiation between knowledge federation and other related research areas. We have developed a reference implementation of KF, called iBond Platform, to offer a production-quality KF platform to enable industrial applications in finance, insurance, marketing, and government. The iBond platform will also help establish the KF community and a comprehensive ecosystem and usher in a novel paradigm shift towards secure, privacy-preserving and responsible AI. As far as we know, knowledge federation is the first hierarchical and unified framework for secure multi-party computing (statistics, queries, searching, and low-level operations) and learning (training, representation, discovery, inference, and reasoning).

There are a wide variety kinds of cryptographic devices involved in the space-ground integrated information network systems, which will generate a large amount of data. How to implement efficient storage and processing of cryptographic data in a big data environment is particularly important. According to the overall technical requirements of integration of heaven and earth cross-domain hierarchical password on-demand service, this paper preliminary designed the cryptographic data management platform architecture based on Hadoop. And proposed a solution for monitoring data storage and processing based on Hadoop. Aiming at massive heterogeneous cryptographic data, designed a layered architecture. Improved system data processing speed by using distributed computing processing. In addition, designed the security of the data management platform to solve the bottleneck problem of massive cryptographic data management in the space-ground integrated information network.

Feature selection plays a crucial role in machine learning. For evolutionary computation based feature selection, the same particles may be repeatedly generated many times during the population iteration process, and recalculating the fitness values of these particles will cost a large amount of computational resources. Therefore, it is necessary to find these particles fast and skip calculating their fitness to save computation resources. This paper proposes the combination of MD5 encryption algorithm and the competitive swarm optimizer (CSO) algorithm for feature selection. Every particle in the population generated by CSO is encoded with MD5 encryption algorithm. Then the HashMap is used to quickly search for the repetitive particles. Through MD5 coding and HashMap searching, the repetitive particles are found fast, and then the recalculation of the repeated particles are avoid. Experimental results show that our proposed algorithm can significantly reduce the running time of feature selection for low-dimensional data, medium-dimensional data and high-dimensional data. Moreover, our algorithm does not change the framework of the competitive population optimization algorithm, only avoids calculation of the repeatedly generated particles, and therefore has no effect on the accuracy and number of features. Our algorithm can be widely applied to other computational intelligence methods.

Identification of Chinese fir family is a key procedure in forestry cultivation. However, it is time consuming depending on experts' identification. Therefore, in this paper, the method of image based recognition of Chinese fir family is proposed to replace experts' identification. For the cones of different Chinese fir families have different texture features, the textures of the cone is modeled using dominant rotated local binary patterns and these textures is selected by combination of binary competitive swarm optimization algorithm and probabilistic collaborative representation based classifier for filtered out irrelevant, noisy or redundant information. Experimental results demonstrate that the proposed approach is effective in recognizing Chinese fir family especially with feature selection.

This paper combines the implementation of “the Belt and Road” initiative, which corresponds to the opportunities and challenges brought by Chinese enterprises “Going Out” and the internationalization of vocational education. Take Harbin Vocational and Technical College for example. It explores the transnational school-enterprise collaborative school-running mode in Vocational Colleges from the analysis of communication background, exploration and practice, thinking and so on. In addition, it introduces the process and achievements of Harbin Vocational and Technical College Transnational School-enterprise Collaborative School-running.

We propose a deep reinforcement learning (DRL) methodology for the tracking, obstacle avoidance, and formation control of nonholonomic robots. By separating vision-based control into a perception module and a controller module, we can train a DRL agent without sophisticated physics or 3D modeling. In addition, the modular framework averts daunting retrains of an image-to-action end-to-end neural network, and provides flexibility in transferring the controller to different robots. First, we train a convolutional neural network (CNN) to accurately localize in an indoor setting with dynamic foreground/background. Then, we design a new DRL algorithm named Momentum Policy Gradient (MPG) for continuous control tasks and prove its convergence. We also show that MPG is robust at tracking varying leader movements and can naturally be extended to problems of formation control. Leveraging reward shaping, features such as collision and obstacle avoidance can be easily integrated into a DRL controller.

The cornerstone of computational drug design is the calculation of binding affinity between two biological counterparts especially a chemical compound, i.e. a ligand, and a protein. Predicting the strength of protein-ligand binding with reasonable accuracy is critical for drug discovery. In this paper, we propose a data-driven framework named DeepAtom to accurately predict the protein-ligand binding affinity. With 3D Convolutional Neural Network (3D-CNN) architecture, DeepAtom could automatically extract binding related atomic interaction patterns from the voxelized complex structure. Compared with the other CNN based approaches, our light-weight model design effectively improves the model representational capacity, even with the limited available training data. With validation experiments on the PDBbind v.2016 benchmark and the independent Astex Diverse Set, we demonstrate that the less feature engineering dependent DeepAtom approach consistently outperforms the other state-of-the-art scoring methods. We also compile and propose a new benchmark dataset to further improve the model performances. With the new dataset as training input, DeepAtom achieves Pearson's $\mathrm{R}=0.83$ and $\text{RMSE}=1.23\ \ pK$ units on the PDBbind v.2016 core set. The promising results demonstrate that DeepAtom models can be potentially adopted in computational drug development protocols such as molecular docking and virtual screening.

Vehicle positioning has played an important role in intelligent transportation systems. Previous research has had difficulties in increasing the sensor aperture and reducing the computational complexity of the vehicle positioning algorithm. This paper proposes a new sensor model to extend the sensor aperture, which is similar to the nested sensor model combined with fourth-order cumulants. The proposed algorithm estimates the number of vehicles, which is much higher than the actual number of sensors. An ideal characteristic equation-based method is used to avoid the use of eigenvalue decomposition and spectrum peak search, thereby greatly reducing the computation complexity. In addition, the weighted coefficient matrix is introduced for optimization. Theoretical analysis and simulation results show that the proposed algorithm has lower computational complexity, avoids 2-D parameter matching, and has a high utilization of arrays while still ensuring accurate parameter estimation.