Reviewerly/exHarmony huggingface dataset:
Bandwidth Enhanced Operation of Photonic Time Delay Reservoir Computing

We show numerically and experimentally that strong optical injection can enhance the operating bandwidth of semiconductor lasers with optical feedback in a photonic time-delay reservoir computing configuration. Its operating conditions can be significantly expanded while maintaining competitive computational performance in a time-series prediction task.
Complementation and anti-complementation in intuitionistic anti-fuzzy graphs

In this paper, we define the notion of intuitionistic anti-fuzzy graphs and their special cases.We illustrate the concepts regarding properties of intuitionistic anti-fuzzy graphs with some examples.Also, we introduce the concept of complement and anti-complement of intuitionistic anti-fuzzy graphs and prove some of their properties.
Policy Gradient Method based Energy Efficience Task Scheduling in Mobile Edge Blockchain

With the explosive growth of emerging applications in wireless networks, high transmission rate, low latency and secure data and resource sharing have become urgent requirements. Mobile edge computing (MEC) technology can provides low latency computing services and high speed content cache to obtain a higher service quality. In combined with blockchain technology, which are capable of establishing trust in an untrustworthy environment, the security function of MEC can be enhanced and satisfies the new application needs. In this paper, the data sharing application in mobile edge blockchain is considered. The BS is responsible of forwarding data package from the source to the target UE. Meanwhile, the MEC server deployed at the BS generates a transaction recording this sharing event and mine the corresponding block to ensure security. The profit of the BS related to the wireless data transmission cost of frequency resource and power, the mining reward and the edge computing cost of the transaction and the competitiveness of the MEC server in the blockchain system is defined as the network utility. The data sharing task scheduling problem at the BS is modeled as a Markov decision process (MDP), and policy gradient based task scheduling (PG-TS) algorithm is proposed and evaluated. Simulation results show that the performance of the proposed algorithm is better than greedy, random, and first in first out (FIFO) algorithm.
Construction of Cyclically Permutable Codes From Prime Length Cyclic Codes

This paper proposes a novel algorithm to find the cyclically permutable codes (CPCs) from a cyclic code. In recent years, the CPCs are increasingly important, and have been applied in the communication network and optical communication. A CPC is a block code such that each codeword has full cyclic order and all codewords are cyclically distinct. In this paper, we use the characteristics of finite fields to develop an efficient algorithm to find a CPC from a q-ary cyclic code with prime length. More precisely, we propose an effective methods to find all codewords with full cyclic order from a cyclic code for prime-primitive length and prime-nonprimitive length respectively.
An Experimental Study on Cultivation Model of Interdisciplinary Innovative and Entrepreneurial Talents in Internet Sports Industry

Abstract Interdisciplinary innovative and entrepreneurial talents can meet the needs of “Internet + sports” Industry development and better serve the local digital economy. Competence-based innovation and entrepreneurship education is a practical and effective teaching model for the artisan cultivation with innovative and entrepreneurial qualities. This article aims to study the craftsmanship talent training model and implement effect of information management and information system specialty. This article analyzes the basic connotation, features, advantages and implement process of craftsmanship talent education is applied innovative and entrepreneurial talents training model. The interdisciplinary undergraduate programs, typified by information management and information systems, have their unique characteristics in promoting innovation and entrepreneurship education. This article proposes a way to build a new model of “artisan” talents training in information management and information system majors to change the traditional Teaching mode to improve teaching quality and enhance students’ ingenuity and awareness. Experimental data show that the innovative and entrepreneurial craftsman-type talent training mode are conducive to the cultivation of talents and the storage of innovative and entrepreneurial talents, and can effectively increase the rate of success, the craftsman-type talent training model and information management system have increased the talent reserve of universities by about 23%, and shortened the talent training cycle by about 15%, which has promoted the development of innovation and entrepreneurship education in China.
An Operation Reliability Analysis Method of Distribution Networks Based on Data and Physical Fusion Models

To ease the hardness of real-time reliability assessment on distribution networks, a data and physical fusion model based on Principal Components Analysis (PCA) and Random Matrix Theory (RMT) was proposed for the operation reliability analysis of distribution networks. Using real-time data collected from substation feeders and transformers, constructing time and space joint matrix, the inner statistical characteristics of data was obtained based on RMT and its augmented matrix technology. Then we used a multi-layer physical index system and PCA to obtain main physical indicators which senses high-risk parts. The K-Means clustering method was used to unite two models and establish the proposed operation reliability analysis method. An IEEE-33 simulation example proved the effectiveness of the method.
Big Data Dimensionality Reduction at IoT Edge Through Optical Graph Signal Processing

We introduce an innovative edge computing paradigm for big data dimensionality reduction in IoT networks through employing an underlying Graph Signal Processing (GSP) model-alternatively to von Neumann architecture for conventional computing. We realize a new breed of graph filters by instituting photonic wave interference in standard WDM optical communication systems, and thereby, we embody GSP in an optical computation context. Our proposed filter instantaneously takes the average of large-scale multidimensional data while is in transit from IoT edge to its core. Through co-using the communication resources for computation, our work motivates a modern IoT edge computing paradigm based on using a collective pool of computation and communication resources. Our solution saves a significant amount of processing delay, investment in cloud structure, and carbon footprint. It is, therefore, instrumental for breaking”the curse of dimensionality” in complex IoT applications such as self-driving vehicles that involve deploying lots of edge nodes and handling large volumes of high dimensional data with very low latency.
Reviving autoencoder pretraining

Abstract The pressing need for pretraining algorithms has been diminished by numerous advances in terms of regularization, architectures, and optimizers. Despite this trend, we re-visit the classic idea of unsupervised autoencoder pretraining and propose a modified variant that relies on a full reverse pass trained in conjunction with a given training task. This yields networks that are as-invertible-as-possible and share mutual information across all constrained layers. We additionally establish links between singular value decomposition and pretraining and show how it can be leveraged for gaining insights about the learned structures. Most importantly, we demonstrate that our approach yields an improved performance for a wide variety of relevant learning and transfer tasks ranging from fully connected networks over residual neural networks to generative adversarial networks. Our results demonstrate that unsupervised pretraining has not lost its practical relevance in today’s deep learning environment.
An interactive instance segmentation system with multi‐resolution convolutional neural networks

In this paper, a fast interactive instance segmentation (IIS) system is proposed and it is composed of an effective heatmap generator, a multi-resolution network (MRNet), and an adaptive threshold refiner to promptly and precisely predict the masks of the objects. The proposed heatmap generator after interaction clicks can help the MRNet to successfully learn the sensitive features for better prediction. Based on convolutional neural network models, the proposed MRNet backbone produces multiple features across multiple resolutions and can intrinsically predict the sharp contour of the object. After the probabilistic prediction achieved by the MRNet, the Otsu's threshold refiner is proposed to further remove some uncertain pixels in the predicted mask. Experimental results demonstrate that the proposed IIS system can promptly predict sharp masks of the targeted objects with mIoU of 89.1% in PASCAL VOC 2012 [1] validation set. Compared to other existing interactive methods, the proposed system can effectively predict the segmentation mask with higher accuracy and less interaction efforts.
Research on Integrated Data Acquisition Method of wind Power Generation based on Deep Learning

The traditional integrated collection method of power generation data uses analog separation components, which are subject to many interference factors of charging and discharging, resulting in the problems of redundancy and data loss in the integrated collection of data. In order to solve the above problems, this paper studies the integrated data acquisition method of wind power generation based on deep learning. Use current sensor, voltage sensor, wind speed and direction meter and other sensing devices to collect data in the process of wind and solar power generation, and use SQL Server 2008 to establish a database to store the collected wind and solar power data. On this basis, deep learning is used to fill the missing data in the database, and then DTS tool is used to complete the data integration after filling. Through comparison experiments with traditional methods, it can be seen that the integrated acquisition method based on deep learning can effectively reduce data redundancy and lack, and has better reliability.
Secure Database on Attendance Verification System by using Data Masking Technique

Abstract Data Masking Technique is a system designed to secure sensitive data by replacing the front data but not the original data. For instance, the data masking techniques capable to hide any data from being viewed by any unauthorized users. However, fewer studies about data masking techniques in the attendance system are implemented. Previous studies have primarily concentrated on defining sensitive data exposure and the technique of securing sensitive data using data masking. Therefore, the aim of this paper is to integrate the use of the data masking technique towards the attendance verification system. Thus, it is sufficient to stop unauthorized users from accessing confidential data. This study also applied the iterative model as a software development methodology as well as integrated with the hashing concept. Therefore, the attendance verification system can be used to perform the functionalities of easily manage records. By using this technique, the attendance of a student was added with a security element. The result shows that data masking and hashing techniques ensure the confidentiality of student data from being viewed by unauthorized parties. The authorized person is the ability to see the masked of sensitive student data. The significance of this study is capable to implement the security element on the attendance system and verify the user before entering the system
Declarative Preferences in Reactive BDI Agents

Current agent architectures implementing the belief-desire-intention (BDI) model consider agents which respond reactively to internal and external events by selecting the first-available plan. Priority between plans is hard-coded in the program, and so the reasons why a certain plan is preferred remain in the programmer’s mind. Recent works that attempt to include explicit preferences in BDI agents treat preferences essentially as a rationale for planning tasks to be performed at run-time, thus disrupting the reactive nature of agents. In this paper we propose a method to include declarative preferences (i.e. concerning states of affairs) in the agent program, and to use them in a manner that preserves reactivity. To achieve this, the plan prioritization step is performed offline, by (a) generating all possible outcomes of situated plan executions, (b) selecting a relevant subset of situation/outcomes couplings as representative summary for each plan, (c) sorting the plans by evaluating summaries through the agent’s preferences. The task of generating outcomes in several conditions is performed by translating the agent’s procedural knowledge to an ASP program using discrete-event calculus.
Neural Data Augmentation Techniques for Time Series Data and its Benefits

Exploring adversarial attacks and studying their effects on machine learning algorithms has been of interest to researchers. Deep neural networks working with time series data have received lesser interest compared to their image counterparts in this context. In a recent finding, it has been revealed that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks. In this paper, we introduce neural data augmentation techniques and show that classifier trained with such augmented data obtains state-of-the-art classification accuracy as well as adversarial accuracy against Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) on various time series benchmarks.
Generating JPEG Steganographic Adversarial Example via Segmented Adversarial Embedding

Nowadays, Convolutional Neural Network (CNN) based steganalytic schemes further improves the detection ability of the steganalyzer comparing with feature based schemes. Besides steganalysis, CNN model can also be used in steganography. Inheriting the mechanism from adversarial attack to CNN model, adversarial embedding is a kind of steganographic scheme that exploits the knowledge of CNN-based steganalyzer. Adversarial embedding can effectively improve security performance of typical adaptive steganographic schemes. In this paper, we propose a novel adversarial embedding scheme for steganography named as Segmented Adversarial Embedding (SAE). The core of SAE is separating the embedding process into several partial embedding processes and performing adversarial embedding in each segment. In each partial embedding process, there is an individual CNN model corresponding to the current embedding stage. In the embedding process, a novel scheme is applied in the cost adjustment. Comparing with the adjustment scheme that utilizes the gradient sign, the new scheme also takes the gradient magnitude into account, which further makes use of the gradient information. Besides the typical implementation of SAE, we also develop a simplified variant with lower complexity. The evaluations on different kinds of steganalyzer prove that SAE is effective to improve the performance of existing steganographic scheme.
An optimized K-Nearest Neighbor algorithm based on Dynamic Distance approach

The k-nearest neighbors algorithm (KNN) is one of the most widely used and effective nonparametric classification algorithms. The classification mechanism of this algorithm involves computing the distance between new instance and the other instances. When the dataset contains non-numerical (ordinal and nominal) attributes, the performance of the algorithm can be significantly affected by how this distance is measured. This paper presents a distance measurement method for improving the performance of KNN. The idea of the proposed method is based on the notion of dynamic distance, which refers to the distance defined between the two values of a non-numerical attribute and depends on the nature of the problem. The determination mechanism of this dynamic distance is formulated in the form of an optimization problem, which is embedded within the structure of KNN and solved using the invasive weed optimization algorithm. The performance of the proposed algorithm is tested on the datasets of the UCI machine learning repository. The results show a minimum of 8% and a maximum of 48.1% improvement in classification accuracy, compared to classic KNN.
Classification Mechanism of Convolutional Neural Network for Facial Expression Recognition

With the development of deep learning, the structures of convolutional neural networks (CNNs) are becoming more complex and the performance of expression recognition is getting better. However, the classification mechanism of CNN is still a black box. The main problem is that CNNs have a great number of parameters, which makes it difficult to analyze them clearly. In this paper, we explain the essence of deep learning from the perspective of manifold geometry. The main purpose of deep learning especially CNN is to learn the probability distributions on manifolds. And we design a neural network based on the facial expression recognition to explore the classification mechanism of CNN. By using the deconvolution visualization method, we qualitatively verify that the trained CNN forms a detector for specific facial action unit (FAU) and each neuron of CNN is a specific manifold feature extractor for facial images. Moreover, we design a distance function to measure the differences of activation value distributions on the same feature map of FAU. The greater the distance, the more sensitive the feature map is to the FAU. The results show that the mapping relationship between FAUs and feature maps of CNN is determined, the trained CNN has generated an internal detector for each FAU to extract the facial manifold feature.
A Method for Lever Arm Estimation in INS/GPS Integration Using Direct Unscented Kalman Filter

In Inertial Navigation System (INS) and Global Positioning System (GPS) integrated system, lever arm effect is an important error source, which makes the integrated system highly nonlinear. In addition to nonlinear problem, the lever arm is difficult to measure in practical applications. To solve the problems, the direct filtering method considering the lever arm effect of Unscented Kalman Filter (UKF) is proposed in this paper. The proposed method adds lever arm to system model of direct Kalman filter and compensates the estimated lever arm, and thus the lever arm is constantly estimated and modified as the filter estimations are updated. Specifically, the system state model and measurement model are established based on the direct filtering method. The navigation parameters of INS and lever arm are taken as system state variable, the velocity and position of GPS are taken as measurement variable. Then the UKF is used for INS/GPS integrated system information fusion. Simulation results show that the proposed direct UKF considering lever arm of integrated navigation system can estimate the lever arm correctly. Furthermore, the accuracy of the proposed method is significantly higher than that of standard indirect KF and standard direct UKF.
Noble Inheritance Mechanism of Digital Content for "Digital-Ji-in" toward Sustainable Society

Various research and development efforts have been made to implement cyber-physical systems, aiming to maintain the associations between digital and physical identities (such as sensors, events, or people). In this study, we consider a scenario with the inheritance of digital content owned by a deceased person. People increasingly store their information (such as digital photos and videos) in cloud services, and this content may become inaccessible to the relatives in case of an unexpected death. In this paper, we propose a method for realizing a noble inheritance mechanism for secret digital content in "Digital-Ji-in" which maintains digital identities toward sustainable society. Specifically, we use a secure secret scheme to realize the noble inheritance mechanism. The "shares" of the encryption key are distributed to future heirs and the trusted third party such as cloud provider. The cryptographic key cannot be recovered until all the heirs' shares are united and the trusted third party's shares are obtained. This paper describes and analyzes the proposed method. Moreover, it outlines a preliminary scenario with "the right to be forgotten." The proposed method can help maintain the connection between the real world and cyberspace in perpetuity.
A Novel Realization of PSK and QPSK Quantum-Noise Randomized Cipher Systems with Optical Domain Decryption

Quantum-noise randomized cipher (QNRC) is an anti-interception communication technology, which can realize high-speed encryption for data stream in true randomness. In this paper, the characteristics of the phase shift keying (PSK) and quadrature phase shift keying(QPSK) QNRC systems are verified by the simulation system with digital coherent demodulation. The decryption process of the QNRC coherent system is carried out in the optical domain for the first time. Specifically, at the quadrature coupler, the modulated local light is coupled with the received encrypted optical signal. The multi-symbol phase estimation(MSPE) module is employed to eliminate the influence of the phase deviation on the signal decision. Finally, error-free secure transmission is realized at 2.5Gsymbol/s over 500km optically amplified links. We reveal the new fact that the mesoscopic power of QPSK-QNRC system is higher than that of PSK-QNRC system.
A Hybrid Method of Cascaded Features for RGB-D Semantic Segmentation

Abstract Fully Convolution Network and its following works has achieved the state-of-art performance on the task of RGB semantic segmentation. However, there still lacks an effective method to sufficiently leverage geometric information of depth image to accomplish RGB-D semantic segmentation. To this end, this paper proposed a new method containing two parts: 1) a simple but useful way based on Fast Marching Method to inpaint area of no-measured-depth pixels, which produces better result than standard dataset we used in this paper; 2) a new fusion architecture of CNN in which we fuse RGB and depth features in shallow layers to make geometry information from depth image a better assistant to RGB semantic segmentation. Besides, we add a feature filter architecture to help model choose the most discriminative features and punish useless or repeating features for better hierarchical expression. The original RGB image and inpainted depth image are fed into two feature extracting streams, one for each modality, and get fused in the fusion layer which is consecutively followed by deeper encoder network and decoder network. We evaluate our method on SUN RGB-D and NYUD dataset and experimental result shows that the proposed model has priority on RGB-D semantic segmentation.