Reviewerly/exHarmony huggingface dataset:
Efficient Generation of Speech Adversarial Examples with Generative Model

Deep neural network-based keyword spotting (KWS) have embraced the tremendous success in smart speech assistant applications. However, the neural network-based KWS have been demonstrated susceptible to be attacked by the adversarial examples. The investigation of efficient adversarial generation would mitigate the security flaws of network-based KWS via adversarial training. In this paper, we propose to use the conditional generative adversarial network (CGAN) to efficiently generate speech adversarial examples. Specifically, we first present a target label embedding method to map the class-wise label into feature maps. Then, we utilize generative adversarial network for constructing the target speech adversarial examples with such feature maps. The target KWS classification network is then integrated with CGAN framework, where the classification error of the target network is back-propagated via gradient flow to guide the generator updating, but the target network itself is frozen. The proposed method is evaluated on a set of state-of-the-art deep learning-based KWS classification networks. The results validate the effectiveness of the generated adversarial examples. In addition, experimental results also demonstrate that the transferability of generated adversarial example among the different KWS classification networks.
Research of Insulator Fault Identification Method Based on Atlas Intelligent Computing Platform

Transmission line monitoring plays a crucial role in the safe operation of the power grid. The application of image recognition technology based on artificial intelligence algorithms can improve the efficiency of fault identification and reduce the labor cost. Object detection is to detect the object of interest in the given picture and is an effective method to recognize the fault location. This paper proposed an insulator fault identification method based on the Atlas intelligent computing platform aiming at the characteristics of small insulator fault data set. This method adopts the SSD300 model for training and inference and analysis is carried out based on the Atlas intelligent computing platform. The experiment results show that the SSD300 model can be ported well to the Atlas intelligent computing platform without reducing the recognition accuracy. At the same time, the model size is decreased.
Improving the Performance of Seabirds Detection Combining Multiple Semantic Segmentation Models

The loss of seabird habitats makes many precious seabirds on the brink of extinction. Monitoring seabirds by unmanned aerial vehicle (UAV) and high-resolution aerial imagery is of vital importance in the long term. The development of convolutional neural network brings great vitality to the detection of small object just like seabird. Due to the complex background and tiny size of seabird in UAV imagery, it is difficult for single semantic segmentation model to detect seabirds accurately. Ensemble learning combines multiple individual learners with a certain strategy to form a committee, which can greatly improve the performance. To realize highly precise and automatic seabird detection from UAV imagery, this paper proposes a deep learning method which combines two semantic segmentation models, as distinct from those generic object detection methods using bounding boxes. The experimental results show that compared to the models such as U-Net and UNet++, the ensemble model performed better overall.
Clustering Using Student t Mixture Copulas

Abstract Tewari et al. (Parametric characterization of multimodal distributions with non-Gaussian modes, pp 286–292, 2011) introduced Gaussian mixture copula models (GMCM) for clustering problems which do not assume normality of the mixture components as Gaussian mixture models (GMM) do. In this paper, we propose Student t mixture copula models (SMCM) as an extension of GMCMs. GMCMs require weak assumptions, yielding a flexible fit and a powerful cluster tool. Our SMCM extension offers, in a natural way, even more flexibility than the GMCM approach. We discuss estimation issues and compare Expectation-Maximization (EM)-based with numerical simplex optimization methods. We illustrate the SMCM as a tool for image segmentation.
Heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder

In Software Effort Estimation (SEE) practice, the data drought problem has been plaguing researchers and practitioners. Leveraging heterogeneous SEE data collected by other companies is a feasible solution to relieve the data drought problem. However, how to make full use of the heterogeneous effort data to conduct SEE, which is called as Heterogeneous Software Effort Estimation (HSEE), has not been well studied. In this paper, we propose a HSEE model, called Dynamic Heterogeneous Software Effort Estimation (i.e., DHSEE), which leverages the adversarial auto-encoder and convolutional neural network techniques. Meanwhile, we have investigated the scenario of conducting HSEE with dynamically increasing effort data. Experiments on ten public datasets indicate that our approach can significantly outperform the state-of-the-art HSEE method and other competing methods on both static and dynamic SEE scenarios.
An ESB-based Architecture for Authentication as a Service Through Enterprise Application Integration

AUTHaaS is a solution for various problems in an enterprise involving different software systems, each of which have a different authentication mechanism. Multiple usernames and passwords for a user, different security vulnerabilities for each software, and possible changes to the authentication mechanism are some of these problems. The solutions proposed for AUTHaaS are based on SOA. As communication in SOA is synchronous, the authentication process can confront problems if the authentication service is delayed for any reason. It is the purpose of this paper to answer these problems. In this paper, a security architecture is proposed for AUTHaaS through enterprise application integration. The core of the integration solution is the Enterprise Service Bus (ESB) technology. Proposed ESB-based architecture allows the user to authenticate only once for using different systems. Once the user is successfully authenticated for an application, other applications receive events through the ESB that indicate the user has successfully authenticated. So they do not need to be authenticated again by the authentication service for further access. The results show that after the 500th request, i.e. the second request of each user, the response time is reduced by 50% and the number of visits to the authentication server for subsequent requests of users will be zero.
Generalized Adversarial Distances to Efficiently Discover Classifier Errors

Given a black-box classification model and an unlabeled evaluation dataset from some application domain, efficient strategies need to be developed to evaluate the model. Random sampling allows a user to estimate metrics like accuracy, precision, and recall, but may not provide insight to high-confidence errors. High-confidence errors are rare events for which the model is highly confident in its prediction, but is wrong. Such errors can represent costly mistakes and should be explicitly searched for. In this paper we propose a generalization to the Adversarial Distance search that leverages concepts from adversarial machine learning to identify predictions for which a classifier may be overly confident. These predictions are useful instances to sample when looking for high-confidence errors because they are prone to a higher rate of error than expected. Our generalization allows Adversarial Distance to be applied to any classifier or data domain. Experimental results show that the generalized method finds errors at rates greater than expected given the confidence of the sampled predictions, and outperforms competing methods.
Application of Pre-installed Smart Box-type Substation in Engineering Project Management under the Environment of Internet of things

In order to improve the progress management of substation project, the application of prefabricated smart box substation in project management under the environment of Internet of things is studied. Based on the analysis of the research status of prefabricated smart box substation project management at home and abroad, combined with the characteristics of substation engineering construction management, the schedule management process is redefined, and the influencing factors of the whole process of substation project implementation progress in the early stage, the early stage and the construction stage are analyzed, and the fuzzy comprehensive evaluation method based on AHP is established The evaluation model of progress management in the whole process of substation construction project is established, the comprehensive quantitative method of schedule management is obtained, and the control countermeasures of substation project schedule management are proposed. And taking the construction of a substation project as an example, the project schedule was compiled, the schedule influencing factors of the various stages of the project were analyzed, and the effectiveness of the power station in the project schedule management work was evaluated, so as to provide future schedule management for similar projects. A basis worthy of reference.
Research on the Construction of Three Dimensional Learning Resources of Information Technology Curriculum in Ethnic Areas

Abstract The teaching of information technology course is of great significance to improve citizens’ information literacy. However, schools in ethnic areas have many deficiencies in terms of teachers, teaching ideas and concepts, software and hardware conditions. In this context, this paper studies the construction of three-dimensional teaching resources of information technology curriculum in ethnic areas. Firstly, it summarizes the concept and characteristics of three-dimensional teaching resources; secondly, it introduces the current situation of information technology curriculum teaching resources in ethnic areas; secondly, it introduces the current situation of information technology curriculum teaching resources in ethnic areas This paper discusses some problems that should be paid attention to in the construction and application of three-dimensional teacahing resources of information technology curriculum in ethnic areas, so as to provide beneficial reference for the reform and resource construction of information technology curriculum in primary and secondary schools in ethnic areas and other areas.
Statistical Distance-Based Acceptance Strategy for Desirable Offers in Bilateral Automated Negotiation

In any negotiation, one of the most important parts of the negotiator's task is deciding whether or not to accept the opponent's offer. Actually, the most challenging thing is answering this question: which offer and when must be accepted? A wide range of simple to sophisticated acceptance strategies have been proposed: simple acceptance strategies which have the constant threshold value and sophisticated strategies that notice both utility and time in order to determine acceptance thresholds. This study introduces a novel statistical acceptance strategy with considering the similarity between the opponent's offer and our previous offers, which is combined with existing usual acceptance strategies. Experiments show our strategy has advantages against the state-of-the-art acceptance strategies.
Research on New Methods and Technologies of Engineering and Environmental Geophysical Exploration Based on Internet of Things

Abstract After years of development, geophysical exploration in macro programming environment through the Internet of things has become a basic international model and has been widely valued and studied. Because of its special advantages in practice, the research of new geophysical methods and technologies has been introduced into the education of computer, law, economics, engineering and other disciplines. With the development of network technology, geophysical exploration of engineering and environment has been combined with network technology, forming problem-oriented research in network environment. In the continuous exploration, there is a stable inertia and activity process between the various elements, which can effectively find problems and solve problems. Therefore, through the Internet of things, this effective bridge combines engineering and environment with “new geophysical methods and technologies” to explore and develop new fields [1] .
A Translator from FDL to SystemVerilog for FPGA Implementation of Fuzzy Inference

Fuzzy inference has been used in a variety of fields, mainly machine control. Either special-purpose programming language or special-purpose development tool is usually used in software implementation of fuzzy inference. For example, EIAJ (Electronic Industries Association of Japan) standardized FDL (Fuzzy System Description Language) to describe fuzzy inference easily and concisely in Japan in 1996. FDL library and a translator named fdltoc to generate C language were developed at the same time. Therefore, it became possible to convert FDL to C language for software implementation. On the other hand, HDL (Hardware Description Language) is usually used in hardware implementation of fuzzy inference, because there is no hardware development environment for fuzzy inference using IF-THEN type rules and fuzzy sets. However, it is not easy for software engineers to design them with HDL. Moreover, it is not possible to generate HDL by high-level synthesis from C language translated by fdltoc, because C language generated by fdltoc has a description of dynamic memory allocation and uses libraries. This paper proposes a translator from FDL to SystemVerilog named FDLtoSV. The comparison result of software implementation using C language generated by fdltoc and hardware implementation using SystemVerilog generated by FDLtoSV with respect to speed show that the proposed hardware implementation result is about 102 times faster than software implementation result. In addition, its circuit scale is available in a low-cost FPGA.
Cross Scene Prediction via Modeling Dynamic Correlation using Latent Space Shared Auto-Encoders

This work addresses on the following problem: given a set of unsynchronized history observations of two scenes that are correlative on their dynamic changes, the purpose is to learn a cross-scene predictor, so that with the observation of one scene, a robot can onlinely predict the dynamic state of the other. A method is proposed to solve the problem via modeling dynamic correlation using latent space shared auto-encoders. Assuming that the inherent correlation of scene dynamics can be represented by shared latent space, where a common latent state is reached if the observations of both scenes are at an approximate time. A learning model is developed by connecting two auto-encoders through the latent space, and a prediction model is built by concatenating the encoder of the input scene with the decoder of the target one. Simulation datasets are generated imitating the dynamic flows at two adjacent gates of a campus, where the dynamic changes are triggered by a common working and teaching schedule. Similar scenarios can also be found at successive intersections on a single road, gates of a subway station, etc. Accuracy of cross-scene prediction is examined at various conditions of scene correlation and pairwise observations. Potentials of the proposed method are demonstrated by comparing with conventional end-to-end methods and linear predictions.
GCN with Clustering Coefficients and Attention Module

Graph convolutional networks (GCN) exploit graph connectivity through their adjacency matrix. However, the assignment of equal importance to every one-hop neighbor and incognizance of intra-neighbor connectivity restricts its performance. Graph attention networks (GAT) address the problem of treating all neighbors equally by employing a self-attention mechanism, which encodes the node of interest using the weighted features of its one-hop neighbors. This imparts appropriate similarity based weight between connected nodes. The learning of weights of nodes takes into consideration the intra-neighbor connectivity between nodes common to a neighborhood. However, this ignores the nature of neighborhood of the node of interest. In this work, we propose to encode the nature of the neighborhood of a node onto itself by appropriately weighing the node's self-loop. The local clustering coefficient of a graph explores to what extent which nodes in a graph tend to bunch together. A vertex with a low clustering coefficient and a large degree shows connectivity between different classes. Similarly, a vertex with a high clustering coefficient and low degree represents tightly coupled similar vertices belonging to the same class. Thus, the node self-loop is initially weighed with the local clustering coefficient. Experiment results on the benchmark graph dataset, including Cora, Citeseer, and Pubmed, shows that even with sparse two layers, the local clustering coefficient based self-loop outperforms the baseline GCN by ≈ 3%. The Ablation study and learning curve prove that the proposed model remains stable and learns the optimal feature representation in comparatively less number of epochs.
Dealing with Ambiguity in Online Customer Reviews: The Topic-Sentiment Method for Automated Content Analysis

Content analysis has become a widely used technique for the analysis of the large quantities of data that are generated online. Especially relevant for marketing researchers are customer reviews on websites such as TripAdvisor and Amazon, because they express customers’ satisfaction and they represent an important source of word-of-mouth for other consumers. Although the recent preference for sentence-constrained approaches has increased the accuracy of analytical methods, in many cases these methods still ignore some of the nuances contained within online reviews. In particular, current methods may not detect when a single topic is discussed both positively and negatively in a single review, or when a single sentence discusses two separate topics.The topic-sentiment method that is proposed in this paper addresses these two issues. It is a sentence-constrained approach that identifies ‘topic-sentiment pairs’; sentences that contain one word that describes the topic, and another that expresses the sentiment (positive or negative). To illustrate the analytical process, the method is applied to a dataset of 17,225 TripAdvisor reviews for restaurants in London. Results indicate that the topic-sentiment method offers a more nuanced approach for the analysis of customer reviews, while it retains the intuitiveness and simplicity of currently used methods.
A Novel Method for Satellite Monitoring With One-Dimension Feature Based on Autoencoder Model

In order to monitor all telemetry data, thresholds are adopted to judge the status of satellite. This method is terrible when some abnormal happened, if the data was not more than pre-set threshold. when the data exceeding the threshold after a period of time, there were a big fault for satellite. This fault would make a huge economic loss especially for the communicate satellite. These are two classes telemetry of satellite about this scenario, one class is continuously changing digital telemetry, the other class is temperature. A method was proposed for solving these problems. An autoencoder model was applied to monitor the telemetry data according to the devices or equipment board. Each device or equipment board has own model, and telemetry data is inputted to the model for compressing a single parameter as one-dimension feature. The operators just only monitor the one-dimension feature, that is simple and fast. If an abnormal appear, the parameter of device or equipment board would be changed to warn the operators, who would check the actual telemetry data of device or equipment board, and the abnormal would be checked out immediately and earlier than the traditional method. For detecting the two kinds of typical abnormal which could not detect by traditional method, two models were built and data was prepared. The results show that auto-decoder model can detect the abnormal accurately and be useful for the operator. A software was built, and some models were trained for a satellite.
A Novel Lossless Image Cryptosystem for Binary Images Using Feed-Forward Back-Propagation Neural Networks

Currently, one of the most challenging areas of research in communication systems is to build a secure communication channel. The existing public key cryptosystem appears to be more complex owing to the consumption of greater computational power. The neural net application is one of the promising developments in cryptography. This chapter examines the application of neural networks in binary image security. The proposed methodology works by encrypting individual sets of binary values for enhanced security with no loss in image quality. We have termed this novel scheme as Individual Pixel Encryption. The presented approach yields more security than the existing methods, which simply generate the cipher using neural networks by performing mix operations on the plaintext. Generally, working with individual pixels has its own share of complexity in terms of implementation and execution, which is why, it has yet to gain traction in the field of image encryption. But with our approach, we have tried to overcome this limitation using neural networks which are best suited for this purpose. The goal of this work is to realize an image cryptosystem using neural networks which is simple and efficient enough to be executed on a daily driver, robust enough that it does not compromise on security aspects and powerful enough that there is no loss in image quality. The proposed work finds its application in the fields of satellite imaging systems, medical imaging systems, military communications systems, social media and peer-to-peer image sharing, confidential imaging systems for civilian and enterprise and other such applications.
Easy Simple Factoring-based Digital Signature Scheme

A newly developed signature scheme, namely the Easy Simple Factoring-based (ESF) signature scheme is proposed in this paper. First, we construct a standard identification scheme based on the ESF assumption. Then, the standard identification scheme is converted to a signature scheme by utilizing the Fiat-Shamir transformation. Our signature scheme is provable secure in the random oracle model, and the security proof is shown. The efficiency analysis of our scheme is conducted and compared with the Full Domain Hash (FDH) signature scheme. Our scheme has a more powerful efficiency performance in signing and verifying algorithms than the FDH signature scheme when the public keys u and γ in our signature scheme are fixed to small bit lengths.