Reviewerly/exHarmony huggingface dataset:
Object Detection of Optical Remote Sensing Image Based on Improved Faster RCNN

Object detection of optical remote sensing image is an important and challenging problem. And it is widely used in the field of aerial and satellite image analysis. With the rapid increase of optical remote sensing image data and popularity of convolutional neural network, the problem has attracted lots of attention recently. However, the detection result of images with complex background is unsatisfactory, so as images with dense and small objects. Aiming at these problems, we propose a method that combined Feature Pyramid Network(FPN) and Deformable Convolution Network(DCN) to improve the Faster RCNN framework, which helps to improve the detection result. The improved network combines the low-level structural information and the high-level semantic information together to enhance the feature representation. The shared convolutional layer makes end-to-end training come true. Additionally, deformable convolution network makes feature extraction better. We adopt the proposed framework to implement experiments on DOTA dataset, attaining mean average precision(mAP)value of 0.834 on the testing dataset, which is an increase of 23% than the classic Faster RCNN.
Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption

This work introduces a fast and lightweight homomorphic-encryption pipeline that enables privacy-preserving machine learning for phishing web page recognition. The primary goals are to use visual features to train an accurate model and to implement an inference pipeline with practical runtime and communication costs. To do so, we deploy a variety of techniques that cover deep learning and optical character recognition to extract salient visual features, and optimize the inner mechanisms of state-of-the-art homomorphic encryption schemes to reduce the encryption-related costs. Our presented system is able to achieve over 90% on the visual classification task, while using less than 250 KB of communication bandwidth and around 0.7 seconds of computation time. We hope our work not only demonstrates a private visual phishing detection pipeline, but also outlines techniques to practically utilize homomorphic encryption in a variety of machine learning tasks.
One-step spectral clustering based on self-paced learning

Aiming at traditional spectral clustering method still suffers from the following issues: 1) unable to handle the incomplete data, 2) two-step clustering strategies tend to perform poorly due to the heterogeneity between the similarity matrix learning model and the clustering model, 3) constructing the affinity matrix from original data which often contains noises and outliers. To address these issues, this paper proposes a robust one-step clustering method based on self-paced learning. Specifically, this paper first designs a missing value mapping matrix to handle missing data, and then employs self-paced regularizer to sort the importance of samples, reduce the interference of noise and outliers of the model, second, fusing affinity matrix learning and spectral decomposition into one model to achieve the purpose of one-step clustering. Experimental results on 8 real data sets, verified the effectiveness of our proposed one-step spectral clustering method, compared to state-of-the-art methods.
Speaker Adaptation of a Multilingual Acoustic Model for Cross-Language Synthesis

Several studies have shown promising results in adapting DNNbased acoustic models as a mechanism to transfer characteristics from pre-trained models.One such example is speaker adaptation using a small amount of data, where fine-tuning has helped train models that extrapolate well to diverse linguistic contexts that are not present in the adaptation data.In the current work, our objective is to synthesize speech in different languages using the target speaker's voice, regardless of the language of their data.To achieve this goal, we create a multilingual model using a corpus that consists of recordings from a large number of monolingual and a few bilingual speakers in multiple languages.The model is then adapted using the target speaker's recordings in a language other than the target language.We also explore if additional speech data from a native speaker of the target language improves the performance.The subjective evaluations show that the proposed approach of cross-language speaker adaptation is able to synthesize speech in the target language, in the target speaker's voice, without data spoken by the target speaker in that language.Also, extra data from a native speaker of the target language can improve model performance.
Dfsmn-San with Persistent Memory Model for Automatic Speech Recognition

Self-attention networks (SAN) have been introduced into automatic speech recognition (ASR) and achieved state-of-the-art performance owing to its superior ability in capturing long term dependency. One of the key ingredients is the self-attention mechanism which can be effectively performed on the whole utterance level. In this paper, we try to investigate whether even more information beyond the whole utterance level can be exploited and beneficial. We propose to apply self-attention layer with augmented memory to ASR. Specifically, we first propose a variant model architecture which combines deep feed-forward sequential memory network (DFSMN) with self-attention layers to form a better baseline model compared with a purely self-attention network. Then, we propose and compare two kinds of additional memory structures added into self-attention layers. Experiments on large-scale LVCSR tasks show that on four individual test sets, the DFSMN-SAN architecture outperforms vanilla SAN encoder by 5% relatively in character error rate (CER). More importantly, the additional memory structure provides further 5% to 11% relative improvement in CER.
Enablers and Barriers for Mobile Commerce and Banking Services Among the Elderly in Developing Countries: A Systematic Review

The rollout of mobile banking has taken over the traditional banking space in both developed and developing countries. Banking institutions are continuously seeking innovative digital solutions to stay ahead of competitors and be the first preference to consumers. Scholars have also shown some interest in investigating mobile commerce and banking implementation and adoption. However, research studies focusing on the elderly and mobile banking are scant. This paper seeks to understand the current state of knowledge regarding the enablers and barriers of mobile banking and commerce among the elderly by providing a systematic review of the existing literature on the phenomenon. The literature review showed that there is minimal research to date that has been conducted on this topic of interest. Consequently, issues of investigating enablers and barriers of mobile banking among the elderly have been fairly neglected. The main barriers noted in the literature include: security concerns, trust and privacy, a lack of personalization and limited technical knowledge of the elderly. Significant enablers that may be considered are: perceived ease of use of mobile banking applications, perceived value, convenience and consumer attitudes. Future directions for research and practice on mobile banking for the elderly are suggested.
Assuring the Integrity of Videos from Wireless-Based IoT Devices using Blockchain

Blockchain technology has drawn attention from various communities. The underlying consensus mechanism in Blockchain enables a myriad of applications for the integrity assurance of stored data. In this paper, we utilize Blockchain technology to verify the authenticity of a video captured by a streaming IoT device for forensic investigation purposes. The proposed approach computes the hash of video frames before they leave the IoT device and are transferred to a remote base station. To guarantee the transmission, we ensure that this hash is sent through a TCP-based connection. The hash is then stored on multiple nodes on a permissioned blockchain platform. In case the video is modified, the discrepancy will be detected by investigating the previously stored hash on the blockchain and comparing it with the hash of the existing frame in question. In this work, we present the prototype as proof-of-concept with experiment results. The system has been tested on a RaspberryPi with different quality of videos to evaluate performance. The results show that the concept can be implemented with moderate video resolutions.
Autonomic Resource Provisioning Framework for Service-based Cloud Applications: A Queuing-Model Based Approach

The user's request changes dynamically in service-based cloud applications, which requires optimal amount of computing resources to meet service-level agreements (SLAs). The existing server-side resource allocation mechanisms have limitations in provisioning the required resources to handle the incoming load on the basis of user's requests. To overcome the aforementioned situation, cloud computing provides ample amount of computing resources to meet the SLAs. There are possibilities that cloud resources might not be properly utilized and might suffer over and under utilization. In this study, the authors have proposed an autonomic resource allocation framework, that automatically provisions (allocate and deallocate) the required computing resources as per the load. In this study, the proposed model leverages the queuing model to optimize the resource allocation process. The primary goal of this study is to improve the virtual resource utilization and response time with respect to the existing methods. Finally, the results have shown that the response time and resource utilization have been improved.
End-To-End Spoken Language Understanding Without Matched Language Speech Model Pretraining Data

In contrast to conventional approaches to spoken language understanding (SLU) that consist of cascading a speech recognizer with a natural language understanding component, end-to-end (E2E) approaches for SLU infer semantics directly from the speech signal without processing it through separate subsystems. Pretraining part of the E2E models for speech recognition before finetuning the entire model for the target SLU task has proven to be an effective method to address the increased data requirements of E2E SLU models. However, transcribed corpora in the target language and domain may not always be available for pretraining an E2E SLU model. This paper proposes two strategies to improve the performance of E2E SLU models in scenarios where transcribed data for pretraining in the target language is unavailable: multilingual pretraining with mismatched languages and data augmentation using SpecAugment[1]. We demonstrate the effectiveness of these two methods for E2E SLU on two datasets, including one recently released publicly available dataset where we surpass the best previously published result despite not using any matched language data for pretraining.
Performance Evaluation of Load Balancing Algorithms Using Cloud Analyst

Today cloud computing is the trending technology and has been proven as a viable business model. We can see fastest growth in the recent years because of it's easy to access mechanism. As the number of cloud users are increasing exponentially, so to handle this, the concept of load balancing is needed to minimize the overhead and maximize the throughput on the cloud. In this paper, we analyze three cloud algorithms namely Round Robin, Throttled, Equally Spread Current Execution Load and their performance in terms of average response time, hourly data center response time and the cost of Virtual Machine (VM) etc. with the help of Cloud Analyst simulator. Cloud Analyst is the simulator which is best among all simulator for algorithm testing in cloud environment. Simulation results demonstrate that Throttled outperforms among these algorithms as number of users increases.
Automated Hand Gesture Recognition using a Deep Convolutional Neural Network model

The tremendous growth in the domain of deep learning has helped in achieving breakthroughs in computer vision applications especially after convolutional neural networks coming into the picture. The unique architecture of CNNs allows it to extract relevant information from the input images without any hand-tuning. Today, with such powerful models we have quite a flexibility build technology that may ameliorate human life. One such technique can be used for detecting and understanding various human gestures as it would make the human-machine communication effective. This could make the conventional input devices like touchscreens, mouse pad, and keyboards redundant. Also, it is considered as a highly secure tech compared to other devices. In this paper, hand gesture technology along with Convolutional Neural Networks has been discovered followed by the construction of a deep convolutional neural network to build a hand gesture recognition application.
A QoS Aware Approach to Service-Oriented Communication in Future Automotive Networks

Service-Oriented Architecture (SOA) is about to enter automotive networks based on the SOME/IP middleware and an Ethernet high-bandwidth communication layer. It promises to meet the growing demands on connectivity and flexibility for software components in modern cars. Largely heterogeneous service requirements and time-sensitive network functions make Quality-of-Service (QoS) agreements a vital building block within future automobiles. Existing middleware solutions, however, do not allow for a dynamic selection of QoS. This paper presents a service-oriented middleware for QoS aware communication in future cars. We contribute a protocol for dynamic QoS negotiation along with a multi-protocol stack, which supports the different communication classes as derived from a thorough requirements analysis. We validate the feasibility of our approach in a case study and evaluate its performance in a simulation model of a realistic in-car network. Our findings indicate that QoS aware communication can indeed meet the requirements, while the impact of the service negotiations and setup times of the network remain acceptable provided the cross-traffic during negotiations stays below 70% of the available bandwidth.
The Adversarial Attacks Threats on Computer Vision: A Survey

Deep learning has shown impressive performance in various complex problems which difficult to the conventional machine learning algorithms, especially in such domains: Medical diagnosis, self-driving, identity verification, social media, and agriculture. However, based on recent research, deep learning algorithms are vulnerable to adversarial attacks with slightly crafted perturbations. The adversarial examples mislead the classifiers to targeted or non-targeted categories, and the perturbations are challenging to be perceptible from the human perspective. The threat raises the severe security problem in deep learning applications. This study aims to summarize recent research for generating adversarial examples in different aspects and reviewed the applications in the physical world. We further considered the difficulties of model robustness and potential countermeasures.
Mobile Robot Path Planning Based Upon Updated Whale Optimization Algorithm

Automated navigation is an important feature of any mobile robot and is a challenge for a robot to plan the path in an unknown environment. We can categorize the path planning strategy of robots in two methods, first is the Classical Methods and second is Heuristic Methods. In this paper, a modified whale optimization algorithm is proposed that ensures an optimal collision-free path. In whale optimization algorithm (WOA) the fitness of any whale will be calculated by taking in consideration the target location and the obstacles in the search space. Many simulations were run in different search spaces to find the optimal path at the end of the iterations when the individual reaches the final destination (target location). The simulation and results show, the proposed modified WOA is feasible for mobile robot path selection problems.
Sentiment Analysis Using Support Vector Machine

Feedback in business is very important because it tells the owner about the condition of their service and then complying with the feedback; owner can modify or upgrades the service quality. Nowadays it becomes simple to give feedback for any kind of service. This feedback can be referred as the sentiment of the customer about the services. In this paper, we are going to analyse this feedback and study how it is extracted for the benefit of the owner. This sentiment analysis is done by various methods and in this paper, we will focus one of those methods named support vector machine method. We will study this method and then implement this method and then analyse the outcome. In this paper we will be using weka to evaluate accuracy of the dataset and then we will analyse the end results.
An Ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers

As automatic speaker recognizer systems become mainstream, voice spoofing attacks are on the rise. Common attack strategies include replay, the use of text-to-speech synthesis, and voice conversion systems. While previouslyproposed end-to-end detection frameworks have shown to be effective in spotting attacks for one particular spoofing strategy, they have relied on different models, architectures, and speech representations, depending on the spoofing strategy. In practice, however, one does not have a priori information regarding the strategy an attacker might employ to fool a speaker recognizer, thus it is necessary to devise approaches which are able to detect attacks regardless of the strategy employed to generate them. In this work, we introduce an end-to-end ensemble based approach such that two models - previously shown to perform well on each considered attack strategy - are trained jointly, while a third model learns how to mix their outputs yielding a single score. Experimental results with replay and text-to-speech/voice conversion attacks show the proposed ensemble method achieving similar or superior performance when compared to systems specialized on each spoofing strategy separately.
EDNFC-Net: Convolutional Neural Network with Nested Feature Concatenation for Nuclei-Instance Segmentation

Accurate nuclei identification is an important step in diagnosis of several diseases. The problem is complex due to heterogeneity in structure, color, and texture among the different categories of cells. The problem is further complicated due to overlapped/clustered nuclei. To address these challenges, we propose an Encoder-Decoder based Convolutional Neural Network (CNN) with Nested-Feature Concatenation (EDNFC-Net) for automatic nuclei segmentation. The feature concatenation cell (FCC) of the EDNFC-Net is made up of two stacks of convolutional filters combined with non-linearity, followed by a concatenation of features. Apart from intra-FCC feature concatenation, a mechanism is also provided for inter-FCC feature concatenation. This arrangement leads to better feature flow and feature-reusability. Similarly, direct feature flow is provided between the encoder and decoder module that preserves the context information. A new loss function with better-penalizing capability is also proposed that helps in the better background and foreground separation. Qualitative and quantitative results are provided on two datasets to validate the proposed architecture and loss function.
Software Defect Prediction based on Machine Learning Algorithms

Software defect prediction is very important in software engineering. It uses the defects found in historical software modules to predict defects in new software modules, and provides decision support for planning and process management in software project. Machine learning is one of the core research directions in the field of artificial intelligence and covers many disciplines. In the research of software defect prediction, various machine learning methods have been widely studied and applied, and have been verified to obtain good performance. In this paper, the basic framework of software defect prediction model based on machine learning is described, several important prediction models are studied and discussed. Finally, the main problems of software defect prediction are summarized, and corresponding solutions are proposed.
Cross Lingual Transfer Learning for Zero-Resource Domain Adaptation

We propose a method for zero-resource domain adaptation of DNN acoustic models, for use in low-resource situations where the only in-language training data available may be poorly matched to the intended target domain. Our method uses a multi-lingual model in which several DNN layers are shared between languages. This architecture enables domain adaptation transforms learned for one well-resourced language to be applied to an entirely different low- resource language. First, to develop the technique we use English as a well-resourced language and take Spanish to mimic a low-resource language. Experiments in domain adaptation between the conversational telephone speech (CTS) domain and broadcast news (BN) domain demonstrate a 29% relative WER improvement on Spanish BN test data by using only English adaptation data. Second, we demonstrate the effectiveness of the method for low-resource languages with a poor match to the well-resourced language. Even in this scenario, the proposed method achieves relative WER improvements of 18-27% by using solely English data for domain adaptation. Compared to other related approaches based on multi-task and multi-condition training, the proposed method is able to better exploit well-resource language data for improved acoustic modelling of the low-resource target domain.
Edgefool: an Adversarial Image Enhancement Filter

Adversarial examples are intentionally perturbed images that mislead classifiers. These images can, however, be easily detected using denoising algorithms, when high-frequency spatial perturbations are used, or can be noticed by humans, when perturbations are large. In this paper, we propose EdgeFool, an adversarial image enhancement filter that learns structure-aware adversarial perturbations. Edge-Fool generates adversarial images with perturbations that enhance image details via training a fully convolutional neural network end-to-end with a multi-task loss function. This loss function accounts for both image detail enhancement and class misleading objectives. We evaluate EdgeFool on three classifiers (ResNet-50, ResNet-18 and AlexNet) using two datasets (ImageNet and Private-Places365) and compare it with six adversarial methods (DeepFool, SparseFool, Carlini-Wagner, SemanticAdv, Non-targeted and Private Fast Gradient Sign Methods). Code is available at https://github.com/smartcameras/EdgeFool.git.
Estimating Centrality Blindly From Low-Pass Filtered Graph Signals

This paper considers blind methods for centrality estimation from graph signals. We model graph signals as the outcome of an unknown low-pass graph filter excited with influences governed by a sparse sub-graph. This model is compatible with a number of data generation process on graphs, including stock data and opinion dynamics. Based on the said graph signal model, we first prove that the folklore heuristics based on PCA of data covariance matrix may fail when the graph filter is not sufficiently low-pass. To remedy, we propose a robust blind centrality estimation method which substantially improves the centrality estimation performance. Numerical results on synthetic and real data support our findings.