Reviewerly/exHarmony huggingface dataset:
CAPC: Packet-Based Network Service Classifier With Convolutional Autoencoder

The Internet has been evolving from a traditional mechanism to a modern service-oriented architecture, such as quality-of-service (QoS) policies, to meet users' various requirements for high service quality.An instant and effective network traffic classification method is indispensable to identify network services to enforce QoS policies on the corresponding service.Network managers can easily flexibly deploy traffic classification modules and configure the network policies with the help of the emerging softwaredefined networking.However, most existing traffic classification solutions, such as port-based methods or deep packet inspection, cannot handle real-time and encrypted traffic classification.In this research, a Convolutional Autoencoder Packet Classifier (CAPC) has been proposed to immediately classify incoming packets in fine-grained and coarse-grained manners, that is, classifying a service to a single application and a rough genre, respectively.The CAPC is a packet-based deep learning model consisting of a 1D convolutional neural network and an autoencoder, which can handle dynamic-port and encrypted traffic and even cluster similar applications.This classifier is verified on not only the private self-captured traffic but also a public VPN dataset to demonstrate its performance.Moreover, the CAPC classifies different types of service traffic with an accuracy of over 99.9% on the private dataset of 16 services and over 97% on the public dataset of 24 services, thereby outperforming other deep learning classifiers.Experimental results also show other performance metrics, including stability, average precision, and recall and the highest F1-score values of 15 and 18 services on the private and public datasets, respectively.
FPGA implementation of eight-direction Sobel edge detection algorithm based on adaptive threshold

Abstract As the basic content of digital image processing, edge detection is increasingly applied in machine vision, face recognition, target tracking and other fields. In particular, edge detection based on Sobel operator is generally applied in industrial practices. However, most current edge detections based on PC-side serial processing are extremely arduous to satisfy real-time processing. This paper proposes FPGA implementation of eight-direction Sobel edge detection algorithm based on adaptive threshold. Specifically, an eight-direction Sobel edge detection algorithm based on adaptive threshold is presented. Subsequently, the specific process of implementing the algorithm on FPGA using the design philosophy of parallel pipeline is described. Finally, a comparison of the performance between traditional Sobel and improved algorithm reveals that the improved algorithm offers distinct advantages in accuracy and detection effectiveness, and the parallel implementation of FPGA hardware can obviously enhance the detection speed and fulfil the real-time requirement.
Sketch-to-Art: Synthesizing Stylized Art Images from Sketches

We propose a new approach for synthesizing fully detailed art-stylized images from sketches. Given a sketch, with no semantic tagging, and a reference image of a specific style, the model can synthesize meaningful details with colors and textures. Based on the GAN framework, the model consists of three novel modules designed explicitly for better artistic style capturing and generation. To enforce the content faithfulness, we introduce the dual-masked mechanism which directly shapes the feature maps according to sketch. To capture more artistic style aspects, we design feature-map transformation for a better style consistency to the reference image. Finally, an inverse process of instance-normalization disentangles the style and content information and further improves the synthesis quality. Experiments demonstrate a significant qualitative and quantitative boost over baseline models based on previous state-of-the-art techniques, modified for the proposed task (17% better Frechet Inception distance and 18% better style classification score). Moreover, the lightweight design of the proposed modules enables the high-quality synthesis at \(512 \times 512\) resolution.
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records

An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018. Despite the common workflow structure appearing in these publications, no trusted and verified software framework exists, forcing researchers to arduously repeat previous work. In this paper, we propose Cardea, an extensible open-source automated machine learning framework encapsulating common prediction problems in the health domain and allows users to build predictive models with their own data. This system relies on two components: Fast Healthcare Interoperability Resources (FHIR) - a standardized data structure for electronic health systems - and several AU TOML frameworks for automated feature engineering, model selection, and tuning. We augment these components with an adaptive data assembler and comprehensive data- and modelauditing capabilities. We demonstrate our framework via 5 prediction tasks on MIMIC-III and KAGGLE datasets, which highlight Cardea's human competitiveness, flexibility in problem definition, extensive feature generation capability, adaptable automatic data assembler, and its usability.
K-Means Genetic Algorithms with Greedy Genetic Operators

The k-means problem is one of the most popular models of cluster analysis. The problem is NP-hard, and modern literature offers many competing heuristic approaches. Sometimes practical problems require obtaining such a result (albeit notExact), within the framework of the k-means model, which would be difficult to improve by known methods without a significant increase in the computation time or computational resources. In such cases, genetic algorithms with greedy agglomerative heuristic crossover operator might be a good choice. However, their computational complexity makes it difficult to use them for large-scale problems. The crossover operator which includes the k-means procedure, taking the absolute majority of the computation time, is essential for such algorithms, and other genetic operators such as mutation are usually eliminated or simplified. The importance of maintaining the population diversity, in particular, with the use of a mutation operator, is more significant with an increase in the data volume and available computing resources such as graphical processing units (GPUs). In this article, we propose a new greedy heuristic mutation operator for such algorithms and investigate the influence of new and well-known mutation operators on the objective function value achieved by the genetic algorithms for large-scale k-means problems. Our computational experiments demonstrate the ability of the new mutation operator, as well as the mechanism for organizing subpopulations, to improve the result of the algorithm.
Wireless Distributed Edge Learning: How Many Edge Devices Do We Need?

We consider distributed machine learning at the wireless edge, where a parameter server builds a global model with the help of multiple wireless edge devices that perform computations on local dataset partitions. Edge devices transmit the result of their computations (updates of current global model) to the server using a fixed rate and orthogonal multiple access over an error prone wireless channel. In case of a transmission error, the undelivered packet is retransmitted until successfully decoded at the receiver. Leveraging on the fundamental tradeoff between computation and communication in distributed systems, our aim is to derive how many edge devices are needed to minimize the average completion time while guaranteeing convergence. We provide upper and lower bounds for the average completion and we find a necessary condition for adding edge devices in two asymptotic regimes, namely the large dataset and the high accuracy regime. Conducted experiments on real datasets and numerical results confirm our analysis and substantiate our claim that the number of edge devices should be carefully selected for timely distributed edge learning.
Transfer Learning for Gender and Age Prediction

In this work, we propose a transfer learning pipeline for gender and age prediction using images from IMDB-WIKI dataset. Firstly, we freeze all layers in pre-trained ImageNet models. Then, the models are trained for four stages with scheduled learning rates and the blocks of layers are unlocked consecutively in accordance to the schedule. We apply multi-output neural network paradigm to predict age and gender simultaneously and the final loss function is based on the combination of age and gender losses. In our approach, the model has better performance than that of the non-pre-trained model because the later stages of our models reuse features extracted from the pre-trained early stages.
Dimensions of Consistency in GSD: Social Factors, Structures and Interactions

Global software development (GSD) implies a distributed development organization, where coordination is needed to efficiently achieve development objectives. So far, socio-technical congruence has examined coordination needs and activities through software code dependencies. However, GSD requires coordination beyond software artifacts. In this paper, we present an interview-based study of software practitioners from companies engaged in GSD. The study examines how different dimensions of interactions are interrelated, and how they affect software development. Our study suggests that, in addition to the relationship between organizational and technical system structure, GSD performance is affected by consistency in communication, operational procedures, and social structures. These can only partially be impacted through formal procedures, and we suggest that distributing coordination work by empowering developers could lead to increased performance.
Two Semi-Supervised Training Approaches for Automated Text Recognition

Automated text recognition is a fundamental problem in Document Image Analysis. Optical models are used for modeling characters while language models are used for composing sentences. Since the scripts and linguistic context differ widely, it is mandatory to specialize the models by training on task-dependent ground-truth. However, to create a sufficient amount of ground-truth, at least for historical handwritten scripts, well-qualified persons have to mark and transcribe text lines, which is very time-consuming. On the other hand, in many cases unassigned transcripts are already available on page-level from another process chain, or at least transcripts from similar linguistic context are available. In this work we present two approaches that make use of such transcripts: whereas the first one creates training data by automatically assigning page-dependent transcripts to text lines, the second one uses a task-specific language model to generate highly confident training data. Both approaches are successfully applied on a very challenging historical handwritten collection.
Biomedical Question Answering: A Survey of Methods and Datasets

Thousands of biomedical research papers are published each day. Now, it takes more time than ever for researchers and healthcare information professionals to find relevant information from this huge literature. Classical information retrieval (IR) systems such as PubMed, while helpful, still provide a large number of search results that needs to be examined manually. Biomedical Question answering (QA) systems on the other hand can extract and provide one direct and exact answer to a question. While huge progress has been made recently in general-domain QA. Biomedical QA still has a long way to go. The number one reason for the slow progress of biomedical QA compared to general-domain QA, is the limited number of biomedical QA datasets and the even smaller number of annotated training instances. The goal of this survey is to list and compare the available biomedical QA datasets, and to provide an overview of traditional and end-to-end neural-based biomedical QA systems.
Recommendation Algorithm of Crowdfunding Platform Based on Collaborative Filtering

Abstract With the continuous progress of Internet technology, the crowdfunding platform has become a new way of network financing. While the generated data keeps increasing, its benefit does not increase in a proportional way, resulting in the “information overload” phenomenon. The personalized recommendation system can solve this problem by mining users’ interests and preferences from a large amount of data. It has achieved success in many fields. This paper applies machine learning algorithm to build a recommendation system based on collaborative filtering. The designed personalized recommendation algorithm can provide accurate and rapid personalized recommendation services, which is convenient for users and conducive to the development of the crowdfunding platform. In addition, this paper uses the data from the crowdfunding platform in practice to complete the performance verification of the algorithm.
SNI-in-the-head: Protecting MPC-in-the-head Protocols against Side-channel Analysis

MPC-in-the-head based protocols have recently gained much popularity and are at the brink of seeing widespread usage. With widespread use come the spectres of implementation issues and implementation attacks such as side-channel attacks. We show that implementations of protocols implementing the MPC-in-the-head paradigm are vulnerable to side-channel attacks. As a case study, we choose the ZKBoo-protocol of Giacomelli, Madsen, and Orlandi (USENIX 2016) and show that even a single leaked value is sufficient to break the security of the protocol. To show that this attack is not just a theoretical vulnerability, we apply differential power analysis to show the vulnerabilities via a simulation.
Computer-Aided Detection and Diagnosis of Thyroid Nodules Using Machine and Deep Learning Classification Algorithms

This paper proposes a computer-aided methodology for detecting and segmenting the tumor regions in ultrasound thyroid images using machine and deep learning algorithms. This proposed tumor detection methodology uses Kirsch's edge detector for enhancing the edge region pixels in thyroid image and then Dual Tree Contourlet Transform (DTCT) was applied on the enhanced image for obtaining the coefficients. Then, features are computed from this transformed thyroid image and these features are trained and classified using the Co-Active Adaptive Neuro Expert System (CANFES) classifier. Then, the morphological segmentation method is applied on the abnormal thyroid image to segment the tumor regions. Finally, the Convolutional Neural Network (CNN) algorithm is applied on the segmented tumor regions for diagnosing them into mild, moderate and severe.
Hap-Cp-Abe Based Encryption Technique With Hashed Access Policy Based Authentication Scheme For Privacy Preserving Of Phr

As a novel abridged record of patients' medical data, PHR would be accessed online. Nevertheless, this very sensitive private information might be attained through illegal parties as of the cloud. Numerous existent algorithms utilized CP-ABE to protect the data as of unapproved users, however, it loses the high-efficiency against the conventional methods on account of the security parameters’ requirement of large size. To surmount these issues, a HAP-CP-ABE-based encryption technique with HAP based authentication scheme intended for PHR is proposed. The proposed work encompasses '3′ phases: (A) the authentication phase, which includes verification before the user uploads the PHR files. (B) Secure upload phase: initially, the features are all extracted as of the PHR files. Then, the features are lessened as of the extracted features utilizing ENT-LDA. Then, HAP will be created using SHA-512. Lastly, securely upload PHR to cloud utilizing HAP-CP-ABE. (C) In the secure download phase, primarily, the system corroborates the HAP for the requested user. If it is matched, then it permits them to access, or else the system declined permission. After authentication, the system permits the requested user to download the file. Extensive analysis along with investigational outcomes shows the proposed system's security together with efficiency.
Impacts of noise and structure on quantum information encoded in a quantum memory

As larger, higher-quality quantum devices are built and demonstrated in quantum information applications, such as quantum computation and quantum communication, the need for high-quality quantum memories to store quantum states becomes ever more pressing. Future quantum devices likely will use a variety of physical hardware, some being used primarily for processing of quantum information and others for storage. Here, we study the correlation of the structure of quantum information with physical noise models of various possible quantum memory implementations. Through numerical simulation of different noise models and approximate analytical formulas applied to a variety of interesting quantum states, we provide comparisons between quantum hardware with different structure, including both qubit- and qudit-based quantum memories. Our findings point to simple, experimentally relevant formulas for the relative lifetimes of quantum information in different quantum memories and have relevance to the design of hybrid quantum devices.
SGNet: Semantics Guided Deep Stereo Matching

Stereovision has been an intensive research area of computer vision. Based on deep learning, stereo matching networks are becoming popular in recent years. Despite of great progress, it’s still challenging to achieve high accurate disparity map due to low texture and illumination changes in the scene. High-level semantic information can be helpful to handle these problems. In this paper a deep semantics guided stereo matching network (SGNet) is proposed. Apart from necessary semantic branch, three semantic guided modules are proposed to embed semantic constraints on matching. The joint confidence module produces confidence of cost volume based on the consistency of disparity and semantic features between left and right images. The residual module is responsible for optimizing the initial disparity results according to its semantic categories. Finally, in the loss module, the smooth of disparity is well supervised based on semantic boundary and region. The proposed network has been evaluated on various public datasets like KITTI 2015, KITTI 2012 and Virtual KITTI, and achieves the state-of-the-art performance.
Evaluating card-based protocols in terms of execution time

Abstract Card-based cryptography is an attractive and unconventional computation model; it provides secure computing methods using a deck of physical cards. It is noteworthy that a card-based protocol can be easily executed by non-experts such as high school students without the use of any electric device. One of the main goals in this discipline is to develop efficient protocols. The efficiency has been evaluated by the number of required cards, the number of colors, and the average number of protocol trials. Although these evaluation metrics are simple and reasonable, it is difficult to estimate the total number of operations or execution time of protocols based only on these three metrics. Therefore, in this paper, we consider adding other metrics to estimate the execution time of protocols more precisely. Furthermore, we actually evaluate some of the important existing protocols using our new criteria.
A Novel Approach for Road Accident Detection using DETR Algorithm

Road accidents are man-made cataclysmic phenomena and are not generally predictable. With increasing numbers of deaths due to accidents in the roadways, a smart and fast detection system for road accidents is the need of the hour. Often, precious few seconds after the accidents make the difference between life and death. To address this problem more efficiently, “A Novel Approach for Road Accident Detection in CCTV Videos using DETR Algorithm” has been developed to aid in notifying hospitals and the local police at places where instant notification is seldom feasible. This paper presents a novel and efficient method for detecting road accidents with DETR (Detection Transformers) and Random Forest Classifier. Objects such as cars, bikes, people, etc. in the CCTV footage are detected using the DETR and the features are fed to a Random Forest Classifier for frame wise classification. Each frame of the video is classified as an accident frame or a non-accident frame. A total count of predicted accident frames from any 60 continuous frames of the video are considered using a sliding window technique before the final decision is made. Simulation results show that the proposed system achieves 78.2% detection rate in CCTV videos.
Neural Image Compression and Explanation

Explaining the prediction of deep neural networks (DNNs) and semantic image compression are two active research areas of deep learning with a numerous of applications in decision-critical systems, such as surveillance cameras, drones and self-driving cars, where interpretable decision is critical and storage/network bandwidth is limited. In this article, we propose a novel end-to-end Neural Image Compression and Explanation (NICE) framework that learns to (1) explain the predictions of convolutional neural networks (CNNs), and (2) subsequently compress the input images for efficient storage or transmission. Specifically, NICE generates a sparse mask over an input image by attaching a stochastic binary gate to each pixel of the image, whose parameters are learned through the interaction with the CNN classifier to be explained. The generated mask is able to capture the saliency of each pixel measured by its influence to the final prediction of CNN; it can also be used to produce a mixed-resolution image, where important pixels maintain their original high resolution and insignificant background pixels are subsampled to a low resolution. The produced images achieve a high compression rate (e.g., about 0.6x of original image file size), while retaining a similar classification accuracy. Extensive experiments across multiple image classification benchmarks demonstrate the superior performance of NICE compared to the state-of-the-art methods in terms of explanation quality and semantic image compression rate. Our code is available at: https://github.com/lxuniverse/NICE.
Advantages and disadvantages of using cryptography in steganography

The use of cryptographic algorithms varies based on the type of application. Also have different uses for steganography algorithms based on media type, format and capacity. Therefore, the combination of these two technologies has definitely had a special sensitivity that must be properly examined. The use of cryptographic algorithms in steganographic systems increases the security of hidden data. But this security should not make the entropy more visible. In this paper presents two steganography algorithms, so that one cryptographic algorithm will be used to encrypt the message before the steganography operation. In the following, the advantages of using and not using these cryptographic algorithms against its disadvantages are reviewed and suggestions about their use in steganography algorithms and systems are presented.
To touch or not to touch? Comparing Touch, mid-air gesture, mid-air haptics for public display in post COVID-19 society

We developed a mid-air touch Mixed Reality application which combined hand tracking sensing and haptic feedback on a desktop display. We evaluated three hand interaction techniques: 1) Touch, 2) Mid Air Gesture Touch, 3) Mid Air Haptic Touch through preliminary user testing with ten adults. Results suggest the user's willingness to use self-service devices in public places increases in the post COVID-19 world, while their concerns about the possibility of getting in touch with the virus reduces. However before the large scale deployment of this technology, accuracy and user experience design need to be improved.