Reviewerly/exHarmony huggingface dataset:
Tail Latency in Datacenter Networks

One of the major challenges in cloud service data centers is to satisfy service-level agreements without significant over-provisioning. Achieving predictable performance is critical for many interactive applications. While the focus, particularly in theoretical models, has been on reducing average latency, the skewed tail of the latency distribution is much harder to reduce despite over-provisioning. In this paper, we take two approaches to mitigate tail latency in data centers. The first approach is based on bridging selected edge and aggregate switches to reduce east-west traffic latency. The second approach is based on task scheduling dependent tasks via their dependency acyclic graph. A queuing network model has been developed which can be used to reduce the average latency. Numerical and simulation results have shown the techniques are effective in terms of reducing the average tail latency within a data center network.
Privacy Preserving Approaches for Online Social Network Data Publishing

Recently, the social network platforms have gained the attention of people worldwide. People post, share, and update their views freely on such platforms. The huge data contained on social networks are utilized for various purposes like research, market analysis, product popularity, prediction, etc. Although it provides so much useful information, it raises the issue regarding user privacy. This chapter discusses the various privacy preservation methods applied to the original social network dataset to preserve privacy against attacks. The two areas for privacy preservation approaches addressed in this chapter are anonymization in social network data publication and differential privacy in node degree publishing.
Application of data Mining in Building Safety Construction

Under the background of big data era, the safety management of construction enterprises should turn their attention to big data, enhance the depth and breadth of information utilization, and explore the safety management of construction enterprises in big data era. In the face of a large number and complex structure of safety production data, the traditional safety management methods can't be used effectively, which leads to the waste of a lot of valuable information and restricts the in-depth understanding and research of improving the level of safety management. According to the characteristics of production safety data, this paper puts forward a data mining algorithm based on natural language processing technology and K-means and Aprior algorithm, combined with visual data mining technology to realize the in-depth analysis of production safety data of a construction project in Shandong Province in 2020.The results show that the distribution law of safety problems in the process of safety production in construction enterprises is obvious, and there is an important relationship between safety problems. If targeted safety management measures are taken, it is bound to improve the efficiency of safety management in construction enterprises.
Determinantal consensus clustering

Random restart of a given algorithm produces many partitions that can be aggregated to yield a consensus clustering. Ensemble methods have been recognized as more robust approaches for data clustering than single clustering algorithms. We propose the use of determinantal point processes or DPPs for the random restart of clustering algorithms based on initial sets of center points, such as k-medoids or k-means. The relation between DPPs and kernel-based methods makes DPPs suitable to describe and quantify similarity between objects. DPPs favor diversity of the center points in initial sets, so that sets with similar points have less chance of being generated than sets with very distinct points. Most current inital sets are generated with center points sampled uniformly at random. We show through extensive simulations that, contrary to DPPs, this technique fails both to ensure diversity, and to obtain a good coverage of all data facets. The latter are two key properties that make DPPs achieve good performance. Simulations with artificial datasets and applications to real datasets show that determinantal consensus clustering outperforms consensus clusterings which are based on uniform random sampling of center points.
SEM Image Quality Assessment Based on Texture Inpainting

This letter presents an image quality assessment (IQA) metric for scanning electron microscopy (SEM) images based on texture inpainting. Inspired by the observation that the texture information of SEM images is quite sensitive to distortions, a texture inpainting network is first trained to extract texture features. Then the weights of the trained texture inpainting network are transferred to the IQA network to help it learn an effective texture representation of the distorted image. Finally, supervised fine-tuning is conducted on the IQA network to predict the image quality score. Experimental results on the SEM image quality dataset demonstrate the advantages of the presented method.
SFM Signal Parameter Estimation Based on Sinusoidal Chirplet Transform

This paper describes a sinusoidal chirplet transform (SCT) to estimate the parameters of a sinusoidal frequency modulated (SFM) signal. With the idea of parametric time-frequency (TF) analysis, the TF concentration could be greatly improved. Moreover, the iteration process provides more accurate and refined parameter estimation results. Numerical evaluation shows the SCT can provide a better TF concentration and achieve balance between estimation accuracy and computational complexity.
Novel Graph-Based Model With Biaffine Attention for Family History Extraction From Clinical Text: Modeling Study

Background Family history information, including information on family members, side of the family of family members, living status of family members, and observations of family members, plays an important role in disease diagnosis and treatment. Family member information extraction aims to extract family history information from semistructured/unstructured text in electronic health records (EHRs), which is a challenging task regarding named entity recognition (NER) and relation extraction (RE), where named entities refer to family members, living status, and observations, and relations refer to relations between family members and living status, and relations between family members and observations. Objective This study aimed to introduce the system we developed for the 2019 n2c2/OHNLP track on family history extraction, which can jointly extract entities and relations about family history information from clinical text. Methods We proposed a novel graph-based model with biaffine attention for family history extraction from clinical text. In this model, we first designed a graph to represent family history information, that is, representing NER and RE regarding family history in a unified way, and then introduced a biaffine attention mechanism to extract family history information in clinical text. Convolution neural network (CNN)-Bidirectional Long Short Term Memory network (BiLSTM) and Bidirectional Encoder Representation from Transformers (BERT) were used to encode the input sentence, and a biaffine classifier was used to extract family history information. In addition, we developed a postprocessing module to adjust the results. A system based on the proposed method was developed for the 2019 n2c2/OHNLP shared task track on family history information extraction. Results Our system ranked first in the challenge, and the F1 scores of the best system on the NER subtask and RE subtask were 0.8745 and 0.6810, respectively. After the challenge, we further fine tuned the parameters and improved the F1 scores of the two subtasks to 0.8823 and 0.7048, respectively. Conclusions The experimental results showed that the system based on the proposed method can extract family history information from clinical text effectively.
Learning Pairwise Preferences from Movie Ratings

Preference Elicitation is now considered a crucial stage in recommender system. That is the stage where we collect and query preferences of the users as part of interactive decision support system. Preference elicitation can be performed using many ways, such as asking the user explicitly to give ratings or feedbacks for some products, or implicitly observing their behaviour. Once this preference has been collected by the system, it can then be used to generate item recommendation. Using explicit feedback itself, such as rating only, has several drawbacks. Using ratings is very subjective. Two users with similar taste might give different rating to the same item. A user also may not be consistent in giving number to express their liking. In other case, a user may like an item more than the other items, but feels difficult to give lower rating on the other item, because it means that the particular item is just as bad as the one he/she does not like. By showing two different items as a pair, it will be easier for the users to pick which one is the best. In this paper, we show the result of our experiment in predicting user pairwise preferences by using their movie ratings data. We evaluated five different machine learning algorithms to predict the data in pairwise preference format. The result shows that K-Nearest Neighbour and Random Forest outperformed the other algorithms.
Evaluating Pairing-Free Identity-Based Identification Using Curve25519

Identification schemes are cryptographic primitives that enable strong authentication for access control mechanisms that are critical to the security of computerized systems. To mitigate the problem of cryptosystems growing large where certificate management becomes a major and costly issue in traditional identification schemes, identity-based identification (IBI) is proposed to eliminate the need for a signature on public keys by using a publicly verifiable ID string as the user’s public key. Schnorr signature scheme is a popular choice used as a building block for several IBI schemes such as Twin-Schnorr, Tight-Schnorr, and Schnorr-IBI. In this work, we present an alternative implementation of the various Schnorr IBI schemes using finite field arithmetic on Curve25519, an elliptic curve implementation known for high-speed and high-security. The results of the hard experimental evidence suggest that the re-implemented IBI schemes outperform the existing works as there is a great improvement in speed for all the algorithms. Specifically, there is a 1.48x speedup corresponding to a reduction of 32.79% in identification runtime. For storage efficiency, the re-implemented IBI schemes achieved a 91% reduction in master public-key size, a 83% reduction in user secret-key sizes on pre-computation setups, and a 84% reduction in bandwidth measured per identification session. These improvements are significantly due to the use of elliptic curve cryptography (ECC) and a high-speed Curve25519 implementation.
Pedestrian-Vehicle Detection Model Based on Optimized YOLOv3

Vehicle and pedestrian detection has always been an important and challenging task in the autonomous driving technology. To solve this problem, this paper proposes an optimized method based on YOLOv3, which is utilized to detect vehicles, pedestrians and cyclists. On the basis of traditional YOLOv3, this algorithm increases the number of deep residual structure and feature map sizes. And then cascades feature maps of various sizes in the backbone network to obtain the Feature Map-Concat (FC) structure. We also optimize the regression loss function at the same time. The evaluation index of this algorithm was implemented on KITTI data set, the results showed that the proposed detection algorithm proposed in this paper achieved encouraging results.
Comparison of LSTM, GRU and Hybrid Architectures for usage of Deep Learning on Recommendation Systems

This article shows the results of a performance analysis from LSTM, GRU and Hybrid Neural Network architectures in Recommendation Systems. To this end, prototypes of the networks were built to be trained using data from the user's browsing history of a streaming website in China. The results were evaluated using the metrics of Accuracy, Precision, Recall and F1-Score, thus identifying the advantages and disadvantages of each architecture in different approaches.
Few training data for Objection Detection

Deep learning method of object detection has achieved excellent results, but most of the object detection network training processes are supervised learning. The performance improvement is driven by a large amount of annotation data to drive deeper and more complex network structures. For object detection tasks, it takes 7-42 seconds to complete the precise labeling of a single target rectangle. As the complexity of the scene and the density of objects increase, the cost of labeling becomes higher. In this paper, we propose a semi-supervised object detection framework based on self-training to solve the problem of few training data object detection. We have improved the method of self-training to generate pseudo-labels for object detection tasks, pseudo-labels made by using automatic threshold search and multi-view detection. This method uses semi-supervised learning to mine and utilize unlabeled data, as well as the use of data augmentation methods to enhance the generalization of labeled data. It can achieve high accuracy in object detection models trained with few training data. We proposed several experiments on two publicly available datasets with few training data, and experiments demon-state that our methods based on semi-supervised learning are better than that based on supervised learning.
UAV Attitude Angle Estimation Based on Door Frame Deformation in Image

In this paper, we introduce a new method for calculating position angle of the barrier-wearing drone through the rectangular change in the image. This method only uses the visual deformation information of rectangular frame to calculate the angle between UAV navigation direction and door frame plane. There is a certain geometric transformation relationship between the change of view angle and the deformation of rectangle. In this paper, we infer the relationship between deformed graphic and UAV attitude under a series of assumptions. This method does not consider the distortion and rotation of rectangle, which are generally very sensitive to noise working conditions. No complicated mathematical computation is involved in the implementation and the required computational storage space is much cheaper compared to the current methods. Experiments with simulated images indicate the effectiveness of the proposed method.
Quantum Error Correction

Active error correction is necessary to counter the effect of noise. Classical error correction is generally used to protect transmission and storage of data, because in these conditions noise has had a chance to accumulate over space or time. Otherwise, there is no active error correction in computation because the signal levels are extremely high compared to the noise. Amplification cannot be used to achieve stable qubits because it cannot be done on quantum information without destroying it, since classical amplification hides an implicit measurement. This leaves error correction as the only protection of quantum information against noise and the high noise levels in quantum systems make error correction necessary at all times, including during any processing. In this chapter, we cover the most basic tools for the construction of quantum error correction codes and the basic principles of error corrected quantum computation.
SS-SF: Piecewise 3D Scene Flow Estimation With Semantic Segmentation

In order to address the issue of edge-blurring and improve the accuracy and robustness of scene flow estimation under motion occlusions, we in this article propose a piecewise 3D scene flow estimation approach with semantic segmentation, named SS-SF. First, we utilize the semantic optical flow to initialize the 3D plane and its rigid motion parameters, and then produce the initial mappings of pixel-to-segment and segment-to-plane of the input left and right image sequences. Second, we plan a novel energy function to optimize the initial mappings by using a semantic segmentation constraint term to regularize the classical scene flow model, which the optimized mappings are employed to update the assignment and motion parameters of each pixel. Third, we adopt the semantic label to extract the occlusion pixels and exploit an occlusion handling constraint to enhance the robustness of the scene flow estimation. Finally, we compare the proposed SS-SF model with several state-of-the-art approaches by using the KITTI and MPI-Sintel databases. The experimental results demonstrate that the proposed method has the advanced accuracy and robustness in scene flow estimation, especially owns the capacities of edge-preserving and occlusion handling.
Prototyping of Machine Learning at Traditional Market as Option for Sales and Purchasing Transaction

Traditional market is an important part of the regional economy. Sales and purchasing transaction in traditional markets in any country is closely related to transactions which are done or executed in physical places. These days, along with technology enhancement, most things may be transformed into virtual reality. Artificial Intelligence technology in the application of machine learning is a potential that can make a significant contribution in various aspects, including the sales system. This study focuses on designing the prototyping of a machine learning application that is purposely used for sales and purchase transactions in the traditional market. This prototyping provides an alternative to traditional market traders who are focused on selling their goods at physical places. This study builds an application that provides a virtual platform for sales and purchase transactions. This study uses Convolutional Neural Networks (CNN) as one of the categories in Deep Learning which is part of Machine Learning. This study finds that the prototyping of machine learning applications for sales and purchase transactions at traditional markets might be a proper and beneficial solution for both traders and customers.
Metric-Based Learning for Nearest-Neighbor Few-Shot Image Classification

Few-shot learning task, which aims to recognize a new class with insufficient data, is an inevitable issue to be solved in image classification. Among recent work, Metalearning is commonly used to Figure out few-shot learning task. Here we tackle a recent method that uses the nearest-neighbor algorithm when recognizing few-shot images and to this end, propose a metric-based approach for nearest-neighbor few-shot classification. We train a convolutional neural network with miniImageNet applying three types of loss, triplet loss, crossentropy loss, and combination of triplet loss and cross-entropy loss. In evaluation, three configurations exist according to feature transformation technique which are unnormalized features, L2-normalized features, and centered L2-norma1ized features. For 1-shot 5-way task, the triplet loss model attains the uppermost accuracy among all three configurations and for 5-shot 5-way task, the identical model reaches the foremost accuracy in unnormalized features configuration.
Smart-YOLO: A Light-Weight Real-time Object Detection Network

Abstract In the computer vision, YOLO has an important position in the field of object detection, but due to its speed limitation, it is not suitable for scenes that require extremely strict real-time performance, such as smart cameras or some mobile devices. However, inverted bottleneck layers, which are built upon depth-wise separable convolution, have been the predominant building blocks in state-of-the art object detection models on mobile devices. In this work, we propose a new lightweight algorithm Smart-YOLO based on the YOLO framework which uses inverted bottleneck blocks and deep-wise separable convolution. We also put forward a new loss function to make up for the loss of accuracy caused by the replacement of the backbone network. The results show that compared with YOLOv3, the accuracy of our model is reduced by about 21%, but achieved up to 4.5× speedup, and the model size is only about 1/8 of the original. This shows that our network is smaller, faster, and more suitable for scenarios that require higher speed and efficient
Agent-Based Modeling on Traveler Behavior and Travel Destination Development with Case Study of Indonesian Tourism

Tourism is one of the important factors to increase foreign exchange, especially in Indonesia, where the competitiveness index is always increasing every year. According to The Travel and Tourism Competitiveness Report released by WEF (World Economic Forum) in 2019, the tourism competitiveness index in Indonesia increases to 40 in 2019 from 42 in 2017. Therefore, the competitiveness between travel destinations is also increasing. Some factors which affect travel destination development are visitors' types and travel destination spot types. Therefore, this paper gives a modeling technique using agent-based modeling to get the effective ratio of the sum of each visitors' type and the sum of each spot type in the travel destination. In this paper, we used two visitors' types based on psychographic personality types, venturer visitor and dependable visitor. In addition, we used three spot types, namely venue spots, rest spots, and photo spots. The modeling could be used on some tourism places in Indonesia which have tourism facilities such as the main tourism superstructure (venue spots and rest spots) and the supplementing tourism superstructure (photo spots). This simulation has a result of a ratio and could be executed with various scenarios so that the travel destinations' owners could determine the exact ratio to get a maximum satisfaction level of every visitor.
Data Hiding in Color Image Using Steganography and Cryptography to Support Message Privacy

The immense measure of classified information has been moved on the internet. Information security turns out to be progressively significant for some applications, for instance, private transmission, video observation, military, and clinical applications. Lately, there has been a great deal of enthusiasm for steganography and steganalysis. Steganography is the specialty of covering up and transmitting information through clearly harmless transporters with an end goal to disguise the presence of information. The advanced picture information, for example, BMP, JPEG, and GIF, are generally utilized as a transporter for steganography. Here the mystery message is implanted into a picture (or any media) called spread picture and afterward sent to the beneficiary who extricates the mystery message from the spread message. This picture ought not to be discernible from the spread picture, with the goal that the aggressor can't find any implanted message. The authors have proposed three approaches of steganography that can easily support message privacy.
Behavioral Biometrics for Human Identity Corroboration based on Gesture-Signature with Deep Learning

Identity corroboration has gained a substantial deal of attention with the high usage of smart devices and dedicated systems accommodating sensitive data and applications magnitudes. In this paper, we develop a behavioral biometric authentication system based on deep learning. Specifically, an android application is developed as a dedicated tool for capturing the touch behavioral biometrics information, the electronic signature information alongside their corresponding accelerometer and gyroscope sensor readings. Finally, the generated sensor readings are fed into a multi-input convolutional neural network architecture for classification. Extensive experimental results show that our proposed approach uniquely identifies users with a classification performance of 93.46% as compared to other baseline approaches, where only a single sensor reading is considered.