Reviewerly/exHarmony huggingface dataset:
Data Analysis for Risk Prediction of Cervical Cancer Metastasis and Recurrence Based on DCNN-RF

Abstract In allusion to the problem of the low survival rate of cervical cancer metastasis and recurrence, combining the advantages of deep learning, a hybrid DCNN-RF method based on patched pathological image was proposed to predict the risk of metastasis and recurrence in cervical cancer patients. In order to improve the generalization ability of the model, according to the features, predicting result could be obtained from random forest, and the integration result was invoked as the result of haematoxylin and eosin pathological whole-slide images (WSI). The experimental results show that the model yielded an accuracy of 90.32% for prediction based on sliding window in cross-validation, and 0.83 AUC in the WSI.
Reinforcing Service Level Agreements in Tourism Sector The Role of Blockchain and Mobile Computing

As service-orientation gathers traction in enterprise engineering, more and more business processes and capabilities alike are wrapped up and viewed as services in today's digital enterprises. Critical to the success of service provisioning is the notion of service level agreement that captures the service provider's commitment in providing a service. Pragmatically, this concept describes not only computer-interpretable factors (e.g., reliability, payment), but also penalty rules of the commitment that to state what to do in case of breach of contract. This paper is dedicated to the reinforcement of the service level agreements. When it comes to service digitization, it is our finding that mobile computing plays a vital role in digitizing the service level agreements, making them reinforceable if coupled with Blockchain's smart contracts. We demonstrate our approach to reinforcing the service level agreements in tourism sector.
An adaptive machine learning methodology to determine manufacturing process parameters for each part

The identification of appropriate manufacturing process parameters typically relies on rule-based schemes, expertise, and domain knowledge of highly skilled workers. Usually, the parameter settings remain the same for each part in an individual production lot once an acceptable quality is reached. Each part, however, has slightly different properties and part-specific parameter settings have the opportunity to increase quality and reduce scrap. We propose a simple linear regression model to identify process parameters based on experimental data and extend that model with ideas from time series analysis to achieve highly-accurate, part-specific parameter settings in a real-world manufacturing use case. We show the usefulness of exploiting the (autocorrelated) structure of regression residuals to improve the predictive performance of regression models in manufacturing environments.
EHANet: Efficient Hybrid Attention Network towards Real-time Semantic Segmentation

Semantic segmentation suffers from the contradiction between inference speed and model accuracy. State-of-the-art real-time methods improve inference rapidity by sacrificing feature representation and model capacity. This paper proposes a novel Efficient Hybrid Attention Network (EHANet) to remedy this dilemma. The EHANet follows an encoder-decoder structure, where the encoder is composed of Reduced Basic-Block (RBB) with very few parameters. At decoding stages, a hybrid attention mechanism is designed to re-weight the feature map. The attention mechanism employs contextual attention for deep features and spatial attention for shallow features. The proposed architecture makes a trade off between inference speed and segmentation performance. As a result, the proposed model achieves 66.1% mIoU on the Cityscapes validation set. Meanwhile, it can operate at a speed of 113 FPS on one NVIDIA Titan XP GPU for an input image with size of 1024×512.
Sparse Online Variational Bayesian Regression

This work considers variational Bayesian inference as an inexpensive and scalable alternative to a fully Bayesian approach in the context of sparsity-promoting priors. In particular, the priors considered arise from scale mixtures of Normal distributions with a generalized inverse Gaussian mixing distribution. This includes the variational Bayesian LASSO as an inexpensive and scalable alternative to the Bayesian LASSO introduced in [65]. It also includes a family of priors which more strongly promote sparsity. For linear models the method requires only the iterative solution of deterministic least squares problems. Furthermore, for p unknown covariates the method can be implemented exactly online with a cost of $O(p^3)$ in computation and $O(p^2)$ in memory per iteration -- in other words, the cost per iteration is independent of n, and in principle infinite data can be considered. For large $p$ an approximation is able to achieve promising results for a cost of $O(p)$ per iteration, in both computation and memory. Strategies for hyper-parameter tuning are also considered. The method is implemented for real and simulated data. It is shown that the performance in terms of variable selection and uncertainty quantification of the variational Bayesian LASSO can be comparable to the Bayesian LASSO for problems which are tractable with that method, and for a fraction of the cost. The present method comfortably handles $n = 65536$, $p = 131073$ on a laptop in less than 30 minutes, and $n = 10^5$, $p = 2.1 \times 10^6$ overnight.
On the Impact of Rain over Semantic Segmentation of Street Scenes

We investigate the negative effects of rain streaks over the performance of a neural network for real time semantic segmentation of street scenes. This is done by synthetically augmenting the CityScapes dataset with artificial rain. We then define and train a generative adversarial network for rain removal, and quantify the benefits of its application as a pre-processing step to both rainy and "clean" images. Finally, we show that by retraining the semantic segmentation network on images processed for rain removal, it is possible to gain even more accuracy, with a model that produces stable results in all analyzed atmospheric conditions. For our experiments, we present a per-class analysis in order to provide deeper insights over the impact of rain on semantic segmentation.
BBSF: A Blockchain Based Secure Framework for The Internet of Things with User Revocation

The Internet of Things (IoT), which uses the internet to connect users (devices) from various fields, has become widely popular nowadays. It is predicted that IoT will play an increasingly important role and be deployed in people ' s daily life. However, there are still various security issues to be addressed in IoT, such as decentralized authentication, low power device and lightweight cryptographic framework, privacy problems, and so on. Blockchain technology, with the characteristics of decentralization, permissionless, transparency, and immutable, has received great attention. To address these security risks, especially the decentralized authentication problem of IoT, and improve the credibility of the authentication, this paper presents a Blockchain-Based Secure Framework (BBSF) for IoT applications. With a smart contract, BBSF supports certificate issuance, update, revocation, and audit functions. We also conduct security analysis and show that the proposed BBSF provides confidentiality, availability, data integrity, and so on. In addition, We deploy BBSF with Ethereum to verify the system stability, scalability, and efficiency.
Simple means Faster: Real-Time Human Motion Forecasting in Monocular First Person Videos on CPU

We present a simple, fast, and light-weight RNN based framework for forecasting future locations of humans in first person monocular videos. The primary motivation for this work was to design a network which could accurately predict future trajectories at a very high rate on a CPU. Typical applications of such a system would be a social robot or a visual assistance system "for all", as both cannot afford to have high compute power to avoid getting heavier, less power efficient, and costlier. In contrast to many previous methods which rely on multiple type of cues such as camera ego-motion or 2D pose of the human, we show that a carefully designed network model which relies solely on bounding boxes can not only perform better but also predicts trajectories at a very high rate while being quite low in size of approximately 17 MB. Specifically, we demonstrate that having an auto-encoder in the encoding phase of the past information and a regularizing layer in the end boosts the accuracy of predictions with negligible overhead. We experiment with three first person video datasets: CityWalks, FPL and JAAD. Our simple method trained on CityWalks surpasses the prediction accuracy of state-of-the-art method (STED) while being 9.6x faster on a CPU (STED runs on a GPU). We also demonstrate that our model can transfer zero-shot or after just 15% fine-tuning to other similar datasets and perform on par with the state-of-the-art methods on such datasets (FPL and DTP). To the best of our knowledge, we are the first to accurately forecast trajectories at a very high prediction rate of 78 trajectories per second on CPU.
SpotTune: Leveraging Transient Resources for Cost-efficient Hyper-parameter Tuning in the Public Cloud

Hyper-parameter tuning (HPT) is crucial for many machine learning (ML) algorithms. But due to the large searching space, HPT is usually time-consuming and resource-intensive. Nowadays, many researchers use public cloud resources to train machine learning models, convenient yet expensive. How to speed up the HPT process while at the same time reduce cost is very important for cloud ML users. In this paper, we propose SpotTune, an approach that exploits transient revocable resources in the public cloud with some tailored strategies to do HPT in a parallel and cost-efficient manner. Orchestrating the HPT process upon transient servers, SpotTune uses two main techniques, fine-grained cost-aware resource provisioning, and ML training trend predicting, to reduce the monetary cost and runtime of HPT processes. Our evaluations show that SpotTune can reduce the cost by up to 90% and achieve a 16.61x performance-cost rate improvement.
Unit Testing Framework for Embedded Component Systems

Effective construction of large, complex embedded systems involve the enhancement of potential reusability by dividing the software into subsystems and converting them into distinct parts (i.e., component-based development (CBD) for embedded systems). CBD can be used to improve development efficiency and reduce costs and is also applied to ensure software reusability: however, there are few approaches for testing CBD systems. General CBD systems do not provide methods for evaluating whether each component behaves as expected, making it necessary to manually connect individual components, which makes it difficult to test and fix bugs. To address these issues, this paper describes a unit testing framework for the embedded component systems (called TECSUnit), allowing the assessment of the behavior of each component. This framework increases the efficiency of testing component systems based on a design focusing on flexibility and efficiency.
DedupCloud: an optimized efficient virtual machine deduplication algorithm in cloud computing environment

The growth of IoT technology and other media is generating a large volume of duplicate data in cloud storage and creates a load on it. Data deduplication is an efficient mechanism for virtual machines (VMs) to eliminate redundant data that improves storage utilization and the cost of storage is reduced as well. DedupCloud, uses a byte comparison technique to minimize the need for hash value computations and also the subsequent comparisons. This chapter proposes a technique which calculates and compares the hash value within the similar category only. It economizes the space required to save the hash values during deduplication as well as helping to make the VM images faster. The results show a benefit of 80% that is achieved in the time required for the deduplication of VM images. Also, there is a saving in storage and in metadata.
Anomaly modelling in machine learning based navigation system of autonomous vehicles

In the past few years, autonomous navigation systems are gradually introduced to the consumer vehicles and the cars using these systems are gaining popularity. The autonomous navigation systems are using machine learning and deep learning models for visual processing of complex road scenes in various scenarios. These machine learning models are trained using numerous test rides and the process of training sometimes continues as the vehicle is navigating the streets. These machine learning models are vulnerable to anomalies which can lead to various issues for the autonomous driving systems. These issues will put the safety of the vehicle in danger. In this paper, the issue of the impact of the anomalies on the deep learning models of smart vehicles is investigated and a method for anomaly modelling in the machine learning based autonomous driving systems is presented. The presented model can increase the robustness of the autonomous vehicles.
Generating Financial Reports from Macro News via Multiple Edits Neural Networks

Automatically generating financial reports given a piece of breaking macro news is quite challenging task. Essentially, this task is a text-to-text generation problem but is to learn long text, i.e., greater than 40 words, from a piece of short macro news. Moreover, the core component for human beings to generate financial reports is the logic inference given a piece of succinct macro news. To address this issue, we propose the novel multiple edits neural networks which first learns the outline for given news and then generates financial reports from the learnt outline. Particularly, the input news is first embedded via skip-gram model and is then fed into Bi-LSTM component to train the contextual representation vector. This vector is used to learn the latent word probability distribution for the generation of financial reports. To train this end to end neural network model, we have collected one hundred thousand pairs of news-report data. Extensive experiments are performed on this collected dataset. The proposed model achieves the SOTA performance against baseline models w.r.t. the evaluation criteria BLEU, ROUGE and human scores. Although the readability of the generated reports by our approach is better than that of the rest models, it remains an open problem which needs further efforts in the future.
Estimation of object class and orientation from multiple viewpoints and relative camera orientation constraints

In this research, we propose a method of estimating object class and orientation given multiple input images assuming the relative camera orientations are known. Input images are transformed to descriptors on 2-D manifolds defined for each class of object through a CNN, and the object class and orientation that minimize the distance between input descriptors and the descriptors associated with the estimated object class and orientation are selected. The object orientation is further optimized by interpolating the viewpoints in the database.The usefulness of the proposed method is demonstrated by comparative evaluation with other methods using publicly available datasets. The usefulness of the proposed method is also demonstrated by recognizing images taken from the cameras on our humanoid robot using our own dataset.
Inter‐frame forgery detection and localisation in videos using earth mover's distance metric

Video forensics is one of the hot topics in multimedia forensics. Nowadays, spreading fake videos across the internet is becoming a profession mainly in politics and entertainment. In this study, a novel two-stage inter-frame video forgery detection technique is proposed. The first stage analyses spatio-temporal feature flow consistency to detect suspicious tamper points. Identifying the type of forgery and validating the recovered video are done in the second stage. Earth mover's distance is used as a similarity metric in both stages. The authors concentrate on a robust inter-frame forgery detection approach which can be applied for any challenging video. Compression at a higher rate, noise addition, and filtering are the anti-forensic tricks used by forgers to fool forensic techniques. However, most of the literature in video forgery detection has handled these issues as post-processing attacks and reported lesser accuracies for it. Hence the authors propose a robust and efficient forgery detection technique capable of identifying all kinds of inter-frame forgeries in videos. Experimental evaluation of the public video data set shows that the proposed approach outperforms existing approaches with an improved rate of robustness.
Measuring and Modeling Software Vulnerability Security Advisory Platforms

In this paper, we report results on a large scale measurement campaign to collect temporal information about events associated with software vulnerabilities. The data is curated so as to extract dates from each of the analyzed security advisories. The resulting time series are our object of study. From our measurements we were able to identify which role was assumed by different platforms (such as websites and forums) in the security landscape, including sources and aggregators of information about vulnerabilities. Then, we propose an analytical model to express the flow of information through security advisories across multiple platforms. The model is based on a queueing network, where each platform corresponds to a queue which adds a delay in the information propagation. Such delays, in turn, have an impact on the visibility of the information at different platforms. Leveraging the proposed model and the collected data, we assess how different system parameters, such as the delays incurred by each platform to propagate its messages, impact the overall flow of information across platforms.
A Tangible Multi-Display Toolkit to Support the Collaborative Design Exploration of AV-Pedestrian Interfaces

The advent of cyber-physical systems, such as robots and autonomous vehicles (AVs), brings new opportunities and challenges for the domain of interaction design. Though there is consensus about the value of human-centred development, there is a lack of documented tailored methods and tools for involving multiple stakeholders in design exploration processes. In this paper we present a novel approach using a tangible multi-display toolkit. Orchestrating computer-generated imagery across multiple displays, the toolkit enables multiple viewing angles and perspectives to be captured simultaneously (e.g. top-view, first-person pedestrian view). Participants are able to directly interact with the simulated environment through tangible objects. At the same time, the objects physically simulate the interface’s behaviour (e.g. through an integrated LED display). We evaluated the toolkit in design sessions with experts to collect feedback and input on the design of an AV-pedestrian interface. The paper reports on how the combination of tangible objects and multiple displays supports collaborative design explorations.
Significant Trajectories and Locality Constrained Linear Coding for Hand Gesture Representation

Recently, action recognition gains a lot of attention of researchers thank to its potential applications in real life. Particularly, hand gestures, which are actions performed by human hand, have been widely studied and started to be deployed as an efficient mean of human machine interaction (HMI). In this paper, we focus on hand gestures recognition in the context of HMI which requires to balance the trade-off between recognition accuracy and computation time. While convolutional neural network (CNN) has been shown to be very effective in many tasks, it requires powerful computer and huge training data which are not always available in common use. In this paper, we study a method based on hand crafted features (i.e. dense trajectories for hand gesture representation). We then select the most significant trajectories and compute a descriptor for each of them. For final representation of a gesture, we utilize locality constrained linear coding (LLC) and compare it with Bag of Words (BoW0 model. Finally, Support Vector Machine (SVM) is deployed to classify gestures. We test the proposed method on a dataset of hand gestures captured from different viewpoints and study the impact of viewpoint changes on such dataset. Experiments show that the proposed method keeps a balance between accuracy and computational time and comparable with CNN based method.
Technical Guidance for Learning Management in a Video Conference with the Zoom and Youtube application in the Covid-19 Pandemic Era

Abstract Learning conducted by teachers and lecturers will not take place without online management in the current pandemic covid-19. Therefore, the technical guidance organized by LPPM University of Muhammadiyah Enrekang aims to provide teachers and lecturers with understanding and skills on how to manage learning online or online. This stage of research includes analyzing the need for an online learning management platform, conducting technical guidance, and conducting evaluations. After the technical guidance activity, the participants liked the material and speakers in the technical guidance which indicated that the participants understood the material presented by the resource persons. Based on the evaluation results it was found that the implementation of this activity was successful with a high level of attendance of the participants, a level of likeness that was evenly distributed in all material presented by the resource persons, and a very high level of satisfaction with the guidance committee services.
Network Latency Estimation with Graph-Laplacian Regularization Tensor Completion

In recent years, with the growing prevalence of personal devices, network latency of devices has drawn much attention due to its significant influence on user experience. Thus network latency estimation is considered to be an important index for network performance evaluation. However, the existing works on network latency estimation are unable to achieve satisfactory estimation accuracy due to the adoption of the conventional matrix or tensor model. In this paper, we construct a novel tensor model based on tensor-SVD for network latency data to make full use of its potential latent factors. Besides, we also propose a graph-laplacian regularization tensor completion algorithm (GLRTC), which mines the underlying spatial information by introducing graph-laplacian regularization constraints to improve the recovery performance. Finally, we conduct extensive simulations on the real-world latency dataset and demonstrate the effectiveness of the proposed algorithm. Comparing with the existing approaches, the proposed algorithm achieves significant improvement in terms of recovery accuracy.