Reviewerly/exHarmony huggingface dataset:
Online Multi-Resource Social Welfare Maximization for Non-Preemptive Jobs

Multi-resource allocation is ubiquitous in datacenters.Usually, the datacenter scheduler associates each user with a utility function and then allocates multiple resources so as to maximize the social welfare, which is the sum of the utilities of the users.We refer to this problem as the Social Welfare Maximization (SWM) problem.Meanwhile, given the dynamic nature of datacenters, the scheduler is supposed to support non-preemptive scheduling, where jobs of users that are being processed would not be interrupted by the reconfiguration of resources.Non-preemptive solutions to SWM incorporating pricing considerations include dynamic pricing and bidding/auctions.However, both strategies are quite complex.Meanwhile, traditional Lagrange solutions to SWM does not support non-preemptive job scheduling.In this paper, we propose Adaptive Dominant Resource Fairness (ADRF), that provides near-optimal social welfare where each user is assigned an α-fair utility function and when 1 < α ≤ 2. Based on ADRF, we propose Cumulative ADRF (C-ADRF), an online algorithm that greedily assigns the idle resources to the current poorest user, making it quite simple to schedule jobs from users non-preemptively.Compared with the polynomial or exponential form of time complexities in other SWM solutions, C-ADRF requires only O(log N ) work, where N is the number of users.Extensive simulations using Google and Facebook cluster-data traces show an impressive behavior of C-ADRF in providing social welfare, only 3% and 2% away from the optimal social welfare, respectively.
Mask Mining for Improved Liver Lesion Segmentation

We propose a novel procedure to improve liver and lesion segmentation from CT scans for U-Net based models. Our method extends standard segmentation pipelines to focus on higher target recall or reduction of noisy false-positive predictions, boosting overall segmentation performance. To achieve this, we include segmentation errors into a new learning process appended to the main training setup, allowing the model to find features which explain away previous errors. We evaluate this on semantically distinct architectures: cascaded two-and three-dimensional as well as combined learning setups for multitask segmentation. Liver and lesion segmentation data are provided by the Liver Tumor Segmentation challenge (LiTS), with an increase in dice score of up to 2 points.
Fast segmentation identification of express parcel barcode based on MSRCR enhanced high noise environment

With the rapid development of the domestic logistics industry, the demand for quick inquiry of express parcel delivery information is becoming more and more urgent. The automatic acquisition of express delivery number is expected to solve this problem. This paper proposes a barcode localization segmentation recognition algorithm for bar code/QR code location segmentation recognition in a single scan image of a parcel in a complex environment. The whole algorithm is fully tested in the actual express single scan image. The results show that the algorithm is fast, accurate and has low bit error rate, and has strong practical value.
Receiver operating characteristic curves with an indeterminacy zone

This work extends Receiver Operating Characteristic (ROC) curve to the situation where some cases, falling in an intermediate ”indeterminacy zone” of the predictor, are not classified. It addresses two challenges: definition of sensitivity and specificity bounds for this case; and summarization of the large number of possibilities arising from different choices of indeterminacy zones.
Prediction of Selected Personality Traits Based on Text Messages from Instant Messenger

The aim of the work was to check effectiveness of machine learning to predict personality traits based on technical parameters of text messages. At the beginning, personality traits of test group (based on Big Five model) were determined. Each person provided a collection of text messages from the instant messenger. The authorial analyzer was used to aggregate the technical parameters: (number of emoji used, average message length, number of punctuation characters used, etc.) During the analysis, the most important parameters of text messages were identified, with the help of which it was possible to predict personality traits. In addition, based on the collected data and conducted analysis, a proprietary system to predict personality traits was created. To this end, methods of supervised machine learning were used. Finally, tests were carried out on the implemented solution, its prediction effectiveness was verified, and conclusions were drawn.
Segmentation and Classification of Cervical Cancer

Uterine cancer comes in second place after breast cancer affecting a large majority of women, especially in poor countries, given the lack of programming for the usual manual/visual screening to detect this cancer, it is in most cases proving insufficient because it is detected in advanced stages, thus the need to produce effective and reliable early detection methods. In this perspective, our work is interested in developing a tool to assist in the diagnosis and early detection of cervical cancer based on the interpretation of MRI images of the cervix, the proposed system takes place in three stages: 1-Pretreatment to remove the noise from the image in general; we opted for the K-means method; 2- Segmentation step in which we used the method of growing regions, 3- Classification or decision step that consists through inferences rules deduced from the FIGO classification to decide which stage it is. The results obtained are satisfactory and demonstrate the effectiveness of our approach to detecting the stage of cervical cancer.
A Compressed Deep Convolutional Neural Networks for Face Recognition

Recent years, deep convolutional neural network has led to significant improvements in face recognition and becomes one of the most popular techniques in computer vision community. However, deep CNN model requires vast amounts of data and time for training and deploying. To solve this problem, we present a deep compact convolutional neural network for face representation. First we apply PCA for initializing convolution filter. Then we adopt DCT and binary hashing for extracting face features. The models are trained on the CASIA-webFace datasets under Caffe framework. Experimental results show that the proposed methods achieve competitive accuracy on the LFW verification benchmark.
Implementation of backpropagation artificial neural network for early detection of vitamin and mineral deficiency

Abstract Basic Health Research data in 2018 show that around 95.5 percent of Indonesian people less consumption of fruit and vegetables. This condition then leads to the suspicion of vitamin and mineral deficiency in Indonesia people. Several methods have been used to detect vitamin and mineral deficiency, such as convolutional rule-based and certainty factor method. However, these methods are less adaptive to adapt to the changes in symptoms when detecting vitamin and mineral deficiencies. This paper proposes an artificial neural network (ANN) using backpropagation (BPN) to detect the vitamin and mineral deficiencies in the human body. Using 107 input of physical symptoms and 17 output of the type of vitamin and mineral, the architecture of the ANN consist of 107-50-17 neurons for the input layer, hidden layer, and output layer respectively. Based on some trial and error experiments, can be determined the epoch, the learning rate, and the error rate to produce the optimal result of the detection. This experiment using 623 epochs, 0.0517 error rate, and 0.1 for the learning rate. The performance measurement conducted using precision, recall, and F-score, for each class output. The experiment shows the proposed ANN using BPN reaches an accuracy level of 73%.
Using informed access network selection to improve HTTP adaptive streaming performance

As end-user devices often have multiple access networks available, choosing the most suitable network can help to improve application performance and user experience. However, selecting the best access network for HTTP Adaptive Streaming (HAS) is non-trivial, e.g., due to complex interactions between network conditions and the Adaptive Bit-Rate algorithm (ABR), which adapts to network conditions by selecting which video representation to load. In this paper, we propose to use an application-informed approach, Informed Access Network Selection (IANS), to select the most suitable access network for each video segment. We evaluate the impact of IANS on HAS performance in a testbed under a variety of network conditions and using different workloads. We find that IANS improves HAS performance substantially, in particular in cases where the available downstream capacity is low. In the Capacity Decrease scenario, where capacity decreases drastically during the video load, IANS can improve the estimated Mean Opinion Score (MOS) compared to using a single network from 2.1 to 2.8. We compare IANS to MPTCP using the Lowest-RTT-first scheduler, which continues to use a low downstream capacity network, resulting in lower performance. This confirms that IANS can improve video streaming performance.
Population-based metaheuristics for Association Rule Text Mining

Nowadays, the majority of data on the Internet is held in an unstructured format, like websites and e-mails. The importance of analyzing these data has been growing day by day. Similar to data mining on structured data, text mining methods for handling unstructured data have also received increasing attention from the research community. The paper deals with the problem of Association Rule Text Mining. To solve the problem, the PSO-ARTM method was proposed, that consists of three steps: Text preprocessing, Association Rule Text Mining using population-based metaheuristics, and text postprocessing. The method was applied to a transaction database obtained from professional triathlon athletes' blogs and news posted on their websites. The obtained results reveal that the proposed method is suitable for Association Rule Text Mining and, therefore, offers a promising way for further development.
Emotion Computing Method Based On Knowledge Representation

In order to solve the shortcomings of traditional affective computing methods with low computational accuracy, a kind of affective computing method based on knowledge representation is proposed. This method is based on the introduction of knowledge representation. According to emotion node vector and emotion control vertex determination. The real number coding scheme is used to calculate the change of emotional parameters and emotional psychological state. Experimental data show that the accuracy of the proposed method is 39.5% higher than that of the traditional method.
Towards a Smart and Socialised Augmented Reality

This paper introduces a nascent project aimed at exploring new avenues to support creativity, socialisation and community through smart interfaces for augment reality. Augmented reality has been so far largely conceptualised from a point of view of 'power users' seeking to support very specific applications, e.g. in training and simulation. With the availability of devices to mass market, new applications become possible, and new research problems open up. We offer a preliminary framework consisting of 2 orthogonal continua (virtual-real and human-thing) and 2 critical perspective (postphenomenology/posthumanism and cultural interface). With this poster we hope to stimulate valuable discussion and seek input from the CHI community about the challenges, opportunities, and theoretical perspectives underpinning a smart, socialised AR.
Research and Practice of Hybrid Teaching Based on AI technology for Foreign Language Translation

In the context of AI technology, it is necessary to reform the education by adopting information technology and intelligent teaching tools. The hybrid teaching and learning based on AI technology integrates online teaching with offline classroom teaching, by taking advantage of both informatic teaching and traditional teaching. This study takes an example of Japanese translation course of junior students majoring in Japanese language in higher education institutions, and conducts the AI-based Japanese translation hybrid teaching practice. The results indicate that the AI-based hybrid teaching mode may motivate students' activity and self-discipline in learning, and increase their capability and motif of autonomous learning.
Review of Imaging Device Identification Based on Machine Learning

Imaging device recognition is an important research hotspot in image tampering analysis. In recent years, it has received extensive research and rapid development. Image tampering analysis based on imaging devices is an important field in image tampering, and the recognition of imaging devices has also become important. In order to promote the recognition research based on imaging equipment, this paper summarizes and discusses the current main methods and representative work of imaging equipment identification. This article compares the similarities and differences of traditional methods and related deep learning methods, respectively, and details the current The main principles and methods of imaging device recognition based on deep learning are discussed. Finally, the problems that need to be solved for imaging device recognition based on deep learning and the future research trends are discussed.
SARA-SDN

Cloud Gaming (CG) solutions run games on cloud servers and stream the compressed video of the game over a network. While this has many benefits for both users and game companies, CG imposes two major challenges: low delay and high bandwidth are essential to ensure QoE. While the tolerable delay varies based on state of the game, previous researches do not fully exploit this concept to enhance QoE. This paper demonstrates the importance of the game-state as a criterion for game requirements. Accordingly, a game-State Aware Resource Allocation for Software-Defined Network (SDN)-based cloud gaming (SARA-SDN) is presented, which incorporates the game state into decision making. First, an SDN architecture is designed to facilitate communication of game information and control commands between game-server, SDN controller, and network switches. Second, a resource allocation algorithm is proposed that dynamically considers game-specific requirements such as delay, bandwidth, and game-state. Finally, SARA-SDN is implemented and evaluated in a simulated environment, where real users played in real time, and different aspects of QoE was measured via extensive subjective tests. Results show the superiority of SARA-SDN over existing methods (on average 1.26, 1.18, 1.21, and 1.12 increase in QoE, responsiveness, controllability, and immediate feedback, respectively, in a scale of 0-5).
Inserting Virtual Static Object with Geometry Consistency into Real Video

Abstract Given the importance of augmented reality in recent years and its wide spread in many fields of media industries, practical fields such as medicine, education, and others. This study present a methodology in order to insert virtual static object into a real video environment for improve the reality with a virtual information that was not actually present. There are several problems needs to be overcome during the process of merging the virtual object such as geometry of shapes, registration, and illumination. The aim of this study is to make the virtual static objects look like it actually exists in the same real video environment in terms of dimensions, sizes and consistency of shapes. The following steps of the proposed method are: Firstly convert original video file into frames. Secondly separate the background from foreground by rebuilding the background model, then obtain the foreground objects by subtract original frames of the video from background. Thirdly apply the background segmentation. Then extracting the features for every one of the segments and extracting the features of virtual static object. Fifthly selecting a suitable segment (area) for insert the virtual object.
Discrete analogues of continuous multivariate probability distributions

In this note we introduce multivariate extensions to the bivariate discrete analogue to continuous probability distributions proposed in Barbiero [Annals of Operations Research, doi: 10.1007/s10479-019-03388-8, 2019]. We furthermore show that identical properties of the bivariate case also hold for n-variate discrete analogues.
SPy: Car Steering Reveals Your Trip Route!

Abstract Vehicular data-collection platforms as part of Original Equipment Manufacturers’ (OEMs’) connected telematics services are on the rise in order to provide diverse connected services to the users. They also allow the collected data to be shared with third-parties upon users’ permission. Under the current suggested permission model, we find these platforms leaking users’ location information without explicitly obtaining users’ permission. We analyze the accuracy of inferring a vehicle’s location from seemingly benign steering wheel angle (SWA) traces, and show its impact on the driver’s location privacy. By collecting and processing real-life SWA traces, we can infer the users’ exact traveled routes with up to 71% accuracy, which is much higher than the state-of-the-art.
Key Schedule against Template Attack-Based Simple Power Analysis on a Single Target

Since 2002, there have been active discussions on template attacks due to the robust performance of such attacks. There are reports of numerous proposals to improve the accuracy of prediction model in order to identify the point of interest. To date, many researchers have only focused on the performance of template attacks. In this paper, we introduce a new approach to retrieve the secret information in key schedules, without the profiling phase utilizing secret information. The template attack allows us to reveal the correct key even though the encryption/decryption processes have powerful countermeasures. More precisely, if the templates are sufficiently built in loading/saving the public information, in the extraction phase, the templates already created can be applied to the identical operation about secret information, which allows us to retrieve the secret information even if the countermeasures are theoretically robust. This suggestion becomes another backdoor to avoid hardened countermeasures. In order to demonstrate our proposal, we consider the Advanced Encryption Standard key schedule as a target for attack; however, it cannot be the target of non-profiling attacks in general. Finally, the Hamming weight information of the correct key could be recovered in an XMEGA128 chip, without the secret information. Moreover, we concentrate on the potential possibility of our suggestion since the performance cannot outperform the original methods used in such attacks.
Evaluation of Technology Accessibility and User Sentiment in Learning Through Virtual Reality Modality

How would your organization's employees respond to a deployment of virtual reality based training? Would they be excited, inspired, or perhaps intimidated by new technology? We tested the accessibility of virtual reality (VR) as a modality for training PricewaterhouseCoopers (PwC) employees, and we observed negative and positive sentiments associated with various approaches to VR engagement. This case study presents our efforts to improve VR headset onboarding and game play experience by analyzing user experience from the time they show up for the VR learning experience to the time they leave. We describe learner capabilities and skills required to interact with the headset and voice-recognition technology in a simulation based learning experience, the methods we used to identify user sentiments and infer causes, as well as our efforts to address user needs by adjusting our VR learner experience design. In selecting our test user population, we attempted to represent the diversity of PwC learner demographics, including gender, ethnicity, employee role / function, practice group within PwC, and other characteristics that could have had a material impact on VR equipment use, such as prior experience with VR, use of eyeglasses, and presence of various accents when speaking. Our heterogenous group of testers pushed us to acknowledge user sensitivities, which we had previously underestimated. We changed our VR onboarding protocol in order to mitigate reticence to learning in VR. We believe that as the technology becomes more prevalent in the enterprise and more employees experience the value VR brings to the training experience, the overall sentiment toward VR will continue to improve. We hope our study motivates continued research to improve accessibility of VR technology, enterprise implementation of VR, user-interface design, and voice recognition technologies used in gameplay.
A Real-Time Chain and Variable Bulk Arrival and Variable Bulk Service (VBAVBS) Model with λF

This paper proposes a real-time chain and a novel embedded Markovian queueing model with variable bulk arrival (VBA) and variable bulk service (VBS) in order to establish and assure a theoretical foundation to design a blockchain-based real-time system with particular interest in Ethereum. Based on the proposed model, various performances are simulated in a numerical manner in order to validate the efficacy of the model by checking good agreements with the results against intuitive and typical expectations as a baseline. A demo of the proposed real-time chain is developed in this work by modifying the open source of Ethereum Geth 1.9.11. The work in this paper will provide both a theoretical foundation to design and optimize the performances of the proposed real-time chain, and ultimately address and resolve the performance bottleneck due to the conventional block-synchrony by employing an asynchrony by the real-time deadline to some extent.
A robust image steganography using teaching learning based optimization based edge detection model for smart cities

Abstract Recently, Internet becomes a most common medium for transferring critical data and the security of the transmitted data gains maximum priority. Image steganography has been developed as a well‐known model of data hiding which verifies the security level of the transferred data. The images offer high capacity, and the occurrence of accessibility over the Internet is more. An effective steganography model is required for achieving better embedding capacity and also maintaining the other variables in an acceptable value. This article introduces a new robust image steganography using Teaching Learning Based Optimization (TLBO) edge detection model. The TBLO is basically a metaheuristic algorithm which is inspired from the teaching and learning procedure in classrooms. The former stage indicates the learning from the teacher and the latter phase represents the interaction among the learners. The experimental validation takes place in a comprehensive way under several views and the outcome pointed out the superior results of the presented model.