Reviewerly/exHarmony huggingface dataset:
Resources Sharing in 5G Networks: Learning-Enabled Incentives and Coalitional Games

Smart systems are often battery-constrained, and compete for resources from remote clouds, which results in high delay. Collaboratively sharing resource among neighbors in proximity is promising to control such delay for time-sensitive applications. Rather few existing studies focus on the design between ubiquitous cooperation and competition with learning-enable incentives. In this article, intelligent algorithms are introduced in a distributed fashion, which encapsulates cooperation and competition to coordinate the overall goal of the cellular system with individual goals of Internet of Things (IoT) devices. First, the utility function of the cell and IoT users are designed, respectively. For the former, an incentive mechanism is constructed, where a novel deep actor-critic learning algorithm is developed with a prioritized queue for continuous action space in the differentiated decision-making procedure. For the latter, the energy model is taken into account. Furthermore, the coalition game combined with deep Q-learning framework is explored so as to model and incentivize the cooperation and competition process. Theoretical analysis and simulation studies demonstrate that the improved algorithms perform better than the original version, and they can converge to a Nash-stable optimal or asymptotically optimal solution.
A Robust Deep-Neural-Network-Based Compressed Model for Mobile Device Assisted by Edge Server

Deep neural networks (DNNs) have been extensively used in multiple applications. Due to the over-parameterized DNN-based model, the mobile device has computation and energy limitations to deploy such a model for machine learning tasks. Thus, many works focus on compressing a large-scale model to a small-scale model. In addition, the mobile device training a model assisted by the edge server is an emerging solution in the edge computing environment. However, recent researches have found that DNNs are vulnerable to adversarial examples. These crafted adversarial examples can fool a DNN-based model incorrect predictions. In particular, the DNN-based model can cause risks when used in safety-critical settings. To address this problem, we design a framework for generating a robust deep-convolutional-neural-network-based compressed model in the edge computing environment. The model is partitioned and trained by the mobile device and the edge server. The robust compressed model is constructed mainly via model compression and model robustness. In model robustness, a defensive mechanism is proposed for enhancing the robustness of the compressed model against adversarial examples. Furthermore, the weight distribution of the compressed model is considered for improving the model's accuracy in the defense method. The small-scale compressed model is effective and robust as a collaborative device-server inference for providing recognition tasks for the near devices. On the other hand, it is practical to deploy on the mobile device due to its small-size. Experimental results show that the generated compressed model has strong robustness against adversarial examples while holding high accuracy.
Improving Follicular Lymphoma Identification using the Class of Interest for Transfer Learning

Follicular Lymphoma (FL) is a type of lymphoma that grows silently and is usually diagnosed in its later stages. To increase the patients' survival rates, FL requires a fast diagnosis. While, traditionally, the diagnosis is performed by visual inspection of Whole Slide Images (WSI), recent advances in deep learning techniques provide an opportunity to automate this process. The main challenge, however, is that WSI images often exhibit large variations across different operating environments, hereinafter referred to as sites. As such, deep learning models usually require retraining using labeled data from each new site. This is, however, not feasible since the labelling process requires pathologists to visually inspect and label each sample. In this paper, we propose a deep learning model that uses transfer learning with fine-tuning to improve the identification of Follicular Lymphoma on images from new sites that are different from those used during training. Our results show that the proposed approach improves the prediction accuracy with 12% to 52% compared to the initial prediction of the model for images from a new site in the target environment.
Multi-branch Face Quality Assessment for Face Recognition

The quality of face images varies due to complex environmental factors, and face images with extremely poor qualities would deteriorate the performance of face recognition. As one of the pre-processing modules of face recognition, face quality assessment needs to consider both environment factors and practical applications. In this paper, we propose a multibranch face quality assessment (MFQA) algorithm considering comprehensive factors acting as a reliable reference for its following recognition. A light-weight convolution neural network (CNN) is used for face image feature extraction, and quality scores corresponding with alignment, visibility, deflection and clarity are predicted by multi-branch layers. Moreover, a score fusion module is implemented by fusing the above scores to obtain a final quality confidence. Compared with other relevant quality assessment works, our method is quite suitable for practical applications because of its better performance, faster speed and smaller model size. Experiments show that our proposed method is able to assess face quality objectively, and the performance of face recognition is significantly improved by introducing our approach into its training and testing procedures.
Initialization for Network Embedding

Network embedding has been intensively studied in the literature and widely used in various applications, such as link prediction and node classification. While previous work focus on the design of new algorithms or are tailored for various problem settings, the discussion of initialization strategies in the learning process is often missed. In this work, we address this important issue of initialization for network embedding that could dramatically improve the performance of the algorithms on both effectiveness and efficiency. Specifically, we first exploit the graph partition technique that divides the graph into several disjoint subsets, and then construct an abstract graph based on the partitions. We obtain the initialization of the embedding for each node in the graph by computing the network embedding on the abstract graph, which is much smaller than the input graph, and then propagating the embedding among the nodes in the input graph. With extensive experiments on various datasets, we demonstrate that our initialization technique significantly improves the performance of the state-of-the-art algorithms on the evaluations of link prediction and node classification by up to 7.76% and 8.74% respectively. Besides, we show that the technique of initialization reduces the running time of the state-of-the-arts by at least 20%.
Multimodal Deep Models for Predicting Affective Responses Evoked by Movies

The goal of this study is to develop and analyze multimodal models for predicting experienced affective responses of viewers watching movie clips. We develop hybrid multimodal prediction models based on both the video and audio of the clips. For the video content, we hypothesize that both image content and motion are crucial features for evoked emotion prediction. To capture such information, we extract features from RGB frames and optical flow using pre-trained neural networks. For the audio model, we compute an enhanced set of low-level descriptors including intensity, loudness, cepstrum, linear predictor coefficients, pitch and voice quality. Both visual and audio features are then concatenated to create audio-visual features, which are used to predict the evoked emotion. To classify the movie clips into the corresponding affective response categories, we propose two approaches based on deep neural network models. The first one is based on fully connected layers without memory on the time component, the second incorporates the sequential dependency with a long short-term memory recurrent neural network (LSTM). We perform a thorough analysis of the importance of each feature set. Our experiments reveal that in our set-up, predicting emotions at each time step independently gives slightly better accuracy performance than with the LSTM. Interestingly, we also observe that the optical flow is more informative than the RGB in videos, and overall, models using audio features are more accurate than those based on video features when making the final prediction of evoked emotions.
Accelerating Large Garbled Circuits on an FPGA-enabled Cloud

Garbled Circuits (GC) is a technique for ensuring the privacy of inputs from users and is particularly well suited for FPGA implementations in the cloud where data analytics is frequently run. Secure Function Evaluation, such as that enabled by GC, is orders of magnitude slower than processing in the clear. We present our best implementation of GC on Amazon Web Services (AWS) that implements garbling on Amazon's FPGA enabled F1 instances. In this paper we present the largest problems garbled to date on FPGA instances, which includes problems that are represented by over four million gates. Our implementation speeds up garbling 20 times over software over a range of different circuit sizes.
Fast Encoding Algorithms for Reed–Solomon Codes With Between Four and Seven Parity Symbols

This article describes a fast Reed-Solomon encoding algorithm with four and seven parity symbols in between. First, we show that the syndrome of Reed-Solomon codes can be computed via the Reed-Muller transform. Based on this result, the fast encoding algorithm is then derived. Analysis shows that the proposed approach asymptotically requires 3 XORs per data bit, representing an improvement over previous algorithms. The simulation demonstrates that the performance of the proposed approach improves with the increase of code length and is superior to other methods. In particular, when the parity number is 5, the proposed approach is about two times faster than other cutting-edge methods.
Social Bot Detection on 2019 Indonesia President Candidate’s Supporter’s Tweets

Nowadays, social media is considered as an essential tool for an organization to communicate with another user, customers and potential customers for gaining popularities, getting ideas and opinion from the users, or affecting another user. One of the problems on social media is the uses of the social bots, that used to make a human user believe the opinion generated from the social bots. These bots can use their ability to post in higher rates than human user to shift the topic that discussed in the time being. To resolve the problems on social bots, there are several ways to identify a bot from an account such as checking the date of account creation and interval on creating between posts with using machine learning.in this paper we will discuss what is the best machine learning algorithm to determine a social bot, and which feature that helps the algorithm the most. In our work, the data that will be used from Indonesia President Candidate's Supporter's Tweets from each side, Joko Widodo (@jokowi) as the first candidate and Prabowo Subianto (@prabowo) as the second candidate from a month. The algorithms that will be used on this paper are Support Vector Machine and Random Forest with cross-validation as a training method. We found that the year of account created has the highest contribution to bot detection.
Two Stream Model for Crowd Video Classification

We propose a novel method for crowd video classification, based on a two-stream convolutional architecture which incorporates spatial and temporal networks. Our proposed method cope with the key challenge of capturing the complementary information on appearance from still frames and motion between frames. In our proposed method, a motion flow field is obtained from the video through dense optical flow. We demonstrate that the proposed method trained on information including dense optical flow achieves significant improvement in performance. We train and evaluate our proposed method on a benchmark crowd video dataset. The experimental results of our method show that it outperforms the reference methods.
Securing Distributed Gradient Descent in High Dimensional Statistical Learning

We consider unreliable distributed learning systems wherein the training data is kept confidential by external workers, and the learner has to interact closely with those workers to train a model. In particular, we assume that there exists a system adversary that can adaptively compromise some workers; the compromised workers deviate from their local designed specifications by sending out arbitrarily malicious messages. We assume in each communication round, up to q out of the m workers suffer Byzantine faults. Each worker keeps a local sample of size n and the total sample size is N=nm. We propose a secured variant of the gradient descent method that can tolerate up to a constant fraction of Byzantine workers, i.e., q/m = O(1). Moreover, we show the statistical estimation error of the iterates converges in O(log N) rounds to O(√/N + √/N ), where d is the model dimension. As long as q=O(d), our proposed algorithm achieves the optimal error rate O(√/N $. Our results are obtained under some technical assumptions. Specifically, we assume strongly-convex population risk. Nevertheless, the empirical risk (sample version) is allowed to be non-convex. The core of our method is to robustly aggregate the gradients computed by the workers based on the filtering procedure proposed by Steinhardt et al. On the technical front, deviating from the existing literature on robustly estimating a finite-dimensional mean vector, we establish a uniform concentration of the sample covariance matrix of gradients, and show that the aggregated gradient, as a function of model parameter, converges uniformly to the true gradient function. To get a near-optimal uniform concentration bound, we develop a new matrix concentration inequality, which might be of independent interest.
Area Definition and Public Opinion Research of Natural Disaster Based on Micro-blog Data

Natural disasters have long disturbed people's normal lives. How to effectively identify and assess natural disaster-affected areas and further control network grievances is of great significance. This paper takes the floods in Hunan Province of China in 2017 as an example, based on the data sources of Sina Weibo, and combines text analysis and image analysis technology to define the disaster scope of natural disasters, and then uses sentimental analysis method and correlation analysis method to study the network public opinion in the disaster area. In this paper, the average accuracy and recall rate of the model which combines image analysis and text analysis to define the disaster area are 72.10% and 81.59% respectively, which effectively realizes the definition of flood disaster area in Hunan Province. In addition, this paper finds out the law of the development and change of network public opinion, and puts forward the opinions and suggestions on the determination of disaster area, the relief of disaster and the control of network public opinion.
Sentiment Analysis to Assess the Community’s Enthusiasm Towards the Development Chatbot Using an Appraisal Theory

PT Kreatif Dinamika Integrasi will develop a chatbot as the computer program designed to simulate intellectual conversation via text and voice by using Artificial Intelligence (AI). However, because development costs are high enough, it is necessary to gather opinions on the community's enthusiasm that will support the decision whether the development should or should not to have proceeded. Community sentiment analysis of data obtained from social media Twitter. Appraisal Theory method is exercised utilizing the R Studio tool to perform tweet crawling, preprocessing, scoring and term weighting. R is an open-source language and environment used for statistical computing and graphics. The results of the analysis indicate positive sentiment where the positive and negative comparison ratio was 4,78. The expression of sentiment analysis results by using the NRC library is 547 for "anticipation" sentiment and 728 for "trust" expression so that the technology of chatbot can be developed by the company. Although the results of the analysis of obtained are positive assessment, there is still the negative sentiment in the community, so it is necessary to act to reduce the risk.
Publicly verifiable and efficiency/security-adjustable outsourcing scheme for solving large-scale modular system of linear equations

Abstract Solving large-scale modular system of linear equations ( $\mathcal {LMSLE}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ℒ</mml:mi><mml:mi>ℳ</mml:mi><mml:mi>S</mml:mi><mml:mi>ℒ</mml:mi><mml:mi>E</mml:mi></mml:math> ) is pervasive in modern computer and communication community, especially in the fields of coding theory and cryptography. However, it is computationally overloaded for lightweight devices arisen in quantity with the dawn of the things of internet (IoT) era. As an important form of cloud computing services, secure computation outsourcing has become a popular topic. In this paper, we design an efficient outsourcing scheme that enables the resource-constrained client to find a solution of the $\mathcal {LMSLE}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ℒ</mml:mi><mml:mi>ℳ</mml:mi><mml:mi>S</mml:mi><mml:mi>ℒ</mml:mi><mml:mi>E</mml:mi></mml:math> with the assistance of a public cloud server. By utilizing affine transformation based on sparse unimodular matrices, our scheme has three merits compared with previous work: 1) Our scheme is efficiency/security-adjustable. Our encryption method is dynamic, and it can balance the security and efficiency to match different application scenarios by skillfully control the number of unimodular matrices. 2) Our scheme is versatile. It is suit for generic m -by- n coefficient matrix A , no matter it is square or not. 3) Our scheme satisfies public verifiability and achieves the optimal verification probability. It enables any verifier which is not necessarily the client to verify the correctness of the results returned from the cloud server with probability 1. Finally, theoretical analysis and comprehensive experimental results confirm our scheme’s security and high efficiency.
On the Usage of a Classical Arabic Corpus as a Language Resource

This article presents a literature review of computer-science-related research applied on hadith, a kind of Arabic narration which appeared in the 7th century. We study and compare existent works in several fields of Natural Language Processing (NLP), Information Retrieval (IR), and Knowledge Extraction (KE). Thus, we illicit their main drawbacks and identify some perspectives, which may be considered by the research community. We also study the characteristics of these types of documents, by enumerating the advantages/limits of using hadith as a language resource. Moreover, our study shows that previous studies used different collections of hadiths, thus making it hard to compare their results objectively. Besides, many preprocessing steps are recurrent through these applications, thus wasting a lot of time. Consequently, the key issues for building generic language resources from hadiths are discussed, taking into account the relevance of related literature and the wide community of researchers that are interested in these narrations. The ultimate goal is to structure hadith books for multiple usages, thus building common collections which may be exploited in future applications.
Bayesianism for Non-ideal Agents

Orthodox Bayesianism is a highly idealized theory of how we ought to live our epistemic lives. One of the most widely discussed idealizations is that of logical omniscience: the assumption that an agent’s degrees of belief must be probabilistically coherent to be rational. It is widely agreed that this assumption is problematic if we want to reason about bounded rationality, logical learning, or other aspects of non-ideal epistemic agency. Yet, we still lack a satisfying way to avoid logical omniscience within a Bayesian framework. Some proposals merely replace logical omniscience with a different logical idealization; others sacrifice all traits of logical competence on the altar of logical non-omniscience. We think a better strategy is available: by enriching the Bayesian framework with tools that allow us to capture what agents can and cannot infer given their limited cognitive resources, we can avoid logical omniscience while retaining the idea that rational degrees of belief are in an important way constrained by the laws of probability. In this paper, we offer a formal implementation of this strategy, show how the resulting framework solves the problem of logical omniscience, and compare it to orthodox Bayesianism as we know it.
EduCoin: a Secure and Efficient Payment Solution for MOOC Environment

Nowadays, the rise in popularity of massive open online courses (MOOCs) is significant. However, current online education service heavily relies on a third-party platform, which acts as a middleman to process transactions occurred among system participants. On the one hand, this centralized business model is based on database management system, once compromised, the data consistency and safety of transaction records can no longer be guaranteed. On the other hand, it's more expensive to resolve disputes and possible fraud transactions via a mediator than a direct peer-to-peer trusted payment. Aiming at solving issues regarding security and inefficiencies, we propose a crypto-asset EduCoin which is designed for secure and efficient payment in MOOC environment. Specially, we present a multi-party business model based on permissioned blockchain, Composer smart contracts and network topologies that utilize the three-tiered functional architecture of Fabric to achieve fast payment and trusted audit. By leveraging benchmark tools Caliper and Hitchhiker, we develop typical workloads based on transaction scenarios applying EduCoin. The experimental results demonstrate the practicality of EduCoin and offer insights into the design trade-offs and performance bottlenecks in production. We have released EduCoin for public use.
I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation

Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense predictions. However, as we show, value-based discrimination between the predictions from the segmentation network and ground-truth annotations can hinder the training process from learning to improve structural qualities as well as disabling the network from properly expressing uncertainties. In this paper, we rethink adversarial training for semantic segmentation and propose to reformulate the fake/real discrimination framework with a correct/incorrect training objective. More specifically, we replace the discriminator with a "gambler" network that learns to spot and distribute its budget in areas where the predictions are clearly wrong, while the segmenter network tries to leave no clear clues for the gambler where to bet. Empirical evaluation on two road-scene semantic segmentation tasks shows that not only does the proposed method re-enable expressing uncertainties, it also improves pixel-wise and structure-based metrics.