Reviewerly/exHarmony huggingface dataset:
TrackDQN: Visual Tracking via Deep Reinforcement Learning

Deep learning is a powerful method for visual object tracking, with excellent tracking accuracy and efficiency. However, the fast motion and larger deformation make a huge impact both on accuracy and efficiency of trackers, which makes more trackers less robustness with deep learning. In this paper, we make a attempt of applying deep reinforcement learning to improve robustness for Visual Tracking. The motivation is that deep Q-network has witnessed a success in solving various problems with computer vision and achieved promising performance on both running time and accuracy. In order to solve visual tracking problem scenario, we propose our Deep Q-Network for Visual Tracking(TrackDQN), which is inspired by deep Q-network and firstly customized from general deep reinforcement learning framework. Specifically, the tracker with TrackDQN can significantly improve the reliability and accuracy, meanwhile maintain higher tracking speed. Experimental results on the OTB benchmarks with fast motion and larger deformation demonstrate that our TrackDQN tracker has a comparable performance over state-of-the-art methods. Our method can also improve robustness and make a improvement of about 1.3% on accuracy with higher speed.
Secret Sharing Scheme Based on Chinese Remainder Theorem in Cloud Computing

For the problem of stealing user privacy data by non-trusted cloud computing service providers, the secret-sharing technique are applied to the cloud computing environment by using the Chinese remainder theorem, elliptic curve discrete logarithm problem and bilinear transformation. As a new verifiable secret sharing scheme, the Chinese residual theorem aims to allocate the special secret share and the common secret share respectively to the user and the cloud service provider. Without the participation of the user, the cloud service provider cannot restore the user's private data. The solution can detect the honesty of the cloud service provider and detect the fraud behavior of the cloud service provider in the system. The elliptic curve and the bilinear pairing technology. Not only reduces the computational cost of the system, but also achieves the verifiability of the solution and enhances the security of the system.
A General Framework for First Story Detection Utilizing Entities and Their Relations

News portals, such as Yahoo News or Google News, collect large amounts of news articles from a variety of sources on a daily basis. Only a small portion of these documents can be selected and displayed on the homepage. Thus, there is a strong preference for major, recent events. In this work, we propose a scalable First Story Detection (FSD) pipeline that identifies fresh news. This pipeline is used in order to instantiate a variety of FSD approaches. In addition we suggest a novel FSD technique that in comparison to existing systems, relies on relation extraction algorithms and exploits the named entities and their relations in order to decide about the freshness of an article. We evaluate our technique by instantiating existing state of art FSD techniques within our generic pipeline. As ground truth we use multiple datasets that cover different categories. Experimental results demonstrate that our FSD method in many cases provides an improvement over state-of-the-art techniques. In addition, we show using a large synthetic dataset that our general FSD pipeline has constant space and time requirements and is suitable for very high volume streams.
Soft Action Particle Deep Reinforcement Learning for a Continuous Action Space

Recent advances of actor-critic methods in deep reinforcement learning have enabled performing several continuous control problems. However, existing actor-critic algorithms require a large number of parameters to model policy and value functions where it can lead to overfitting issue and is difficult to tune hyperparameter. In this paper, we introduce a new off-policy actor-critic algorithm, which can reduce a significant number of parameters compared to existing actorcritic algorithms without any performance loss. The proposed method replaces the actor network with a set of action particles that employ few parameters. Then, the policy distribution is represented using state action value network with action particles. During the learning phase, to improve the performance of policy distribution, the location of action particles is updated to maximize state action values. To enhance the exploration and stable convergence, we add perturbation to action particles during training. In the experiment, we validate the proposed method in MuJoCo environments and empirically show that our method shows similar or better performance than the state-of-the-art actor-critic method with a smaller number of parameters. The experimental video can be found at http: //rllab.snu.ac.kr/multimedia.
The Brazilian Presidential Election of 2018 and the relationship between technology and democracy in Latin America

Purpose The purpose of this paper selected by ICIL 2019 committee in Rome is to demonstrate the current importance of the internet in the protection of democracy in developting countries. Design/methodology/approach It is intended to make a comparison with the growing and current phenomenon of Brazilian disinformation with other contemporary phenomena related to new technologies through literature review methodology. Findings The Brazilian elections in 2018 represent an authentic model in a post-Cambridge Analytical phase where the myth of the sanctity of data has been broken. The big influence of the algorithmic revolution on democracies in Latin America has never been more evident. The misuse of algorithms created an artificial environment that does not put us in contact with different realities; the consequences of this conjuncture have the deepest impacts, especially in countries that rely on a deficient educational system. Social implications Besides that, the broad use of zero-rating on the internet delivery in developing countries is also considered a factor of fake news dissemination. The information bubbles promote political polarization to the detriment of diversity – and the diversity is par excellence one of the pillars of democracy. Originality/value The research about the impact that the phenomenon of disinformation has on underdeveloped countries, it is essential to analyze the new role of the legislator in the elaboration of hypercomplex laws with multi-stakeholder interests that respect the essential core of digital human rights as the freedom of expression online.
Comparative Performance Analysis of Digital Modulation Schemes with Digital Audio Transmission through AWGN Channel

With the dawn of internet and smart phones, modern communication systems have consistently evolved as they became more advanced and sophisticated and less prone to distortions. This paper analyses the different digital modulation techniques employed during transmission of signal via Additive White Gaussian Noise (AWGN) channel. Comparative analysis is carried out in case of M-ary Phase Shift Modulation (MPSK) techniques viz. BPSK, QPSK, 8-PSK and 16-PSK and consequently their Bit Error Rates (BER) are analyzed. Furthermore, a standard audio signal is passed through AWGN channel while employing these M-ary Phase Shift Modulation (MPSK) techniques and result is analyzed. In the next part, a real time speech signal is transmitted and passed through AWGN channel where BPSK and QPSK are deployed and the results are analyzed.
PTAD:Provable and Traceable Assured Deletion in Cloud Storage

As an efficient deletion method, unlinking is widely used in cloud storage. While unlinking is a kind of incomplete deletion, `deleted data' remains on cloud and can be recovered. To make `deleted data' unrecoverable, overwriting is an effective method on cloud. Users lose control over their data on cloud once deleted, so it is difficult for them to confirm overwriting. In face of such a crucial problem, we propose a Provable and Traceable Assured Deletion (PTAD) scheme in cloud storage based on blockchain. PTAD scheme relies on overwriting to achieve assured deletion. We reference the idea of data integrity checking and design algorithms to verify if cloud overwrites original blocks properly as specific patterns. We utilize technique of smart contract in blockchain to automatically execute verification and keep transaction in ledger for tracking. The whole scheme can be divided into three stages-unlinking, overwriting and verification-and we design one specific algorithm for each stage. For evaluation, we implement PTAD scheme on cloud and construct a consortium chain with Hyperledger Fabric. The performance shows that PTAD scheme is effective and feasible.
Comparative Study Indian Electoral Reforms in Indian Context

The world's largest democracy has adopted electoral reforms in Assembly as well as Parliamentary elections. Electronic Voting Machine (EVM) has replaced the paper ballot system. However, there are serious concerns being raised with regards to the credibility and reliability of the EVM's. This resulted in Voter Verified Paper Audit Trail (VVPAT) being attached to the EVM, which was found to be unverifiable and non-auditable. The primary focus of this paper is to provide a comparison between Ballot Paper Voting System and EVM highlighting the various challenges of the existing electoral system. This paper provides an insight into Block chain technology, its impact and revolution it can bring in the field of Electoral reforms in India. The comparison parameters like Time, Cost, Transparency, Risk factor, Verification/Auditing process, Authentication etc. are considered for evaluation of the different methodology discussed.
Home Computerization for the Elderly And Disabled with Internet of Things

The smart home for disabled people is a resolution to the various day-to-day difficulties faced by physically challenged people. The advancement in home automation is quite significant in the past few years, the idea here is to develop a system that would provide ease and comfort to the physically challenged in everyday life. The suggested system operates remotely through Voice Control, Wi-Fi and Bluetooth, which would make people self-dependent and enable them to access the appliances with ease. The emergence of affordable and cheap microcontrollers has enabled the implementation of low-cost smart homes. This special-purpose system would result in improved standards of living for every individual. In this paper, an effort to present an affordable, multi-facility, home system has been done which is capable of integrating appliances.
Blockchain Trust and Decentralization in Supply Chain Management

The supply chain management poses significant challenges with respect to solving the issues of trust and open participation when dealing with processes such as management of product supply information, product lifecycle, transport history, etc. This paper explores the trust and decentralization issues prevalent in the main stages of the supply chain and checks their candidacy for the implementation using blockchain technology. The analysis is performed in terms of the characteristics of trust and decentralization with respect to forming a generalized framework. The main contribution of this work is a conceptual overview of the areas where blockchain technology could efficiently support the supply chain management in order to benefit further research and development.
A Class of Warped Filter Bank Frames Tailored to Non-linear Frequency Scales

A method for constructing non-uniform filter banks is presented. Starting from a uniform system of translates, generated by a prototype filter, a non-uniform covering of the frequency axis is obtained by composition with a warping function. The warping function is a $${\mathcal {C}}^1$$-diffeomorphism that determines the frequency progression and can be chosen freely, apart from minor technical restrictions. The resulting functions are interpreted as filter frequency responses. Combined with appropriately chosen decimation factors, a non-uniform analysis filter bank is obtained. Classical Gabor and wavelet filter banks are special cases of the proposed construction. Beyond the state-of-the-art, we construct a filter bank adapted to a frequency scale derived from human auditory perception and families of filter banks that can be interpreted as an interpolation between linear (Gabor) and logarithmic (wavelet) frequency scales. We derive straightforward conditions on the prototype filter decay and the decimation factors, such that the resulting warped filter bank forms a frame. In particular, a simple and constructive method for obtaining tight frames with bandlimited filters is derived by invoking previous results on generalized shift-invariant systems.
Automated Detection System for Adversarial Examples with High-Frequency Noises Sieve

Deep neural networks are being applied in many tasks with encouraging results, and have often reached human-level performance. However, deep neural networks are vulnerable to well-designed input samples called adversarial examples. In particular, neural networks tend to misclassify adversarial examples that are imperceptible to humans. This paper introduces a new detection system that automatically detects adversarial examples on deep neural networks. Our proposed system can mostly distinguish adversarial samples and benign images in an end-to-end manner without human intervention. We exploit the important role of the frequency domain in adversarial samples, and propose a method that detects malicious samples in observations. When evaluated on two standard benchmark datasets (MNIST and ImageNet), our method achieved an out-detection rate of 99.7–100% in many settings.
PowerStar: Improving Power Efficiency in Heterogenous Processors for Bursty Workloads with Approximate Computing

Modern Data Centers have increasingly adopted heterogeneous processors in their server nodes to maximize power efficiency. However, there are still challenges in how to properly configure these processors such that throughput can be maximized under fluctuating workload while optimizing system power consumption. In this paper, we propose PowerStar, a framework that maximizes power efficiency and reduces the number of reconfigurations needed in heterogeneous processors during periods of fluctuations in job arrival patterns while handling latency-critical workloads. PowerStar is built based on the following two key observations: (i) reconfiguration of heterogeneous processors to add more cores and enable higher performance and/or re-allocation of computing cores can be costly due to the extra latency involved and the associated energy overheads; (ii) a considerable amount of energy savings can be achieved by keeping the system in most power-efficient configurations capable of absorbing short bursts in job arrivals without needing to reconfigure the system. PowerStar operates by carefully choosing the most power-efficient configurations (states) and judiciously maximizing the state residency through the controlled use of approximate computing, when feasible. We implement PowerStar as a prototype on a 6-core ARM big.LITTLE heterogeneous platform and evaluate it with a variety of workloads. Our results show that, compared to a baseline of performance-driven power management policy, our power efficiency-aware PowerStar can reduce the average power by up to 11% under tight QoS (95th percentile latency under 3× job execution latency), and can save even higher average power of up to 32% under relaxed QoS (95th percentile latency under 10× job execution latency) constraints when compared to the baseline.
A Study on Deep Learning Architecture and Their Applications

Deep learning (DL) techniques are currently drawn more research interest including computer vision, pattern recognition, speech recognition, and robotics due to its higher accuracy and self-feature extraction property compared to the traditional algorithms that necessitate hand-crafted features. The success with the DL is due to the advancement in technology and the availability of the high-power devices that are achieved with high computational complexity. In this paper, we aim to provide an intrinsic investigation of the DL architectures and their applications in the practical world. Specifically, the overview of autoencoder, restricted Boltzmann machine, generative adversarial network, and convolutional neural network are provided. Different aspects and applications in real-world cases are surveyed and summarized.
Measuring Inconsistency in a General Information Space

AI systems often need to deal with inconsistent information. For this reason since the early 2000s some AI researchers have developed ways to measure the amount of inconsistency in a knowledge base. By now there is a substantial amount of research about various aspects of inconsistency measuring. The problem is that most of this work applies only to knowledge bases formulated as sets of formulas in propositional logic. Hence this work is not really applicable to the way that information is actually stored. The purpose of this paper is to extend inconsistency measuring to real world information. We first define the concept of general information space which encompasses various types of databases and scenarios in AI systems. Then, we show how to transform any general information space to an inconsistency equivalent propositional knowledge base, and finally apply propositional inconsistency measures to find the inconsistency of the general information space. Our method allows for the direct comparison of the inconsistency of different information spaces, even though the data is presented in different ways. We demonstrate the transformation on three general information spaces: a relational database, a graph database, and a Blocks world scenario, where we apply several inconsistency measures after performing the transformation.
Rubber Tapping Position and Harvesting Cup Detection Using Faster-RCNN with MobileNetV2

This paper presents the detection of rubber tree (Hevea brasiliensis) tapping position (tapping-path) and trunk-mounted harvesting cup on RGB-D images, which is the machine vision part of automatic rubber farming system. RGB-D images are collected in real tapping environment. Camera is placed about 1.00-meter distance to the trunk which is feasible for robot platform. Faster-RCNN (Region-based Convolutional Neural Network) object detector is proposed with ImageNet pretrained Mobilenet-v2 as the feature extraction layers to detect multiple object's class - tapping path and harvesting cup. Our experiments show that cup detection achieve higher average precision on grayscale depth image detector than only RGB image detector. We also propose 3-channel combinations from 4 grayscales (RGB-D) then, put into specific detectors. Detector's performances are computed using 0.5 and 0.75 intersection over union thresholds (IoU). The results show that Faster-RCNN with Mobilenet-v2 tapping position and cup detection can reach highest 0.95 mAP@0.5 IoU estimated with k-fold cross validation (k=5). Our main contribution is that detection of harvesting cup using preprocessed grayscale depth with color images are more well-localized than only color image, observed at 0.75 IoU but tapping position detection is highly depended on color images and this can be impacted to practical application since cup detection are not required lighting.
Online Budgeted Least Squares with Unlabeled Data

The scarcity of labeled data in real streaming environments has boosted the study of online semi-supervised learning (SSL). However, existing online SSL models often rely on some specific assumptions (e.g., manifold assumption) and need to maintain some extra constraints (e.g., the Laplacian matrix) on the fly, which is usually time and resource consuming. In this paper, we propose an efficient and effective online semi-supervised learning approach via Budgeted Least Square (BLS). Specifically, we first derive both closed-form transductive and inductive solutions for kernel least squares classification in the semi-supervised setting. Then, together with online kernel learning, BLS allows a concise online update. Besides, the theoretical regret bound of BLS is analysed, and empirical experiments on both static and streaming data further demonstrate its superiority over state-of-the-art algorithms.
Unsupervised OCR Model Evaluation Using GAN

Optical Character Recognition (OCR) has achieved its state-of-the-art performance with the use of Deep Learning for character recognition. Deep Learning techniques need large amount of data along with ground truth. Out of the available data, small portion of it has to be used for validation purpose as well. Preparing ground truth for historical documents is expensive and hence availability of data is of utmost concern. Jenckel et al. jenckel came up with an idea of using all the available data for training the OCR model and for the purpose of validation, they generated the input image from Softmax layer of the OCR model; using the decoder setup which can be used to compare with the original input image to validate the OCR model. In this paper, we have explored the possibilities of using Generative Adversial Networks (GANs) [6] for generating the image directly from the text obtained from OCR model instead of using the Softmax layer which is not always accessible for all the Deep Learning based OCR models. Using text directly to generate the input image back gives us the advantage to use this pipeline for any OCR models even whose Softmax layer is not accessible. In the results section, we have shown that the current state of using GANs for unsupervised OCR model evaluation.
EASVD: A Modified Method to Enhance the Authentication for SPICE Virtual Desktop

Virtual Desktop (VD) provides a convenient way for users to operate their virtual machines on different types of terminals. SPICE is an excellent protocol for the VD display. However, there are still several threats when using the original SPICE in the high-level scenarios. The threats are mainly due to the fact that the original SPICE ignores the confidentiality of the VD. The threats contain the data interaction like copying, falsifying, and deleting between VDs & different physical terminals. Furthermore, the original SPICE authentication is a one-factor method based on the IP address and password of the user. We bind each VD to a specific authorized terminal to reduce the threat posed by the data interaction between VDs and physical terminals in the high-level security scenarios proposed in our paper. At the same time, we propose a two-factor authentication method which includes physical terminals authentication and user-password authentication. We also reinforce the security of the VD by putting forward a modified way to storage the password. Our approach to intensify the VDs authentication has been implemented in terminals based on SPICE and Libvirt. Our method can effectively prevent the unauthorized users from illegal logging into the VD on the illegal physical terminals, even when the correct password is intercepted by unauthorized users. We demonstrate the effectiveness of our method by experimental results.