Reviewerly/exHarmony huggingface dataset:
Securely Storing and Sharing Memory Cues in Memory Augmentation Systems: A Practical Approach

A plethora of sensors embedded in wearable, mobile, and infrastructure devices allow us to seamlessly capture large parts of our daily activities and experiences. It is not hard to imagine that such data could be used to support human memory in the form of automatically generated memory cues, e.g., images, that help us remember past events. Such a vision of pervasive "memory-augmentation systems", however, comes with significant privacy and security implications, chief among them the threat of memory manipulation: without strong guarantees about the provenance of captured data, attackers would be able to manipulate our memories by deliberately injecting, removing, or modifying captured data. This work introduces this novel threat of human memory manipulation in memory augmentation systems. We then present a practical approach that addresses key memory manipulation threats by securing the captured memory streams. Finally we report evaluation results on a prototypical secure camera platform that we built.
NEAR

Many explainable recommender systems construct explanations of the recommendations these models produce, but it continues to be a di cult problem to explain to a user why an item was recommended by these high-dimensional latent factor models. In this work, We propose a technique that joint interpretations into recommendation training to make accurate predictions while at the same time learning to produce recommendations which have the most explanatory utility to the user. Our evaluation shows that we can jointly learn to make accurate and meaningful explanations with only a small sacri ce in recommendation accuracy. We also develop a new algorithm to measure explanation delity for the interpretation of top-n rankings. We prove that our approach can form the basis of a universal approach to explanation generation in recommender systems.
Comparison of generalized cross validation and unbiased risk method for selecting optimal knot in spline truncated

Abstract Gini ratio is an indicator to measure income inequality. Gini ratio of Indonesia in 2017 is 0.391, still far from Gini ratio target by Bappenas in 2019, that is 0.36. The Gini ratio modeling in this study uses a nonparametric regression approach because the form of the regression curve between the Gini ratio and its predictive variables is unknown. One of the estimators in nonparametric regression is spline truncated. Spline truncated has a knot that adjusts to the local characteristics of a function or data more effectively. The number of knots and their location affect the form of regression curve estimation, so it’s important to obtain optimal knot. There are methods for selecting optimal knots, such as Generalized Cross Validation (GCV) and Unbiased Risk (UBR). This study compares GCV and UBR in selecting optimal knots on Gini Ratio data in Indonesia 2017. The criteria of the best model are based on Mean Squared Error (MSE) and R 2 values. From the result, the optimal knot from GCV was a combination of 3-2-2-3 knot with MSE of 0.00085 and R 2 of 79.18%. Meanwhile, by using UBR, the optimal knot is three knots with MSE of 0.00095 and R 2 of 66.42%. In conclusion, GCV generated better model than UBR.
Adaptive Particle Sampling and Resampling in Parallel/Distributed Particle Filters

Particle filters have been widely used in estimating the states of dynamic systems by using Bayesian interference and stochastic sampling techniques. Parallel computing techniques were introduced to improve the performance of sequential particle filters with multiple processing units (PUs). However, the unavoidable communications between Pus, lower the performance. The hybrid and adaptive resampling algorithms were proposed to improve the performance of parallel/distributed particle filters by reducing the communication costs without loss of estimation accuracy. In this paper, we propose an adaptive sampling and resampling technique in particle filters. In the proposed algorithm, the number of particle is dynamically adjustable based on the model convergence. As a result, less particles will be used if the current convergence is good and more particles will be used if the convergence is getting bad. The experimental results show the improved performance by using less particles and reducing the communication cost compared with other algorithms.
Assuring Correctness, Completeness, and Performance for Model-Based Fault Diagnosis Systems

The robotic scientific and commercial spacecraft industries are currently trending towards the development of onboard autonomous capabilities for responding quickly to dynamic environments and rapidly changing situations. Model-based fault diagnosis (MBFD) is an approach to estimating a spacecraft's health state by continuously verifying accurate behavior and diagnosing off-nominal behavior. Proper functioning of MBFD depends on 1) the quality of the diagnostic system model that is analyzed and compared to commands and onboard measurements to estimate a system's health state, and 2) the correct functionality of the diagnosis engine interrogating the model and comparing its analyses to observed system behavior. Our goal is to develop Verification and Validation (V&V) techniques for a MBFD system to provide the necessary confidence in its ability to estimate the health of on-board spacecraft components and systems accurately and precisely. Our effort is investigating two areas. First, we build on our previously-reported work in developing techniques for checking the correctness and completeness of MBFD systems. Second, we develop techniques for analyzing performance characteristics of MBFD systems (e.g., runtime, memory usage) to provide assurance that they will function within the resource-constrained environments found on spacecraft. This paper describes work we have done in both areas. For the first area, we describe our approach to developing formal definitions for a correct and complete fault diagnosis system, the application of those definitions to a systematic way of checking the correctness and completeness of a diagnostic model independently from the diagnosis engine, and the results of these checks. We also discuss the ways in which V&V techniques are related to our correctness and completeness checking techniques for a MBFD system development process. For the second area, we develop analytical expressions as functions of key parameters to estimate runtime/processing performance bounds. In parallel, we define various model structures (topologies) to enable parameterized testing to determine how specific parameters affect performance, providing a way to identify the significant contributors. These analyses lay the foundation for developing techniques and tests for evaluating false-positive and false-negative metrics, which are used to characterize MBFD diagnosis performance.
Recursive update filtering-based variational Bayesian approach for extended target or group target tracking with nonlinear measurements

For extended target and group target tracking, most existing random matrix approaches assume that the measurements are linear in the kinematic state and in the noise with its covariance being a random matrix to represent the extension of an extended target or target group. However, in practice, the measurements (e.g., range and bearing) are nonlinear in the kinematic state and extension noise. Only the matched linearization (ML) approach has been proposed to linearize the nonlinear measurement function based on minimum mean square error criterion. However, if the state prediction error is not small, it will incur large linearization error and then the variational Bayesian measurement update approach produces inaccurate estimation results. To this point, using the ML technology, we present a recursive update filtering-based variational Bayesian update (RUF-VBU) approach for better estimation performance. In addition, the square-root implementation of RUF-VBU is proposed for the recursive interruption problem caused by the nonpositive definite covariance matrix. The simulation results demonstrate the effectiveness of the proposed approach.
Design and Fabrication of 6-axis Gesture Controlled Robot

In this paper, a six-axis robot which can be controlled by gestures is fabricated. The robot has six degrees of freedom and is easily accessible since the robot does not need higher efforts of controlling. A human hand glove is integrated with transceiver and flex sensors, the resistance variation of the flex sensor is transmitted to the robot axis. Based on the variation the robot can rotate angular or in a linear manner about its axis. A simple transceiver is used in order to control the signals which are sent and received between the robotic arm and the human hand. Arduino programming is used for controlling the system.
Blurring Political Leanings of Messages on Social Networks Using Deniable Steganography

While social network services have become popular platforms where large amounts of user-generated content are published, individuals, election campaigns, and political organizations leverage the services to publish, promote, and exchange political messages.However, sometimes, political discussions resulted in heated debates, doxing, and even cyberbullying.These conflicts may decrease the engagement of users and prevent them from publishing articles with apparent political orientation.To help users to post political opinion among the Internet freely, in this paper, we employ the concept of deniable encryption to generate neutral-like articles composed of two critics toward both sides of the discussion, while one of the critics is the actual message from the author to particular recipients.For receivers who know the credential information (e.g., key and codebook), they can communicate with each other by extracting the real information from the neutral-like articles.For people with the opposite stand, they cannot claim the message that is supporting specific political entity, as it contains disapproval to both stands; besides, they cannot prove that which part of the message is the plaintext even if they intercept the messages.We provide a generalized process of our proposal and present a practical scenario using transcripts, Facebook posts of Donald Trump, and posts of Hillary Clinton to demonstrate the usability of our scheme.
A study of hardware architecture based attacks to bypass operating system security

Abstract Malware target a vulnerability, bug or loophole in software to exploit the system, escalate privileges, extract inaccessible data, or execute code for malicious purpose. We can assert that over the period of time, both hardware and software have evolved and so are their vulnerabilities. But most of the vulnerabilities identified or researched are software oriented. On similar lines, there can be vulnerabilities in the hardware architecture that can be exploited, hidden from upper layer securities like operating system (OS) reference monitor, antivirus, etc. It is difficult to trace the vulnerabilities in the hardware primarily due to complex architecture and difficulties to observe the effects of operations performed across the system. Just like the software, the hardware architecture can also be exploited to develop malware and to gain access to sensitive data. This paper discusses the attacks that exploit the operations performed at the hardware level. We have discussed the different exploits that use the hardware architecture to extract data, their limitations and the future of hardware architecture based exploits. At the end, the data extraction process is validated through our implementation of one of the hardware architecture based exploits. We question about the impact on system security if the hardware architecture is exploited.
Error compensation method of large size steel sheet measurement based on control field

Abstract Aiming at the problem of low accuracy of large size sheet edge, a method of sheet size error measurement based on control field is proposed. Firstly, an error compensation model of the measuring system based on control field is established by analyzing the causes of the errors of the measuring system. A grid standard plate is designed and the error distribution on the grid line is obtained by using the measurement results of the standard plate. Secondly, the error curve is established according to the distribution, and the trig function theorem is used to project the curve into the image pixel coordinate system. Finally, the control field of the whole measurement area is reconstructed by linear interpolation, and the measurement results are compensated and corrected. The steel sheet is measured in the measuring area of 1.2m × 2.6m on the basis of these theories and technologies. The experiment shows that the precision of the measuring system can reach 1mm per meter, which satisfies the accuracy and speed requirements of large-size steel sheet measurement in industry, and has high application value.
Analysis on the Protection of Genetic Resources from the Perspective of Intellectual Property

Considering the frequent plagiarism and plunder of genetic resources in China, the analysis of genetic resources protection has become a very important issue.This paper used literature analysis method to discuss the protection of genetic resources from the perspective of intellectual property protection.This paper reviewed current practices and policies on intellectual property, and access to genetic resources, in an attempt to better understand the nature of genetic resources and the difficulties of intellectual property protection, expound theoretical framework of genetic resource protection, so as to propose corresponding improvement suggestions under the context of China.
Sustaining the IT Workforce

Attracting, recruiting and retaining a sufficient number of information technology professionals remains a key issue for organizations. Although research and practice call upon this issue for quite some time and suggest various interventions, the shortage of qualified IT professionals does not decline, but seems to increase. Thus, sustaining the IT workforce represents a recurring issue faced with challenges that are fixed firmly within the domain. This literature review aims to identify the major issues related to sustaining IT professionals from a human resource management perspective. Sixty-seven research articles from the AIS Senior Scholars' basket are reviewed to identify the major issues respectively to the past times from the dot com boom during the 1990s, across industry recession in 2001 up to the present day. Four major issues are explained and discussed.
Coalitional Game Simulation Platform

Some negotiation platforms have been designed for the negotiation research in the past decades. Few of them discuss the coalition game. In this paper, coalition game simulation platform -- an online real-time multilateral negotiation environment simulator-- is introduced. The negotiation domain for this platform is derived from the weighted voting game, an instance of coalition game. For the purpose of research, we equipped this platform with several good features, like human-agent negotiation, extensible API for negotiation agent development, configurable game parameters and game history data.
FSM based High Speed VLSI Architecture for DBUTVF Algorithm

A Novel Finite state machine with data based VLSI architecture for fixed 3×3 kernal for Decision based unsymmetrical trimmed variants filter (DBUTVF) is proposed in this paper. The proposed architecture uses one hot encoding to assign the different states that were used in state diagram. A novel rank ordering algorithm that is better than its 1D counterpart is also a novel feature of this architecture. The architecture was targeted for the device XC3s5000-5fg900. The result of the architecture is evaluated in terms of area, speed and power for the targeted device.
The efficient method of mixed reality light restoration using HDR image of 3D scene

One of the main problems of MR devices is a physically correct representation of luminance distribution for virtual objects and its shadows in the real world. In other words, reconstruction of the luminance distribution over the scene is one of the key parameters that allow solving the problem of "seamless" interaction between the virtual and real worlds and serves as the primary means for improving the quality of perception when we use the augmented reality system. By using neuro networks, it becomes possible to estimate the number of light sources, their positions and radiation goniodiagrams. Authors use the fast and high-quality photorealistic image synthesis to synthesize the virtual image of the real world under the restored illumination conditions. Having both real world image and synthesized real world image at restored illumination conditions it becomes possible to estimate the error of the roughly estimated light source positions to correct it. It is possible by application of the special shading analysis method. The first step requires two kind of information: reconstructed geometry of surrounding space and HDR image, with luminance data. One of the main issues is that there are two kinds of illumination: primary illumination, from light sources, and secondary illumination or background illumination, from non-luminous objects. The task is to remove background illumination and define primary illumination. After restoration illumination condition, a virtual object could be properly added to visible image of real environment.
Structural-Based Graph Publishing Under Differential Privacy

Mining data from social and communication network have been attracting recent attention from various research fields. However, these data represented by large-scale graphs are often sensitive and private. It is a necessity of developing algorithms to publish large-scale graph while not revealing sensitive information. As a standard for data privacy preservation, differential privacy based algorithm are also widely used in publishing graph-based dataset. However, previous differential privacy based methods often bring huge computational cost and lack the capability of modeling complicated graph structure. To address these challenge, we propose a novel graph publishing algorithm which combines community detection with differential privacy method. By segmenting the graph into several sub-graphs by community detection, differential privacy methods is able to handle large-scale graphs with complex structure. Experimental results on several datasets demonstrates the promising performance of the proposed algorithm compared with original differential privacy methods.
Multi-rate reference embedding for highly-scalable self-recovery using fuzzy mamdani

This paper proposes a self-recovery method of fragile watermarking. Generally, self-recovery methods embed two types of data into the original image: check-bits for tamper detection and reference data for image recovery. Generating reference data is the primary challenge of every self-recovery meth od for more tamper resiliency and higher reconstruction quality. The proposed Multi-Rate Reference Embedding (MRRE) method makes unique reference data with several redundancy rates, instead of generating multiple reference data. According to the proposed methodology, the image is compressed by a source coding algorithm and the compressed data is separated into ten parts. Each part is protected by a channel coding algorithm based on pre-assigned redundancy rates. A fuzzy-based rate allocation system is used to assign the redundancy rates based on the importance of data. The generated data is packetized and randomly embedded into an image block. For tamper detection purpose, check-bits are generated by an MD5 hash function for every block. Both reference data and check-bits are embedded into three least significant bits (LSB) of the image pixels. To increase restoration efficiency, the proposed MRRE method provides ten scales of image recovery named highly-scalable self-recovery. The simulation results show an improvement in both tamper tolerability and reconstruction quality in comparison with the most recent methods.
Balance with the interactive size effects of display, target, and key spacing in tablet tapping, dragging, and typing tasks

Abstract This study aimed to expand the current demonstration of display size effects from pointing tasks to dragging and tap‐typing tasks to investigate the effects of influential factors on the performance of touchscreen tablets. From 7‐ to 11‐in. displays, the index of difficulty was kept constant by increasing the target amplitudes and widths (6.0–9.5 mm) proportionally, and the key spacing were, respectively, 13.5 × 7.3–21.5 × 11.5 mm with 0‐mm key gaps. The results demonstrated that simple task types (complexities) reinforced the display size effects and that the effects interacted with target sizes (including amplitude/width scaling) and key spacing to reflect increasing efficiency and usability with increasing sizes. Gender effects were only significant in subjective assessments. Display sizes approaching 10 in., target sizes not less than 7 mm (optimal 9–10 mm) and key spacing larger than 17.5 mm induced the effects of motor scale (the scale of the upper limb joint coordination) and interacted with one another to result in the optimal performance and usability.
Development of a Song Lyric Corpus for the English Language

Web Scraping Tools are simplifying the task of creating large databases for various applications such as the construction of corpus aimed at the development of applications for natural language processing. Many of these applications require a large amount of data, and in that sense, the Web presents itself as an important data source. Among the various tasks in the NLP scope, one of the most challenging is automatic text generation. In this task the objective is to generate syntactically and semantically correct texts after a training process on a particular corpus. This article presents the elaboration of an English song lyrics Corpus, extracted from the Web, that can be used to train applications for automatic generation of lyrics, poems, or other NPL related tasks. After its normalization, an analysis of the Corpus is presented, as well as analyzes performed after the corpus vectorization (embedding) generated with the use of two current techniques.
General Image Segmentation by Deeper Residual U-Net

With the development of deep learning, using convolutional neural networks for semantic segmentation has received a large amount of attention. Numerous convolutional neural networks architecture has been proposed. In biomedical image processing, U-Net has achieved great remarkable achievement. However, due to the feeble convolution operations for extracting complex image information, the U-Net presents a poor performance in general semantic segmentation. Therefore, in this paper, we propose a new neural network framework, called 'Deeper Residual U-Net' for general image semantic segmentation. In our method, we apply ResNet101 for extracting features and use a double features fusion mechanism compared to U-net. In the first time, the Deeper Residual U-Net up sample each stage features and fuses them with features of the previous layer one by one, which make low-level features contain more abstract information. In the second time, it upsamples all fused features of different stages to the same size and combines them to predict. We test our network in Pascal VOC 2012 dataset and get mean accuracy 80.9, mIoU accuracy 74.3, which already available for general image segmentation.