Reviewerly/exHarmony huggingface dataset:
Appearance Frequency-Based Ranking Method for Improving Recommendation Diversity

Recommender systems are used to analyze users' preferences through their past activities and to personalize recommendations for each user based on what they might be interested in. The performance of the recommender system is most commonly measured using only recommendation accuracy. However, recommending accurate items does not mean that the generated recommendation is the best for the user because it can be biased towards some items that have a higher chance of being liked by users, such as popular items. Recommendations become repetitive and obvious with biased item selection and are less likely to be personalized. To mitigate bias and repetitiveness, recommendation diversity has been studied. However, diversity has a trade-off relationship with accuracy. Modifying the recommendation algorithm to consider diversity while learning about user preferences would not only cause loss in accuracy, but also lead to less precise reading of user preferences. Instead, using ranking methods to re-rank the priority of items predicted, the recommendation algorithm would keep the preciseness of the algorithm. In this study, a ranking method using the appearance frequency of items to restrict the items from being frequently recommended will be proposed. The experimental results showed that the proposed method consistently improved diversity in multiple diversity metrics.
Realizing integrated prioritized service in the Hadoop cloud system

Cloud computing has profoundly influenced how people receive IT services nowadays. As the cloud offers more and more services to users, the Quality of Service (QoS) in cloud has become an important issue that could determine the success or failure of services on the cloud. Among the aspects of cloud QoS, job performance is an area which most cloud systems often have little or no control over it. To effectively manage job performance in cloud QoS, the collaboration between the cloud system and its underlying operating system is a must. Hadoop is one of the most popular cloud systems used today. Unfortunately, it does not support efficient schemes to manage job performance. Previously, we proposed a new approach, namely PYARN, to enable Hadoop to provide prioritized job scheduling service to help manage job performance in cloud QoS. In this paper, we report our efforts to make PYARN collaborate with PMMDO, a Linux module providing prioritized service we developed earlier. Compared with the original Hadoop, experiment results showed that the cooperation between PYARN and PMMDO could further expedite the execution of prioritized jobs by up to around 30% more than what PYARN could achieve alone. Our integrated system demonstrates that the cloud system and its host operating system should work together to help manage cloud QoS with regard to job performance.
HSIM-DNN

Deep learning that utilizes large-scale deep neural networks (DNNs) is effective in automatic high-level feature extraction but also computation and memory intensive. Constructing DNNs using block-circulant matrices can simultaneously achieve hardware acceleration and model compression while maintaining high accuracy. This paper proposes HSIM-DNN, an accurate hardware simulator on the C++ platform, to simulate the exact behavior of DNN hardware implementations and thereby facilitate the block-circulant matrix-based design of DNN training and inference procedures in hardware. Real FPGA implementations validate the simulator with various circulant block sizes and data bit lengths taking into account accuracy, compression ratio and power consumption, which provides excellent insights for hardware design.
A Survey of Scheduling Algorithms in Cloud Computing

In cloud computing, resources are provided as a public utility and the user can lease and release those resources via the Internet by an on-demand fashion. Since cloud resources are meant to be utilized properly, the role of scheduling algorithms is vital to ensure an appropriate resource is available to every request. In this paper, major cloud computing scheduling algorithms are reviewed, and discussion is made accordingly. This paper also compares cloud computing scheduling from the perspective of makespan, load balancing, CPU utilization, deadline, response time, and allocation cost. In addition, the paper proposes an abstract model to integrate desirable features the of algorithm suitable to cloud environment. Future research opportunities are highlighted and the end of the paper.
Repairing Learned Controllers with Convex Optimization: A Case Study

Despite the increasing popularity of Machine Learning methods, their usage in safety-critical applications is sometimes limited by the impossibility of providing formal guarantees on their behaviour. In this work we focus on one such application, where Kernel Ridge Regression with Random Fourier Features is used to learn controllers for a prosthetic hand. Due to the non-linearity of the activation function used, these controllers sometimes fail in correctly identifying users’ intention. Under specific circumstances muscular activation levels may be misinterpreted by the method, resulting in the prosthetic hand not behaving as intended. To alleviate this problem, we propose a novel method to verify the presence of this kind of intent detection mismatch and to repair controllers leveraging off-the-shelf LP technology without using additional data. We demonstrate the feasibility of our approach using datasets gathered from human participants.
An explanation-based approach for experiment reproducibility in recommender systems

The offline evaluation of recommender systems is typically based on accuracy metrics such as the Mean Absolute Error and the Root Mean Squared Error for error rating prediction and Precision and Recall for measuring the quality of the top-N recommendations. However, it is difficult to reproduce the results since there are various libraries that can be used for running experiments and also within the same library there are many different settings that if not taken into consideration when replicating the results might vary. In this paper, we show that within the use of the same library an explanation-based approach can be used to assist in the reproducibility of experiments. Our proposed approach has been experimentally evaluated using a wide range of recommendation algorithms ranging from collaborative filtering to complicated fuzzy recommendation approaches that can solve the filter bubble problem, a real dataset, and the results show that it is both practical and effective.
A binocular parallel rendering method for VR globes

The scene-rendering mechanism based on binocular vision is one of the key techniques for the VR globe to achieve immersion-type visualization of global 3D scenes. However, this special rendering mechanism also requires that the 3D scene is continuously drawn twice within one frame, which significantly affects the rendering efficiency of VR globes. Therefore, we propose a binocular parallel rendering method. This method first improves the current rendering process of VR globes by assigning the rendering tasks for the left and right camera of VR to be processed on different CPU cores, thereby achieving parallel rendering of binocular scenes. Second, due to the problem of inconsistent resolution of binocular scenes caused by different viewpoints for the left and right cameras, we propose a resolution synchronize algorithm. this algorithm conducts real-time synchronization on the resolution of scene in the rendering process and thus avoids the problem of erroneous binocular stereo matching. Finally, we validate the effectiveness of the method in this paper through experiments. The results of experiments indicate that while the method in this paper can ensure the consistency of binocular scene resolution, it can decrease the frame time of VR globes by approximately 27% on average.
TPPG: Privacy-preserving trajectory data publication based on 3D-Grid partition

The issue of privacy preservation is receiving more and more attention when publishing trajectory data. In this paper, we study the challenges of published trajectory data anonymization. Most existing anonymization methods directly delete the trajectories or locations violating specific constraints , it is likely to cause a large loss of information. To address the problem, this paper proposes a trajectory privacy preservation method based on 3D-Grid partition in order to reduce information loss in the process of trajectory anonymization. This method first divides the trajectory region into several spatio-temporal units (denoted as 3D-cells), and then conducts location exchange or suppression in each spatio-temporal unit. Based on the trajectory data partition, within each 3D-cell, the proposed method exchanges locations among trajectories or removes very few locations of some sub-trajectories which do not meet the conditions rather than the whole trajectory. Our method considers three scenarios of trajectory distribution and measures trajectory similarity based on time, orientation, spatial locations and other features of trajectory. After the reconstruction of the related anonymous sub-trajectories, an anonymized trajectory dataset is obtained. Theoretical analysis and experimental results show that, compared to other methods, the proposed algorithm effectively preserves trajectory data privacy and improves the anonymous results of trajectory data in terms of accuracy and availability.
A contextual family tree visualization design

With the increase in availability of online national archives and software to manage genealogical records, genealogy studies are growing in popularity. While conducting research, genealogists communicate their findings either in written narratives or in genealogical charts. In that context, visualization methods can be very effective for promoting the understanding of the intricacies of a family tree and the relations among its individuals. Most of the software designed for genealogy provides a collection of standard charts to plot family trees, despite having limited analysis capabilities in general. In addition, most of the research in family tree visualization designs have been focused on methods to represent very large trees in a restricted space. Herein, we propose the contextual family tree, a new visualization design for family trees that represents individuals and their spouses with enhanced details about their families’ context. The design was developed through an iterative prototype-evaluation design cycle. For illustrating the potential of our new visualization design, we used contextual family trees created from publicly available genealogical data communication files, showing that the design can be useful to provide a better understanding of the data and also for validating the consistency of the genealogical data.
RoD: Evaluating the Risk of Data Disclosure Using Noise Estimation for Differential Privacy

Differential privacy is a paradigm of big data privacy protection that offers protection even when an attacker has arbitrary background knowledge in advance. Consequently, it is viewed as a reliable protection mechanism for sensitive information. Differential privacy introduces noise, such as Laplace noise, to obfuscate the true values in a data set while preserving its statistical properties. However, a large amount of Laplace noise added into a data set is typically defined by the discursive scale parameter of Laplace distribution. The privacy budget <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> in differential privacy has been theoretically interpreted, but the implication on the risk of data disclosure ( <i>RoD</i> ) in practice has not yet been well studied. Moreover, choosing an appropriate value for <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> is not straightforward because it considerably affects the level of privacy in a data set. In this paper, we define and evaluate <i>RoD</i> in a data set with either numerical or binary attributes for numerical or counting queries with multiple attributes based on noise estimation. Through confidence probability of noise estimation, we provide a simple method to select the privacy budget <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> for differential privacy and associate differential privacy with <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymization. Finally, we show the relationship between the <i>RoD</i> and <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> as well as between <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> in our experimental results. To the best of our knowledge, this is the first study using the quantity of noise as a bridge to evaluate <i>RoD</i> for multiple attributes (either numerical or binary data) and determine the relationship between differential privacy and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymization.
Optical wide-field tomography of sediment resuspension

We present a wide-field imaging approach to optically sense underwater sediment resuspension events. It uses wide-field multi-directional views and diffuse backlight. Our approach algorithmically quantifies the amount of material resuspended and its spatiotemporal distribution. The suspended particles affect the radiation that reaches the cameras, hence the captured images. By measuring the radiance during and prior to resuspension, we extract the optical depth on the line of sight per pixel. Using computed tomography (CT) principles, the optical depths yield estimation of the extinction coefficient of the suspension, per voxel. The suspended density is then derived from the reconstructed extinction coefficient.
Bayesian variable selection in a finite mixture of linear mixed-effects models

Mixture of linear mixed-effects models has received considerable attention in longitudinal studies, including medical research, social science and economics. The inferential question of interest is often the identification of critical factors that affect the responses. We consider a Bayesian approach to select the important fixed and random effects in the finite mixture of linear mixed-effects models. To accomplish our goal, latent variables are introduced to facilitate the identification of influential fixed and random components and to classify the membership of observations in the longitudinal data. A spike-and-slab prior for the regression coefficients is adopted to sidestep the potential complications of highly collinear covariates and to handle large p and small n issues in the variable selection problems. Here we employ Markov chain Monte Carlo (MCMC) sampling techniques for posterior inferences and explore the performance of the proposed method in simulation studies, followed by an actual psychiatric data analysis concerning depressive disorder.
Cooperation of Virtual Reality and Real Objects with HoloLens

We propose several approaches when programming applications for Microsoft HoloLens. These applications aim at close cooperation of real world with holographic objects (virtual reality). Often a precise placement that does not change with the position of a user can be challenging. One way of avoiding such problems is to use technology of anchors. Another way proposed in this paper is a prior scanning of object of interest with an application aimed for Vuforia plugin. Both concepts use close cooperation with user-friendly environment for developing of mixed reality applications – Unity. We present and discuss examples in this article. The area of precise location for mixed reality (augmented reality mixed with real reality in a way that one can interact witch each other) is of paramount importance. If we know precise position of virtual objects in the scene and precise position of the device in the scene, we can alleviate the burden of massive computing within HoloLens. As a result, we gain better experience from mixed reality.
Movement to market, currency to property: the rise and fall of Bitcoin as an anti-state movement, 2009–2014

Can social movements mobilize market devices to challenge the political–economic order? Focusing on Bitcoin, we argue that an effective anti-state market device needs to be durably 'counterearmarked', to use Viviana Zelizer's term, with radical meaning. This durability, however, requires that the movement build alliances with holders of political and economic power who also embrace the device's radical meaning, lest those actors reformat the device to suit their purposes. To make this case, we locate Bitcoin's radical origins in a performative project built on elements of Austrian monetary theory. We then track Bitcoin's dual transformation between 2009 and 2014: the anti-state movement gave way to a market featuring big financial players, and the Internal Revenue Service officially redefined the bitcoin currency as property. Understanding this dual transformation requires joining Zelizerian conceptions of money with theories of markets-and-movements on the one hand, and symbolic-cultural conceptions of the classificatory state on the other.
On the potential of Google AMP to promote local content in developing regions

The Google Accelerated Mobile Pages (AMP) project has gained lot of interest from content providers as a means to improve user experience. It introduces a number of innovations to streamline the downloading, parsing and rendering of pages. Google recently announced the hosting of more than 2B+ web pages, covering more than 900K domains. Due to the constrained nature of Internet connectivity in developing regions, AMP offers particularly exciting potential for improving web user experience in these countries. This paper provides a first look at Google's AMP performance in Africa. We start by outlining the current web infrastructure provisioning in Africa, using local news websites as a case study. Discovering a sparse and low performance environment, we then evaluate the benefits that AMP can introduce in terms of accessibility to local content in developing regions. This study reveals that in Africa, AMP is able to reduce page load time and page size by a factor of 3 and 8 respectively. However, AMP is not a neutral technology as the search engine favours content that is using Google AMP. This raises an important question of search neutrality.
Countering hate speech through arts and arts education

Hate speech has become a growing topic of discussion and debate on a global scale, especially as advances in the internet transform communication on many levels. Among scholars, hate speech has been defined as any form of expression – for example by means of speech, images, videos or online activity – that has the capacity to increase hatred against a person or people because of a characteristic they share or a group to which they belong. In order to maintain the integrity of a functioning democracy, it is important to identify the best balance between allowing freedom of expression and protecting other human rights by countering hate speech. In addition to strengthening the legal framework to address the cases when hate speech can be considered criminal, and developing automated monitoring of online systems to prevent the spreading of cyberhate, counter narratives can be utilized by the targets of hate speech and their communities to create campaigns against hate speech. The employment of artists’ expression and arts education have great potential for creating different counter narratives to challenge one-sided narratives and hate speakers’ simplified generalizations. Because hate speech is not an easy issue to address in schools, clear research evidence, concrete guidelines and practical examples can help teachers to contribute, along with their students, in combating it. A great body of evidence supporting the beneficial social impacts of the arts and culture fields is already available, but much more research, backed by sufficient resources, is needed to evaluate the impact and effectiveness of intervention strategies in countering hate speech through arts education.
Computation time optimization of a model-based predictive roll stabilization by neuro-fuzzy systems

The present article discusses the possibility to reduce the computational effort of complex control algorithms by neuro-fuzzy systems. Thereby, great potentials can be released, especially in the automotive sector. A limiting factor for the design of control algorithms is the task of a real-time execution on cost-optimized control units [1]. The influence of this limitations can be reduced by neuro-fuzzy systems. This is shown exemplary for the model-based predictive control of the roll motion presented by Sieberg et al. [2]. The controller based on the adaptive neuro-fuzzy inference system is validated regarding the control quality and the computational effort. Thus it is compared to the origin model-based predictive control algorithm. The implementation and validation are based on a co-simulation of MATLAB/SIMULINK and IPG CarMaker.
Relationship Among Knowledge Management, Innovation, and Performance: A Systematic Literature Review

The role of knowledge management in improving organizational performance has been discussed in previous studies. Some other studies indicate that innovation is the enabler for organizational performance. Therefore, this study aims to identify the current research main findings related to the relationship among knowledge management, innovation, and performance. The methodology approach used in this study is the systematic literature review. There are 22 primary articles published from 2005 until 2018. The primary articles are classified according to the study context, research variables, and statistical tools used. The target sample population discussed in the 22 primary articles is both service and manufacturing industry with various size of the organization. Six of the primary articles identify the mediating role of innovation in the relationship between knowledge management and performance.
Electric Wheelchair Trajectory Tracking Control based on Fuzzy Logic Controller

Autonomous mobile robot navigation is a very interesting problem to researchers. The electric wheelchair is a mobile robot for the disabled. The electrical wheelchair offers the possibility of outdoor or indoor autonomy navigation for a disabled person. Trajectory Tracking is a current topic in mobile robotics. In this paper a control strategy is developed. This strategy enables the electric wheelchair to follow a desired trajectory. A fuzzy logic controller is adopted in this approach. Also, a mathematical model is proposed as a kinematic model of the electric wheelchair. Then, a simulation results is presented to justify the efficiency of the method.
Integrating Mathematics and Educational Robotics: Simple Motion Planning

This paper shows how students can be guided to integrate elementary mathematical analyses with motion planning for typical educational robots. Rather than using calculus as in comprehensive works on motion planning, we show students can achieve interesting results using just simple linear regression tools and trigonometric analyses. Experiments with one robotics platform show that use of these tools can lead to passable navigation through dead reckoning even if students have limited experience with use of sensors, programming, and mathematics.