Reviewerly/exHarmony huggingface dataset:
Nurturing Collaboration in an Undergraduate Computing Course with Robot-themed Team Training and Team Building

Group projects are a common feature of undergraduate degree programmes in computing. Early and sustained collaboration helps students to strive beyond introductory programming towards professional software development. However, during their first year of study, students can find teamwork challenging. To equip learners with the foundational knowledge, skills, and experience that they need to collaborate effectively so early in their studies, a 3-day Robot Olympics using Lego Mindstorms EV3 robots can be deployed. The exercise draws upon Salas' big-five model of teamwork, making first-year students aware of coordinating mechanisms that aid in clarifying expectations and managing conflicts. These then act as lenses for reflection and feedback. Comparing a baseline cohort in 2015-16 to a cohort in 2016-17, after the introduction of the Robot Olympics, reveals a statistically significant reduction in team discord in an assessed collaborative programming project (d = 0.76). This suggests that the Robot Olympics made a positive contribution to the design of the first computing module. Notably, helping students to enact and reflect upon their group work and related employability skills.
Truck active reversing control strategy based on modified particle filter and multi‐sensors environment perception

Due to the limit of the drivers' sight sheltered by the large body and the complexity of driving, heavy-duty trucks usually cause serious reversing traffic accidents. In this study, the authors present a new strategy to settle this problem by using the multi-sensors system, modified unscented particle filter, and an active safety control strategy. First, multi-sensors including binocular cameras, ultrasonic sensors, and laser rangefinders are used to perceive the environmental information to help drivers to decrease the blind area and obtain the distance between obstacles and the truck. Second, a federal information fusion mechanism based on the unscented Kalman filter is used to fuse the information so that the proposed system can eliminate spurious data and improve the robustness. Next, a framework combining low-rank representation with unscented particle filter that is used for target tracking is proposed. Third, a speed control method is introduced to realise the active speed control of a truck. Lastly, the experiment and the results prove that the proposed algorithm has higher accuracy than others. Moreover, the proposed system can not only help drivers decrease the burden while reversing but also increase the active safety ability to reverse, which has great significance for heavy-duty trucks.
Promises of the virtual museum

Have we escaped the hype and death cycles that have plagued the history of virtual reality? In the last few years, as commercial virtual reality headsets and systems have become accessible, content developers, film festivals, and---most intriguing---galleries and museums are dipping their toes in the virtual pool.
Clearing the Smoke

The impact of computing devices on the nature of work has been a long-standing topic of inquiry. Removing the boundaries of traditional corporate organizations, mobile IoT has enabled a technology driven future, taking trans-formative technology off the desk and placing it in the field. The exponential increase in mobility and reduction in cost have expanded accessibility of computing technologies to whole new categories of work including emergency re-sponse. As new kinds of workplaces adopt and adapt to computing, we want to better understand how these tech-nologies impact the organization and change the types of work people do. In order to answer those questions, we present a qualitative investigation examining the implemen-tation of a wearable device into two fire departments in the Southeastern United States. Our analysis demonstrates how the particulars of these kinds of workplaces and organiza-tions will shape how we design new digital technologies for the next generation workforce.
A novel compression based neuronal architecture for memory encoding

Research in neuro-biological memory encoding suggests that it takes place through various biophysical and biochemical mechanisms during synaptic transmission of information between neurons. However, there are no mathematical models to explain how these processes result in real-time memory encoding which is compressed and distributed in different neuronal pathways across different brain regions. Biologically inspired artificial neural networks that accomplish learning by updating its synaptic weights, lack a theoretical justification. In this work, we propose a novel biologically inspired network architecture of neural memory encoding, preserving its various attributes including compression, non-linearity, distributed processing and dynamical nature. We demonstrate that our model is capable of universal computation and satisfies the approximation theorem.
Online distributed fuzzy modeling of nonlinear PDE systems: Computation based on adaptive algorithms

With the emergence of novel model-based controllers for partial differential equation (PDE) systems, identifying the mathematical model of PDE systems has become a promising and complicated research topic. This paper suggests a new method to identify an adaptive Takagi–Sugeno (TS) fuzzy PDE model for nonlinear multi-input multi-output (MIMO) first-order PDE systems. The proposed approach is performed online based on the measured input and output data of the nonlinear PDE systems. Furthermore, the identification process will be obtained for the cases that the noise is either white or colored. For the case of white noise, a nonlinear recursive least square (NRLS) approach is applied to identify the nonlinear system. On the other hand, when the colored noise is exerted to the nonlinear PDE system, the fuzzy PDE model of the nonlinear PDE system and also nonlinear colored noise are identified based on the nonlinear extended matrix methods (NEMM). Moreover, the problem of identification for both colored and white noise cases is investigated when premise variables of membership functions are known or unknown. Finally, in order to illustrate the effectiveness and merits of the proposed methods, the identification method is applied to a practical nonisothermal Plug-Flow reactor (PFR) and a hyperbolic PDE system with Lotka–Volterra type applications. As it is expected, the evolutions of the error between the state variables for the obtained TS fuzzy PDE model and the output data converge to the zero in the steady-state conditions. Thus one concludes, the proposed identification algorithm can accurately adjust both consequents and antecedents parameters of TS fuzzy PDE model.
Optimizing Health, Education and Governance Delivery Through Blockchain

Blockchain technology and cryptocurrencies are generating profound transformations in most sectors of the society, including healthcare, education and governance sectors that are uplifting communities out of despair in regions around the world. In these sectors, they are remaking financing and usher in an era of how institutions are structured, functioning and their relationships with their publics. This chapter identifies transformative research papers, White Papers, reports on these themes, and then analyzes their contexts, relevance and contributions to the transformations underway in the selected sectors. Using modified Cornell Notes template, for the healthcare, education and governance sectors data is collected, analyzed and categorized into four approaches to technology adoption: transformation, Substitution, Localization and Single Use. The chapter is organized in four main parts: the introductory, background, focus of the study, and the conclusion part.
Reinforcement Learning to Create Value and Policy Functions Using Minimax Tree Search in <i>Hex</i>

Recently, the use of reinforcement-learning algorithms has been proposed to create value and policy functions, and their effectiveness has been demonstrated using Go, Chess, and Shogi. In previous studies, the policy function was trained to predict the search probabilities of each move output by Monte Carlo tree search; thus, a number of simulations were required to obtain the search probabilities. We propose a reinforcement-learning algorithm with game of self-play to create value and policy functions such that the policy function is trained directly from the game results without the search probabilities. In this study, we use Hex, a board game developed by Piet Hein, to evaluate the proposed method. We demonstrate the effectiveness of the proposed learning algorithm in terms of the policy function accuracy, and play a tournament with the proposed computer Hex algorithm DeepEZO and 2017 world-champion programs. The tournament results demonstrate that DeepEZO outperforms all programs. DeepEZO achieved a winning percentage of 79.3% against the world-champion program MoHex2.0 under the same search conditions on 13 × 13 board. We also show that the highly accurate policy functions can be created by training the policy functions to increase the number of moves to be searched in the loser position.
Probabilistic Path Planning using Obstacle Trajectory Prediction

In this paper, we describe a novel approach for navigation of a robot in a dynamic environment by exploiting the benefits of time series analysis of Long Short Term Memory (LSTM) architectures on obstacle trajectories. Most path planning algorithms consider the instantaneous position of the obstacles while generating the path, resulting in frequent re-planning and extended traversal time. However, moving obstacles more often than not tend to follow certain motion patterns in real life scenarios such as the motion of people on roads, building lobbies, shops, etc. In our algorithm, LSTM-based Dynamic Rapidly-exploring Random Trees star (LD-RRT*), obstacle trajectories are treated as sequences where their future position estimates are incorporated in the state validity checker of planning algorithms to generate an optimal path. Taking the future obstacle motion into account reduces the total traversal time and the frequency of re-planning. We propose the use of an LSTM model for future trajectory prediction and an extension of Rapidly-exploring Random Trees for path planning. Comparisons with other state of the art planners show the effectiveness of our algorithm.
Architectural Protection of Trusted System Services for SGX Enclaves in Cloud Computing

Data security and privacy are of great concern for users of cloud computing. In order to provide such guarantees in public clouds, hardware manufacturers have designed trusted execution environments such as Intel’s Software Guard eXtensions (SGX). Intel SGX supports privacy-preserving, tamper-proof containments called enclaves. Regrettably, an SGX enclave has to rely on the untrusted operating system or hypervisor for underlying services, which contradicts the threat model of Intel SGX. Whereas much of the previous work concentrates on protecting trusted applications by means of modifying a hypervisor, we tackle the problem by reusing existing drivers and leveraging processor-enforced protection. We propose a novel approach, named SMK, to provide trusted system services for SGX enclaves. SMK leverages existing Intel architecture features, i.e., System Management Mode (SMM) and Uniform Extensible Firmware Interface (UEFI). Specifically, we retrofit UEFI firmware and design an isolated micro-kernel inside SMM to securely provision critical system services for enclaves. To highlight the effectiveness and extensibility of SMK, we implement two system services: trusted clock and trusted network. Furthermore, we harden two real-world security-sensitive applications, OpenSSL and OpenVPN, with SMK’s system services. Our evaluation indicates that SMK can supply trusted system services for enclaves with modest runtime overheads.
To Chain or Not to Chain? A Case from Energy Sector

During the last decade, Blockchain technology has attracted a lot of attention as it has the potential to disrupt the existing ecosystem and transform the way we do business. In this paper we investigate the energy supply chain and we examine the impact of the development of a blockchain solution in the area of solar energy. Our findings illustrate that blockchain technology can disrupt many areas of this sector including disruptions in the product, process, position and paradigm.
A Differential Privacy Framework for Collaborative Filtering

Focusing on the privacy issues in recommender systems, we propose a framework containing two perturbation methods for differentially private collaborative filtering to prevent the threat of inference attacks against users. To conceal individual ratings and provide valuable predictions, we consider some representative algorithms to calculate the predicted scores and provide specific solutions for adding Laplace noise. The DPI (Differentially Private Input) method perturbs the original ratings, which can be followed by any recommendation algorithms. By contrast, the DPM (Differentially Private Manner) method is based on the original ratings, which perturbs the measurements during implementation of the algorithms and releases the predicted scores. The experimental results showed that both methods can provide valuable prediction results while guaranteeing DP, which suggests it is a feasible solution and can be competent to make private recommendations.
Tampering Detection in Digital Audio Recording Based on Statistical Reverberation Features

Audio authentication has become a challenging task with an increase in easily available manipulation tools which can be used to forge the audio recordings. This paper focuses on audio tampering detection which is a task that involved in the area of audio forensics. Reverberation-based acoustic features are considered to distinguish between original recordings and their tampered versions. Two types of feature sets have been considered which depend on the decay rate distribution of the signal in each frequency band. The statistical features of the decay distribution and that of the Mel-frequency cepstral coefficient matrix of the reverberant component have been used. A threshold-based technique that considers the percentage error between these statistical parameters of the original audio recordings and their tampered versions has been employed. The methodology has been tested on synthetically created data set which consisting of original recordings and their tampered versions recorded in different acoustic environments.
Simulate-the-hardware

This work investigates how to effectively train binarized neural networks (BNNs) for the specialized low-precision neural accelerators. When mapping BNNs onto the specialized neural accelerators that adopt fixed-point feature data representation and binary parameters, due to the operation overflow caused by short fixed-point coding, the BNN inference results from the deep learning frameworks on CPU/GPU will be inconsistent with those from the accelerators. This issue leads to a large deviation between the training environment and the inference implementation, and causes potential model accuracy losses when deployed on the accelerators. Therefore, we present a series of methods to contain the overflow phenomenon, and enable typical deep learning frameworks like Tensorflow to effectively train BNNs that could work with high accuracy and convergence speed on the specialized neural accelerators.
Ensemble and Deep Learning for Language-Independent Automatic Selection of Parallel Data

Machine translation is used in many applications in everyday life. Due to the increase of translated documents that need to be organized as useful or not (for building a translation model), the automated categorization of texts (classification), is a popular research field of machine learning. This kind of information can be quite helpful for machine translation. Our parallel corpora (English-Greek and English-Italian) are based on educational data, which are quite difficult to translate. We apply two state of the art architectures, Random Forest (RF) and Deeplearnig4j (DL4J), to our data (which constitute three translation outputs). To our knowledge, this is the first time that deep learning architectures are applied to the automatic selection of parallel data. We also propose new string-based features that seem to be effective for the classifier, and we investigate whether an attribute selection method could be used for better classification accuracy. Experimental results indicate an increase of up to 4% (compared to our previous work) using RF and rather satisfactory results using DL4J.
Sensitivity to Time Delays in VDM-Based Navigation

A recently proposed navigation methodology for aerial platforms based on the vehicle dynamic model (VDM) has shown promising results in terms of navigation autonomy. Its practical realization requires that control inputs are related to the same absolute time frame as inertial measurement unit (IMU) data and all other observations when available (e.g., global navigation satellite system (GNSS) position, barometric altitude, etc.). This study analyzes the (non-) tolerances of possible delays in control-input command with respect to navigation performance on a fixed-wing unmanned aerial vehicle (UAV). Multiple simulations using two emulated trajectories based on real flights reveal the vital importance of correct time-tagging of servo data while that of motor data turned out to be tolerable to a considerably large extent.
The Technology Explorers: Partnering with Older Adults to Engage with Virtual Reality and Virtual Avatars

Participatory research methods are increasingly being adopted when designing and evaluating emerging technologies for older adults. This chapter details our approach to applying participatory methods to our work with a group of older adults during series of workshops. The workshops involved working with 25 adults from both regional and metropolitan areas, who were over the age of 70. We referred to these participants as the Technology Explorers. The Technology Explorers partnered with us to explore how Virtual Reality (VR) technology could be harnessed to provide older adults with opportunities to participate in meaningful social activities. This chapter focuses on the initial engagement phase of the project that involved introducing the Technology Explorers to a range of VR systems and interaction techniques, co-designing 3D Avatars, and interacting with each other in two social VR technology probes. We describe the participatory methods that helped us to build a strong collaborative partnership with the Technology Explorers, before detailing the techniques used to engage with group members who had vastly different levels on technical knowledge. As none of the Technology Explorers had any experience with VR or virtual avatars, in a final section, we describe three techniques we employed to develop this capacity in the group, with a particular focus on the deployment of two technology probes that marked the end of the workshop series.
Discontinuity adaptive SAR image despeckling using curvelet-based BM3D technique

Synthetic Aperture Radar (SAR) is an all-weather, day and night satellite imaging technology where the radar is mounted on aircraft and successive pulses of radio waves are transmitted to illuminate the target scene. The signal processing of the recorded backscattered echoes produce SAR images. SAR images contain inherent multiplicative speckle noise which is formed due to the constructive and destructive interference of transmitted signals with the returning signals. Speckle noise appears as granular patterns and makes the image interpretation difficult. Non-local means approaches like Block Matching and 3D filtering (BM3D) are effective scheme for removing speckle noise from SAR images. This method gives good performance for additive noise but is not adaptive to curved edges and discontinuities that occur in SAR images affected by multiplicative noise. This paper proposes a three-step refined algorithm to adapt BM3D for despeckling multiplicative speckle noise. In the proposed scheme curvelet is used to find the transform coefficients and this modification in the transform domain improves the despeckling accuracy of BM3D. Also Wiener filtering is replaced with Importance Sampling Unscented Kalman Filtering (ISUKF) for better adapting to discontinuities in the real SAR image. An improved method of grouping is proposed here based on Manhattan distance which better adapts to constantly changing multiplicative noise statistics. A detailed comparative study is carried out on each step using various well-known performance measures. From the results, it is found that the proposed Curvelet-ISUKF-Manhattan BM3D (CIM-BM3D) method of despeckling has better values for all the performance measure and the results are also visually verified.
Simulation analysis of spherical panoramic mosaic

Multi-camera mosaic has become mainstream for obtaining panoramic images. Current mosaic methods based on intensity, frequency, or feature of images are limited in terms of image quality. We simulate the process of spherical panoramic mosaic and propose the corresponding calibration method based on the relationship among multiple cameras. This method is independent of image content but utilizes the orientation and position of each camera directly. Experimental results demonstrate that (1) the simulated imaging model and mosaic method are effective under different numbers of horizontal/vertical cameras; (2) that the influence of rotation angle error on panoramic mosaic can be eliminated entirely through calibration; and (3) that the influence of center offset error on panoramic mosaic is directly proportional to the center offset and inversely proportional to the object distance. This misalignment, which cannot be eliminated theoretically because the object distance is unknown, is limited when the center offset is small.
Improving Usability Perception of Error-Prone AI Speakers: Elaborated Feedback Mitigates Negative Consequences of Errors

Users of voice user interface (VUI) often encounter errors, such as when a VUI attempts to recognize a user’s voice inputs or execute tasks. Conversation is prone to errors, and in the collaborative perspective, communicators manage common ground together to handle erroneous situations. Adopting a collaborative view of conversation, we propose that a VUI can address different types of errors by providing users with feedback to aid them in developing common ground to communicate more effectively. To test this proposal, we conducted a 2 (error type: recognition vs. execution error) × 2 (feedback elaboration: present vs. absent) mixed-design experiment in which users interacted with a VUI speaker and evaluated its usability in these four modes. Participants reported greater acceptance of feedback and higher usability perception for a speaker returning execution errors than for one returning recognition errors, particularly when the speaker presented feedback articulating reasons for the errors. This finding indicates that a VUI can employ feedback explaining the causes of errors to facilitate the development of common ground and to minimize the negative consequences of errors.