Reviewerly/exHarmony huggingface dataset:
Velocity-Dependent Perception Threshold for Discrete Imperceptible Repositioning in a Virtual Environment During Eye Blinks

In this study, the relationship between a person’s walking speed and the perception threshold for discrete implicit repositioning during eyeblinks in a virtual environment is investigated. The aim is to estimate the perception thresholds for forward and backward repositioning during forward translation following eyeblink occurrences. A psychophysical method called Staircase Transformed and Weighted up/down is utilized to quantify the perception thresholds for forward and backward repositioning. The perception thresholds for this repositioning are estimated for three different walking speeds: slow (0.58 m/s), moderate (0.86 m/s), and fast (1.1 m/s). The collected observations are then analyzed using regression analysis. The estimated perception threshold values for imperceptible forward repositioning were 0.374, 0.635, and 0.897 meters for the abovementioned walking speeds, respectively. Moreover, the respective perception threshold values for imperceptible backward repositioning were 0.287, 0.430, and 0.572 meters for the same walking speeds. The findings reveal a proportional relationship between the perception threshold values and the participant’s walking speed. As such, it is possible to imperceptibly reposition a participant at a greater distance when they are walking faster relative to the same situation when the participant is walking slower. In addition, the results show that there is more tolerance in forward discrete repositioning compared to backward discrete repositioning during forward translation. These findings enable the extension of the manipulation types utilized by the Redirected Walking Technique. More specifically, this allows for implementing a sophisticated composite redirected walking controller, which utilizes continuous and discrete translation gains simultaneously; this helps not only with reducing the cognitive load, but also with reducing the amount of physical space required to support infinite free exploration in an immersive virtual environment.
Online Access and Resources for Open Self-directed Learning in Africa

Learning online has grown significantly in the African context, and hence access and the nature of resources in this area have become more important. This chapter explores the context of online access and resources on the continent as contextualisation for open education and self-directed learning in Africa. This conceptual chapter provides an overview of the relevant scholarship on the nature of online access and resources in this context. To this end, published statistics and research were critically analysed. Successful online access does not only imply formal access to resources, but also require epistemic, demiurgic as well as axiopistical access. Consequently, these issues are also regarded in terms of what is required from students to reach all these levels of access. Furthermore, this chapter considers resources in terms of it being either material or human within the self-directed learning context and this chapter also focuses on how this concept is viewed within open education. The chapter concludes with a discussion and overview of how online access and resources and its role in education should be viewed within the African context and in terms of the concept of ubuntu where communal aspects of learning are highlighted.
RL-GEP: Symbolic Regression via Gene Expression Programming and Reinforcement Learning

Symbolic regression has become a hot topic in recent years due to the surging demand for interpretable machine learning methods. Traditionally, symbolic regression problems are mainly solved by genetic algorithms. Nonetheless, with the development of deep learning, reinforcement learning based symbolic regression methods have received attention gradually. Unfortunately, hardly any of those reinforcement learning based methods have been proven effectively to solve real world regression problems as genetic algorithm based methods. In this paper, we find a general reinforcement learning based symbolic regression method is difficult to solve real world problems since it is hard to balance between exploration and exploitation. To deal with this problem, we propose a hybrid method to use both genetic algorithm and reinforcement learning for solving symbolic regression problems. By doing so, we can combine the advantages of reinforcement learning and genetic algorithm and achieve better performance than using them alone. To validate the effectiveness of the proposed method, we apply the proposed method to ten benchmark datasets. The experimental results show that the proposed method achieves competitive performance compared with several well-known symbolic regression methods on those datasets.
Enhancing the Performance of Image Classification Through Features Automatically Learned from Depth-Maps

This paper approaches a problem from Computer Vision, that of classifying indoor and outdoor images using machine learning and deep learning models. To do so, we are going to perform an unsupervised learning based analysis with the aim of determining the relevance of depth maps in the context of classification. For further tests to decide on the granularity of information extraction means, features are aggregated from sub-images of different sizes from DIODE data set to compare multiple scales of region attention. Four feature sets will be proposed and comparatively analysed in the context of indoor-outdoor image classification. To empirically confirm the advantage of using features automatically learned from depth maps, the features are fed into a supervised classification model. The performance of the classification using the proposed feature sets is then compared with the results of existing related work, highlighting the clear advantage of using features encoding depth information. Overall accuracy improvements consist of 18.8% for the machine learning approach and 1.1% for the deep learning one when depth information was added to the data.
Fuzzy-Based Workflow Scheduling in Multi-Cloud Environment

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to the sharable computing resources. The services of cloud could be effectively used for running large-scale data and computation-intensive scientific workflow applications. Workflow scheduling (WFS) plays an important role in the integration of complex applications in multi-cloud computing environment. Normally cloud users want to get rid of vendor lock-in problem and try to use multi-cloud based on their optimal criteria satisfaction. Workflows also constitute different applications. Hence in this chapter we propose an effective service aggregation methodology for different services in the workflow structure. Scheduling the individual applications in the workflow to various cloud services needs to follow a good heuristic and decision model. Here we propose a fuzzy-based multi-objective service selection algorithm, which can be used for WFS. The objectives considered in this work are time, cost, and trust. We device a fuzzy logic-based decision for solving the multi-objective problem. The proposed fuzzy logic-based workflow scheduling (FLWS) provides an optimal schedule for the given workflow, considering the services provided by different cloud providers. The FLWS finds the optimal services for each application in the workflow based on the weights got for the three objectives time, cost, and trust. Hence our FLWS provides a flexible system for optimal decision based on the users’ priority.
Cost-Effective Memory Replay for Continual Relation Extraction

Continual relation extraction incrementally learns to extract the relations between entities from unstructured text with a series of tasks. The state-of-the-art methods of continual relation extraction are based on memory replay, which allocate a fixed memory for each continuously coming task to store part of the training data and replay them in subsequent tasks. However, memory resources are usually limited in real scenarios. Existing methods haven't considered the limitation and use efficiency of memory. This paper introduces cost-effective memory replay (CEMR) based on existing methods, which stores as much training data as possible for each task and adopts effective strategies of samples selection and replacement. CEMR efficiently uses limited memory for memory replay and knowledge consolidation in the new tasks, which alleviates the catastrophic forgetting problem. Experiments are conducted on three different relation extraction datasets using multiple comparison methods. The final results show that CEMR outperforms the state-of-the-art methods, which proves the effectiveness of CEMR.
Geopositioned 3D Areas of Interest for Gaze Analysis

To understand driver’s gaze behavior, the gaze is usually matched to surrounding objects or static areas of interest (AOI) at fixed positions around the car. Full surround object tracking allows for an understanding of the traffic situation. However, because it requires an extensive sensor set and a lot of processing power, it’s not yet broadly available in production cars. The use of static AOIs only requires the addition of eye tracking sensors. They are at fixed positions around the car and can’t adapt to the environment, therefore their usefulness is limited. We propose geopositioned 3D AOIs. With adaptability and the use of a small sensor set, they combine the strengths of both methods. To test 3D AOIs’ capabilities for gaze analysis, a driving simulator study with 74 participants was conducted. We show that 3D AOIs are suitable for driver’s gaze analysis and a promising tool for driver intention prediction.
A Knowledge Growing System-based Decision Making-Support System Application for Forces Command and Control in Military Operations

Decision-making in Commanding and Controlling (C2) of forces in a Military Operations (MILOPS) is a complex and tiring task, especially it requires a sustainable cognitive activity where the safety of the soldiers and the success of the mission objective are the primary consideration. In this research, we have developed a new Cognitive Artificial Intelligence (CAI)-based application for supporting the commander in carrying out C2 in a MILOPS. The core of the application is Knowledge Growing System (KGS) with ASSA2010 information-inferencing method that computes the three MILOPS primary parameters, namely Weather, Field, and Enemy (WFE). The computation results are the most probable route for delivering the forces considering the conditions of the WFE. The results of the test show that the Knowledge Growing System-based Decision Making-Support System for MILOPS (KGS-DMSSMILOPS) application has successfully worked properly.
Content-Aware Playback Speed Control for Low-Latency Live Streaming of Sports

There are two main factors that determine the viewer experience during the live streaming of sports content: latency and stalls. Latency should be low and stalls should not occur. Yet, these two factors work against each other and it is not trivial to strike the best trade-off between them. One of the best tools we have today to manage this trade-off is the adaptive playback speed control. This tool allows the streaming client to slow down the playback when there is a risk of stalling and increase the playback when there is no risk of stalling but the live latency is higher than desired. While adaptive playback generally works well, the artifacts due to the changes in the playback speed should preferably be unnoticeable to the viewers. However, this mostly depends on the portion of the audio/video content subject to the playback speed change. In this paper, we advance the state-of-the-art by developing a content-aware playback speed control (CAPSC) algorithm and demonstrate a number of examples showing its significance. We make the running code available and provide a demo page hoping that it will be a useful tool for the developers and content providers.
Back to the Future: Efficient, Time-Consistent Solutions in Reach-Avoid Games

We study the class of reach-avoid dynamic games in which multiple agents interact noncooperatively, and each wishes to satisfy a distinct target criterion while avoiding a failure criterion. Reach-avoid games are commonly used to express safety-critical optimal control problems found in mobile robot motion planning. Here, we focus on finding time-consistent solutions, in which future motion plans remain optimal even when a robot diverges from the plan early on due to, e.g., intrinsic dynamic uncertainty or extrinsic environment disturbances. Our main contribution is a computationally-efficient algorithm for multi-agent reach-avoid games which renders time-consistent solutions for all players. We demonstrate our approach in two- and three-player simulated driving scenarios, in which our method provides safe control strategies for all agents.
Monitory Consensus Mechanism of Blockchain for Internet of Things

As a blockchain consensus mechanism based on Byzantine fault tolerance, PBFT has a very important role to play in the field of consortium blockchains. However, in the context of the Internet of Things, the poor scalability of PBFT limits the application of blockchain in the face of a large number of replicas and an unstable network structure. To achieve the consistency of replicas in the Internet of Things, this article proposes a monitory Byzantine fault tolerant mechanism, a type of monitor node is added to the network model. The monitor nodes measure the behavior and judge the status of the replicas in the blockchain. The monitor node can not only guide the new node join the blockchain, but also use the trust measurement table to sense the status of the nodes, and eliminate malicious nodes or crashed nodes. To verify the effectiveness of the proposed replica elimination method, a verification simulation experiment is performed. And the experiments show that the proposed mechanism has good stability, good effectiveness and accuracy for Byzantine node elimination, and improves the security of the consensus mechanism.
Tone and training: Teaching drum kit students on acoustic versus electronic instruments

This article explores elements of tonal production in acoustic drums to support an argument for its use in the learning environment, as opposed to electronic drums. Aspects discussed include tonal production, range of dynamics, dynamics between components of the drum kit, articulation, specific sticks, specific types of strokes and stylistic elements. The argument focuses on describing how each of these factors work on acoustic drum kits, in what ways they differ on electronic kits and how auditory perception and training can work hand in hand with developing technical facility. Tonal quality is an important aspect for all musicians, irrespective of the instrument. An important thread in this discussion is tonal quality on the drum kit and how it is valuable in the process of training musical drummers.
Depth Embedded and Dense Dilated Convolutional Network for Crowd Density Estimation

In recent years, due to the rapid growth of the urban population, the management of public security has become extremely necessary. Therefore, accurate crowd counting and density distribution estimation play an important role in many situations especially during the Covid-19 pandemic which has been spreading around the world. Although many studies have been proposed, it remains to be a challenging task because of the vivid intra-scene scale variations of people caused by depth effects. In this paper, we propose a novel unified system that allows the scale variation problem to be solved both directly and indirectly. To allow the network to have an understanding of depth when estimating crowd density, we first propose to embed this information into the crowd density estimation network indirectly through the training process by mean of multi-task learning. Our network is now designed to solve not only the main task of estimating crowd density, but also a side task: depth estimation. Besides, to learn the large-scale features directly, dense dilated convolution blocks were proposed to be used in our encoder. The experimental results demonstrate that by using both such direct and indirect methods, we can boost the performance and achieve good results compared to existing methods. Besides, with the multi-task design, we can completely cut off the unnecessary branches of the network related to the side task to speed up computation during the testing phase.
Learning Dynamic Insurance Recommendations from Users’ Click Sessions

While personalised recommendations have been most successful in domains like retail due to large volume of users' feedback on items, it is challenging to implement traditional recommender systems into the insurance domain where such prior information is very small in volume. This work addresses the problem of sparse feedback by studying users' click sessions as signals for learning insurance recommendations. Our preliminary results show limitations in representing click sessions by manually engineered features. The proposed framework uses an autoencoder approach to automatically learns representation of sessions, then a neural network approach to model dependencies across sessions that can be used to predict recommendations. Thereby, it is further able to capture users' dynamic needs of insurance products evolving over time.
Explainable Debugger for Black-box Machine Learning Models

The research around developing methods for debugging and refining Machine Learning (ML) models is still in its infancy. We believe employing tailored tools in the development process can help developers in creating more trustworthy and reliable models. This is particularly essential for creating black-box models such as deep neural networks and random forests, as their opaque decision-making and complex structure prevent detailed investigations. Although many explanation techniques provide interpretability in terms of predictive features for a mispredicted instance, it would be beneficial for a developer to find a partition of the training data that significantly influences the anomaly. Such responsible partitions can be subjected to data visualization and data engineering in the development phase to improve the model's accuracy. In this paper, we propose a systematic debugging framework for the development of ML models that guides the data engineering process using the model's decision boundary. Our approach finds the influential neighborhood of anomalous data points using observation-level feature importance and explains them via a novel quasi-global explanation technique. It is also equipped with a robust global explanation approach to reveal general trends and expose potential biases in the neighborhoods. We demonstrate the efficacy of the devised framework through several experiments on standard data sets and black-box models and propose various guidelines on how the framework's components can be practically useful from a developer's perspective.
An Arbitrary Kernel-size Applicable NoC-based DNN Processor Design with Hybrid Data Reuse

In recent years, Deep neural networks (DNNs) accelerator designs receive much attention due to the dramatically increasing demands of real-time applications. Because of the different computing requirements according to various applications, the involved convolution kernel sizes in the target DNN model are not fixed, which increases the difficulty of the DNN accelerator design. In a practical way, the processing element (PE) array is usually adopted to implement the DNN accelerator based on the largest kernel size in the target DNN model. However, this kind of worst-case design consideration leads to low hardware utilization. Besides, the dedicated array-based PE interconnection restricts the efficiency, bringing from the data-reuse computing method and increases the memory access. The reason is that it is difficult to design a proper dataflow to support multiple data-reuse methods in the PE array-based processor. To address the aforementioned problems, we propose weight-wise convolution processing mechanism and employ Network-on-Chip (NoC) interconnection in this work. The proposed mechanism can support high applicability for arbitrary kernel size. Besides, the NoC-based DNN design provides elastic dataflow, which leverages the data reuse for arbitrary kernel sizes in the target DNN model. Compared with the related works, the experimental results show that the proposed approach can improve 2% to 21% average utilization of computational capability of a PE and reduce 25% to 60% memory access, which further save 20% to 56% energy consumption.
Determination of Suitable Configuration Parameters for Linear Feedback Shift Register using Binary Bat Optimization Algorithm

It is a difficult problem in the field of computer science to be able to answer the question of what configuration parameters will be used to generate long bit strings with linear feedback shift register structures, which is a component widely used in many practical applications that require random behavior. Since this difficult challenge is a problem with NP computational complexity, the difficulty of developing solutions in classical ways has been tried to be solved by using optimization algorithms. In this study, all possible configurations that can be used for all linear feedback shift register structures up to eight degrees have been determined using the binary bat optimization algorithm. Statistical properties of random sequences generated using these configurations have been tested using the chi-square test. All the resulting configurations are shared as a public dataset. It is thought that these successful results can be used effectively in various practical applications by many researchers in the future.
Web Data APIs for Knowledge Graphs: Easing Access to Semantic Data for Application Developers

of the University of Texas at Austin and Paul Groth of the University of
Cost Effective Annotation Framework Using Zero-Shot Text Classification

Manual and high-quality annotation of social media data has enabled companies and researchers to develop improved implementations using natural language processing. However, human text-annotation is expensive and time-consuming. Crowd-sourcing platforms such as Amazon's Mechanical Turk (MTurk) can be leveraged for the creation of large training corpora for text classification tasks using social media data. Nevertheless, the quality of annotations can vary significantly, based on the interpretations and motivations of annotators completing the tasks. Further, the labelling cost of data through MTurk will increase if target messages are small and having a significant amount of noise (e.g. promotional messages on Twitter). In this work, we propose a new annotation framework to create high-quality human-annotated datasets for text classification from social media data. We present a zero-shot text classification based pre-annotation technique reducing the adverse effects arising due to the highly skewed distribution of data across target classes. The proposed framework significantly reduces the cost and time while maintaining the quality of the annotations. Being generic, it can be applied to annotating text data from any discipline. Our experiment with a Twitter data annotation using the proposed annotation framework shows a cost reduction of 80% with no compromise to quality.