Miracle-dz/UniFAR-FTUs huggingface dataset:
Uplink Time Scheduling with Power Level Modulation in Wireless Powered Communication Networks.

In this paper, we propose downlink signal design and optimal uplink scheduling for the wireless powered communication networks (WPCNs). Prior works give attention to resource allocation in a static channel because users are equipped with only energy receiver and users cannot update varying uplink schedulling. For uplink scheduling, we propose a downlink signal design scheme, called a power level modulation, which conveys uplink scheduling information to users. First, we design a downlink energy signal using power level modulation. Hybrid-access point (H-AP) allocates different power level in each subslot of the downlink energy signal according to channel condition and users optimize their uplink time subslots for signal transmission based on the power levels of their received signals. Further, we formulate the sum throughput maximization problem for the proposed scheme by determining the uplink and downlink time allocation using convex optimization problem. Numerical results confirm that the throughput of the proposed scheme outperforms that of the conventional schemes.
A Deep Value-network Based Approach for Multi-Driver Order Dispatching

Recent works on ride-sharing order dispatching have highlighted the importance of taking into account both the spatial and temporal dynamics in the dispatching process for improving the transportation system efficiency. At the same time, deep reinforcement learning has advanced to the point where it achieves superhuman performance in a number of fields. In this work, we propose a deep reinforcement learning based solution for order dispatching and we conduct large scale online A/B tests on DiDi's ride-dispatching platform to show that the proposed method achieves significant improvement on both total driver income and user experience related metrics. In particular, we model the ride dispatching problem as a Semi Markov Decision Process to account for the temporal aspect of the dispatching actions. To improve the stability of the value iteration with nonlinear function approximators like neural networks, we propose Cerebellar Value Networks (CVNet) with a novel distributed state representation layer. We further derive a regularized policy evaluation scheme for CVNet that penalizes large Lipschitz constant of the value network for additional robustness against adversarial perturbation and noises. Finally, we adapt various transfer learning methods to CVNet for increased learning adaptability and efficiency across multiple cities. We conduct extensive offline simulations based on real dispatching data as well as online AB tests through the DiDi's platform. Results show that CVNet consistently outperforms other recently proposed dispatching methods. We finally show that the performance can be further improved through the efficient use of transfer learning.
Identifiability of Cause and Effect using Regularized Regression

We consider the problem of telling apart cause from effect between two univariate continuous-valued random variables X and Y. In general, it is impossible to make definite statements about causality without making assumptions on the underlying model; one of the most important aspects of causal inference is hence to determine under which assumptions are we able to do so. In this paper we show under which general conditions we can identify cause from effect by simply choosing the direction with the best regression score. We define a general framework of identifiable regression-based scoring functions, and show how to instantiate it in practice using regression splines. Compared to existing methods that either give strong guarantees, but are hardly applicable in practice, or provide no guarantees, but do work well in practice, our instantiation combines the best of both worlds; it gives guarantees, while empirical evaluation on synthetic and real-world data shows that it performs at least as well as the state of the art.
Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.
#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media

The availability of large-scale online social data, coupled with computational methods can help us answer fundamental questions relat- ing to our social lives, particularly our health and well-being. The #MeToo trend has led to people talking about personal experiences of harassment more openly. This work at- tempts to aggregate such experiences of sex- ual abuse to facilitate a better understanding of social media constructs and to bring about social change. It has been found that disclo- sure of abuse has positive psychological im- pacts. Hence, we contend that such informa- tion can leveraged to create better campaigns for social change by analyzing how users react to these stories and to obtain a better insight into the consequences of sexual abuse. We use a three part Twitter-Specific Social Media Lan- guage Model to segregate personal recollec- tions of sexual harassment from Twitter posts. An extensive comparison with state-of-the-art generic and specific models along with a de- tailed error analysis explores the merit of our proposed model.
Co-sleep: Designing a workplace-based wellness program for sleep deprivation

Sleep deprivation is a public health issue. Awareness of sleep deprivation has not been widely investigated in workplace-based wellness programmes. This study adopted a three-stage design process with nine participants from a local manufacturing company to help raise awareness of sleep deprivation. The common causes of sleep deprivation were identified through the deployment of technology probes and participant interviews. The study contributes smart Internet of things(IoT) workplace-based design concepts for activity tracking that may aid sleep and explore ways of sharing personal sleep data within the workplace. Through the use of co-design methods, the study also highlights prominent privacy concerns relating to use of personal data from different stakeholders' perspectives, including the unexpected use of sleep data by organisations for fatigue risk management and the evaluation of employee performance. The Actigrahy and sleep diary data can be accessed online through https://github.com/famousgrouse/pervasivehealth/
Recommended software/packages for meta-analysis of diagnostic accuracy

When performing a meta-analysis of diagnostic accuracy, authors usually need to use a statistical package. Many different packages are available but not all of them are well developed. In this review, we first make an inventory of most popular packages providing meta-analysis of diagnostic accuracy, and then evaluate them by their accessibility, usability, and functions and methodology, and give an overall assessment for each package. This methodological review will enable review authors to understand what the packages can do for them and to choose the correct methods.
End-user involvement in rehabilitation virtual reality implementation research.

Purpose Despite increasing evidence for the effectiveness of off-the-shelf and rehabilitation-specific active video games (AVGs) and virtual reality (VR) systems for rehabilitation, clinical uptake remains poor. A better match between VR/AVG system capabilities and client/therapist needs, through improved end-user involvement (UI) in VR/AVG implementation research, may increase uptake of this technology. The purpose of this paper is to review four case examples from the authors' collective experience of including end users in VR/AVG research to identify common benefits, challenges and lessons learned. Design/methodology/approach The authors apply knowledge and lessons learned from the four cases to make recommendations for subsequent user-engaged research design and methods, including evaluation of the impact of end UI. Findings A better match between VR/AVG system capabilities and client/therapist needs leads to improved end UI in all stages of VR/AVG implementation research. There are common benefits of increasing buy-in and soliciting early on the knowledge and skills of therapists as well as input from the ultimate end users: people participating in rehabilitation. Most settings have the challenges of balancing the technology requirements with the needs and goals of the practice setting and of the end users. Research limitations/implications Increasing end UI in VR/AVG implementation research may address issues related to poor clinical uptake. In the VR/AVG context, end users can be therapists, clients or technology developers/engineers. This paper presented four case scenarios describing the implementation of different VR/AVG systems and involving a variety of populations, end users and settings. Originality/value The set of recommendations for subsequent user-engaged research design and methods span the process of development, research and implementation. The authors hope that these recommendations will foster collaborations across disciplines, encourage researchers and therapists to adopt VR/AVGs more readily, and lead to efficacious and effective treatment approaches for rehabilitation clients.
Modular soft pneumatic actuator system design for compliance matching

The future of robotics is personal. Never before has technology been as pervasive as it is today, with advanced mobile electronics hardware and multi-level network connectivity pushing “smart” devices deeper into our daily lives through home automation systems, virtual assistants, and wearable activity monitoring. As the suite of personal technology around us continues to grow in this way, augmenting and offloading the burden of routine activities of daily living, the notion that this trend will extend to robotics seems inevitable. Transitioning robots from their current principal domain of industrial factory settings to domestic, workplace, or public environments is not simply a matter of relocation or reprogramming, however. The key differences between “traditional” types of robots and those which would best serve personal, proximal, human interactive applications demand a new approach to their design. Chief among these are requirements for safety, adaptability, reliability, reconfigurability, and to a more practical extent, usability. These properties frame the context and objectives of my thesis work, which seeks to provide solutions and answers to not only how these features might be achieved in personal robotic systems, but as well what benefits they can afford. I approach the investigation of these questions from a perspective of compliance matching of hardware systems to their applications, by providing methods to achieve mechanical attributes complimentary to their environment and end-use. These features are fundamental to the burgeoning field of Soft Robotics, wherein flexible, compliant materials are used as the basis for the structure, actuation, sensing, and control of complete robotic systems. Combined with pressurized air as a power source, soft pneumatic actuator (SPA) based systems offers new and novel methods of exploiting the intrinsic compliance of soft material components in robotic systems. While this strategy seems to answer many of the needs for human-safe robotic applications, it also brings new questions and challenges: What are the needs and applications personal robots may best serve? Are soft pneumatic actuators capable of these tasks, or “useful” work output and performance? How can SPA based systems be applied to provide complex functionality needed for operation in diverse, real-world environments? What are the theoretical and practical challenges in implementing scalable, multiple degrees of freedom systems, and how can they be overcome? I present solutions to these problems in my thesis work, elucidated through scientific design, testing and evaluation of robotic prototypes which leverage and demonstrate three key features: • Intrinsic compliance: provided by passive elastic and flexible component material properties
Studying Geometric Optical Illusions through the Lens of a Convolutional Neural Network

Geometrical optical illusions such as the Muller Lyer illusion and the Ponzo illusion have been widely researched over the past 100+ years, yet researchers have not reached a consensus on why human perception is deceived by these illusions or which illusions are the results of the same effects. In this paper, I study these illusions through the lens of a convolutional neural network. First, I successfully train the network to correctly classify how a human would perceive a particular class of illusion (such as the Muller Lyer illusion), then I test the network’s ability to generalize to illusions that it was not trained on (like the Ponzo illusion). I do not find that these networks generalize effectively. Tests to better understand how the network learns to classify these illusions suggest the networks are checking for image data in specific ‘activation regions’ in order to make classifications rather than analyzing the entire illusions.
When Language Instruction and Second Language Acquisition Meet: An Interdisciplinary Approach

There exist misconceptions regarding the objectives of language instruction and those of Second Language Acquisition (SLA). Many language instructors teach the grammar rules of the target language expecting students to internalize knowledge and produce immediately. But research on SLA shows a different picture; there are different stages of language acquisition that should not be changed deliberately through instruction of some grammar rules before or instead of other ones (cf. Van Patten, 2010). It is not the case that all language instructors are aware of or familiar with linguistic research on SLA. Another major concern is that standardization of models on teaching meets the objectives of the institution, but not always the needs and demands of each learner. Each classroom can count on a diverse audience, different types of learners and therefore diverse expectations. This workshop is intended for language instructors, and could be adapted for instructors of other disciplines since the main principles acknowledged here apply in other learning scenarios (i.e. boost the amount of practice of the most complex issues, recognize the diverse audience). This workshop combines the situations described above – considering what research shows us about SLA and a diverse audience in a language classroom (e.g. Foreign language learners vs. Heritage speaker learners). It adapts a practice activity integrating SLA research, and two instruction approaches: one for a diverse audience, and the other focused on form.
3D Object Categorization Using Spin-Images with MPI Parallel Implementation

Object recognition and categorization are two important key features of computer vision. Accuracy aspects representresearchchallengeforbothobjectrecognitionandcategorization techniques. High performance computing (HPC) technologies usually manage the increasing time and complexity of computations. In this paper, an approach utilizing3Dspin-imagesfor3Dobjectcategorizationisproposed.Themaincontributionofthisapproach is that item ploys the MPI techniques in a unique way to extract spin-images. The technique proposed utilizes the independence between spin-images generated a teach point. Time estimation of this technique has shown dramatic decrease of the categorization time proportion alto number of workers used.
Distributed Knowledge Based Clinical Auto-Coding System

Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) methods and techniques to resolve the problem of manual coding of clinical narratives. Most of the studies are focused on classification systems relevant to the U.S and there is a scarcity of studies relevant to Australian classification systems such as ICD-10-AM and ACHI. Therefore, we aim to develop a knowledge-based clinical auto-coding system, that utilise appropriate NLP and ML techniques to assign ICD-10-AM and ACHI codes to clinical records, while adhering to both local coding standards (Australian Coding Standard) and international guidelines that get updated and validated continuously.
3D Design For Test Architectures Based on IEEE P1687

3D stacked integrated circuits based on Through Silicon Vias (TSV) are promising with their high performances and small form factor. However, these circuits present many test issues. In this paper we propose a novel 3D Design for Test (DFT) architecture based on IEEE P1687. The proposed test architecture enables test at all 3D fabrication levels: pre, mid, and post-bond levels. We discuss 3 DFT architecture proposals and we show one practical implementation using a commercial EDA tool.
EUHSA: Extending Usability Heuristics for Smartphone Application

Emerging development trends in smartphone computing assist the user to enhance skills, health improvement, earning opportunities, sharing ideas, providing entertainment, and so on. Appropriate transfer of these assistances demands a vibrant appearance to a smartphone user. End users emphasize easy-to-use, ergonomics, efficiency, and visual aesthetic, among others. Measuring usability issues allows for improving these features. An inspection-based heuristics set identifies the usability issues for improving the quality of smartphone interfaces. Using suitable heuristics is highly relevant. In this paper, we present an extended and relevant set of 14 Usability Heuristics for Smartphone (EUHSA) application, where 13 heuristics were selected after cross-linking the identified usability flaws with the previously proposed heuristics through a user study with 800 students from a human–computer interaction course. An additional heuristic is proposed against uncaptured usability flaws. Furthermore, the EUHSA was validated using an expert evaluation study. Findings were compared with the previously proposed Joyce and SMASH sets of heuristics. The results explicitly justify the EUHSA and prove its effectiveness in evaluating the usability issues.
Location-Specific Orientation Set Is Independent of the Horizontal Benefit with or Without Object Boundaries

Chen and Cave (2019) showed that facilitation in visual comparison tasks that had previously been attributed to object-based attention could more directly be explained as facilitation in comparing two shapes that are configured horizontally rather than vertically. They also cued the orientation of the upcoming stimulus configuration without cuing its location and found an asymmetry: the orientation cue only enhanced performance for vertical configurations. The current study replicates the horizontal benefit in visual comparison and again demonstrates that it is independent of surrounding object boundaries. In these experiments, the cue is informative about the location of the target configuration as well as its orientation, and it enhances performance for both horizontal and vertical configurations; there is no asymmetry. Either a long or a short cue can enhance performance when it is valid. Thus, Chen and Cave’s cuing asymmetry seems to reflect unusual aspects of an attentional set for orientation that must be established without knowing the upcoming stimulus location. Taken together, these studies show that a location-specific cue enhances comparison independently of the horizontal advantage, while a location-nonspecific cue produces a different type of attentional set that does not enhance comparison in horizontal configurations.
Wavelet packet transform for feature extraction of EEG during mental tasks

Wavelet packet transform (WPT) based feature extraction of the electroencephalogram (EEG) is introduced. Six-channel EEG data of four subjects were recorded while they performed three different mental tasks. Approximate one-second data segments were divided and transformed to multi-scale representations by dyadic wavelet packet decomposition channel by channel. Power values of different sub-spaces of six-channel EEG signals formed the feature vectors. A radial basis function (RBF) network was applied to classify the three task pairs. The average classification accuracy of four subjects over three task pairs is 85.3%. Compared with the two autoregressive (AR) model methods, wavelet packet transform would be a promising method to extract features from EEG signals.