Reviewerly/exHarmony huggingface dataset:
Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements

In this letter, we propose a derivative-free model learning framework for Reinforcement Learning (RL) algorithms based on Gaussian Process Regression (GPR). In many mechanical systems, only positions can be measured by the sensing instruments. Then, instead of representing the system state as suggested by the physics with a collection of positions, velocities, and accelerations, we define the state as the set of past position measurements. However, the equation of motions derived by physical first principles cannot be directly applied in this framework, being functions of velocities and accelerations. For this reason, we introduce a novel derivative-free physically-inspired kernel, which can be easily combined with nonparametric derivative-free Gaussian Process models. Tests performed on two real platforms show that the considered state definition combined with the proposed model improves estimation performance and data-efficiency w.r.t. traditional models based on GPR. Finally, we validate the proposed framework by solving two RL control problems for two real robotic systems.
Learning to infer human attention in daily activities

The first attention model in the computer science community is proposed in 1998. In the following years, human attention has been intensively studied. However, these studies mainly refer human attention as the image regions that draw the attention of a human (outside the image) who is looking at the image. In this paper, we infer the attention of a human inside a third-person view video where the human is doing a task, and define human attention as attentional objects that coincide with the task the human is doing. To infer human attention, we propose a deep neural network model that fuses both low-level human pose cue and high-level task encoding cue. Due to the lack of appropriate public datasets for studying this problem, we newly collect a video dataset in complex Virtual-Reality (VR) scenes. In the experiments, we widely compare our method with three other methods on this VR dataset. In addition, we re-annotate a public real dataset and conduct the extensional experiments on this real dataset. The experiment results validate the effectiveness of our method.
A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique

Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) "skeleton" joints on the human body at 30 frames per second, and the skeleton data often have acceptable accuracy. However, the skeleton data obtained from the sensor sometimes exhibit a high level of jitter due to noise and estimation error. This jitter is worse when there is occlusion or a subject moves slightly out of the field of view of the sensor for a short period of time. Therefore, this paper proposed a novel approach to simultaneously handle the noise and error in the skeleton data derived from Kinect. Initially, we adopted classification processing to divide the skeleton data into noise data and erroneous data. Furthermore, we used a Kalman filter to smooth the noise data and correct erroneous data. We performed an occlusion experiment to prove the effectiveness of our algorithm. The proposed method outperforms existing techniques, such as the moving mean filter and traditional Kalman filter. The experimental results show an improvement of accuracy of at least 58.7%, 47.5% and 22.5% compared to the original Kinect data, moving mean filter and traditional Kalman filter, respectively. Our method provides a new perspective for Kinect data processing and a solid data foundation for subsequent research that utilizes Kinect.
Exploring the use of virtual reality by pre-service elementary teachers for teaching science in the elementary classroom

(2020). Exploring the use of virtual reality by pre-service elementary teachers for teaching science in the elementary classroom. Journal of Research on Technology in Education: Vol. 52, No. 2, pp. 163-177.
A Smart Virtual Assistant for Students

Chatbot has become more popular in business as they can reduce customer service cost and handles multiple users at a time with a round the clock availability, reliability, and accessibility. However, chatbot uses in education are still in their infancy. At the same time, college students must recurrently navigate challenging tasks, such as building graduation plans, learning about majors, retrieving information about courses and scheduling among others. Without timely and on-demand assistance and support, many students fail to raise these challenges. To address this problem, we present a smart virtual assistant for students that provides continuous instant support to student, staff and faculty communities. In order to verify and validate our proposed model, a pilot projet has been set up involving three leading academic institutions in Minnesota; Century College, Metro State University and St. Cloud State University
Adversarial Learning for Cross-Project Semi-Supervised Defect Prediction

Cross-project defect prediction (CPDP) aims to build a prediction model on existing source projects and predict the labels of target project. The data distribution difference between different projects makes CPDP very challenging. Besides, most existing CPDP methods usually require sufficient and labeled data. However, acquiring lots of labeled data for a new project is difficult while obtaining the unlabeled data is relatively easy. A desirable approach is building a prediction model on unlabeled data and labeled data. CPDP in this scenario is called cross-project semi-supervised defect prediction (CSDP). Recently, generative adversarial networks have achieved impressive results with these strong ability of learning data distribution and discriminative representation. For effectively learning the discriminative features of data from different projects, we propose a Discriminative Adversarial Feature Learning (DAFL) approach for CSDP. DAFL consists of feature transformer and project discriminator, which compete with each other. A feature transformer tries to generate feature representation, which learns the discriminant information and preserves intrinsic structure inferred from both labeled and unlabeled data. A project discriminator tries to discriminate source and target instances on the generated representation. Experiments on 16 projects show that DAFL performs significantly better than baselines.
Impact of Node Churn in the Bitcoin Network

The aim of this work is to evaluate the impact of node churn -nodes leaving and rejoining the network- on the Bitcoin network. We provide a comprehensive analytical model for the churning process. We use a Continuous Time Markov Chain (CTMC) to describe the behavior of a node, and then apply the results to model the changes in connectivity and the impact on network performance. We analyze the time needed to resynchronize a node upon rejoining the network and find that sleep times of the order of hours require synchronization times limited by a minute. We estimate the impact of sleep and synchronization time on overall network connectivity and block/transaction distribution time. Our results show that networks with less than 4000 nodes are sensitive to churn. This occurs due to opposing impact of decrease in network size (and diameter) due to sleep time and increase of communication load per node. However, the impact of churn on network with more than 4000 nodes is noticeable but small enough to make a large Bitcoin network fairly resilient to churn.
An Image Encryption Algorithm Based On Fractal Geometry

This paper introduces an effective image encryption method based on fractals for colored images. Fractals are now widely used in image security due to its randomness property and infinite boundaries. Since images are traversed over the public channel in day-to-day life confidentiality, integrity and authenticity must be pledged for prosperous transmission. This method capsulizes a larger key space and complex behavior to accomplish a productive confusion and diffusion techniques in a fruitful way. Julia set and Mandelbrot set are constructed and active confusion is exerted till the image pixels are altogether disordered and ensuing diffusion with XOR operation. Experimental results show the ability of the proposed system with respect to various statistical measures.
The Evolution of Architectural Decision Making as a Key Focus Area of Software Architecture Research: A Semi-Systematic Literature Study

Literature review studies are essential and form the foundation for any type of research. They serve as the point of departure for those seeking to understand a research topic, as well as, helps research communities to reflect on the ideas, fundamentals, and approaches that have emerged, been acknowledged, and formed the state-of-the-art. In this paper, we present a semi-systematic literature review of 218 papers published over the last four decades that have contributed to a better understanding of architectural design decisions (ADDs). These publications cover various related topics including tool support for managing ADDs, human aspects in architectural decision making (ADM), and group decision making. The results of this paper should be treated as a getting-started guide for researchers who are entering the investigation phase of research on ADM. In this paper, the readers will find a brief description of the contributions made by the established research community over the years. Based on those insights, we recommend our readers to explore the publications and the topics in depth.
FPGA Implementation of Real-Time Pedestrian Detection Using Normalization-Based Validation of Adaptive Features Clustering

Presently, many researchers and engineers have investigated autonomous driving, which has significantly influenced the revolution of artificial intelligence (AI). One of the critical challenges for autonomous driving is the inadequate precision of autonomous vehicles in detecting pedestrians, which is a major safety hazard to human beings. In this paper,a Field Programmable Gate Array (FPGA) demonstration system with a normalization-based validity index (NbVI) has been proposed for real-time pedestrian detection. The proposed algorithm can accurately detect pedestrians by calculating the Manhattan distance between the target histogram of oriented gradient (HOG) features and real-time pedestrian HOG features. In lieu of sophisticated circuit layout and substantial training burden with neuron computation circuit, the proposed detection system with adaptive features clustering is hardware-friendly and is capable of real-time pedestrian detection using fewer training images with high detection rate (up to 99.2%). Moreover, the function execution time of pedestrian detection is shortened by 25% using FPGA acceleration.
Mediating cross-cultural differences in research article rhetorical moves in academic translation: A pilot corpus-based study of abstracts

Research on mediating cross-cultural differences in rhetorical conventions of research article abstracts (RAAs) between such culturally distant languages as English and Chinese is rare. This article aims to explore the differences in rhetorical preferences between independently produced English and Chinese research article abstracts and the strategies used in translating Chinese RAAs into English. RAAs published in three journals between 2006 and 2016 were selected to form three corpora: corpus A consisting of 32 English RAAs, corpus B made up of 38 Chinese RAAs, and corpus C including 38 English RAAs which are translations of the RAAs in corpus B. Corpus A and B form a comparable corpus while corpus B and C constitute a parallel corpus. Results suggest that Chinese and English RAAs have different preferences in rhetorical move patterns because the rhetorical moves of Product and Method are much more frequent in English RAAs compared with their Chinese counterparts, and that rhetorical foreignization is the dominant strategy in translating Chinese RAAs into English. Possible reasons of such rhetorical differences and the impact of this resistant strategy on the reception of the translated RAAs in the international community is discussed.
Reuse Kernels or Activations?

Spectral-domain CNNs have been shown to be more efficient than traditional spatial CNNs in terms of reducing computation complexity. However they come with a 'kernel explosion' problem that, even after compression (pruning), imposes a high memory burden and off-chip bandwidth requirement for kernel access. This creates a performance gap between the potential acceleration offered by compression and actual FPGA implementation performance, especially for low-latency CNN inference. In this paper, we develop a principled approach to overcoming this performance gap and designing a low-latency, low-bandwidth, spectral sparse CNN accelerator on FPGAs. First, we analyze the bandwidth-storage tradeoff of sparse convolutional layers and locate communication bottlenecks. We then develop a dataflow for flexibly optimizing data reuse in different layers to minimize off-chip communication. Finally, we propose a novel scheduling algorithm to optimally schedule the on-chip memory access of multiple sparse kernels and minimize read conflicts. On a state-of-the-art FPGA platform, our design reduces data transfers by 42% with DSP utilization up to 90% and achieves inference latency of 9 ms for VGG16, compared to the baseline state-of-the-art latency of 68 ms.
Towards Power Efficiency in Deep Learning on Data Center Hardware

Deep learning (DL) is a computationally intensive workload that is expected to grow rapidly in data centers in the near future. Its high energy demand necessitates finding ways to improve computational efficiency. In this work, we directly measure power used by the whole system as well as that used by GPU, CPU, and RAM during DL training to determine their contributions to the overall energy consumption. We find that while GPUs use most of the power - about 70 % - the consumption of other components is also significant and their optimizations can bring important power savings. Evaluating a multitude of options, we identify the parameters that bring in the most power savings. Overall, an energy savings of over 20% of can be obtained by adjusting system settings alone without changing the workload, at the cost of a minor increase in runtime. Alternatively, if runtime needs to stay constant, an 18% energy savings is identified. In distributed multi-server DL, we find that scale-out overhead has only a small energy cost, making distributed training more energy-efficient than expected. Implications for the field and ways to make DL more energy-efficient going forward are also discussed.
A channel-based perspective on conjugate priors

Abstract A desired closure property in Bayesian probability is that an updated posterior distribution be in the same class of distributions – say Gaussians – as the prior distribution. When the updating takes place via a statistical model, one calls the class of prior distributions the ‘conjugate priors’ of the model. This paper gives (1) an abstract formulation of this notion of conjugate prior, using channels, in a graphical language, (2) a simple abstract proof that such conjugate priors yield Bayesian inversions and (3) an extension to multiple updates. The theory is illustrated with several standard examples.
Quantum Cognitive Triad: Semantic Geometry of Context Representation

The paper describes an algorithm for semantic representation of behavioral contexts relative to a dichotomic decision alternative. The contexts are represented as quantum qubit states in two-dimensional Hilbert space visualized as points on the Bloch sphere. The azimuthal coordinate of this sphere functions as a one-dimensional semantic space in which the contexts are accommodated according to their subjective relevance to the considered uncertainty. The contexts are processed in triples defined by knowledge of a subject about a binary situational factor. The obtained triads of context representations function as stable cognitive structure at the same time allowing a subject to model probabilistically-variative behavior. The developed algorithm illustrates an approach for quantitative subjectively-semantic modeling of behavior based on conceptual and mathematical apparatus of quantum theory.
Improved Mapping of Soil Heavy Metals Using a Vis-NIR Spectroscopy Index in an Agricultural Area of Eastern China

Visible and near-infrared reflectance (Vis-NIR) spectroscopy can provide low-cost and high-density data for mapping various soil properties.However, a weak correlation between the spectra and measurements of soil heavy metals makes spectroscopy difficult to use in predicting incipient risk areas.In this study, we introduce a new spectral index (SI) based on Vis-NIR spectra and use it as a covariate in ordinary cokriging (OCK) to improve the mapping of soil heavy metals.The SI was defined from the highest covariance between spectra and heavy metal content in the partial least squares regression (PLSR) model.The proposed mapping approach was compared with an ordinary kriging (OK) predictor that uses only soil heavy metal data and an OCK predictor that uses soil organic matter (SOM) and Fe as covariates.To this end, a total of 100 topsoil (0-20 cm) samples were collected in an agricultural area near Longkou City, and the contents of As, Pb and Zn in the soil were determined.The results showed that OCK with the SI provided better results in terms of unbiasedness and accuracy compared to other comparative methods.Additionally, we explored the SI through simple strategies based on spectral analysis and correlation statistics and found that the SI synthesized most of the soil properties affected by heavy metals and was not limited to Fe and SOM.In summary, the SI method is cost-effective for improving soil heavy metal mapping and can be applied to other areas.
Improving Mandarin End-to-End Speech Synthesis by Self-Attention and Learnable Gaussian Bias

Compared to conventional speech synthesis, end-to-end speech synthesis has achieved much better naturalness with more simplified system building pipeline. End-to-end framework can generate natural speech directly from characters for English. But for other languages like Chinese, recent studies have indicated that extra engineering features are still needed for model robustness and naturalness, e.g, word boundaries and prosody boundaries, which makes the front-end pipeline as complicated as the traditional approach. To maintain the naturalness of generated speech and discard language-specific expertise as much as possible, in Mandarin TTS, we introduce a novel self-attention based encoder with learnable Gaussian bias in Tacotron. We evaluate different systems with and without complex prosody information and results show that the proposed approach has the ability to generate stable and natural speech with minimum language-dependent front-end modules.
Spacecraft Anomaly Detection via Transformer Reconstruction Error

Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. Nowadays anomaly detection method deployed to production is based on reconstruction error generated by LSTM sequence modeling. Recently, the remarkable improvement achieved by BERT model in language translation demonstrated that the self-attention-based transformer is superior to LSTM models, due to its ignoring distance. In this paper, we continue the research on transformer and propose a transformer-based architecture, masked time series modeling, applying transformer in data stream, which has two novel components (1) the attention mechanism used for updating timestep in parallel and (2) the mask strategy used to detect the anomaly in advanced time. We compared the performances of our method with state-of-the-art AD methods on challenging public NASA telemetry dataset. The experiment results demonstrated our method saves about 80% time cost because of parallel computing compared with LSTM methods and achieves 0.78 F1 point-based score, moreover achieving a better score on range-based indicators.
Style-Aware Neural Model with Application in Authorship Attribution

Writing style is a combination of consistent decisions associated with a specific author at different levels of language production, including lexical, syntactic, and structural. In this paper, we introduce a style-aware neural model to encode document information from three stylistic levels and evaluate it in the domain of authorship attribution. First, we propose a simple way to jointly encode syntactic and lexical representations of sentences. Subsequently, we employ an attention-based hierarchical neural network to encode the syntactic and semantic structure of sentences in documents while rewarding the sentences which contribute more to capturing the writing style. Our experimental results, based on four benchmark datasets, reveal the benefits of encoding document information from all three stylistic levels when compared to the baseline methods in the literature.
Bayesian Contextual Bandits for Hyper Parameter Optimization

Hyper parameter optimization (HPO) is a crucial step in modern machine learning systems.Bayesian optimization (BO) has shown great promise in HPO, where the parameter evaluation is conducted through a black-box optimization procedure.However, the main drawback of BO lies in the expensive computation cost, which limits its application in large deep models.Hence there is strong motivation to reduce the number of epochs of training required by leveraging the partial information provided by iterative training procedures.Recent advancements use probabilistic models that extrapolate learning curves explicitly to early-stop the training of poor-performing configurations.However, these approaches impose a strong prior assumption on the distribution of learning curves and involve much larger computational complexity or rely heavily on predefined rules, which is not general for different cases.To tackle the challenge of training resource allocation in infinite parameter search space and in time horizon, we study HPO problem in Bayesian contextual bandits setting and derive several fully-dynamic strategies that are information-efficient and scalable.Extensive experiments demonstrate that the proposed method can significantly speed up the HPO process.