Reviewerly/exHarmony huggingface dataset:
Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM)

Comparing alternative explanations for behavioral phenomena is central to the process of scientific inquiry. Recent research has emphasized the efficacy of Information Theoretic model selection criteria in partial least squares structural equation modeling (PLS-SEM), which has gained massive dissemination in a variety of fields. However, selecting one model over others based on model selection criteria may lead to a false sense of confidence as differences in the criteria values are often small. To overcome this limitation researchers have proposed Akaike weights, whose efficacy however, has not been assessed in the PLS-SEM context yet. Addressing this gap in research, we analyze the efficacy of Akaike weights in PLS-SEM-based model comparison tasks. We find that Akaike weights derived from BIC and GM are well suited for separating incorrectly specified from correctly specified models, and that Akaike weights based on AIC are useful for creating model-averaged predictions under conditions of model selection uncertainty.
RetroWrite: Statically Instrumenting COTS Binaries for Fuzzing and Sanitization

Analyzing the security of closed source binaries is currently impractical for end-users, or even developers who rely on third-party libraries. Such analysis relies on automatic vulnerability discovery techniques, most notably fuzzing with sanitizers enabled. The current state of the art for applying fuzzing or sanitization to binaries is dynamic binary translation, which has prohibitive performance overhead. The alternate technique, static binary rewriting, cannot fully recover symbolization information and hence has difficulty modifying binaries to track code coverage for fuzzing or to add security checks for sanitizers.The ideal solution for binary security analysis would be a static rewriter that can intelligently add the required instrumentation as if it were inserted at compile time. Such instrumentation requires an analysis to statically disambiguate between references and scalars, a problem known to be undecidable in the general case. We show that recovering this information is possible in practice for the most common class of software and libraries: 64-bit, position independent code. Based on this observation, we develop RetroWrite, a binary-rewriting instrumentation to support American Fuzzy Lop (AFL) and Address Sanitizer (ASan), and show that it can achieve compiler-level performance while retaining precision. Binaries rewritten for coverage-guided fuzzing using RetroWrite are identical in performance to compiler-instrumented binaries and outperform the default QEMU-based instrumentation by 4.5x while triggering more bugs. Our implementation of binary-only Address Sanitizer is 3x faster than Valgrind's memcheck, the state-of-the-art binary-only memory checker, and detects 80% more bugs in our evaluation.
X-Vectors Meet Emotions: A Study On Dependencies Between Emotion and Speaker Recognition

In this work, we explore the dependencies between speaker recognition and emotion recognition. We first show that knowledge learned for speaker recognition can be reused for emotion recognition through transfer learning. Then, we show the effect of emotion on speaker recognition. For emotion recognition, we show that using a simple linear model is enough to obtain good performance on the features extracted from pre-trained models such as the x-vector model. Then, we improve emotion recognition performance by finetuning for emotion classification. We evaluated our experiments on three different types of datasets: IEMOCAP, MSP-Podcast, and Crema-D. By fine-tuning, we obtained 30.40%, 7.99%, and 8.61% absolute improvement on IEMOCAP, MSP-Podcast, and Crema-D respectively over baseline model with no pre-training. Finally, we present results on the effect of emotion on speaker verification. We observed that speaker verification performance is prone to changes in test speaker emotions. We found that trials with angry utterances performed worst in all three datasets. We hope our analysis will initiate a new line of research in the speaker recognition community.
No-Reference Video Quality Assessment Using Natural Spatiotemporal Scene Statistics

Robust spatiotemporal representations of natural videos have several applications including quality assessment, action recognition, object tracking etc. In this paper, we propose a video representation that is based on a parameterized statistical model for the spatiotemporal statistics of mean subtracted and contrast normalized (MSCN) coefficients of natural videos. Specifically, we propose an asymmetric generalized Gaussian distribution (AGGD) to model the statistics of MSCN coefficients of natural videos and their spatiotemporal Gabor bandpass filtered outputs. We then demonstrate that the AGGD model parameters serve as good representative features for distortion discrimination. Based on this observation, we propose a supervised learning approach using support vector regression (SVR) to address the no-reference video quality assessment (NRVQA) problem. The performance of the proposed algorithm is evaluated on publicly available video quality assessment (VQA) datasets with both traditional and in-capture/authentic distortions. We show that the proposed algorithm delivers competitive performance on traditional (synthetic) distortions and acceptable performance on authentic distortions. The code for our algorithm will be released at https://www.iith.ac.in/~lfovia/downloads.html.
CIF: Continuous Integrate-And-Fire for End-To-End Speech Recognition

In this paper, we propose a novel soft and monotonic alignment mechanism used for sequence transduction. It is inspired by the integrate-and-fire model in spiking neural networks and employed in the encoder-decoder framework consists of continuous functions, thus being named as: Continuous Integrate-and-Fire (CIF). Applied to the ASR task, CIF not only shows a concise calculation, but also supports online recognition and acoustic boundary positioning, thus suitable for various ASR scenarios. Several support strategies are also proposed to alleviate the unique problems of CIF-based model. With the joint action of these methods, the CIF-based model shows competitive performance. Notably, it achieves a word error rate (WER) of 2.86% on the test-clean of Librispeech and creates new state-of-the-art result on Mandarin telephone ASR benchmark.
Comparing reading comprehension between children reading augmented reality and print storybooks

Reading in early ages can be a challenge due to difficulties in recognizing new words and may result in lack of proper comprehension of the text. To address this problem, technology has been used in the form of electronic books and more recently Augmented Reality (AR) books. In this study, we measure reading comprehension of children reading an AR storybook and compare it with their counterparts reading the traditional print version of the same book. Reading comprehension was tested in this study by measuring children’s ability in retelling and recalling of the story. A quasi-experimental methodology was employed with post-test only design. The participants were 34 children aged 7 to 9 in Tehran (Iran) which were randomly assigned to either experimental or control group and read the print storybook with and without a tablet. Participants were asked to retell the story and answer comprehension questions right after reading the storybook. Children were observed during storytelling and interviewed. Mann-Whitney test was used for data analysis. The results showed a significant difference between the control and the experimental groups in terms of overall reading comprehension. Children who experienced augmented storybook were better in retelling and answering comprehension questions. However, this was not the case in all subcategories, as there was no significant difference between the groups in retelling theme and setting. Also, the experimental group scored higher in answering implicit questions. Overall, the presentation of related multimedia content with the print storybook will result in improvement in reading comprehension. Possible explanation and implications are discussed.
Breaking The Experience: Effects of Questionnaires in VR User Studies

Questionnaires are among the most common research tools in virtual reality (VR) evaluations and user studies. However, transitioning from virtual worlds to the physical world to respond to VR experience questionnaires can potentially lead to systematic biases. Administering questionnaires in VR (inVRQs) is becoming more common in contemporary research. This is based on the intuitive notion that inVRQs may ease participation, reduce the Break in Presence (BIP) and avoid biases. In this paper, we perform a systematic investigation into the effects of interrupting the VR experience through questionnaires using physiological data as a continuous and objective measure of presence. In a user study (n=50), we evaluated question-asking procedures using a VR shooter with two different levels of immersion. The users rated their player experience with a questionnaire either inside or outside of VR. Our results indicate a reduced BIP for the employed inVRQ without affecting the self-reported player experience.
A Cost-Efficient Container Orchestration Strategy in Kubernetes-Based Cloud Computing Infrastructures with Heterogeneous Resources

Containers, as a lightweight application virtualization technology, have recently gained immense popularity in mainstream cluster management systems like Google Borg and Kubernetes. Prevalently adopted by these systems for task deployments of diverse workloads such as big data, web services, and IoT, they support agile application deployment, environmental consistency, OS distribution portability, application-centric management, and resource isolation. Although most of these systems are mature with advanced features, their optimization strategies are still tailored to the assumption of a static cluster. Elastic compute resources would enable heterogeneous resource management strategies in response to the dynamic business volume for various types of workloads. Hence, we propose a heterogeneous task allocation strategy for cost-efficient container orchestration through resource utilization optimization and elastic instance pricing with three main features. The first one is to support heterogeneous job configurations to optimize the initial placement of containers into existing resources by task packing. The second one is cluster size adjustment to meet the changing workload through autoscaling algorithms. The third one is a rescheduling mechanism to shut down underutilized VM instances for cost saving and reallocate the relevant jobs without losing task progress. We evaluate our approach in terms of cost and performance on the Australian National Cloud Infrastructure (Nectar). Our experiments demonstrate that the proposed strategy could reduce the overall cost by 23% to 32% for different types of cloud workload patterns when compared to the default Kubernetes framework.
Balancing efficiency and fairness in heterogeneous GPU clusters for deep learning

We present Gandivafair, a distributed, fair share scheduler that balances conflicting goals of efficiency and fairness in GPU clusters for deep learning training (DLT). Gandivafair provides performance isolation between users, enabling multiple users to share a single cluster, thus, maximizing cluster efficiency. Gandivafair is the first scheduler that allocates cluster-wide GPU time fairly among active users.
Using augmented reality to experiment with elements in a chemistry course

The use of augmented reality (AR) in education is an emerging trend aimed at improving students' learning outcomes and affective factors. This study further investigated the effects of AR learning activities combined with different approaches, including teacher-centered demonstration and student-centered hands-on, on the conceptual understanding of chemistry and interest in science. Participants of this study included 104 ninth graders from four classes at a junior high school, who were taught by the same chemistry teacher. A quasi-experimental research design was conducted to compare students' pretest, immediate posttest, and delayed posttest scores. Statistical findings were further supplemented by student interviews toward the activities. Results showed that the hands-on learning group performed significantly better on the chemical reactions concept test and interest questionnaire of the immediate posttest than the demonstration learning group. Retention effects also revealed that the students’ conceptual understanding of chemical elements remained effective four months after completion of the learning activities. No significant decline of situational interest and a slightly upward trend of individual interest were found in both groups. According to the interview responses, students were satisfied with the hands-on activities and appreciated the opportunity to be active learners. From this study, hands-on AR could serve as a promising strategy to motivate students in learning chemistry not only for immediate effectiveness but longstanding influence.
Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach

Feature selection is an effective approach to reduce the number of features of data, which enhances the performance of classification in machine learning. In this paper, we formulate a joint feature selection problem to reduce the number of the selected features while enhancing the accuracy. An improved binary particle swarm optimization (IBPSO) algorithm is proposed to solve the formulated problem. IBPSO introduces a local search factor based on Lévy flight, a global search factor based on weighting inertia coefficient, a population diversity improvement factor based on mutation mechanism and a binary mechanism to improve the performance of conventional PSO and to make it suitable for the binary feature selection problems. Experiments based on 16 classical datasets are selected to test the effectiveness of the proposed IBPSO algorithm, and the results demonstrate that IBPSO has better performance than some other comparison algorithms.
The Current State of Research, Challenges, and Future Research Directions of Blockchain Technology in Patient Care: Systematic Review

Background Blockchain offers a promising new distributed technology to address the challenges of data standardization, system interoperability, security, privacy, and accessibility of medical records. Objective The purpose of this review is to assess the research on the use of blockchain technology for patient care and the associated challenges and to provide a research agenda for future research. Methods This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We queried the Cumulative Index of Nursing and Allied Health Literature (CINAHL), PubMed, Excerpta Medica dataBASE (EMBASE), and Web of Science databases for peer-reviewed research articles published up to December 2019 that examined the implementation of blockchain technology in health care settings. We identified 800 articles from which we selected 70 empirical research articles for a detailed review. Results Blockchain-based patient care applications include medical information systems, personal health records, mobile health and telemedicine, data preservation systems and social networks, health information exchanges and remote monitoring systems, and medical research systems. These blockchain-based health care applications may improve patient engagement and empowerment, improve health care provider access to information, and enhance the use of health care information for medical research. Conclusions Blockchain health information technology (HIT) provides benefits such as ensuring data privacy and security of health data, facilitating interoperability of heterogeneous HIT systems, and improving the quality of health care outcomes. However, barriers to using blockchain technology to build HIT include security and privacy vulnerabilities, user resistance, high computing power requirements and implementation costs, inefficient consensus algorithms, and challenges of integrating blockchain with existing HIT. With 51% of the research focused on medical information systems such as electronic health record and electronic medical record, and 53% of the research focused on data security and privacy issues, this review shows that HIT research is primarily focused on the use of blockchain technologies to address the current challenges HIT faces. Although Blockchain presents significant potential for disrupting health care, most ideas are in their infancy.
Twin-Delayed DDPG

In this current research, Twin-Delayed DDPG (TD3) algorithm has been used to solve the most challenging virtual Artificial Intelligence application by training a 4-ant-legged robot as an Intelligent Agent to run across a field. Twin-Delayed DDPG (TD3) is an incredibly smart AI model of a Deep Reinforcement Learning which combines the state-of-the-art methods in Artificial Intelligence. These includes Policy gradient, Actor-Critics, and continuous Double Deep Q-Learning. These Deep Reinforcement Learning approaches trained an Intelligent agent to interact with an environment with automatic feature engineering, that is, necessitating minimal domain knowledge. For the implementation of the TD3, we used a two-layer feedforward neural network of 400 and 300 hidden nodes respectively, with Rectified Linear Units (ReLU) as an activation function between each layer for both the Actor and Critics. We, then added a final tanh unit after the output of the Actor. The Critic receives both the state and action as input to the first layer. Both the network parameters were updated using Adam optimizer. The idea behind the Twin-Delayed DDPG (TD3) is to reduce overestimation bias in Deep Q-Learning with discrete actions which are ineffective in an Actor-Critic domain setting. Based on the Maximum Average Reward over the evaluation time-step, our model achieved an approximate maximum of 2364. Therefore, we can truly say that, TD3 has obviously improved on both the learning speed and performance of the Deep Deterministic Policy Gradient (DDPG) in a challenging environment in a continuous control domain.
Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study

In the active research area of employing embedding models for knowledge graph completion, particularly for the task of link prediction, most prior studies used two benchmark datasets FB15k and WN18 in evaluating such models. Most triples in these and other datasets in such studies belong to reverse and duplicate relations which exhibit high data redundancy due to semantic duplication, correlation or data incompleteness. This is a case of excessive data leakage---a model is trained using features that otherwise would not be available when the model needs to be applied for real prediction. There are also Cartesian product relations for which every triple formed by the Cartesian product of applicable subjects and objects is a true fact. Link prediction on the aforementioned relations is easy and can be achieved with even better accuracy using straightforward rules instead of sophisticated embedding models. A more fundamental defect of these models is that the link prediction scenario, given such data, is non-existent in the real-world. This paper is the first systematic study with the main objective of assessing the true effectiveness of embedding models when the unrealistic triples are removed. Our experiment results show these models are much less accurate than what we used to perceive. Their poor accuracy renders link prediction a task without truly effective automated solution. Hence, we call for re-investigation of possible effective approaches.
Towards Non-I.I.D. image classification: A dataset and baselines

I.I.D.2 hypothesis between training and testing data is the basis of numerous image classification methods. Such property can hardly be guaranteed in practice where the Non-IIDness is common, causing instable performances of these models. In literature, however, the Non-I.I.D.3 image classification problem is largely understudied. A key reason is lacking of a well-designed dataset to support related research. In this paper, we construct and release a Non-I.I.D. image dataset called NICO4, which uses contexts to create Non-IIDness consciously. Compared to other datasets, extended analyses prove NICO can support various Non-I.I.D. situations with sufficient flexibility. Meanwhile, we propose a baseline model with ConvNet structure for General Non-I.I.D. image classification, where distribution of testing data is unknown but different from training data. The experimental results demonstrate that NICO can well support the training of ConvNet model from scratch, and a batch balancing module can help ConvNets to perform better in Non-I.I.D. settings.
A novel hybrid bidirectional unidirectional LSTM network for dynamic hand gesture recognition with Leap Motion

Due to the recent development of machine learning and sensor innovations, hand gesture recognition systems become promising for the digital entertainment field. In this paper, we propose a dynamic hand gesture recognition approach using touchless hand motions over a Leap Motion device. First, we analyze the sequential time series data gathered from Leap Motion using Long Short-Term Memory (LSTM) recurrent neural networks for recognition purposes. We exploit basic unidirectional LSTM and bidirectional LSTM separately. Then, we propound novel architecture by combining the aforementioned models with additional components to give a final prediction network, named Hybrid Bidirectional Unidirectional LSTM (HBU-LSTM). The suggested network improves the model performance significantly by considering the spatial and temporal dependencies between the Leap Motion data and the network layers during the forward and backward pass. The recognition models are examined on two available benchmark datasets, named the LeapGestureDB dataset and the RIT dataset. Experiments demonstrate the potential of the proposed HBU-LSTM network for dynamic hand gesture recognition, with an average recognition rate reaching approximately 90%. Our suggested approach reaches superior performance, in terms of accuracy and computational complexity, over some existing methods for hand gesture recognition.
On the training of a neural network for online path planning with offline path planning algorithms

One of the challenges in path planning for an automated vehicle is uncertainty in the operational environment of the vehicle, demanding a quick but sophisticated control of the vehicle online. To address this online path planning issue, neural networks, which can derive a heading for an operating vehicle in a given situation, have been actively studied, demonstrating their satisfactory performance. However, the study on the training path data, which specifies the desired output of a neural network and in turn influences the behavior of the neural network, has been neglected in the literature. Motivated by this fact, in this paper, we first generate different training path data sets applying two different offline path planning algorithms and evaluate the performance of a neural network as an online path planner depending on the training data under a simulation environment. We further investigate the properties of the training data that make a neural network more reliable for online path planning.