Reviewerly/exHarmony huggingface dataset:
Dynamic phase-deforming interferometry: suppression of errors from vibration and air turbulence

In this Letter, dynamic phase-deforming interferometry (PDI) is proposed to solve the impact of environmental vibration and air turbulence on the measurement accuracy. Two interference passes, namely, the measurement pass (MP) and carrier-frequency pass (CP), are constructed, utilizing one detector to acquire the fringe patterns of the two passes simultaneously. The relative deformation phases of the CP fringe patterns are calculated with the Fourier transform method; then the phase extraction of the MP is performed via the least squares method to obtain the final phase distribution. The optical layout and phase-extraction algorithm of the PDI method are investigated, and the principle simulation and measurement experiments are performed. The experimental results show that the PDI can provide not only high-precision phase measurement under the influence of environmental vibration and air turbulence, but also has low system complexity and fast measurement speed.
A Comparative Performance of Real-time Big Data Analytic Architectures

Nowadays, many organizations pay attention to the relevant technologies of Big Data to analyze more accurately, quickly, and efficiently. Real-time Big Data analytics is challenging due to the massive volume of complex data needed to distribute in processing. Therefore, in this research, we investigate two state-of-the-art architectures: Lambda and Kappa. The Kappa architecture is simply the Lambda architecture without the batch layer. To help businesses decide on the right architecture, their processing time, and resource utilization in the same environment need to be found out. Experiments had been carried out with the data size 3 MB, 30 MB, and 300 MB. The results showed that Lambda architecture outperforms Kappa architecture around 9% for the accuracy test when using processing time approximately 2.2 times more than Kappa architecture. Lambda architecture also used more 10-20% of CPU usage and 0.5 GB of RAM usage than Kappa architecture.
Human, Virtual Human, Bump! A Preliminary Study on Haptic Feedback

How does haptic feedback during a human-virtual human interaction affect emotional arousal in virtual reality? In this between-subjects study, we compare haptic feedback and no haptic feedback conditions in which a virtual human “bumps” into the participant in order to determine the influence of haptic feedback on emotional arousal, sense of presence, and embodiment in virtual reality, as well as compare self-report measures of emotional arousal to those objectively collected via event-related galvanic skin response (GSR) recordings. We plan to extend the current preliminary study by adding three more conditions as described in the future work section. Participants are students age 18-32 with at least moderate experience in virtual reality. Preliminary results indicate significant differences in presence and embodiment between haptic feedback and no haptic feedback groups. With our small sample size at the current time, GSR does not show significant differences between haptic and no haptic feedback conditions.
Optimal binary representation via non-convex optimization on tomographic displays

There have been many recent developments in 3D display technology to provide correct accommodation cues over an extended focus range. To this end, those displays rely on scene decomposition algorithms to reproduce accurate occlusion boundaries as well asretinal defocus blur. Recently, tomographic displays have been proposed with improved trade-offs of focus range, spatial resolution, and exit-pupil. The advantage of the system partly stems from a high-speed backlight modulation system based on a digital micromirror device, which only supports 1-bit images. However, its inherent binary constraint hinders achieving the optimal scene decomposition, thus leaving boundary artifacts. In this work, we present a technique for synthesizing optimal imagery of general 3D scenes with occlusion on tomographic displays. Requiring no prior knowledge of the scene geometry, our technique addresses the blending issue via non-convex optimization, inspired by recent studies in discrete tomography. Also, we present a general framework for this rendering algorithm and demonstrate the utility of the technique for volumetric display systems with binary representation.
Assessing Media QoE, Simulator Sickness and Presence for Omnidirectional Videos with Different Test Protocols

QoE for omnidirectional videos comprises additional components such as simulator sickness and presence. In this paper, a series of tests is presented comparing different test protocols to assess integral quality, simulator sickness and presence for omnidirectional videos in one test run, using the HTC Vive Pro as head-mounted display. For quality ratings, the five-point ACR scale was used. In addition, the well-established Simulator Sickness Questionnaire and Presence Questionnaire methods were used, once in a full version, and once with only one single integral scale, to analyze how well presence and simulator sickness can be captured using only a single scale.
All watched over by machines of loving grace: A critical look at smart contracts

Smart contracts are coded parameters written into an immutable distributed ledger called a blockchain. There has been increasing legal interest in the application of these self-executing programs to conduct transactions. Most of the scholarly and practical analysis so far has been taken the claims of this technology being akin to a contract at face value, with legal analysis of contract formation, performance, and enforcement at the forefront of the debate. This article discusses that while smart contracts may pose some interesting legal questions, most of these are irrelevant, and smart contracts should be understood almost strictly from a technical perspective, and that any legal response is entirely dependent on the technical capabilities of the smart contract. The article proposes that smart contracts are not contracts for all practical purposes.
The syllabus as a student privacy document in an age of learning analytics

Purpose The purpose of this paper is to reveal how instructors discuss student data and information privacy in their syllabi. Design/methodology/approach The authors collected a mixture of publicly accessible and privately disclosed syllabi from 8,302 library and information science (LIS) courses to extract privacy language. Using privacy concepts from the literature and emergent themes, the authors analyzed the corpus. Findings Most syllabi did not mention privacy (98 percent). Privacy tended to be mentioned in the context of digital tools, course communication, policies and assignments. Research limitations/implications The transferability of the findings is limited because they address only one field and professional discipline, LIS, and address syllabi for only online and hybrid courses. Practical implications The findings suggest a need for professional development for instructors related to student data privacy. The discussion provides recommendations for creating educational experiences that support syllabi development and constructive norming opportunities. Social implications Instructors may be making assumptions about the degree of privacy literacy among their students or not value student privacy. Each raises significant concerns if privacy is instrumental to intellectual freedom and processes critical to the educational experience. Originality/value In an age of educational data mining and analytics, this is one of the first studies to consider if and how instructors are addressing student data privacy in their courses, and the study initiates an important conversation for reflecting on privacy values and practices.
Deep Transfer Collaborative Filtering for Recommender Systems

Collaborative filtering (CF) is among the most effective techniques for recommendations. However, it suffers from data sparsity and cold-start issue. One solution is to incorporate the side information and the other is to learn knowledge from relevant domains. In this paper, we consider both aspects and propose a generic deep transfer collaborative filtering (DTCF) architecture, which integrates collective matrix factorization and deep transfer learning. We exhibit one instantiation of our architecture by employing non-negative matrix tri-factorization and stacked denoising autoencoder (SDAE) in both source and target domains. Deep learning copes with both the ratings’ statistic characteristics and the side information to generate effective latent representations. Matrix tri-factorization produces private latent factors linked with per SDAE and common latent factors connected with different domains. Extensive experimental results on real datasets exhibit a superiority of our approach in comparison to state-of-the-art works.
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization

For optimization of a large sum of functions in a distributed computing environment, we present a novel communication efficient Newton-type algorithm that enjoys a variety of advantages over similar existing methods. Our algorithm, DINGO, is derived by optimization of the gradient's norm as a surrogate function. DINGO does not impose any specific form on the underlying functions and its application range extends far beyond convexity and smoothness. The underlying sub-problems of DINGO are simple linear least-squares, for which a plethora of efficient algorithms exist. DINGO involves a few hyper-parameters that are easy to tune and we theoretically show that a strict reduction in the surrogate objective is guaranteed, regardless of the selected hyper-parameters.
A Combined Vision-Based Multiple Object Tracking and Visual Odometry System

Detection and tracking of different moving objects in the surrounding environment is an essential capability for any robotic perception system. This enables forecasting of object trajectories and safe navigation of a mobile robot, such as a self-driving car, toward its goal. In this paper, we demonstrate a multi-object tracker from image data in a tracking-by-detection paradigm. The tracking algorithm imposes no prior requirements on the tracked objects. This paper demonstrates how with a simple and intuitive cost function, competitive and real-time performance is achieved. Furthermore, the multi-object tracker is combined with a stereo visual odometry system to obtain a more complete knowledge of the environment. Beside 2D object detections and tracking in the image domain, this provides robot motion estimates and 3D object trajectories in the world frame. Evaluation and discussion of the system is presented along with a study on the effect of removing features lying on tracked objects on visual odometry performance.
Virtualization and its Role in Cloud Computing Environment

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Dense prediction for micro-expression spotting based on deep sequence model

Micro-expression (ME) analysis has been becoming an attractive topic recently. Nevertheless, the studies of ME mostly focus on the recognition task while spotting task is rarely touched. While micro-expression recognition methods have obtained the promising results by applying deep learning techniques, the performance of the ME spotting task still needs to be largely improved. Most of the approaches still rely upon traditional techniques such as distance measurement between handcrafted features of frames which are not robust enough in detecting ME locations correctly. In this paper, we propose a novel method for ME spotting based on a deep sequence model. Our framework consists of two main steps: 1) From each position of video, we extract a spatial-temporal feature that can discriminate MEs among extrinsic movements. 2) We propose to use a LSTM network that can utilize both local and global correlation of the extracted feature to predict the score of the ME apex frame. The experiments on two publicly databases of ME spotting demonstrate the effectiveness of our proposed method.
Modeling of the Acute Lymphoblastic Leukemia Detection based on the Principal Object Characteristics of the Color Image

The objective of the research is to develop a model to detect the Acute Lymphoblastic Leukemia. This research contributes a model for Acute Lymphoblastic Leukemia Detection using Principal Object Characteristics of the Color Image. We developed method consist of four main stages as follows: enhancement, segmentation, feature extraction, and accuracy measurement. This research proposed seven characteristics as the object feature, which are Energy (EN), Entropy(EP), Shanon Entropy (H(X)), Log Energy Entropy (EE), Mean (ME), Variance (VA), and Correlation (CO). The feature extraction results have been calculated using four measurement methods, i.e. Euclidean Distance, Manhattan, Canberra, and Chebyshev methods. Our proposed method has produced maximum accuracy of 81.54%, 81.54%, 76.92%, and 82.31% for Euclidean Distance, Manhattan, Canberra, and Chebyshev methods, respectively. Lastly, we applied a confusion matrix to compute the accuracy of the experiment. Our proposed method has been evaluated using Acute Lymphoblastic Leukemia- Image Database (ALL-IDB).
Fast phase denoising using stationary wavelet transform in speckle pattern interferometry

Phase fringe patterns in speckle pattern interferometry need to be denoised before they are processed further to ensure phase unwrapping reliability and phase determination accuracy. A fast phase denoising method combining the sine–cosine transform and the stationary wavelet transform, with a detailed, unified and adaptive parameter setting of the wavelet function, decomposition level, threshold selection and threshold function, is proposed for a speckle interference image. Owing to the ability of the sine–cosine transform to retain an abrupt phase change, and the stationary wavelet transform to quickly remove the phase noise, the proposed approach can be used to denoise the phase fringe pattern effectively and quickly. These characteristics are verified by simulation as well as practical experiments. Compared with the popular phase denoising method—the windowed Fourier transform with a window size of 20—the proposed method can save the running time by at least 16 times in a practical application test. This speed advantage will be more important when a high-resolution camera is used in dynamic measurement.
On the effects of pseudorandom and quantum-random number generators in soft computing

Abstract In this work, we argue that the implications of pseudorandom and quantum-random number generators (PRNG and QRNG) inexplicably affect the performances and behaviours of various machine learning models that require a random input. These implications are yet to be explored in soft computing until this work. We use a CPU and a QPU to generate random numbers for multiple machine learning techniques. Random numbers are employed in the random initial weight distributions of dense and convolutional neural networks, in which results show a profound difference in learning patterns for the two. In 50 dense neural networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification at + 0.1%, and QRNG exceeded PRNG for mental state EEG classification by + 2.82%. In 50 convolutional neural networks (25 PRNG/25 QRNG), the MNIST and CIFAR-10 problems are benchmarked, and in MNIST the QRNG experiences a higher starting accuracy than the PRNG but ultimately only exceeds it by 0.02%. In CIFAR-10, the QRNG outperforms PRNG by + 0.92%. The n -random split of a Random Tree is enhanced towards and new Quantum Random Tree (QRT) model, which has differing classification abilities to its classical counterpart, 200 trees are trained and compared (100 PRNG/100 QRNG). Using the accent and EEG classification data sets, a QRT seemed inferior to a RT as it performed on average worse by − 0.12%. This pattern is also seen in the EEG classification problem, where a QRT performs worse than a RT by − 0.28%. Finally, the QRT is ensembled into a Quantum Random Forest (QRF), which also has a noticeable effect when compared to the standard Random Forest (RF). Ten to 100 ensembles of trees are benchmarked for the accent and EEG classification problems. In accent classification, the best RF (100 RT) outperforms the best QRF (100 QRF) by 0.14% accuracy. In EEG classification, the best RF (100 RT) outperforms the best QRF (100 QRT) by 0.08% but is extremely more complex, requiring twice the amount of trees in committee. All differences are observed to be situationally positive or negative and thus are likely data dependent in their observed functional behaviour.
Bitcoin: Evolution of Blockchain Technology

In the past few years, blockchain thechnologies have sparked a lot of interest in the scientific and businness communities. The working Bitcoin blockchain has spawned a number of scientific research on the technology, security and reliability of the model. It managed to generate a new kind of economy with a market cap of around $ 300 billion at its peak. This article aims to elaborate on the development of blockchain technologies, to discuss the main problems, as well as to show various solutions.
Deep Learning Architectures

Deep learning is one of the most widely used machine learning techniques which has achieved enormous success in applications such as anomaly detection, image detection, pattern recognition, and natural language processing. Deep learning architectures have revolutionized the analytical landscape for big data amidst wide-scale deployment of sensory networks and improved communication protocols. In this chapter, we will discuss multiple deep learning architectures and explain their underlying mathematical concepts. An up-to-date overview here presented concerns three main categories of neural networks, namely, Convolutional Neural Networks, Pretrained Unspervised Networks, and Recurrent/Recursive Neural Networks. Applications of each of these architectures in selected areas such as pattern recognition and image detection are also discussed.
Efficient Object Annotation via Speaking and Pointing

Deep neural networks deliver state-of-the-art visual recognition, but they rely on large datasets, which are time-consuming to annotate. These datasets are typically annotated in two stages: (1) determining the presence of object classes at the image level and (2) marking the spatial extent for all objects of these classes. In this work we use speech, together with mouse inputs, to speed up this process. We first improve stage one, by letting annotators indicate object class presence via speech. We then combine the two stages: annotators draw an object bounding box via the mouse and simultaneously provide its class label via speech. Using speech has distinct advantages over relying on mouse inputs alone. First, it is fast and allows for direct access to the class name, by simply saying it. Second, annotators can simultaneously speak and mark an object location. Finally, speech-based interfaces can be kept extremely simple, hence using them requires less mouse movement compared to existing approaches. Through extensive experiments on the COCO and ILSVRC datasets we show that our approach yields high-quality annotations at significant speed gains. Stage one takes $$2.3{\times }-14.9{\times }$$ less annotation time than existing methods based on a hierarchical organization of the classes to be annotated. Moreover, when combining the two stages, we find that object class labels come for free: annotating them at the same time as bounding boxes has zero additional cost. On COCO, this makes the overall process $$1.9\times $$ faster than the two-stage approach.
What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning

Pretrained transformer-based language models have achieved state of the art across countless tasks in natural language processing. These models are highly expressive, comprising at least a hundred million parameters and a dozen layers. Recent evidence suggests that only a few of the final layers need to be fine-tuned for high quality on downstream tasks. Naturally, a subsequent research question is, how many of the last layers do we need to fine-tune? In this paper, we precisely answer this question. We examine two recent pretrained language models, BERT and RoBERTa, across standard tasks in textual entailment, semantic similarity, sentiment analysis, and linguistic acceptability. We vary the number of final layers that are fine-tuned, then study the resulting change in task-specific effectiveness. We show that only a fourth of the final layers need to be fine-tuned to achieve 90% of the original quality. Surprisingly, we also find that fine-tuning all layers does not always help.
Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles.

Model interpretability has become an important problem in ML due to the increased effect algorithmic decisions have on humans. Counterfactual explanations can help users understand not only why ML models make certain decisions, but also how these decisions can be changed. We frame the problem of finding counterfactual explanations as an optimization task and extend previous work that could only be applied to differentiable models. In order to accommodate non-differentiable models such as tree ensembles, we propose using probabilistic model approximations in the optimization framework. We introduce a novel approximation technique that is effective for finding counterfactual explanations for predictions of the original model and show that our counterfactual examples are significantly closer to the original instances compared to other methods specifically designed for tree ensembles.