Reviewerly/exHarmony huggingface dataset:
LOSA-X: Expandable 3D Motion Tracking System

This paper presents a novel, low-cost, expandable 3D motion tracking system with the ability to adapt to dynamic background illumination. The proposed system consists of multiple tracker modules equipped with a linear optical sensor arrays and a 9-DOF inertial measurement unit for position and orientation tracking of a wireless IR-LED illuminated active marker. This paper presents an extended Kalman filter-based sensor fusion architecture for combining data acquired from multiple trackers and marker for enhanced accuracy and operational range. The marker system adaptively adjusts the LED intensity to optimize energy consumption and measurement accuracy. The proposed system's ability to extract background illumination allows it to be robust toward differences in scene illuminations, both static and dynamic. Detailed hardware design and performance evaluation through experimentation have been discussed in this paper.
Testing Conditional Independence on Discrete Data using Stochastic Complexity

Testing for conditional independence is a core aspect of constraint-based causal discovery. Although commonly used tests are perfect in theory, they often fail to reject independence in practice---especially when conditioning on multiple variables.We focus on discrete data and propose a new test based on the notion of algorithmic independence that we instantiate using stochastic complexity. Amongst others, we show that our proposed test, SCI, is an asymptotically unbiased as well as L2 consistent estimator for conditional mutual information (CMI). Further, we show that SCI can be reformulated to find a sensible threshold for CMI that works well on limited samples. Empirical evaluation shows that SCI has a lower type II error than commonly used tests. As a result, we obtain a higher recall when we use SCI in causal discovery algorithms, without compromising the precision.
Stochastic Gradient MCMC for Nonlinear State Space Models

State space models (SSMs) provide a flexible framework for modeling complex time series via a latent stochastic process. Inference for nonlinear, non-Gaussian SSMs is often tackled with particle methods that do not scale well to long time series. The challenge is two-fold: not only do computations scale linearly with time, as in the linear case, but particle filters additionally suffer from increasing particle degeneracy with longer series. Stochastic gradient MCMC methods have been developed to scale inference for hidden Markov models (HMMs) and linear SSMs using buffered stochastic gradient estimates to account for temporal dependencies. We extend these stochastic gradient estimators to nonlinear SSMs using particle methods. We present error bounds that account for both buffering error and particle error in the case of nonlinear SSMs that are log-concave in the latent process. We evaluate our proposed particle buffered stochastic gradient using SGMCMC for inference on both long sequential synthetic and minute-resolution financial returns data, demonstrating the importance of this class of methods.
SacCalib

Recent methods to automatically calibrate stationary eye trackers were shown to effectively reduce inherent calibration distortion. However, these methods require additional information, such as mouse clicks or on-screen content. We propose the first method that only requires users' eye movements to reduce calibration distortion in the background while users naturally look at an interface. Our method exploits that calibration distortion makes straight saccade trajectories appear curved between the saccadic start and end points. We show that this curving effect is systematic and the result of distorted gaze projection plane. To mitigate calibration distortion, our method undistorts this plane by straightening saccade trajectories using image warping. We show that this approach improves over the common six-point calibration and is promising for reducing distortion. As such, it provides a non-intrusive solution to alleviating accuracy decrease of eye tracker during long-term use.
Enhanced Augmented/Mixed Reality and Process Safety Applications

Abstract As the oil and gas industry undergoes a digital transformation, the massive volume and variety of information being ingested at increasing velocity necessitates new methods of data interaction for decision making. Additionally, effective management of safety risks and flawless operational execution in an evolving oil and gas industry requires innovative applications of digital technology. By superimposing contextually-relevant digital information on the physical world, augmented and mixed reality (AR/MR) technologies have tremendous potential to meet these challenges by providing a more intuitive way to interact with data, train personnel, and ensure process safety. However, a major challenge with AR and MR technologies is the limited processing power and capability of available hardware. A cloud-based software platform has been developed to overcome computational limitations of AR and MR devices, enabling interaction with significantly more complex 3D content. Additionally, this enhanced AR/MR software platform enables real-time connectivity across different hardware architectures – such as smartphones and Microsoft HoloLens devices – creating powerful new capability for remote collaboration. This unique software platform transforms consumer-grade AR and MR devices into powerful industrial tools useful for a variety of oil and gas applications. This study will illustrate the functionality enhancements provided by this software platform and how it greatly increases the application potential of AR and MR, including a case study on adoption of this enhanced AR/MR technology for process safety using threat response drill (TRD) scenarios. Enhanced AR and MR provides full-scale virtual TRD scenarios that enable practical demonstration of operational readiness and proactive risk management. Crew response capability and human performance can be collaboratively evaluated with gamified AR/MR techniques, allowing for multiple outcomes based on user inputs through multiple interaction modalities, enabled by the underlying software platform. Enhanced AR/MR enabled by this software platform can drive major improvements in process safety and ultimately help reduce CAPEX, increase efficiency, and mitigate risk across the oil and gas industry.
Pruning the Convolution Neural Network (SqueezeNet) based on L<inf>2</inf> Normalization of Activation Maps

In recent years, deep learning models have become popular in real-time embedded application, but there are many complexities for the hardware deployment because of limited resources such as memory, computational power, and energy. Network pruning is one of the promising technique to solve these problems. This paper presents an approach to prune the Convolution Neural Network (SqueezeNet) based on L 2 nor-malization of activation maps on the CIFAR-10 dataset without introducing the network sparsity in the pruned model. Results show that proposed approach reduces the SqueezeNet model by 67% without a significant drop in the accuracy of the model (optimal pruning efficiency result)
A Scalable Automated System to Measure User Experience on Smart Devices

In this paper, we present an automated scalable system that measures user experience on smart devices such as TVs, tablets and smart phones. The system consists of three parts: (i) a robot with a mobile arm to perform touches and clicks on a tested device such as a tablet or a phone, and sensors to capture the video signals, (ii) a signal capturing process records the input video in real time, controlled by algorithms that use a deep detection model and a text matching model to estimate app state, and a deep classification model to navigate, and (iii) a quality metrics computation process that uses spatial and temporal computer vision algorithms. We show that this system can continuously evaluate major streaming apps such as the Prime Video app, popular shopping apps such as Amazon retail app, and other mobile apps. We also do manual validation for a subset of the metrics to evaluate the reliability of our system.
Improving Side-Channel Analysis Through Semi-supervised Learning

The profiled side-channel analysis represents the most powerful category of side-channel attacks. In this context, the security evaluator (i.e., attacker) gains access to a profiling device to build a precise model which is used to attack another device in the attacking phase. Mostly, it is assumed that the attacker has significant capabilities in the profiling phase, whereas the attacking phase is very restricted. We step away from this assumption and consider an attacker restricted in the profiling phase, while the attacking phase is less limited. We propose the concept of semi-supervised learning for side-channel analysis, where the attacker uses a small number of labeled measurements from the profiling phase as well as the unlabeled measurements from the attacking phase to build a more reliable model. Our results show that the semi-supervised concept significantly helps the template attack (TA) and its pooled version (TA $$_p$$ ). More specifically, for low noise scenario, the results for machine learning techniques and TA are often improved when only a small number of measurements is available in the profiling phase, while there is no significant difference in scenarios where the supervised set is large enough for reliable classification. For high noise scenario, TA $$_p$$ and multilayer perceptron results are improved for the majority of inspected dataset sizes, while for high noise scenario with added countermeasures, we show a small improvement for TA $$_p$$ , Naive Bayes and multilayer perceptron approaches for most inspected dataset sizes. Current results go in favor of using semi-supervised learning, especially self-training approach, in side-channel attacks.
LPM: Learnable Pooling Module for Efficient Full-Face Gaze Estimation

Gaze tracking is an important technology in many domains. Techniques such as Convolutional Neural Networks (CNNs) have allowed the invention of the gaze tracking method that relies only on commodity hardware such as the camera on a personal computer. It has been shown that the full-face region for gaze estimation can provide a better performance than the one obtained from eye image alone. However, a problem with using the full-face image is the heavy computation due to the larger image size. This study tackles this problem through compression of the input full-face image by removing redundant information using a novel learnable pooling module. The module can be trained end-to-end by backpropagation to learn the size of the grid in the pooling filter. The learnable pooling module keeps the resolution of valuable regions high and vice versa. This proposed method preserved the gaze estimation accuracy at a certain level when the image was reduced to a smaller size.
Design and Implementation of Ideological and Political Education Evaluation System

With the development of informatization, in order to improve office efficiency and manage students more effectively, universities have produced many management systems, such as library management systems and educational management systems. These systems mostly manage student information and other information in an integrated manner. The refinement of each module, due to the large number of colleges and universities, graduates and enrolls a large number of students every year, and the archiving of students' ideological and political courses and students' ideological and political evaluation is quite complicated. Some college management systems may only provide ideological and political courses. Course information and student test scores did not specifically manage the student's ideological and political education assessment and assess the student's psychological activities based on the student's daily performance, nor did they provide detailed course scheduling information for specific ideological and political related courses. Therefore, colleges and universities should also implement detailed information management for students' ideological and political education methods, which can improve management efficiency and reduce unnecessary human resources. The management system is developed by the Java[1-3]development language through Myeclipise as a development tool. The mysql database is used as the background to realize the information management of the ideological and political education of college students. It can promote the detailed management of all aspects of students' information management. Service school as well as students. And the machine learning algorithm is added. Through the previous students' data, the classification model is trained by Logistic regression algorithm to evaluate the students' ideological and political good condition.
Normalized quadrature four-frame phase shift algorithm based on crossed fringe patterns

In the phase measurement deflectometry, sinusoidal fringe patterns are separately projected on a specular test surface, and the distorted fringes reflected by the surface are recorded by a charge-coupled device (CCD) camera. The phase shift algorithm is used to find the position of the light source on the display corresponding to the pixel point on the CCD, thereby calculating the slope of each point on the element to be tested, and finally retrieving the surface shape of the test element by using the surface reconstruction algorithm. The phase information is usually obtained by traditional 16-step phase-shifting algorithm, which involves certain complicated and time-consuming procedures, for instance, fringe projection in the horizontal and vertical directions. Therefore, to avoid these procedures, a four-step phase shift algorithm based on crossed fringes is proposed. Based on Gram–Schmidt orthonormalization method, only four crossed fringe patterns are needed to determine the phase in both directions simultaneously. Both numerical simulation and experiment are conducted to verify the validity of the algorithm.
Evaluation of Accuracy: A Comparative Study Between Touch Screen and Midair Gesture Input

This paper presents a user study on the accuracy measure between Touch Screen and Leap Motion Controller. Touch screen is a popular way to input text whereas leap motion controller is an emerging input device that captures the natural movement of hand. The object of this paper was to approach an affordable and more accurate method of input technology as well as to specify in what cases Leap motion could act as good as or better than Touch screen. For this purpose a program was developed that generated targets that had to be selected by the user. On the basis of the performance of users in the experiment the results were generated. There was a need to be able to compare both input modalities from human performance perspective. New methods of user interface require the developers to rethink about the technologies incorporated in the new products.
Shared-Autocorrelation Binary Codes Found by Exhaustive Search

Binary code negation and reversal are known to leave the autocorrelation sequence (ACS) unchanged. However, for some code lengths, there exist “shared-autocorrelation” code pairs for which the mechanism behind shared autocorrelation is not so simple. In this paper, exhaustive search is used to find representatives for all such code pairs for lengths 2 to 39. The resulting code pairs are used to discover code structures associated with the shared autocorrelation property. Some of the code pairs discovered exhibit either Golay skew-symmetry or are found to be Kronecker products of smaller shared-autocorrelation pairs. All four-code sets (or quads) for length $N\leq 36$ having the shared- $\rm ACS$ property were also found.
Multi-layer attention for person re-identification

Person re-identification has been a significant application in the field of video surveillance analysis, yet it remains a challenging work to recognize the person of interest across disjoint cameras of different viewpoints. The factors affecting the identification results include the variation in background, different illumination conditions and the changes of human body poses. Existing person re-identification methods mainly focus on the feature extraction of the whole frame and metric learning functions. However, most of those algorithms treat different areas without distinction. It is worth emphasizing that different local regions make different contributions to image representaion, which exactly conforms to the attention mechanism. In this paper, we introduce a novel attention network which explores spatial attention in a convolutional neural network. Our algorithm learns the visual attention in multi-layer feature maps. The proposed model not only pays attention to the spatial probabilities of local regions, but also takes the features in different levels into consideration. We evaluate this multi-layer spatial attention model on three benchmark person re-identification datasets: Market-1501, CUHK03, and DukeMTMC-reID. The experiment results validate the advances of our adopted network by comparing with state-of-the-art baselines.
Use of the Internet Technology in the Intelligent Library Services of Colleges and Universities in the Big Data Era

With the development and application of the intelligent technologies, the Internet of things has become a hot word in recent years. Intelligent libraries and smart libraries have also entered our real world and become a major research hotspot in the library academia in recent years. The continuous development and progress of our society has accelerated the arrival of the era of the big data. In such an environment, how to continuously improve the application level of the mobile augmented reality technologies in the university libraries has become a very important task. The service of university libraries has distinct characteristics of knowledge and specialization, and its intellectualization construction is also unique. It is of great significance for the development of university intelligent libraries to define its intellectualized functions and structures.
Quality of barrier cover with wireless sensors

A set of wireless sensors is called a barrier cover if they can monitor the boundary of an area so that an intruder cannot enter the area without being found by any sensor. The quality of a barrier cover is the shortest length of path along which an intruder can enter the area from outside. We study four problems, in this paper, related to the quality of the barrier cover and give their computational complexity and algorithmic solutions.
Taxiing Characteristic Analysis and Control for Full-Wing Solar-Powered Unmanned Aerial Vehicle

To solve the taxiing control problem of the full-wing solar-powered unmanned aerial vehicle (UAV) without front wheel steering servo and rudder, a control approach using differential propeller thrust to control the taxiing is proposed in this paper. Firstly, the taxiing mathematical models of two kinds of full-wing solar-powered UAVs with the front wheels turning freely or fixed are established. Meanwhile, the taxiing characteristics of full-wing solar-powered UAV in different taxiing speeds are analyzed. Secondly, based on the linear active disturbance rejection control (LADRC) theory, a yaw angle controller is designed by using differential propeller thrust as the control output. Finally, a straight line trajectory tracking scheme which is suitable for take-off and landing taxiing is designed on the base of improved vector field theory. Simulation results show that the designed controller has a good control effect on full-wing solar-powered UAV's take-off and landing taxiing periods, and better robustness.
A Two-Phase Algorithm for Recognizing Human Activities in the Context of Industry 4.0 and Human-Driven Processes

Future industrial systems, a revolution known as Industry 4.0, are envisioned to integrate people into cyber world as prosumers (service providers and consumers). In this context, human-driven processes appear as an essential reality and instruments to create feedback information loops between the social subsystem (people) and the cyber subsystem (technological components) are required. Although many different instruments have been proposed, nowadays pattern recognition techniques are the most promising ones. However, these solutions present some important pending problems. For example, they are dependent on the selected hardware to acquire information from users; or they present a limit on the precision of the recognition process. To address this situation, in this paper it is proposed a two-phase algorithm to integrate people in Industry 4.0 systems and human-driven processes. The algorithm defines complex actions as compositions of simple movements. Complex actions are recognized using Hidden Markov Models, and simple movements are recognized using Dynamic Time Warping. In that way, only movements are dependent on the employed hardware devices to capture information, and the precision of complex action recognition gets greatly increased. A real experimental validation is also carried out to evaluate and compare the performance of the proposed solution.
A Training Procedure for Quantum Random Vector Functional-link Networks

Quantum computing ideally allows designers to build much more efficient computers than the existing classical ones. By exploiting quantum parallelism and entanglement, it is possible to solve signal processing tasks on high throughput data coming from multiple sources. Random Vector Functional-Link is a neural network model usually adopted in such contexts, although quantum implementations have not been considered so far. This paper proposes a quantum version of this neural model, by introducing a specific learning algorithm to find the coefficients of the adopted quantum gates and focusing on the finite precision arithmetic imposed by the qubit strings that are used to represent the model parameters.
Mobile Technologies in Education: Student Expectations – Teaching Reality Gap

In the world where mobile technologies are generally available to the majority of population, there is an obvious demand to make use of them in education. But, despite the fact that there is no lack in creative ideas on how to incorporate them in the learning process, most formal educational systems do not specifically support nor encourage teachers to use mobile devices in their teaching. As a result, there is a discrepancy between the expectations of students and working practices of teachers. The paper intends to discuss this discrepancy using the data of the surveys on the implementation of mobile learning conducted among the students and teachers of Tambov State Technical University (Tambov, Russia).
Structured reward functions using STL

In this work we present a new method for shaping reward functions to train reinforcement learning agents using signal temporal logic (STL) formulas. The proposed approach uses the robustness metric of partial signal traces against STL specifications to generate locally shaped rewards, doing this in a manner that is agnostic of the learning algorithm used by the reinforcement learning agent.
Jointly Considering Siamese Network and MatchPyramid Network for Text Semantic Matching

When users ask questions in the Q&A website, the Q&A website often recommends similar questions to users. In order to improve the accuracy of recommendation, we propose a new text matching model: a text matching model based on Siamese semantic network and MatchPyramid model. Specifically, the algorithm combines the advanced features of Siamese semantic network with MatchPyramid model, and use these advanced features for classification. We compare our proposed model with several other models on the dataset provided by Quora and Alibaba. The results show that our proposed model performs better than other models.