Reviewerly/exHarmony huggingface dataset:
PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor

Most human activity recognition datasets that are publicly available have data captured by using either smartphones or smartwatches, which are usually placed on the waist or the wrist, respectively. These devices obtain one set of acceleration and angular velocity in the x-, y-, and z-axis from the accelerometer and the gyroscope planted in these devices. The PLHI-MC10 dataset contains data obtained by using 3 BioStamp nPoint® sensors from 7 physically healthy adult test subjects performing different exercise activities. These sensors are the state-of-the-art biomedical sensors manufactured by MC10. Each of the three sensors was attached to the subject externally on three muscles-Extensor Digitorum (Posterior Forearm), Gastrocnemius (Calf), and Pectoralis (Chest)-giving us three sets of 3 axial acceleration, two sets of 3 axial angular velocities, and 1 set of voltage values from the heart. Using three different sensors instead of a single sensor improves precision. It helps distinguish between human activities as it simultaneously captures the movement and contractions of various muscles from separate parts of the human body. Each test subject performed five activities (stairs, jogging, skipping, lifting kettlebell, basketball throws) in a supervised environment. The data is cleaned, filtered, and synced.
Construction of Maker Competencies-Oriented Smart Classroom Model in College English Listening and Speaking Teaching

Abstract Smart classroom, as a new kind of teaching model, has triggered a revolution in China’s educational field. This paper clarifies the teaching model of smart classroom established on the maker competency-oriented learning activities, which generates learners’ English productive ability, innovative thinking, and highlights the self-directed, personalized and creative teaching design in classroom teaching.
Artificial Intelligence and Machine Learning in Manufacturing

The diagnosis of manufacturing processes and systems, prediction of machine health for corrective measures are mainly achieved through various machine learning techniques. In the previous chapters, discussions were held around the signal and image processing techniques, using which meaningful information was gathered from the raw data. The results are validated by correlating with the experiments.
An Approximate Bribe Queueing Model for Bid Advising in Cloud Spot Markets

We consider the scheduling system of a container cloud spot market where the user specifies the requested number of containers and their resource requirements, along with a bid value. Jobs are preemptively ordered based on their bid values as the available capacity, which is excess capacity made available for the spot market, may vary over time. Due to this variation, the number of allocated containers to a job may vary during its lifetime, resulting in users experiencing periods of degraded performance, potentially leading to job slowdown. We want to model and analyze such a scheduling system starting from first principles, inspired by the M/M/1 bribe queue. Thus, we introduce a simple, empirical queueing model which parametrically relates job slowdown to bid values given load and bid distribution. We demonstrate the accuracy of our approximation and parameter estimation through simulation.
Configuring Waypoints and Patterns for Autonomous Arduino Robot with GPS and Bluetooth Using an Android App

The challenge is to build a low-cost robot as a basis for personal uses such as mowing or line trimming or moving items from one place to another that is easily configured using a ubiquitous user interface like an android app to autonomously move the robot from one location to another. This paper describes such a robot and user interface for use by non-technical users. A user can record a GPS waypoint and then when directed, the robot will move to that location in a relatively straight line, guided by GPS. In addition, a user can record a pattern while remote controlling the robot with their phone, and then when directed, the robot will replay the same pattern without GPS.
Perceptual and cognitive processes in augmented reality – comparison between binocular and monocular presentations

In the present study, we investigated the difference between monocular augmented reality (AR) and binocular AR in terms of perception and cognition by using a task that combines the flanker task with the oddball task. A right- or left-facing arrowhead was presented as a central stimulus at the central vision, and participants were instructed to press a key only when the direction in which the arrowhead faced was a target. In a small number of trials, arrowheads that were facing in the same or opposite direction (flanker stimuli) were presented beside the central stimulus binocularly or monocularly as an AR image. In the binocular condition, the flanker stimuli were presented to both eyes, and, in the monocular condition, only to the dominant eye. The results revealed that participants could respond faster in the binocular condition than in the monocular one; however, only when the flanker stimuli were in the opposite direction was the response faster in the monocular condition. Moreover, the results of event-related brain potentials (ERPs) showed that all stimuli were processed in both the monocular and the binocular conditions in the perceptual stage; however, the influence of the flanker stimuli was attenuated in the monocular condition in the cognitive stage. The influence of flanker stimuli might be more unstable in the monocular condition than in the binocular condition, but more precise examination should be conducted in a future study.
Enhanced Separable Disentanglement for Unsupervised Domain Adaptation

Domain adaptation aims to mitigate the domain gap when transferring knowledge from an existing labeled domain to a new domain. However, existing disentanglement-based methods do not fully consider separation between domain-invariant and domain-specific features, which means the domain-invariant features are not discriminative. The reconstructed features are also not sufficiently used during training. In this paper, we propose a novel enhanced separable disentanglement (ESD) model. We first employ a disentangler to distill domain-invariant and domain-specific features. Then, we apply feature separation enhancement processes to minimize contamination between domain-invariant and domain-specific features. Finally, our model reconstructs complete feature vectors, which are used for further disentanglement during the training phase. Extensive experiments from three benchmark datasets outperform state-of-the-art methods, especially on challenging cross-domain tasks.
SQKT: A Student Attention-Based and Question-Aware Model for Knowledge Tracing

The goal of Knowledge Tracing (KT) is to trace student’s knowledge states in relation to different knowledge concepts and make prediction of student’s performance on new exercises. With the growing number of online learning platforms, personalized learning is more and more urgently required. As a result, KT has been widely explored for recent decades. Traditional machine learning based methods and Deep Neural Network based methods have been constantly introduced for improving prediction accuracy of KT models and have achieved some positive results. However, there are still some challenges for KT research, such as information representation of high-dimentional question data, consideration of personalized learning ability, and so on. In this paper we propose a novel Student attention-based and Question-aware model for KT (SQKT), which can address the challenges by estimating student attention on different type of questions through history exercise trajectory. Firstly, we devise a weighted graph and propose a weighted deepwalk method to get the question embedding which is combined with the correlated skills as question representation. Secondly, we propose a novel student attention mechanism, which is dedicated for the updating of student’s knowledge state. Finally, comprehensive experiments are conducted on 4 real world datasets, the results demonstrate that our SQKT model outperforms the state-of-the-art KT models on all datasets.
Opening Deep Neural Networks With Generative Models

Image classification methods are usually trained to perform predictions taking into account a predefined group of known classes. Real-world problems, however, may not allow for a full knowledge of the input and label spaces, making failures in recognition a hazard to deep visual learning. Open set recognition methods are characterized by the ability to correctly identifying inputs of known and unknown classes. In this context, we propose GeMOS: simple and plug-and-play open set recognition modules that can be attached to pretrained Deep Neural Networks for visual recognition. The GeMOS framework pairs pre-trained Convolutional Neural Networks with generative models for open set recognition to extract open set scores for each sample, allowing for failure recognition in object recognition tasks. We conduct a thorough evaluation of the proposed method in comparison with state-of-the-art open set algorithms, finding that GeMOS either outperforms or is statistically indistinguishable from more complex and costly models.
Design of District-level Photovoltaic Installations for Optimal Power Production and Economic Benefit

PhotoVoltaic (PV) installations are a widespread source of renewable energy, and are quite common urban buildings’ roofs. To soften both the initial investment and the recurrent maintenance costs, the current market trends delegate the construction of PV installations to Energy Aggregators, i.e., grouping of consumers and producers that act as a single entity to satisfy local energy demand and to sell the surplus energy to the grid. In this perspective, PV installations can be designed with a larger perspective, i.e., at district level, to maximize power production not of a single building but rather of a number of blocks of a city. This implies new challenges, including efficient data management (the covered area can be squared kilometers wide) and optimal PV installation (the number of PV modules can be in the order of hundreds or even thousands). This paper proposes a framework to combine detailed geographic and irradiance information to determine an optimal PV installation over a district, by maximizing both power production and economic convenience. Our simulation results run on a real-world district prove that the framework allows an advanced evaluation of costs and benefit, that can be used by Energy Aggregators to design a new PV installation, and demonstrate an improvement on power generation up to 20% w.r.t. standard installations.
Position Estimation of Pedestrians in Surveillance Video Using Face Detection and Simple Camera Calibration

Pedestrian position estimation in videos is an important technique for enhancing surveillance system applications. Although many studies estimate pedestrian positions by using human body detection, its usage is limited when the entire body expands outside of the field of view. Camera calibration is also important for realizing accurate position estimation. Most surveillance cameras are not adjusted, and it is necessary to establish a method for easy camera calibration after installation. In this paper, we propose an estimation method for pedestrian positions using face detection and anthropometric properties such as statistical face lengths. We also investigate a simple method for camera calibration that is suitable for actual uses. We evaluate the position estimation accuracy by using indoor surveillance videos.
Image Enhancement Methods for Remote Sensing: A Survey

The quality of the images obtained from remote sensing devices is very important for many image processing applications. Most of the enhancement methods are based on histogram modification and transform based methods. Histogram modification based methods aim to modify the histogram of the input image to obtain a more uniform distribution. Transform based methods apply a certain transform to the input image and enhance the image in transform domain followed by the inverse transform. In this work, both histogram modification and transform domain methods have been considered, as well as hybrid methods. Moreover, a new hybrid algorithm is proposed for remote sensing image enhancement. Visual comparisons as well as quantitative comparisons have been carried out for different enhancement methods. For objective comparison quality metrics, namely Contrast Gain, Enhancement Measurement, Discrete Entropy and Average Mean Brightness Error have been used. The comparisons show that, the histogram modification methods have a better contrast improvement, while transform domain methods have a better performance in edge enhancement and color preservation. Moreover, hybrid methods which combine the two former approaches have higher potential.
Teaching Bot to Play Thousand Schnapsen

During recent years, AI found its ways to games. Here, imperfect information games, such as Thousand Schnapsen, bring about major challenges. In this work, the rules and characteristics of this game have been described. Next, an overview of existing literature, focusing on similar problems, has been presented, followed by a summary of selected methods for finding an optimal strategy along with applied modifications. Finally, the results of the experiments are discussed.
Research and Analysis of One-dimensional Discrete Wavelet Transform

In the digital information age, signal processing technology has become an increasingly popular research direction. Among them, discrete wavelet transform, which is widely used in signal analysis, image compression, and video coding, is an important research topic. In the process of multi-resolution analysis, each layer of decomposition causes the signal to pass through a low-pass filter and a band-pass filter, and the signal is decomposed into a low-frequency part and a high-frequency part. The characteristics of the low-pass filter are determined by the scaling function φ(x), and the characteristics of the band-pass filter are determined by the wavelet function ψ(x). The decomposed coefficient consists of two parts: low frequency coefficient vector cA and high frequency coefficient vector cD. The low frequency coefficient vector cA is obtained by convolution of the impulse response of the signal and the low-pass filter, which reflects the low frequency component of the signal. High-frequency coefficient vector cD is obtained by convolution of signal and bandpass filter, which reflects the high frequency component of signal.
Fast and accurate phase-unwrapping algorithm based on the transport of intensity equation: comment

In [ Appl. Opt. 56 , 7079 ( 2017 ) APOPAI 0003-6935 10.1364/AO.56.007079 ], an attractive phase unwrapping algorithm based on the transport of intensity equation is proposed. Although the effectiveness is experimentally evaluated, the derivation of the angular spectrum based expression is ambiguous. In this paper, the ambiguity is clarified with the Helmholtz equation.
Estimating Ensemble Bias using Bayesian Convolutional Neural Network

Convolutional neural networks (CNN) are powerful tools in many medical imaging applications including denoising. However, a major concern in deploying a CNN in safety-critical areas is to access its prediction accuracy on out-of-distribution test samples. Our previous study showed that while a CNN could generate similar ensemble standard deviation levels on inputs with different noise levels, the magnitude and location of bias could be substantially different. These observations imply that although CNNs can generate visually high-quality images from each individual dataset, the bias might be higher in one image than the other, which might lead to misdiagnosis. Therefore, it is crucial for us to understand when and where a CNN is uncertain about its prediction in real clinical datasets. In this study, we investigate the use of a Bayesian Convolutional Neural Network (BCNN) to estimate an uncertainty map of its prediction, and assess its correlations with the ensemble bias of its own and a conventional CNN's prediction ensemble bias, respectively. The BCNN is implemented by adding Monte Carlo dropout layers after each convolution and ReLU layer, which is equivalent to imposing a Bernoulli prior distribution on the network weights. At the testing stage, we acquire multiple stochastic forward passes through the network to generate the uncertainty map. We applied BCNN and CNN on two 18F-FDG scans, with one whole-body scan which is considered as an in-distribution test sample, and one brain only study which is considered as an out-of-distribution sample. For the whole-body scan, we generated 10 noise realizations for ensemble analysis using bootstrap. Our results demonstrate that BCNN can generate a prediction uncertainty map that highly correlates with the ensemble bias map. This technique could provide important guidance on interpreting the denoising results from a neural network where ground truth is unknown.
Achieving Effective Secrecy based on Blockchain and Data Sharing in Cloud Computing

Cloud protection is a collection of policies, rules, appropriate technical and organizational measures which work with each other to protect cloud-based systems, critical infrastructure. These protections are set up for the safety of cloud information, regulatory enforcement and safety of the privacy of consumers, as well as the authentication of individual users and devices. Cloud protection can be tailored to the exact needs of the firm from validating access to filtering traffic. And since these regulations can be set up and handled in a single location, overhead administration is minimized and IT teams can concentrate on other business areas. Depending upon the cloud provider or the cloud safety solutions in place, the way cloud protection is provided. However, it should be a shared responsibility of the company owner and the solutions provider to execute cloud protection processes. This is the content-centered cloud face serious challenges as they meet a wide variety of content demands, secure and express complaints material how effectively and safely to protect data over the network seems to be an issue. In this paper, a blockchain-based framework has been proposed to address data security problems in content-centered cloud. Here, we exercise reciprocal trust between users and service providers. The transparency and the resistance to exploitation of the blockchain network protect the provider's protection and access control. Selected from the users with the aid of a common record can be kept secretly by the content owners. The article shares the low overhead interesting data, delay and congestion of the network and then green contact. To fix this, we suggest a solution cross-cut the gap with the use of a local peer network. It limits the number of international transactions by using a scalable local chief, without blockchain compromise on the authentication of local and peer transactions W\world level. The tests indicate a large decrease Weight and height of the block for global peers. The resolution often increases the rate of all transactions indirectly the load distribution is induced by peers.
Watching the Watchers: Resource-Efficient Mobile Video Decoding through Context-Aware Resolution Adaptation

Mobile computing evolution is critically threatened by the limitations of the battery technology, which does not keep pace with the increase in energy requirements of mobile applications. A novel approach for reducing the energy appetite of mobile apps comes from the approximate Computing field, which proposes techniques that in a controlled manner sacrifice computation accuracy for higher energy savings. Building on this philosophy we propose a context-aware mobile video quality adaptation that reduces the energy needed for video playback, while ensuring that a user's quality expectations with respect to the mobile video are met. We confirm that the decoding resolution can play a significant role in reducing the overall power consumption of a mobile device and conduct a user study with 22 participants to investigate how the context in which a video is played modulates a user's quality expectations. We discover that a user's physical activity and the spatial/temporal properties of the video interact and jointly influence the minimal acceptable playback resolution, paving the way for context-adaptable approximate mobile computing.
The impact of contextual information on decision‐making in footwear examination: An eye‐tracking study

In order to investigate whether context factors and expectations might potentially influence the decision-making of forensic footwear examiners, we collected the gaze process of experts examining the shoeprint image sets through the eye-tracking recorder. Additional to eye movement data as an objective measure, questionnaires were completed, and survey was conducted afterwards. Twenty-three qualified examiners assessed the similarity among shoe images for 22 different cases, including three sets were laterally reversed. We divided the experiment into two sessions, and then compared the examiners' performance with and without contextual information. The results showed the effects of contextual bias manipulate on both behavioral data and eye tracking data. The consensus and accuracy of examiners with contextual information were higher than those without contextual information. In the eye-tracking data, there is a significant difference between fixation counts and saccadic counts under contextual information. In addition, we found that the contextual information produced significant changes in inter-examiner consistency as measured by the Earth Mover Distance metric. However, there is no significant statistic differences in saccadic amplitude and total fixation duration of the examiners after exposure to contextual information. Our research results are instructive for understanding the cognitive process of shoeprint examination involved in real cases. In this process, stimuli related to context factors may affect decision-making and behavior. Implications for contextual effect causes are discussed.
GRHAM: Towards Group Recommendation Using Hierarchical Attention Mechanism

Group recommendations extend individual user recommendations to groups, which have become one of the most prevalent topics in the recommender system and widely applied in catering, tourism, movies, and many other fields. The key to group recommendation is how to aggregate the preferences of different group members and calculate the group's preferences. However, the existing aggregation strategy is static and simple. First, they ignore that the preferences of members will change over time. Second, they fail to consider that member's influence is different when the group makes decisions on different activities. To this end, this paper proposes a new novel model for Group Recommendation using Hierarchical Attention Mechanism (GRHAM), which can dynamically adjust the weight of members in group decision-making. Our model consists of two layers of attention neural networks, the first attention layer learns the influence weights of members when the group makes the decision, and the second attention layer learns the influence weights between group members. Besides aggregating the preference of group members, we further learn group topic preferences from the historical data. We conduct experiments on two real datasets, and the experimental results show that our model outperforms other group recommendation models.