Reviewerly/exHarmony huggingface dataset:
Combining Clustering Undersample and Ensemble Learning for Wearable Fall Detection

Accidental falls often cause serious harm to the human body, especially for the elderly. But falls tend to be infrequent, making it difficult to collect large amounts of data for research. In reality, there is a large gap between the amount of sensor data collected by falling activities and daily activities, which will lead to class imbalance. When using machine learning algorithms to detect falls, class imbalance will cause the performance of the classifier to be biased towards most classes and reduce the detection accuracy of a few classes. When faced with the problem of binary class imbalance, selecting an effective machine learning algorithm and resampling data can effectively improve the accuracy of classification. In this paper, an ensemble learning algorithm and clustering undersampling method are used for fall detection. The ensemble learning algorithm can reduce the impact of imbalanced datasets on the training model through multiple classifier iterations. Clustering undersampling method can change the dataset distribution and balance the number of positive and negative samples. The method in this paper is evaluated on the public dataset Sisfall. Compared with the traditional machine learning algorithms, the ensemble learning has higher accuracy and faster training speed. Combined with the clustering undersampling method, the method has a higher recall and precision.
Lightweight Generative Adversarial Networks Based on Ghost Module

Generative adversarial networks are widely used in computer vision tasks like image translation and image style transfer. Most of mainstream methods including CycleGAN and pix2pix use the stacking of residual blocks to deepen the number of network layers, which makes the networks have a large number of parameters and floating point operations. This paper presents a ghost-module-based generative adversarial networks. We use the ghost module to replace the residual blocks in the traditional generative adversarial network for building lightweight generative adversarial networks. Experiments shows that our method significantly reducing the parameters and floating point operations of the generative adversarial network on the precondition of assuring the quality of the generated images.
Pedestrian Detection Based on Improved Faster-RCNN Algorithm

In recent years, pedestrian detection based on image recognition has become an important research topic in vehicle assisted driving. For the question of poor detection accuracy resulted from missing detection and small targets in pedestrian detection, proposes a pedestrian detection method based on improved Faster-RCNN. First, ResNet34 residual network was used to replace VGG-16 as the backbone feature extraction network, and then SENet mechanism was introduced to further enhance and suppress the weight vector. Then, aiming at the multi-scale problem in the detection set, FPN network is added to further strengthen the feature extraction ability of the network. The k-means algorithm is introduced to generate appropriate anchors according to the characteristics of the dataset. The experimental results show that, compared with the classic network, the average precision (mAP) of the improved algorithm reaches 93.36%, which is 5.34% higher than the original Faster-RCNN algorithm, which proves the effectiveness of the algorithm.
An Efficient Learning Based Autonomous Exploration Algorithm For Mobile Robots

In this paper, a novel autonomous exploration algorithm is proposed to achieve efficient exploration task of an unknown environment in terms of the shortest path. First, a new neural network based on the variational autoencoder, LMPnet, is proposed to predict a series of local maps with projected obstacles of unknown areas. Then, a deep Q-network with long-short term memory (LSTM) structure, ETPNet, is proposed to generate piecewise local target points based on the predicted local maps where the reward function is designed to favor shorter length of the local path and larger information gain. Experimental results demonstrate that the proposed algorithm achieves good performance in reducing exploration time.
Research on Humanistic Innovation Accomplishment Cultivation in Intercultural Communication Based on Virtual Simulation Technology

This paper conducts quantitative research on the experimental method in accordance with the “hypothesis-testing” model. By using questionnaires and interviews, the participants’ cultural integration in the process of organic integration of virtual simulation technology and college students’ humanistic innovation accomplishment in intercultural communication are analyzed. Based on the data, the research designs a slightly modified Likert five-level scale according to the specific situation, and uses SPSS to conduct frequency statistics and average statistics and constructs an intercultural communication humanistic innovation accomplishment model based on the results of the factor analyses, comprehensively improving students’ intercultural communication and humanistic innovation accomplishment skills.
Livestock Posture Recognition Using Deep Learning

Calf posture recognition could be one of the required steps for a complete automated calf monitoring system, as sometimes the calf is required to be in a standing posture before being able to proceed to the next stage. To distinguish calf postures such as between standing or lying, machines require complicated frameworks, especially one that involves deep learning models. Previously, most of the works utilized video inputs rather than image inputs, which would make the model unnecessarily complicated compared to a conventional Convolutional Neural Network (CNN) model, which accepts image inputs. In this paper, to overcome all the problems mentioned earlier, two deep learning models with the exact same ResNet-50 based architecture have been built and trained on two different image datasets, respectively sourced from separate cameras placed at different angles to be compared and analyzed. The performance for both CNN models were 99.7% and 99.99% in accuracy, respectively, significantly better than the 92.61% accuracy of a similar work, and is adequate for a real-time calf monitoring system.
Blockchain-Based Distributed File System Security and Privacy: A Systematic Mapping Study

Blockchain-based distributed file system is an emerging, decentralized technology that enhances blockchain for storing data and general-purpose files off-chain. The platform integrates blockchain technology with the distributed file system to give users the ability to store data and content respectively on the technology which offers distributed access to data safe and secure among a community of trusted or untrusted networks. This advancement is gaining traction in researchers, with a growing number of research papers and applications being built based on this technology. However, the combination raises doubt on the security and privacy issues of the system. The blockchain-based distributed file system also is still young, unstructured, and in its early stages of research. Thus, a systematic mapping study was carried out and 23 primary studies were identified. Extraction of data is then carried out based on their research type and contribution type facets to gain a wide overview of blockchain and distributed file systems focusing on security and privacy.
Multiscale importance evaluation pruning algorithm

Abstract With the continuous increase of the number of network layers, while the network performance is constantly improving, the complexity of the network is also increasing exponentially, which limits the application scenarios of neural networks. To solve this problem, many network compression and acceleration methods have been proposed. Unstructured pruning of networks is a common method that generates compact networks by pruning each parameter of the network. A common operation of pruning is to evaluate the importance of the network at a certain scale to allocate the proportion of pruning. We propose a method that combines the importance of multiple scales to comprehensively evaluate the importance of parameters. Firstly, the network is pruned layer by layer, and the sum of the classification accuracy of the network after pruning and training network for one epoch as the sensitivity of the layer network. Secondly, on the filter, the channel attention module is used to evaluate the importance of each channel of the filter, thirdly, the KL (Kullback Leibler) divergence is used to evaluate the importance of the parameters in the convolution kernel. Finally, the importance of each parameter is generated by multiplying the importance of the three scales, and the result is obtained after fine-tuning the network.
Web Solution Based on QR Code for the Traceability of the Wood Transformation Process

The information’s management on the traceability of wood in Peru is complicated by multiple causes. In this article we present our project, which seeks to propose a technological model that allows greater efficiency in the management of timber information so that sawmills in Peru can improve their administration. Our proposed model allows us to appreciate a possible improvement in the time it takes to obtain information on the traceability of wood compared to the traditional way in which work continues in Peru. Also, in the investigation a prototype application of traceable information was developed. The main contribution of the research consists in the design of a technological model that serves as a reference to develop a technological solution for the traceability of wood in Peru.
Mobile Application for the Timely Detection of Potential COVID-19 Infections

The objective of this study is to determine user acceptance of a mobile application for the timely detection of possible COVID-19 infections in the city of Cajamarca. For this purpose, the mobile application Covid Alerta (Covid Alert) was developed for devices with the Android operating system. The OpenUp software development methodology was used for the development of this application. The applied, quantitative, exploratory research used 2 survey forms: one to measure how the detection of COVID-19 was carried out in the city of Cajamarca, and another to measure the impact of using a mobile application to detect infected contacts. The results indicated that the most reliable screening tests are molecular tests; in addition, 85% of respondents felt much safer receiving alerts of infected contacts on their mobile devices. Likewise, 88.5% indicated confidently that the application complies with registering and reporting infected contacts. This shows that 88.8% of users accepted the use of mobile applications for the timely detection of possible COVID-19 infections.
Real–Time Implementation of Fuzzy Control Blocks for Process Control

In this paper, the application of the controller based on fuzzy logic is evaluated as an alternative to the standard PID controller applied in PLC (Programmable Logic Controller), since currently the large firms dedicated to the automation field although they offer the fuzzy control tool for their PLC these have a high acquisition cost. This work deals with the design and implementation in real time of control blocks based on fuzzy logic using a Siemens PLC to control the level and temperature processes of a multipurpose plant. And with the results obtained from the real-time implementation, it can be affirmed that the controller based on fuzzy logic presents a better performance when compared to the PID controller in terms of settling time, stable state error and overshoot percentage.
Learning spectral transform for 3D human motion prediction

In existing motion prediction methods that use graph convolution networks, motion sequences are transformed to a spectral domain, and future motions are predicted through graph spectral filtering for the transformed spectral sequences. However, because the conventional spectral transform method uses a predetermined spectral basis, the motion prediction does not work well for aperiodic or complicated motions. To overcome this problem, we propose a method to learn spectral domain transforms from motion sequences in the training dataset. To this end, two methods are attempted: one for learning the frequency of each spectral basis, and another for learning the values of the basis function directly. Through experiments on representative 3D human motion benchmarks, H3.6M and CMU Mocap, we demonstrate that both of the proposed methods consistently outperform the baseline method. In particular, the method of directly learning the basis function outperforms the state-of-the-art methods. We also demonstrate that the proposed method yields realistic predictions, even for aperiodic and complicated action categories.
Probabilistic mediator: A coalgebraic perspective

Mediator is a component-based modeling language where components and systems can be specified separately and precisely. In this paper, we show how to analyze probabilistic Mediator, which supports both non-deterministic and probabilistic behavior of component-based systems, from a coalgebraic perspective. Probabilistic Mediator components are modeled as coalgebras, with the powerset monad P and the distribution monad D to capture their non-determinsitic and probabilistic behavior, respectively. A coalgebraic semantics of combinators on Mediator components is developed. Such a coalgebraic view induces a suitable notion of bisimulation for comparing probabilistic Mediator components and systems.
Kolmogorov and Kuroda Translations Into Basic Predicate Logic

Abstract Kolmogorov established the principle of the double negation translation by which to embed Classical Predicate Logic ${\operatorname {CQC}}$ into Intuitionistic Predicate Logic ${\operatorname {IQC}}$. We show that the obvious generalizations to the Basic Predicate Logic of [3] and to ${\operatorname {BQC}}$ of [12], a proper subsystem of ${\operatorname {IQC}}$, go through as well. The obvious generalizations of Kuroda’s embedding are shown to be equivalent to the Kolmogorov variant. In our proofs novel nontrivial techniques are needed to overcome the absence of full modus ponens in Basic Predicate Logic. In [3] we argued that ${\operatorname {IQC}}$ is not the logic of constructive mathematics. Our doubts were far from new. New was that we put forward an alternative, ${\operatorname {BQC}}$. One concern is that ${\operatorname {BQC}}$ is too weak for serious mathematics, or even trivial. This paper is one step to alleviate such concerns.
A conversation with Elja Arjas (Helsinki, November 2021 and March 2022)

Abstract Statistics as an independent scientific discipline is relatively young in Finland. Its active history stretches back roughly a century, with the past 50 years signifying a period of growth. Few other academics such as Elja Arjas, now professor emeritus at University of Helsinki, have played a prominent role in establishing statistics in Finland. This conversation tries to illuminate how this came to happen and what was needed to push statistics as a discipline to a firmer ground. We do not have a looking glass at our disposal but will nevertheless also try make some predictions about the future.
التطوير المهني لأعضاء هيئة التدريس : مدخل لتحقيق الجودة في التعليم الجامعي

This study tackled the professional development of staff members in higher education institutions, with emphasis on the diverse roles of the staff members they are associated with the spirit of the currently accepted educational theories and thoughts.The study aimed to clarify the roles, tasks and competences of staff members in higher education institutions and the need and justifications of the importance of professional development. The study tried to find answers for a number of questions; the most important are: What are the roles, tasks and competencies of staff members? Why is it necessary to pay attention for the staff members' performance development? What are the competencies the teaching staff in these institutions should be equipped with? What are the means and patterns of training needed to develop competencies of the teaching staff members?The study adopted the descriptive analytical methodology by analyzing the views and ideas of experts on the topic. The study summed up with a number of conclusions and recommendations.
Upper bound estimations of misclassification rate in the heteroscedastic clustering model with sub‐Gaussian noises

Clustering is an important tool in statistics, machine learning and applied mathematics. This paper considers the clustering model , where the noise matrix consists of independent sub‐Gaussian entries and the variance may vary across different coordinates. Our aim is to estimate the error between the label vector and its defined estimator . We provide upper bound estimations for the misclassification rate in the sense of expectation and probability, respectively. Finally, some simulations have been carried out to support our theoretical results and illustrate the advantage of our proposed estimator.
When Shall I Estimate Your Intent? Costs and Benefits of Intent Inference in Multi-Agent Interactions

This paper addresses incomplete-information dynamic games, where reward parameters of agents are private. Previous studies have shown that online belief update is necessary for deriving equilibrial policies of such games, especially for high-risk games such as vehicle interactions. However, updating beliefs in real time is computationally expensive as it requires continuous computation of Nash equilibria of the sub-games starting from the current states. In this paper, we consider the triggering mechanism of belief update as a policy defined on the agents’ physical and belief states, and propose learning this policy through reinforcement learning (RL). Using a two-vehicle uncontrolled intersection case, we show that intermittent belief update via RL is sufficient for safe interactions, reducing the computation cost of updates by 59% when agents have full observations of physical states. Simulation results also show that the belief update frequency will increase as noise becomes more significant in measurements of the vehicle positions.
Deep Interacting Multiple Model Filtering

In this paper, a deep learning-based multiple model estimation framework is presented for the state estimation of hybrid dynamical systems from high dimensional observations such as camera images. A low dimensional vector which represents the measurement of the latent dynamical system and its corresponding variance are learned using a deep encoder neural network. An Interacting Multiple Model (IMM) filter is used to generate the latent state estimates and covariances using multiple dynamical models, which can be learned using backpropagation through time. The state estimates of the dynamical system and the corresponding covariance matrix are generated from the latent state estimates and covariance using a deep decoder neural network. The whole network is trained in an end-to-end manner using a loss function which minimizes the negative log-likelihood of the neural network parameters. Simulation results are presented using a 2D bouncing ball example and estimation error statistics are computed which demonstrates the accuracy and consistency of the estimation.
Challenges in Automated Measurement of Pedestrian Dynamics

Analyzing pedestrian dynamics has since long been an active and practical field of interest. Since the introduction of, in particular, smartphones, various organizations saw a simple means for automatically measuring pedestrian dynamics. The basic idea is simple: network packets sent by WiFi-enabled devices can be collected by sensors and by extracting the unique MAC address from each packet, it should be possible to count how many devices are detected by a single sensor, as well as how devices move between sensors. Although this approach has been commercially deployed for many years, it is now largely forbidden (at least in the EU) due to obvious privacy infringements. In this paper, we address challenges and some potential solutions to automated measurement of pedestrian movements while protecting privacy. The results come from learning the hard way: having run experiments extensively over the past years, we have gradually gained considerable insight in what is possible and what may lie ahead.
Filtering of systems with heavy tailed noise: application to 3D target tracking with glint noise

In this paper a filtering method for non-Gaussian linear systems is adopted to face the problem of the target tracking in the presence of the glint noise. In particular, we extend the quadratic filtering method with virtual measurements to the three-dimensional case of the target tracking problem. Moreover, we present extensive numerical simulation by comparing our method with several filtering algorithms used in the case of heavy tailed noises. The latter numerical results confirm the effectiveness of the proposed approach.
Passivity-Based Target Tracking Robust to Intermittent Measurements

A passivity-based switched systems analysis of an estimator-predictor framework is presented for mobile target tracking. Measurements of the target are provided by a mobile sensor with limited field-of-view (e.g., a camera). The analysis demonstrates that passivity-based dwell-times exist to ensure the tracking objective is achieved, despite intermittent feedback of the target pose. Specifically, a maximum ratio is determined between the length of time the target is unobserved and the length of time the target is observed. After a period of time where the target is unobserved, this provides a minimum time that the target must be observed to ensure passivity of the system. An additional result demonstrates conditions under which the estimator-predictor framework is uniformly ultimately bounded. These results show that the framework is robust to intermittent feedback, which is an alternative approach to relaxing the continuous observation constraint required by many existing results.
Target Tracking with Frame- and Event-based Cameras Involving Delayed and Irregularly-Sampled Visual Feedback for a Robotic Air-Hockey System

We present a target tracking system for a robotic air-hockey player, where the state of the puck is optimally estimated by fusing measurements from frame- and event-based vision sensors. In addition to the jumping velocity of the tracked object during the game, the technical challenge of the problem is amplified by the variable delay and irregular sampling intervals of the measurements — a thematic challenge for controls under such visual feedback. An auto-restart Kalman filter is first proposed for compensating sudden jumps in the puck state. Then a memory-enabled auto-restart Kalman filter is derived to additionally accommodate delays and sensing irregularities. Building on physics-based modeling, model-based filtering, and mixed sensor management, the proposed method applies to other vision-based control systems from motion tracking to manufacturing automation. The tracking performance is analyzed in simulation that shows the effectiveness of the proposed tracking algorithm.
Catching Objects with a Robot Arm using Model Predictive Control

This paper presents a model predictive control (MPC)-based planning and control approach for catching objects in flight with a robotic arm. The core of the approach is to combine the three elementary tasks of the catching process, namely predicting the flight trajectory, determining the catching pose and the motion planning and control of the robot in one optimization problem. Thereto, a time-optimal problem formulation is chosen with additional robot-specific inequality constraints. Based on a parametric description of the flight parabola, terminal equality constraints are defined ensuring that the end effector position lies on the flight parabola with an orientation in tangential direction of the trajectory. The approach is successfully applied in simulation and experiments in real-time for a 7-degrees-of-freedom (DOF) robot arm with the nonlinear model predictive control toolbox GRAMPC.