Reviewerly/exHarmony huggingface dataset:
An Evaluation On The Entropy Supplying Capability Of Smartphone Sensors

Abstract Random numbers are very important for the security of computer system. However, generating qualified random numbers is difficult because we cannot always successfully introduce dedicated random number hardware into computer system. Although most operating systems provide random number generation capabilities, the effective entropy supply is still dependent on the hardware platform including memory and clocks etc. However, obtaining hardware events such as clocks requires system privileges, which is not conducive for entropy estimation at the application layer. In contrast, data related to the sensor hardware can be extracted directly at the application layer. These sensor data contain some randomness and may be used as a noise source. In this way, applications can use these sensors to implement their own proprietary random number generators. Before taking these sensors as the noise source, it is necessary to fully evaluate their entropy supply capability. In this paper, 300 Android smartphones and 30 iOS smartphones are selected as samples and their sensor entropy supply capabilities are comprehensively evaluated. Based on the entropy evaluation results, we give some suggestions on how to generate random numbers using these sensor data. We first design a framework for evaluating the entropy supply capability for smartphone sensors, based on the min-entropy estimation method proposed in NIST SP 800-90B. According to this framework, we simulate stationary and mobile working states for each smartphone, and collect sufficient sensor data as the min-entropy estimation dataset. The min-entropy estimation results show that in the stationary working state, each ACCELEROMETER sensor data collection can obtain at least 1.5 bits of entropy in Android, while each GYROSCOPE sensor data collection can obtain at least 20 bits of entropy in iOS. In the mobile working state, each ACCELEROMETER sensor data collection can obtain at least 1.9 bits of entropy, while each GYROSCOPE sensor data acquisition in iOS system can obtain at least 27 bits of entropy. This means that we can still get a stable entropy output from the sensor even when the smartphone is in stationary working state. Statistical analysis of the data using cross correlation methods suggests it is hard for an attacker to guess or predict the random numbers generated by a smartphone through another smartphone put in the similar external environment.
Minimum-Time Energy-Optimal Path Following Control for a Wheeled Mobile Robot subject to Velocity Constraints using Linear Programming

This paper focuses on developing a path planning algorithm for a wheeled mobile robot with actuator constraints. The proposed motion planning approach is divided into two stages: path planning, followed by path following. In the path planning stage, the problem is formulated in a path coordinate frame and solved using an optimal control framework. The Bernstein basis function weighted benchmark minimum energy cost function is used to achieve the rest-to-rest motion. In the path-following stage, the optimal controls from the first stage are discretized and posed as a Linear programming problem to determine the time scaling to satisfy the actuator limits. Control in the path frame is transformed into time domain while satisfying the velocity constraints. The Pioneer3-DX test platform is used to validate the control method experimentally. Multiple trials are conducted to statistically validate the proposed approach.
Understanding calibration of deep neural networks for medical image classification

Background and Objective - In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play a pivotal role in explainability by providing insights into the model's certainty, identifying cases that require attention, and establishing trust in its predictions. Consequently, the significance of a well-calibrated model becomes paramount in the medical imaging domain, where accurate and reliable predictions are of utmost importance. While there has been a significant effort towards training modern deep neural networks to achieve high accuracy on medical imaging tasks, model calibration and factors that affect it remain under-explored. Methods - To address this, we conducted a comprehensive empirical study that explores model performance and calibration under different training regimes. We considered fully supervised training, which is the prevailing approach in the community, as well as rotation-based self-supervised method with and without transfer learning, across various datasets and architecture sizes. Multiple calibration metrics were employed to gain a holistic understanding of model calibration. Results - Our study reveals that factors such as weight distributions and the similarity of learned representations correlate with the calibration trends observed in the models. Notably, models trained using rotation-based self-supervised pretrained regime exhibit significantly better calibration while achieving comparable or even superior performance compared to fully supervised models across different medical imaging datasets. Conclusion - These findings shed light on the importance of model calibration in medical image analysis and highlight the benefits of incorporating self-supervised learning approach to improve both performance and calibration.
Research and reflection on college physical education classroom teaching based on SSGAN model

Abstract Teaching behavior recognition has a wide range of applications in the smart classroom and is one of the important means to achieve educational intelligence. To improve the performance of indoor teaching behavior recognition using CSI in complex scenes, this paper proposes an indoor teaching behavior recognition algorithm based on multi-feature fusion MLSTM by eliminating background noise to circumvent the influence of the experimental environment on CSI. To address the problem, the model cannot generalize in recognizing new users, and the labeled samples of new users are difficult to obtain in large quantities in a short period of time. In this paper, a new user recognition algorithm based on the SSGAN model is constructed, and then the input and output of MLSTM are modified as the discriminator of SSGAN to improve the recognition performance of the model for new users by semi-supervised learning. The recognition accuracy of the M-LSTM model on the sports, daily, and dance datasets is 0.985, 0.966, and 0.944, respectively, and the recognition accuracy of the SSGAN model on the three datasets is also around 90%, as verified by different experiments. The p-value is less than 0.05, and the student’s interest in physical education in the experimental group is 2.6 times higher than that in the control group. Therefore, the model proposed in this paper has good practicality.
Temporality in Cultural Informatics

The present work wishes to highlight the role of temporality in cultural informatics and provides a review of concepts and practices. Temporality is often overlooked during the interaction design of cultural technologies, although aspects of it are crucial for the success of a cultural experience. Many researchers decide to support the visitor before, during and after her visit, planning for a continuation of the visit and providing an extended time frame. In addition, in technology assisted cultural experiences, temporality is again a key concept, since in different types of experiences can be synchronous or asynchronous. Furthermore, there are apps that directly ask the visitor to provide the estimated time of visit, in order to plan content delivery and experiences accordingly. Visitor studies also reveal that the amount of time people spend at a cultural space depends on external factors like tiredness, weather, etc, but also on internal factors such as visitor's personality. For all the above, specific examples are provided. When interaction in cultural informatics is designed, aspects of temporality should guide the design processes, thus the paper concludes with a summary of important design points.
MfvPose: A multi-scale hybrid framework for human pose estimation

Human pose estimation is a challenging visual task that relies on spatial location information. To improve the performance of human pose estimation, it is important to accurately determine the constraint relationship among keypoints. To address this, we propose MfvPose, a novel hybrid model that leverages rich multi-scale information. The proposed model incorporates the HRFOV module, which uses cascaded atrous convolution to maintain high-resolution representations of the backbone extractor and enrich the multi-scale information. In addition, we introduce learnable scalar weights to the Transformer encoder. In detail, it involves a multiplication by a diagonal matrix with learnable scalar weights on output of each residual block, which improves the dynamics of model training and enhances the accuracy of human pose estimation. It is experimentally shown that our proposed MfvPose achieves promising results on various benchmarks.
SecK2 – A novel machine learning algorithm for detecting data poisoning attacks

Machine learning (ML) techniques play a crucial role in producing precise predictions without the use of explicit programming by utilizing representative and unbiased data. These methods, which are a subset of artificial intelligence (AI), are used in a variety of settings, including recommendation engines, spam filtering, malware detection, classification, and predictive maintenance. While ML algorithms improve results, they also present security and privacy threats, especially in the face of adversarial ML attacks such as data poisoning assaults that can undermine data modeling applications. This study introduces SecK2, a cutting-edge ML method developed to stop dangerous input from entering ML models. The scalability of SecK2 is proved through meticulous experimental research, revealing its astonishing capacity to identify data poisoning attacks at a previously unheard-of pace. As a result, SecK2 becomes a valuable tool for guaranteeing the reliability and security of ML models. Our suggested method produces outstanding results by a variety of criteria. Notably, it achieves a noteworthy 61% convergence rate and an exceptional 89% attack detection rate. Additionally, it offers a phenomenal 96% throughput while protecting data integrity at 53%. The technique also boasts impressive Validation accuracy of 96% and Training accuracy of 92%. The suggested technology offers a strong and reliable barrier against the rising danger of data poisoning attacks. ML practitioners can have more faith in their models, thanks to SecK2’s capabilities, protecting against potential adversarial assaults and preserving the dependability of ML-based applications.
Investigating Applicability Heuristics of Answer Set Programming in Game Development: Use Cases and Empirical Study

The game industry is continuously growing and evolving, with new ways of creating games being developed. However, even with the availability of powerful game engines, developers are still forced to spend time and effort implementing common game features, such as basic AI, path-finding, and simple scene variations. This can become a serious detriment for indie game developers. The present research focuses on the application of Answer Set Programming (ASP) methods within the game development process, aiming to support rapid and cost-effective game prototyping for indie game developers. Specifically, we present a pragmatic approach to the usage of ASP for game development within certain use cases and we report on evaluation results based on feedback that was received from end-users. Analysis of results demonstrates how ASP can be used, providing new ways of thinking about game mechanics and content creation, and eventually paving the way for new game design frameworks and possibilities. On the downside, adoption of the suggested method can be difficult due to unfamiliarity with the ASP programming paradigm.
Concept and dependencies enhanced graph convolutional networks for short text classification

Short text classification task is a special kind of text classification task in that the text to be classified is generally short, typically generating a sparse text representation that lacks rich semantic information. Given this shortcoming, scholars worldwide have explored improved short text classification methods based on deep learning. However, existing methods cannot effectively use concept knowledge and long-distance word dependencies. Therefore, based on graph neural networks from the perspective of text composition, we propose concept and dependencies enhanced graph convolutional networks for short text classification. First, the co-occurrence relationship between words is obtained by sliding window, the inclusion relationship between documents and words is obtained by TF-IDF, long-distance word dependencies is obtained by Stanford CoreNLP, and the association relationship between concepts in the concept graph with entities in the text is obtained through Microsoft Concept Graph. Then, a text graph is constructed for an entire text corpus based on the four relationships. Finally, the text graph is input into graph convolutional neural networks, and the category of each document node is predicted after two layers of convolution. Experimental results demonstrate that our proposed method overall best on multiple classical English text classification datasets.
Exploring Runners' Preferences of Drone Based Feedback to Support their Well-Being

While drones are frequently used to capture aerial footage of runners, their utilization to enhance well-being of runners through real time feedback has not been fully explored. In this paper we investigate runners’ feedback preferences regarding drone-feedback and its implications for drone-feedback design. Using an embodied storming approach, we engaged 25 participants in a running activity to gather their preferences on running-related feedback delivered through drones. Our analysis uncovered runners’ top three preferred feedback parameters were pace, trunk lean, and time. Additionally, participants preferred instructive feedback for posture/technique-related parameters, and activity-related feedback focused on their current state. Furthermore, we present results from a reflexive thematic analysis, highlighting design considerations for drone feedback and its impact on designing drones for runners. We hope that these findings will inspire future researchers to explore the use of drones in promoting runners’ well-being.
A Cuckoo search-based optimized ensemble model (CSOEM) for the analysis of human gait

The use of mobile devices has contributed greatly to the expansion of Human Activity Recognition (HAR) studies in recent years. Researchers find it attractive because of its versatility, low cost, compact size, ease of usage, and wide range of possible applications. Conventional, biological, and control-based systems are just some of the methods that have been created for humanoid robot movement in recent years. This article specifically targeted improvement in the proposed method, which is different from previous papers. This is being done with the use of the publicly available Human Activity Gait (HAG) data set, which documents a wide range of different types of activities. IMU sensors were used to collect this data set. Several experiments were conducted using different machine-learning strategies, each with its own set of hyper-parameters, to determine how best to utilize these data. In our proposed model Cuckoo Search Optimization is being used for optimum feature selection. On this data set, we have tested a number of machine learning models, including LR, KNN, DT, and proposed CSOEM (Cuckoo Search-Based Optimized Ensemble Model). The simulation suggests that the proposed model CSOEM achieves an impressive accuracy of 98%. This CSOEM is built by combining the feature selection strategy of Cuckoo Search Optimizations with the ensembling of the LR, KNN, and DT.
Dataset for detecting motorcyclists in pedestrian areas

This paper presents a semi-automated, scalable, and homologous methodology towards IoT implemented in Python for extracting and integrating images in pedestrian and motorcyclist areas on the road for constructing a multiclass object classifier. It consists of two stages. The first stage deals with creating a non-debugged data set by acquiring images related to the semantic context previously mentioned, using an embedded device connected 24/7 via Wi-Fi to a free and public CCTV service in Medellin, Colombia. Through artificial vision techniques, and automatically performs a comparative chronological analysis to download the images observed by 80 cameras that report data asynchronously. The second stage proposes two algorithms focused on debugging the previously obtained data set. The first one facilitates the user in labeling the data set not debugged through Regions of Interest (ROI) and hotkeys. It decomposes the information in the nth image of the data set in the same dictionary to store it in a binary Pickle file. The second one is nothing more than an observer of the classification performed by the user through the first algorithm to allow the user to verify if the information contained in the Pickle file built is correct.
Human factors in software development: A study on database systems adoption by developers.

Ease of use and usability of tools and programming languages used in software development has been a topic of interest of the field of human-computer interaction. Various studies have examined these technologies from the point of view of the user, focusing either in general purpose programming languages, or specific tools. The area of database design and development and the adopted data models and tools is of high importance for most modern information systems, as the explosion of data generation has triggered a rapid development in databases. So, an interesting research question is to investigate the factors that influence practitioners in adopting database models and tools today. Factors to be considered relate to business, organizational and social factors, as well as the usability of the tools and representations used. In this explorative study we investigate (1) choices by professionals in relation to different types of projects (2) the criteria programmers use when selecting technological models; (3) possible reasons for reluctance in adoption; and (4) Perception of different models. The research is based on interviews with 9 professional developers in both their work as independent consultants and in corporate environments. The results revealed a strong influence of background knowledge acquired during university studies and experience as well as other social and organizational factors that influence technology selection by IT professionals concerned.
A multidimensional feature fusion network based on MGSE and TAAC for video-based human action recognition

With the maturity of intelligent technology such as human-computer interaction, human action recognition (HAR) technology has been widely used in virtual reality, video surveillance, and other fields. However, the current video-based HAR methods still cannot fully extract abstract action features, and there is still a lack of action collection and recognition for special personnel such as prisoners and elderly people living alone. To solve the above problems, this paper proposes a multidimensional feature fusion network, called P-MTSC3D, a parallel network based on context modeling and temporal adaptive attention module. It consists of three branches. The first branch serves as the basic network branch, which extracts basic feature information. The second branch consists of a feature pre-extraction layer and two multiscale-convolution-based global context modeling combined squeeze and excitation (MGSE) modules, which can extract spatial and channel features. The third branch consists of two temporal adaptive attention units based on convolution (TAAC) to extract temporal dimension features. In order to verify the validity of the proposed network, this paper conducts experiments on the University of Central Florida (UCF) 101 dataset and the human motion database (HMDB) 51 dataset. The recognition accuracy of the proposed P-MTSC3D network is 97.92% on the UCF101 dataset and 75.59% on the HMDB51 dataset, respectively. The FLOPs of the P-MTSC3D network is 30.85G, and the test time is 2.83 s/16 samples on the UCF101 dataset. The experimental results demonstrate that the P-MTSC3D network has better overall performance than the state-of-the-art networks. In addition, a prison action (PA) dataset is constructed in this paper to verify the application effect of the proposed network in actual scenarios.
Exploring the co-manipulation of physical and virtual objects in tangible mobile augmented reality for collaborative learning

Collaborative mobile augmented reality (MAR) has emerged as a promising tool in the field of education. This technology enables multiple users to interact with digital content overlaid on the physical world through their mobile devices. Collaborative MAR combined with tangible elements enhances learning by integrating physical objects that can be manipulated and interacted with in the augmented reality environment, providing a hands-on and immersive educational experience. This study explores the impact of tangible mobile augmented reality on collaboration and object manipulation. Our goal is to understand how mobile devices’ manipulation affects collocated students' collaboration in tangible MAR in terms of ease of use and collaboration. The study involves participants working in pairs to facilitate collaboration through a MAR game for developing the computational thinking skills of primary school students. Our goal is to compare the perceived behaviors and interactions that emerged in two distinct MAR settings: (1) Stand-mounted device condition, where the device is fixed on a mobile stand, and (2) Hand-held device condition, where the device is held by one of the team's players. The same task is simulated in both settings to allow for direct comparison. The results of this study can help inform the design and development of future MAR systems and provide insights into the potential benefits and challenges in terms of collaboration and object manipulation.
Data and Artistic Creation: challenges and opportunities of online mediation

Artistic creation using data that come from different domains such as the human body, environment, scientific measures etc. is not new to the contemporary artistic scene. The transformation of bodily data into other modalities through sonification or visualization in interactive music, dance performances, projections, procedural animation and live-coding is a common practice in the realm of digital and hybrid arts. From the perspective of Human-Computer Interaction, in this paper we pose the research question of if and how different forms of data that capture the embodied reactions to social issues can be re-usable for artistic creations for the global community through an online platform. Applying user-centred design methods, we report on the feasibility, implications and opportunities of such an approach.