Reviewerly/exHarmony huggingface dataset:
Reimagining the cycle: interaction in self-tracking period apps and menstrual empowerment

FemTech, technology often in the forms of apps developed to specifically target female health issues, have billions of users globally. Yet, despite the popularity of e.g., period trackers or pregnancy apps, we know little about the potential impact of these technologies, often developed outside controlled and regulated healthcare. How interactive technology is designed, and in our case the cycle represented through the design, has the potential to shape women's understanding of menstruation. In this study we analyse the interaction design of nine of the most downloaded menstruation apps, asking how commercial menstruation apps represent the cycle through their interaction design. While previous research has criticized these types of apps for lacking privacy and for enforcing normative ideals on women, they are often marketed in terms of female empowerment and users do indeed seem to find them very useful for period and fertility tracking. However, the term 'empowerment' is today used broadly and is known for having many potential meanings. Even within the field of Human Computer Interaction (HCI), 'empowerment' is used frequently but rarely explicitly defined. The question then becomes what empowerment could mean for menstruation tracking. In order to begin exploring the way menstruation and the period is represented in current apps and the way that future apps could design for empowerment, we engage in a comparative design investigation using what we call critical app-walkthrough methodology where an app's design is explored comprehensibly at one point in time and apps interacted with over a longer time period through researcher use-diares. Our results center around three ways in which these apps represent the cycle to users through design. We analyse; (1) interface metaphors used to represent the temporality of bleeding, (2) datafication of menstruation through input and output for intimate data tracking, and (3) the ways fertility predictions convey certainty over uncertainty. In a second step, we then explore what empowerment could mean for period trackers and how design could support empowering experiences. Finally, we present four design sensitivities meant to inspire designers to design for other types of period tracking experiences that might better empower bleeders. These are: support lived temporalities, embrace uncertainty, empower the self , and design less .
Analysis of the Pedagogical Experiment Results on the Implementation of Distance Learning Technologies in the Teaching of Mathematical Disciplines for Technical Specialties of Universities

&lt;p&gt;The article considers the results of a pedagogical experiment on the implementation of modern distance educational technologies in the teaching of mathematical disciplines to students of technical specialties of higher education. As a basis for the experiment, an electronic textbook on the course "Theory of the function of a complex variable" was used. It was used as part of the distance learning system CLASS.NET to teach this discipline at the Moscow Aviation Institute. The article presents the results of the preliminary statistical analysis of the homogeneity of experimental and control groups of students and the results of their questionnaire in order to identify the motivation for the use of distance learning tools in the educational process. A comparative analysis of the current ratings of students of experimental and control groups, calculated at the time of conducting control measures during the semester, is provided. The effectiveness of using the developed distance learning technology (LMS) in the educational process is confirmed by the results of statistical studies of the final assessments of students obtained during full-time testing.&lt;/p&gt;
An Efficient and Robust Scalar Auxialiary Variable Based Algorithm for Discrete Gradient Systems Arising from Optimizations

.We propose in this paper a new minimization algorithm based on a slightly modified version of the scalar auxialiary variable (SAV) approach coupled with a relaxation step and an adaptive strategy. It enjoys several distinct advantages over popular gradient based methods: (i) it is unconditionally energy diminishing with a modified energy which is intrinsically related to the original energy, and thus no parameter tuning is needed for stability; (ii) it allows the use of large step-sizes which can effectively accelerate the convergence rate. We also present a convergence analysis for some SAV based algorithms, which include our new algorithm without the relaxation step as a special case. We apply our new algorithm to several illustrative and benchmark problems and compare its performance with several popular gradient based methods. The numerical results indicate that the new algorithm is very robust, and its adaptive version usually converges significantly faster than those popular gradient decent based methods.Keywordsdiscrete gradient systemoptimizationscalar auxiliary variableadaptivestabilityconvergenceMSC codes65K1049M0590C26
A Graph Convolutional Siamese Network for the Assessment and Recognition of Physical Rehabilitation Exercises

Recently, due to the attention of physical rehabilitation improves markedly, several researchers attempt to implement automatic rehabilitation exercise analysis. However, most of the existing methods only focus on the assessment of a single action class, which limits the application scenario of multi-type action assessment. To advance the prior work, we present a novel graph convolutional siamese network to combine action classification and action assessment task. Specifically, a test action and a standard action form a pair as input to our model, which assesses the correctness of the test action compared with the standard action. Meanwhile, our model adopts a graph convolutional network to extract a feature from the input 3D skeleton data and recognize the action. Finally, we evaluate our model on UI-PRMD and IntelliRehabDS two popular datasets. Experiments demonstrate that the proposed model reaches state-of-the-art performance on action classification and outperforms the Dynamic Time Warping algorithm and hidden Markov model method by a large margin in terms of assessment accuracy.
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning

Self-supervised representation learning (SSRL) methods have shown great success in computer vision. In recent studies, augmentation-based contrastive learning methods have been proposed for learning representations that are invariant or equivariant to pre-defined data augmentation operations. However, invariant or equivariant features favor only specific downstream tasks depending on the augmentations chosen. They may result in poor performance when the learned representation does not match task requirements. Here, we consider an active observer that can manipulate views of an object and has knowledge of the action(s) that generated each view. We introduce Contrastive Invariant and Predictive Equivariant Representation learning (CIPER). CIPER comprises both invariant and equivariant learning objectives using one shared encoder and two different output heads on top of the encoder. One output head is a projection head with a state-of-the-art contrastive objective to encourage invariance to augmentations. The other is a prediction head estimating the augmentation parameters, capturing equivariant features. Both heads are discarded after training and only the encoder is used for downstream tasks. We evaluate our method on static image tasks and time-augmented image datasets. Our results show that CIPER outperforms a baseline contrastive method on various tasks. Interestingly, CIPER encourages the formation of hierarchically structured representations where different views of an object become systematically organized in the latent representation space.
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning

Sequential decision making in the real world often requires finding a good balance of conflicting objectives. In general, there exist a plethora of Pareto-optimal policies that embody different patterns of compromises between objectives, and it is technically challenging to obtain them exhaustively using deep neural networks. In this work, we propose a novel multi-objective reinforcement learning (MORL) algorithm that trains a single neural network via policy gradient to approximately obtain the entire Pareto set in a single run of training, without relying on linear scalarization of objectives. The proposed method works in both continuous and discrete action spaces with no design change of the policy network. Numerical experiments demonstrate the practicality and efficacy of our approach in comparison to standard MORL baselines.
Performance Evaluation of K-SVCR in Multi-class Scenario

The support vector classification regression machine for K-class classification (K-SVCR) based on the “1-verses-1-verses-rest” structure is a unique multi-class classification method, and it generates ternary output {−1, 0, 1}. Since it generates ternary output, the training data requires corresponding labels. In this article, we have checked the performance of K-SVCR to explore the impact of (1) the labeling of the class and (2) the relative location of class clouds. Many artificially generated datasets and a real-world dataset are considered to understand the impact of the choice of labels and relative location of the class distribution. Accuracy is used for K-SVCR concerning the choice of class labels and the relative location of data clouds. We found that the change in the class labels—positive + , negative −, and neutral 0 affects the accuracy of classification significantly.
Suitability Analysis Using GIS-Based Analytic Hierarchy Process (AHP) for Solar Power Exploration

Sibuyan Island is experiencing a significant increase in electricity demand due to population growth, urbanization, and industrial development. The island plans to use solar energy, recognizing its abundance and renewable nature; thus, this study was conducted to visualize the spatial distribution of solar exploration suitability using a geographic information system (GIS). Various criteria, including climatology, location, geography, meteorology, and disaster susceptibility, were considered in the assessment. Parameters affected by government policies, such as protected areas, proximity to rivers, roads and faults, ancestral domains, and proclaimed watersheds, were also considered. Parameters were weighted, and suitability levels were highlighted using AHP. The study revealed that about 5.88% (2674.06 km2) of the island was categorized as highly suitable for a solar farm, 34.99% (15,908.21 km2) as suitable, 2.49% (1129.95 km2) as moderately suitable, and the majority, 56.64% (25,754.47 km2), was considered not suitable for solar projects. A solar power suitability map was developed as a reference for local governments and residents in establishing solar PV systems in their respective sites, thus maximizing the full potential of their land. The study also directs future solar power exploration studies in Sibuyan Island, supporting ongoing efforts to maximize solar energy utilization.
Anomaly Detection Methods for Industrial Applications: A Comparative Study

Anomaly detection (AD) algorithms can be instrumental in industrial scenarios to enhance the detection of potentially serious problems at a very early stage. Of course, the “Industry 4.0” revolution is fostering the implementation of intelligent data-driven decisions in industry based on increasingly efficient machine learning (ML) algorithms. Most well-known AD methods use a supervised learning approach focusing on fault classification. They assume the availability of labeled data for both normal and anomalous classes. However, in many industrial environments, a labeled set of anomalous data instances is more challenging to obtain than a labeled set of normal data. Hence, this work implements an unsupervised approach based on two different methods using a typical benchmark bearing-fault dataset. The first method relies on the manual extraction of typical vibration metrics provided as input to an ML algorithm. The second one is based on a deep learning (DL) approach, automatically learning latent representation from raw data. The performance metrics demonstrate that both approaches can distinguish the state of a bearing from normal to faulty. DL methodology proves a higher accuracy rate in recognizing faults and a better ability to provide information about the fault size.
Verifiable and Searchable Symmetric Encryption Scheme Based on the Public Key Cryptosystem

With the rapid development of Internet of Things technology and cloud computing technology, all industries need to outsource massive data to third-party clouds for storage in order to reduce storage and computing costs. Verifiable and dynamic searchable symmetric encryption is a very important cloud security technology, which supports the dynamic update of private data and allows users to perform search operations on the cloud server and verify the legitimacy of the returned results. Therefore, how to realize the dynamic search of encrypted cloud data and the effective verification of the results returned by the cloud server is a key problem to be solved. To solve this problem, we propose a verifiable dynamic encryption scheme (v-PADSSE) based on the public key cryptosystem. In order to achieve efficient and correct data updating, the scheme designs verification information (VI) for each keyword and constructs a verification list (VL) to store it. When dynamic update operations are performed on the cloud data, it is easy to quickly update the security index through obtaining the latest verification information in the VL. The safety and performance evaluation of the v-PADSSE scheme proved that the scheme is safe and effective.
Improved Network and Training Scheme for Cross-Trial Surface Electromyography (sEMG)-Based Gesture Recognition

To enhance the performance of surface electromyography (sEMG)-based gesture recognition, we propose a novel network-agnostic two-stage training scheme, called sEMGPoseMIM, that produces trial-invariant representations to be aligned with corresponding hand movements via cross-modal knowledge distillation. In the first stage, an sEMG encoder is trained via cross-trial mutual information maximization using the sEMG sequences sampled from the same time step but different trials in a contrastive learning manner. In the second stage, the learned sEMG encoder is fine-tuned with the supervision of gesture and hand movements in a knowledge-distillation manner. In addition, we propose a novel network called sEMGXCM as the sEMG encoder. Comprehensive experiments on seven sparse multichannel sEMG databases are conducted to demonstrate the effectiveness of the training scheme sEMGPoseMIM and the network sEMGXCM, which achieves an average improvement of +1.3% on the sparse multichannel sEMG databases compared to the existing methods. Furthermore, the comparison between training sEMGXCM and other existing networks from scratch shows that sEMGXCM outperforms the others by an average of +1.5%.
An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems

The Hunger Games Search (HGS) is an innovative optimizer that operates without relying on gradients and utilizes a population-based approach. It draws inspiration from the collaborative foraging activities observed in social animals in their natural habitats. However, despite its notable strengths, HGS is subject to limitations, including inadequate diversity, premature convergence, and susceptibility to local optima. To overcome these challenges, this study introduces two adjusted strategies to enhance the original HGS algorithm. The first adaptive strategy combines the Logarithmic Spiral (LS) technique with Opposition-based Learning (OBL), resulting in the LS-OBL approach. This strategy plays a pivotal role in reducing the search space and maintaining population diversity within HGS, effectively augmenting the algorithm's exploration capabilities. The second adaptive strategy, the dynamic Rosenbrock Method (RM), contributes to HGS by adjusting the search direction and step size. This adjustment enables HGS to escape from suboptimal solutions and enhances its convergence accuracy. Combined, these two strategies form the improved algorithm proposed in this study, referred to as RLHGS. To assess the efficacy of the introduced strategies, specific experiments are designed to evaluate the impact of LS-OBL and RM on enhancing HGS performance. The experimental results unequivocally demonstrate that integrating these two strategies significantly enhances the capabilities of HGS. Furthermore, RLHGS is compared against eight state-of-the-art algorithms using 23 well-established benchmark functions and the CEC2020 test suite. The experimental results consistently indicate that RLHGS outperforms the other algorithms, securing the top rank in both test suites. This compelling evidence substantiates the superior functionality and performance of RLHGS compared to its counterparts. Moreover, RLHGS is applied to address four constrained real-world engineering optimization problems. The final results underscore the effectiveness of RLHGS in tackling such problems, further supporting its value as an efficient optimization method.
Learning Template-Constraint Real-Time Siamese Tracker for Drone AI Devices via Concatenation

Significant progress has been made in object tracking tasks thanks to the application of deep learning. However, current deep neural network-based object tracking methods often rely on stacking sub-modules and introducing complex structures to improve tracking accuracy. Unfortunately, these approaches are inefficient and limit the feasibility of deploying efficient trackers on drone AI devices. To address these challenges, this paper introduces ConcatTrk, a high-speed object tracking method designed specifically for drone AI devices. ConcatTrk utilizes a lightweight network architecture, enabling real-time tracking on edge devices. Specifically, the proposed method primarily uses the concatenation operation to construct its core tracking steps, including multi-scale feature fusion, intra-frame feature matching, and dynamic template updating, which aim to reduce the computational overhead of the tracker. To ensure tracking performance in UAV tracking scenarios, ConcatTrk implements a learnable feature matching operator along with a simple and efficient template constraint branch, which enables accurate tracking by discriminatively matching features and incorporating periodic template updates. Results of comprehensive experiments on popular benchmarks, including UAV123, OTB100, and LaSOT, show that ConcatTrk has achieved promising accuracy and attained a tracking speed of 41 FPS on an edge AI device, Nvidia AGX Xavier. ConcatTrk runs 8× faster than the SOTA tracker TransT while using 4.9× fewer FLOPs. Real-world tests on the drone platform have strongly validated its practicability, including real-time tracking speed, reliable accuracy, and low power consumption.
Pandemic surveillance and mobilities across Sydney, New South Wales

Abstract In response to the acute public health crisis of COVID‐19 in 2020 and 2021, governments in Australia and around the world rushed to institute technologies to track human bodies with “live” surveillance. In Australia, “lockdowns” halted human mobilities in all states for different periods, and technologies for tracking bodies and collecting data were introduced after and between physical lockdowns. In this article, we analyse both the monitoring technologies developed to contend with COVID‐19 and the complex array of regulations and public health orders governing space and mobility in New South Wales and Sydney. Our focus on monitoring technologies is based on the COVIDSafe App (National) and the New South Wales COVIDSafe Check‐in App. These apps enabled the surveillance of individual mobilities before their sudden demise apparently unrelated to the public health scenario at the time. We argue that these technologies are examples of sensory power which rapidly enrolled human bodies in systems of surveillance that were difficult to unravel through 2022 and beyond. Our focus on regulations and public health orders outlines the shifting legal geographies during public health crisis and the ways these were enacted as mobility restrictions, surveillance, and punishment in western Sydney. We argue that the scars of the peak pandemic will endure in particular locations and communities, signalling the persistence of sensory power beyond the life of specific COVID‐19 tools.
Smart speakers and customer experience in service contexts

Abstract The use of artificial intelligence‐based devices, such as smart speakers, is rising in frontline services, as such devices can perform several tasks for customers. However, little is known about customer responses to interactions with smart speakers that occur during service encounters. This research encompasses three studies intended to enhance our currently scarce knowledge of how customers respond to interactions with smart speakers during service encounters. Focusing on the hospitality industry, the first study shows that smart speakers improve evaluations of the hotels that use them in terms of customer ratings and positive affects. The second study demonstrates that automated social presence and psychological ownership feelings are key mechanisms that explain the development of valuable customer responses. The third study, which uses virtual reality, suggests that better actual behaviors are exhibited by people who appreciate the incorporation of smart speakers into services. Thus, the findings indicate that smart speakers improve service experiences, especially if used to carry out hedonic tasks usually performed by employees.
Principal balances of compositional data for regression and classification using partial least squares

Abstract High‐dimensional compositional data are commonplace in the modern omics sciences, among others. Analysis of compositional data requires the proper choice of a log‐ratio coordinate representation, since their relative nature is not compatible with the direct use of standard statistical methods. Principal balances, a particular class of orthonormal log‐ratio coordinates, are well suited to this context as they are constructed so that the first few coordinates capture most of the compositional variability of data set. Focusing on regression and classification problems in high dimensions, we propose a novel partial least squares (PLS) procedure to construct principal balances that maximize the explained variability of the response variable and notably ease interpretability when compared to the ordinary PLS formulation. The proposed PLS principal balance approach can be understood as a generalized version of common log‐contrast models since, instead of just one, multiple orthonormal log‐contrasts are estimated simultaneously. We demonstrate the performance of the proposed method using both simulated and empirical data sets.