Reviewerly/exHarmony huggingface dataset:
Dancing Delicacies: Designing Computational Food for Dynamic Dining Trajectories

Contemporary human-food interaction design is often a technology-driven endeavor in which food's materiality has been largely underexplored. Building on the concept of “computational food”, this paper explores the design of food as a material realization of computation through a material-centered approach. We engaged with a “Research through Design” exploration by designing a computational food system called “Dancing Delicacies”, which enables food items to be “programmed” and “reconfigured” within dynamic trajectories. Our practice led to a design framework resulting in four original dish designs. Our dishes aim to illustrate the richness of this new design space for computational food. Furthermore, through engaging with expert practitioners from the hospitality industry, we provide a first account of understanding the design of computational food for dynamic dining trajectories and its speculative use contexts in the industry. With this work, we hope to inspire researchers and designers to envision a new future of human-food interaction.
Understanding Women's Perspectives on Smart Home Security Systems in Patriarchal Societies of Malawi.

According to the World Bank, 33% of women in Sub-Saharan Africa experience domestic violence due to men's social status over them. Prior research suggests that smart home security systems can perpetuate patriarchal attitudes at the expense of women's rights. However, women's perspectives on smart home security systems are not well understood. This paper presents a study with 33 Malawian women to understand how these technologies might be used in their homes. Based on interviews and focus group discussions, we present women's perspectives on using smart home security systems and how they might exacerbate patriarchy. We found that while these technologies provide opportunities for supporting domestic security, they may affect women's privacy as they complete everyday activities, like cooking. We use design sessions to explore opportunities for designing these technologies in patriarchal societies. This study expands existing discussion about smart home security systems by considering how patriarchal norms shape their utilization.
The Inner Ear: Capturing and Physicalizing Home Vibrations

We present the Inner Ear: a porcelain device that both captures and represents data. In particular, we focus on sensing vibrations—for their hidden yet omnipresent qualities in domestic environments. We designed the Inner Ear in response and in contrast to a growing collection of 'always on and recording' smart home devices. With the Inner Ear, we purposefully let participants choose when to capture vibrations and which capture should be physicalized. In this pictorial, we describe the design and fabrication process of the capturing device as well as the data physicalization workflow. We contribute insights on (1) the design rationale and development of a double function artifact (to both capture and represent), as well as (2) design decisions involved in balancing legibility with leaving room for meaning making during the transcription of home vibration data.
Forking a Sketch: How the OpenProcessing Community Uses Remixing to Collect, Annotate, Tune, and Extend Creative Code

Creative coders create programs that generate visual output. Frameworks such as p5.js support sketching with creative code. Given the focus on expressivity over functionality, code reuse in creative coding practice is distinct from other programming contexts. Remixing facilitates iteration on existing code, but we have yet to understand how creative coders use remixing in practice. To understand creative coder remixing strategies, we studied the community of OpenProcessing, a site dedicated to sharing code-generated artworks. We found that 30% of the 1.2 million sketches in our data set were involved in remixing. For in-depth insight, we qualitatively analyze source code and visual output of 350 antecedent-remix pairs. We present on the diversity of ways that authors remix to curate projects, annotate process, explore variations, and transform existing sketches. We discuss the prevalence of these types and implications for supporting a multiplicity of remixing strategies in creative work.
Increasing Literacy Through Interactive Media In Early Childhood

The purpose of the study is to develop interactive learning media to improve early childhood literacy. The type of research is Research and Development/RD using the ADDIE model development procedure. The subjects of the study included 21 groups of Class A in Al Azhar kindergarten. Data collection techniques use interviews, FGD (Focus Group Discussion), observation, and validation of material and media experts. Data were analyzed with descriptive statistics and effectiveness tests. Research results in the development of interactive learning multimedia products that are valid, effective, and feasible to be used to improve early childhood literacy. The effectiveness of the media is shown from the results of the t-test, which is known that that learning with interactive media can improve early childhood literacy.
Reso-Net: Generic Image Resolution Enhancement Using Convolutional Autoencoders

Images are created in a variety of ways in various industries. These images are tough to work with, and as a result, they can't be used effectively in a variety of fields. In this paper, Image Resolution is improved to carry out the process of generic image enhancement tasks. In this process, the low-resolution image is enhanced so that the high-resolution image is achieved. With the help of Image enhancement, the perception or in other words the process of interpreting information present in images by the human viewers is enhanced and the quality is improved to a large extent. Image resolution augmentation has traditionally been accomplished using a variety of classic image processing approaches. However, these methods are not as robust as they should be in dealing with any form of noise signal associated with the image and unable to handle the problems of Error Control Mechanism, Optimization and some other problems. Therefore, this paper presents a method of image resolution enhancement using Advanced Hybrid Neural Network architecture which brings about significant improvements in the entire process.
Gene expression programming with dual strategies and neighborhood search for symbolic regression problems

As an evolutionary algorithm for automatic generation of computer programs, Gene expression programming (GEP) has received a lot of attention because it can generate concise and highly accurate solutions. However, GEP also has some unsatisfactory aspects, such as too many control parameters and a lack of balance between exploration and exploitation. Therefore, a new GEP with dual strategies and neighborhood search (DNSGEP) is proposed in this paper for symbolic regression problems. As the core part, a new multi-parent crossover operator with neighborhood search is proposed to replace the original transposition and recombination operators in GEP, which greatly reduces the excessive control parameters in GEP. To assist the multi-parent crossover operator in selecting parents, selection weight is used to assess the quality of individuals in the population. The multi-parent crossover operator combines selection weight to learn more from the better parents. In order to satisfy the different needs of global search and local search of the algorithm, two different crossover strategies are extended on the basis of the multi-parent crossover operator. Furthermore, a population distribution entropy strategy to GEP is proposed for determining when the algorithm should choose which crossover strategy to use. DNSGEP is compared with GEP and three GEP variants on 14 symbolic regression problems and 6 even parity problems. Experimental results show that DNSGEP has the success rate and accuracy that are fully superior to GEP, GEPADF and OGEP, not inferior to SLGEP.
Assessing data remnants in modern smartphones after factory reset

It is commonly believed by end-users that factory reset on an electronic device restores the state of the device back to when it was shipped from the factory. Nevertheless, user data has reportedly been recovered after a factory reset by applying forensic data recovery techniques. In order to protect end-users’ privacy, smartphone manufacturers started implementing security countermeasures such as encryption. Specifically for Android smartphones, the encryption scheme advanced as the Android version became higher. Meanwhile, the effectiveness of a factory reset on a modern Android device has not been publicly explored. In this paper, the effectiveness of factory reset on modern devices running Android 11 or 12 is investigated. This is done by looking at low level differences on data extractions before and after creating data and after resetting a device. Results show that some parts of the encrypted data are still accessible in their binary form as not all data is reset to factory state. Furthermore, different partitions do not wipe data that was created during device usage, from which information about the use of a device may be deduced.
Trajectory tracking control of an unmanned aerial vehicle with deep reinforcement learning for tasks inside the EAST

The robotic arms inside the EAST (Experimental Advanced Superconducting Tokamak) are bulky and slow, making them unable to efficiently complete remote handling tasks such as inspection and grasping. Miniature intelligent UAVs have the potential to assist in remote handling tasks. A key challenge is to achieve autonomous flight along a set trajectory within the EAST's vacuum vessel. This paper presents an autonomous UAV system with deep reinforcement learning for this purpose. The autonomous flight of a quadrotor UAV within the EAST was simulated using OpenAI Gym-style environment. To verify that the trained policy is transferable, we experimentally verified the trajectory tracking of UAVs along specific trajectories in real scenarios. The results show that our autonomous UAV system can complete trajectory-tracking flight tasks inside the EAST vacuum vessel.
Coordination-free Multi-robot Path Planning for Congestion Reduction Using Topological Reasoning

Abstract We consider the problem of multi-robot path planning in a complex, cluttered environment with the aim of reducing overall congestion in the environment, while avoiding any inter-robot communication or coordination. Such limitations may exist due to lack of communication or due to privacy restrictions (for example, autonomous vehicles may not want to share their locations or intents with other vehicles or even to a central server). The key insight that allows us to solve this problem is to stochastically distribute the robots across different routes in the environment by assigning them paths in different topologically distinct classes, so as to lower congestion and the overall travel time for all robots in the environment. We outline the computation of topologically distinct paths in a spatio-temporal configuration space and propose methods for the stochastic assignment of paths to the robots. A fast replanning algorithm and a potential field based controller allow robots to avoid collision with nearby agents while following the assigned path. Our simulation and experiment results show a significant advantage over shortest path following under such a coordination-free setup.
A deep-learning-based grading system (ASAG) for reading comprehension assessment by using aphorisms as open-answer-questions

Abstract Today reading comprehension is considered an essential skill in modern life, therefore, higher education students require more specific skills to understand, interpret and evaluate texts effectively. Short answer questions (SAQs) are one of the relevant and proper tools for assessing reading comprehension skills. Unlike multiple-choice questions, SAQs allow for the assessment of cognitive abilities such as attention, language, perception, and problem solving. However, the task of SAQs scoring is time-consuming and susceptible to ambiguity. Automatic Short Answer Grading (ASAG) is a new paradigm that could help solve these problems. This experimental analysis aims to implement ASAG using several approaches to sentence embedding based on deep learning with a multilayer perceptron regression layer on the top, trained with a reading comprehension dataset based on aphorisms. For experimental testing, the available dataset is composed of answers given by 199 undergraduate students in Spanish. BERT and Skip-Thought models are tested with different hyperparameters to find the best performance in terms of Pearson correlation coefficient and RMSE against human experts grades. The result of the current study showed that BERT model performed better than other approaches.
Opinion Mining with Density Forests

In this paper, we propose a new approach for opinion mining with density-based forests. We apply Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify clusters of data points in a space of feature vectors that are important features of hotel and restaurant reviews, and then use the clusters to construct random forests to classify whether the opinions expressed about features in the reviews are positive or negative. Our experiment uses two standard datasets of hotel and restaurant reviews in two different scenarios. The experimental results show the effectiveness of our proposed
Dried Droplets of Diluted Blood to Detect a High Concentration of Lipids

Hyperlipidemia is the elevated concentration of lipids in the blood, and it increases the probability of arterial obstruction, infarctions, and other complications of the circulatory system. While there are indications that qualitative analysis of blood stains could potentially identify patients with this pathology, the efficacy of this method remains uncertain. In this paper, we report an experimental study that investigates the formation of patterns in dried blood droplets with varying concentrations of ultrapure water. Two blood samples, one healthy and one with moderate hyperlipidemia, were examined to determine the ideal water and blood mixtures for detecting high lipid concentrations. Numerous intricate patterns were observed throughout the central region and periphery of the dried droplet. These patterns encompass various forms, such as plaques, bump-like patterns, and a range of cracks including random, radial, and ortho-radial configurations. By calculating the entropy of the Gray Level Co-occurrence Matrix (GLCM) and analyzing ROC curves, we determined that solutions with 4% and 12% hematocrit (indicating a high percentage of ultrapure water) exhibit over 95% accuracy in differentiating high lipid concentrations. These findings provide a promising outlook for the development of diagnostic methods based on the study of diluted blood coatings.
Anti-Similar Visual Target Tracking Algorithm Based on Filter Peak Guidance and Fusion Network

Visual tracking is a key research area in computer vision, as tracking technology is increasingly being applied in daily life, it has high-research significance. Visual tracking technology usually faces various challenging interference factors, among which, a similar background is one of the factors that has a greater impact on the tracking process. Kernelized Correlation Filter (KCF) tracking algorithm can track targets quickly by using circulant matrix, and has good tracking effect, so it is widely used in the tracking field. However, when the target is interfered by similar objects, the filter template in KCF cannot effectively distinguish between the target and the interfering object. This is because the filter only uses the texture gradient feature as the description object of the target, which will make the KCF algorithm extremely sensitive to the change of the target; therefore, the filter has difficultly making a judgment in the unstable scene, cannot accurately describe the target state, and finally leads to tracking failure. Therefore, this paper fuses Color Names (CN) on the basis of the original Histogram of Oriented Gradients (HOG) feature of KCF, which can obtain a more comprehensive feature representation, and realize the application of combined features to improve the anti-interference ability of KCF in complex scenes. In addition, this paper also uses the peak response of correlation filtering as the judgment condition to determine whether the current tracking result is stable. When the filter is in an unstable tracking state, the proposed algorithm will select the value with high confidence from its multiple responses as the candidate target of the Siamese network, and the deep learning network is used as the incremental learning method of the filter. The Channel Attention is introduced into the network layer, so that the network can adaptively reason and adjust the extracted universal features, and the enhanced feature information is used as the final discriminant basis. Finally, according to the response, the target with the smallest error compared with the target template is selected from multiple candidate targets as the final tracking result. The experimental results show that the average accuracy and average success rate of the proposed algorithm are significantly improved compared with the classical tracking algorithm, especially in dealing with similar target interference.
A Super-Resolution Reconstruction Network of Space Target Images Based on Dual Regression and Deformable Convolutional Attention Mechanism

High-quality space target images are important for space surveillance and space attack defense confrontation. To obtain space target images with higher resolution and sharpness, this paper proposes an image super-resolution reconstruction network based on dual regression and a deformable convolutional attention mechanism (DCAM). Firstly, the mapping space is constrained by dual regression; secondly, deformable convolution is used to expand the perceptual field and extract the high-frequency features of the image; finally, the convolutional attention mechanism is used to calculate the saliency of the channel domain and the spatial domain of the image to enhance the useful features and suppress the useless feature responses. The experimental results show that the method outperforms the comparison algorithm in both objective quality evaluation index and localization accuracy on the space target image dataset compared with the current mainstream image super-resolution algorithms.
Distributed Cooperative Automatic Modulation Classification Using DWA-ADMM in Wireless Communication Networks

Automatic modulation classification (AMC) is an important component in non-cooperative wireless communication networks to identify the modulation schemes of the received signals. In this paper, considering the multipath effect in practical propagation environments, a distributed cooperative AMC (Co-AMC) network based on machine learning is proposed to identify the modulation scheme in non-cooperative wireless communication networks. Specifically, feature vectors are first obtained by applying a cyclic spectrum to facilitate the feature extraction of the received signal. Then, a classifier based on the K-nearest neighbor (KNN) method is designed to obtain the local decision for modulation classification at each distributed node. Meanwhile, the reliability of the local decision is estimated by applying two loss functions to assess the quality of the local decision. Finally, the unified classification result is obtained to fuse the local decisions according to their reliabilities by applying a designed decision fusion algorithm based on the distributed weighted average alternating direction method of multipliers (DWA-ADMM). The simulation results demonstrate that the proposed Co-AMC network achieves superior classification accuracy compared to existing AMC methods across a range of modulation schemes and SNRs. More importantly, the proposed Co-AMC exhibits great flexibility and practicability since it is adaptive to wireless networks with various scales and topologies.
A Comprehensive Survey of Facet Ranking Approaches Used in Faceted Search Systems

Faceted Search Systems (FSSs) have gained prominence as one of the dominant search approaches in vertical search systems. They provide facets to educate users about the information space and allow them to refine their search query and navigate back and forth between resources on a single results page. Despite the importance of this problem, it is rare to find studies dedicated solely to the investigation of facet ranking methods, nor to how this step, aside from other aspects of faceted search, affects the user’s search experience. The objective of this survey paper is to review the state of the art in research related to faceted search systems, with a focus on existing facet ranking approaches and the key challenges posed by this problem. In addition to that, this survey also investigates state-of-the-art FSS evaluation frameworks and the most commonly used techniques and metrics to evaluate facet ranking approaches. It also lays out criteria for dataset appropriateness and its needed structure to be used in evaluating facet ranking methods aside from other FSS aspects. This paper concludes by highlighting gaps in the current research and future research directions related to this area.
AutoFL: Towards AutoML in a Federated Learning Context

Federated learning (FL) is a decentralized machine learning (ML) technique that learns from distributed data by moving the training process from a centralized server towards many clients rather than centralizing the client data, as is common with classical machine learning. The recent literature on federated learning often focuses on domain-specific use cases (e.g., IoT), investigates various privacy concerns (e.g., membership inference), or analyzes the impact of adversarial attacks (e.g., poisoning) and possible countermeasures. In these works, it is common for the server to have already chosen a specific machine-learning model and predefined hyperparameters prior to initiating the distributed training process. This decision is based on the server’s ability to accomplish the task by either reusing well-established neural network architectures suitable for the specific task (e.g., ResNet-50 for image classification) or evaluating the adequacy of a model using the limited data it has access to. Additionally, the server may also assess publicly available datasets, which may or may not accurately represent real-world data distributions. In this paper, we address the challenge where this step—i.e., the ML model selection and hyperparameter optimization—is not possible in a centralized manner. In such a context, the data of a single client may not be sufficient or not representative enough to construct an ML model configuration that is effective for all clients. In real-world deployments, the data on the different clients may be imbalanced and heterogeneously distributed, and the performance impact of countermeasures is often unclear upfront. While various automated machine learning (AutoML) frameworks have been proposed for classical machine learning and deep learning in a centralized setting, we investigated the practical feasibility of AutoML in a federated learning context while taking into account the presence of security and privacy countermeasures. We implemented and validated our proof-of-concept framework, called AutoFL, on top of open-source libraries for machine learning, federated learning, and hyperparameter optimization, and have demonstrated the added value of our framework with public datasets in different scenarios.