Reviewerly/exHarmony huggingface dataset:
Embodied Embroidery: Somaesthetic Interaction Design for Women's Masturbation

Though a taboo topic, women's masturbation is the most effective technique in producing orgasms among all sexual behaviors [37]. This project explores how somaesthetic interaction design can contribute to designing for women's sexual pleasure, challenging androcentric discourses on women's sexuality, and also the desexualization of women with dis/abilities. In the study, the first author, who identifies as a woman with an invisible disability, experiments with other women's masturbatory techniques using her own body as a design resource. She then articulated that intersubjective engagement using her own body as an artistic medium in the form of Embodied Embroidery, a practice inspired by women's artmaking, and which seeks to foreground the aesthetic dimensions of experiential knowledge to support theory-making in design. Guided by three key features of somaesthetic interaction—first-person perspective, intersubjectivity, and articulation—this pictorial contributes to pleasure activism in the domain of HCI and interaction design.
Exploring the Embodied Experience of Walking Meetings through Bodystorming – Implications for Design

Walking meetings are a promising way to reduce unhealthy sedentary behavior at the office. Some aspects of walking meetings are however hard to assess using traditional research approaches that do not account well for the embodied experience of walking meetings. We conducted a series of 16 bodystorming sessions, featuring unusual walking meeting situations to engage participants (N=45) in a reflective experience. After each bodystorming, participants completed three tasks: a body map, an empathy map, and a rating of workload using the NASA-TLX scale. These embodied explorations provide insights on key themes related to walking meetings: material and tools, physical and mental demand, connection with the environment, social dynamics, and privacy. We discuss the role of technology and opportunities for technology-mediated walking meetings. We draw implications for the design of walking meeting technologies or services to account for embodied experiences, and the individual, social, and environmental factors at play.
Prototyping with Monkeys: Uncovering What Buttons for Monkeys Look Like

Although much work has focused on designing touch interfaces for primates, little is known about how physical computer buttons for monkeys would look. Here, we employ the rapid prototyping method commonly used in human–computer interaction to design tangible buttons for monkeys allowing them to interact with computer enrichment. Our findings reflect on the process of altering rapid prototyping from humans to animals and how computer buttons for monkeys might look. On this basis, we make suggestions for monkey buttons, highlighting colour and pull/swing over push/touch interactions. We also reflect on lessons learned from transferring prototyping across species, such as the need to iterate on a few variables and for initial prototypes to be varied, and speculate on how to balance the designer (human) and user (animals) needs. More broadly, this paper builds upon HCI prototyping techniques for unconventional users, creating a method for rapid iterative prototyping with animals.
EScope: Effective Event Validation for IoT Systems Based on State Correlation

Typical Internet of Things (IoT) systems are event-driven platforms, in which smart sensing devices sense or subscribe to events (device state changes), and react according to the preconfigured trigger-action logic, as known as, automation rules."Events" are essential elements to perform automatic control in an IoT system.However, events are not always trustworthy.Sensing fake event notifications injected by attackers (called event spoofing attack) can trigger sensitive actions through automation rules without involving authorized users.Existing solutions verify events via "event fingerprints" extracted by surrounding sensors.However, if a system has homogeneous sensors that have strong correlations among them, traditional threshold-based methods may cause information redundancy and noise amplification, consequently, decreasing the checking accuracy.Aiming at this, in this paper, we propose "EScope", an effective event validation approach to check the authenticity of system events based on device state correlation.EScope selects informative and representative sensors using an Neural-Network-based (NN-based) sensor selection component and extracts a verification sensor set for event validation.We evaluate our approach using an existing dataset provided by Peeves.The experiment results demonstrate that EScope achieves an average 67% sensor amount reduction on 22 events compared with the existing work, and increases the event spoofing detection accuracy.
Feature generation based on relation learning and image partition for occluded person re-identification

In order to solve the challenging tasks of person re-identification(Re-ID) in occluded scenarios, we propose a novel approach which divides local units by forming high-level semantic information of pedestrians and generates features of occluded parts. The approach uses CNN and pose estimation to extract the feature map and key points, and a graph convolutional network to learn the relation of key points. Specifically, we design a Generating Local Part (GLP) module to divide the feature map into different units. Based on different occluded conditions, the partition mode of GLP has high flexibility and variability. The features of the non-occluded parts are clustered into an intermediate node, and then the spatially correlated features of the occluded parts are generated according to the de-clustering operation. We conduct experiments on both the occluded and the holistic datasets to demonstrate its effectiveness.
Completely characterizing a class of permutation quadrinomials

In the present article, we provide a complete characterization of permutations on the finite field F4m of shape fϵ_(X):=ϵ1X‾q+1+ϵ2X‾qX+ϵ3X‾Xq+ϵ4Xq+1, definitively (where q=2k, Q=2m, m is odd, (m,k)=1, X‾=XQ). Our achievement firstly extends the very recent literature (a long series of interesting recent (2019-2022) results derived in (at least) seven articles) about the bijectivity on FQ2 of fϵ_. It also gives a complete proof of the conjecture (Conjecture 19) posed in the recent literature by Li et al. (2021) [24] about the characterization of permutations fϵ_ with 4-uniform BCT (which can be viewed, thanks to their nice cryptographic properties, as promising candidates as S-boxes to be used in designing secure block ciphers in symmetric cryptography). To derive our results about the quadrinomials fϵ_, we shall keep the essence of our successful approach initiated by Kim et al. in [23] by relating the problem of characterization of the bijectivity of fϵ_ over FQ2 to the equation Xq+1+X+a=0 but also by performing algebraic developments with new nature by considering some rational functions over finite fields. Besides, we shall employ novel algebraic techniques and derive new auxiliary results (some of them have their independent interest) going beyond the recent results given in [23]. Notably, we present a complete proof of the bijectivity of fϵ_ over FQ2 without any restriction. Specifically, our results are valid when the coefficients ϵi (i∈{1,2,3,4}) lie in FQ2 and for any value of k≥1 (at the opposite of the results in [23] valid with the condition that the coefficients ϵi (i∈{1,2,3,4}) lie in the finite field FQ while covering all values of k≥1). Finally, we emphasize that very recently, Göloğlu has presented in (Göloğlu, 2022 [13]), an alternative novel approach providing a complete description of permutations of the form mentioned above using biprojective polynomials.
Syntactic complexity in adapted extracurricular reading materials

Extracurricular reading is important for language learning since it is not just beneficial for vocabulary and grammar acquisition, but also useful for other aspects such as reading comprehension. Previous research has focused on the role of linguistic features such as syntactic complexity in adapted textbooks, while little attention has been paid to that in other reading materials such as extracurricular ones. This study examined the syntactic complexity in adapted extracurricular reading texts at five difficulty levels based on five machine learning algorithms. The results showed that the adapted texts differed significantly across the five levels in terms of syntactic complexity, with large effect sizes. In addition, three indices of syntactic complexity, i.e., DC/T, MLS, and CN/C, were found to well distinguish texts at different levels. The findings of the present study have significant implications for the adaptation of language learning materials such as extracurricular reading texts and test materials.
PFYOLOv4: An Improved Small Object Pedestrian Detection Algorithm

With the development of deep convolutional neural networks, the effect of pedestrian detection has been rapidly improved. However, there are still many problems in small target pedestrian detection, for example noise (such as light) interference, target occlusion, and low detection accuracy. In order to solve the above problems, based on YOLOv4 algorithm, this paper proposes an improved small target pedestrian detection algorithm named PF_YOLOv4. The algorithm is improved in three aspects on the basis of the YOLOv4 algorithm: firstly, a soft thresholding module is added to the residual structure of the backbone network to perform noise reduction process on interference factors, such as light to enhance the robustness of the algorithm; secondly, the depthwise separable convolution replaces the traditional convolution in the YOLOv4 residual structure, to reduce the number of network model parameters; finally, the Convolutional Block Attention Module (CBAM) is added after the output feature map of the backbone network to enhance of the network feature expression. Experimental results show that the PF_YOLOv4 algorithm outperforms most of the state-of-the-art algorithms in detecting small target pedestrians. The mean Average Precision (mAP) of the PF_YOLOv4 algorithm is 2.35% higher than that of the YOLOv4 algorithm and 9.67% higher than that of the YOLOv3 algorithm, while the detection speed is slightly higher than that of YOLOv4 algorithm.
A Methodological Framework for AI-Assisted Security Assessments of Active Directory Environments

The pervasiveness of complex technological infrastructures and services coupled with the continuously evolving threat landscape poses new sophisticated security risks. These risks are mostly associated with many diverse vulnerabilities related to software or hardware security flaws, misconfigurations and operational weaknesses. In this scenario, a timely assessment and mitigation of the security risks affecting technological environments are of paramount importance. To cope with these compelling issues, we propose an AI-assisted methodological framework aimed at evaluating whether the target environment is vulnerable or safe. The framework is based on the combined application of graph-based and machine learning techniques. More precisely, the components of the target together with their vulnerabilities are represented by graphs whose analysis identifies the attack paths associated with potential security threats. Machine learning techniques classify these paths and provide the security assessment of the target. The experimental evaluation of the proposed framework was performed on 220 artificially generated Active Directory environments, half of which injected with vulnerabilities. The results of the classification process were generally good. For example, the F1-score obtained by the Random Forest classifier for the assessment of vulnerable networks was equal to 0.91. These results suggest that our approach could be applied for automating the security assessment procedures of complex networked environments.
Non-Co-Occurrence Enhanced Multi-Label Cross-Modal Hashing Retrieval Based on Graph Convolutional Network

Supervised cross-modal retrieval has significant advantages in retrieval efficiency and storage cost.In the field of hashing retrieval, existing supervised methods are divided into single-label and multilabel methods.For the single-label method, simply using a single label to measure the semantic relevance between instances will cause an error in supervision information.However, the existing multi-label hashing methods also have some problems.For example, only considering the co-occurrence of multiple labels among instances may not accurately reflect their similarity.At the same time, in the previous methods, the text modality processing did not reach the fine level of image modality, resulting in insufficient use of text information.We proposed Non-co-occurrence enhanced Multi-label cross-modal hashing retrieval based on Graph Convolutional Network (MHGCN) to address these issues.Firstly, we introduced a multi-label non-co-occurrence similarity measurement method, which adds multi-label non-co-occurrence information among instances in the multi-label similarity measurement to measure the differences between instances; Secondly, we used Graph Convolutional Networks (GCNS) to process the information on text modality; Thirdly, we introduced the memory mechanism to restrict the difference of hash code learning.Many experiments on three commonly used benchmark datasets show that the proposed Non-co-occurrence enhanced Multi-label cross-modal hashing retrieval based on Graph Convolutional Network (MHGCN) method has excellent performance.
Semi-Supervised Gaussian Processes Active Learning Model for Imbalanced Small Data Based on Tri-Training With Data Enhancement

To solve the problem that some imbalanced small sample datasets only contain a few labeled samples, a semi-supervised gaussian processes active learning model based on improved tri-training with enhanced data is proposed. Firstly, the label samples are balanced and enhanced, and we present a quantitative enhanced data evaluation criteria based on the JS distance and the similarity of information entropy between enhanced data and original data to select the best enhanced data. Secondly, an improved semi-supervised learning method based on tri-training is proposed to find the unlabeled samples which have high confidence, so the certainty of the labeled samples group can be increased, in order to ensure that the three classifiers of tri-training have both difference and robustness, random forest is introduced to divide the features of the dataset into three groups with equal contribution, and each classifier trains different combinations of two feature groups. Thirdly, in order to query and classify the most informative unlabeled samples more precisely, active learning based on the Gaussian process and JS distribution range is structured, because of the high uncertainty of the unlabeled samples predicted by active learning, the similarity distribution range of JS distance is introduced to compare the similarity of unlabeled samples and labeled samples in active learning`s classifier, so the model can classify more diverse samples. The final experimental results show that compared with several traditional models, the proposed model performs better on artificial datasets and imbalanced small-size UCI datasets.
MARS-GAN: Multilevel-Feature-Learning Attention-Aware Based Generative Adversarial Network for Removing Surgical Smoke

Surgical smoke caused poor visibility during laparoscopic surgery, the smoke removal is important to improve the safety and efficiency of the surgery. We propose the Multilevel-feature-learning Attention-aware based Generative Adversarial Network for Removing Surgical Smoke (MARS-GAN) in this work. MARS-GAN incorporates multilevel smoke feature learning, smoke attention learning, and multi-task learning together. Specifically, the multilevel smoke feature learning adopts the multilevel strategy to adaptively learn non-homogeneity smoke intensity and area features with specific branches and integrates comprehensive features to preserve both semantic and textural information with pyramidal connections. The smoke attention learning extends the smoke segmentation module with the dark channel prior module to provide the pixel-wise measurement for focusing on the smoke features while preserving the smokeless details. And the multi-task learning strategy fuses the adversarial loss, cyclic consistency loss, smoke perception loss, dark channel prior loss, and contrast enhancement loss to help the model optimization. Furthermore, a paired smokeless/smoky dataset is synthesized for elevating smoke recognition ability. The experimental results show that MARS-GAN outperforms the comparative methods for removing surgical smoke on both synthetic/real laparoscopic surgical images, with the potential to be embedded in laparoscopic devices for smoke removal.
A Learning-Based End-to-End Wireless Communication System Utilizing a Deep Neural Network Channel Module

The existing end-to-end (E2E) wireless communication systems require fewer communication modules and have a simple processing signal flow, compared to conventional wireless communication systems.However, in the absence of a differentiable channel model, it is impossible to train transmitters, used in such systems, which makes impossible to get optimal system performance.To solve this problem, an E2E wireless communication system, learned with conditional generative adversarial networks (CGANs) for channel modeling, has been proposed recently.Unfortunately, the CGAN training is prone to instability, slow convergence, and inaccurate channel modeling, which affects the system performance.To this end, a learning-based E2E wireless communication system, utilizing a deep neural network (DNN) channel module to model an unknown channel, is proposed in this paper.Simulation results show that the proposed DNN channel modeling has faster convergence, simpler network structure, and can reflect the behavior of real channels more accurately.In addition, the proposed E2E wireless communication system performs better, in terms of bit error rate (BER) and block error rate (BLER), than the E2E wireless communication system, using CGAN as unknown channel, and a traditional communication system, designed based on the prior knowledge of the channel.Compared to these two systems, at high signal-to-noise ratio (SNR) values, the proposed system can achieve a SNR gain of at least 2 dB, in communication scenarios involving frequency-selective multi-path channels. INDEX TERMSAutoencoder, end-to-end communication systems, deep learning, deep neural network (DNN) I. INTRODUCTION Wireless communication has played an important role in our daily life in recent years due to the rapid development of relevant technologies.In the fifth generation of mobile communications (5G), especially, many new technologies, such as massive multiple-input multiple-output (MIMO) [1], [2], beamforming [3], [4], and edge computing [5], were utilized, which has enabled the expansion of wireless communication applications to industrial, medical, and educational fields.The conventional wireless communication system architecture consists of multiple signal processing sub-modules, shown in Fig. 1.Message , which must be sent to the other side, is transformed by the transmitter into a suitable (encoded and modulated) signal , which after passing through the channel, becomes signal, which arrives at the receiver and is converted into message by performing opposite procedures to that of the transmitter.
An Anonymous Authentication With Received Signal Strength Based Pseudonymous Identities Generation for VANETs

Our proposed key stream derived from shared key generation process based on received signal strength (RSS) represents pseudonymous identities (PIDs) utilized in an anonymous authentication is obtained through an interactive connection between communicating mobile users and an authorized entity taken over by a Group Manager (GM). This process goal is fetching some credentials in advance, when mobile users actively and anonymously are playing their role in the system. These PIDs could be embedded in the credential equipped to the participating mobile users. They should be usually unique and must be secured from any adversary when doing any either attack or treat. In this paper, our proposed shared key generation derived from RSS between communicating mobile users and GM provides unique PIDs. Then, the embedded PID at an epoch time as a part of mobile users secret key together with credential components are used for signing a message anonymously. Based on PID at epoch time and updated revocation list obtained from GM, verifier user ensures the validity of signature. The measurement scenario of system performance is done by varying the traffic conditions either quiet or crowded to create communication impairment combined with mobile users’ speed varying on 20 km/h, 40 km/h, and 60 km/h respectively. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ping</i> time interval is varied as well, it is configured to 7 ms, 10 ms, and 20 ms, respectively. Here, the result of evaluation showed that PIDs generation works properly by the number of generated PIDs up to 11 with the highest correlation is achieved up to 0.99. Meanwhile, quantization algorithm works properly for 3000 or more ICMP packets and achieves zero key disagreement rate (KDR). Total signing and verification times are sufficient practical about 90 ms and 100 ms, respectively.
Privacy vs. Efficiency: Achieving Both Through Adaptive Hierarchical Federated Learning

As a decentralized training paradigm, Federated learning (FL) promises data privacy by exchanging model parameters instead of raw local data. However, it is still impeded by the resource limitations of end devices and privacy risks from the ‘curious’ cloud. Yet, existing work predominately ignores that these two issues are non-orthogonal in nature. In this article, we propose a joint design (i.e., AHFL) that accommodates both the efficiency expectation and privacy protection of clients towards high inference accuracy. Based on a cloud-edge-end hierarchical FL framework, we carefully offload the training burden of devices to one proximate edge for enhanced efficiency and apply a two-level differential privacy mechanism for privacy protection. To resolve the conflicts of dynamical resource consumption and privacy risk accumulation, we formulate an optimization problem for choosing configurations under correlated learning parameters (e.g., iterations) and privacy control factors (e.g., noise intensity). An adaptive algorithmic solution is presented based on performance-oriented resource scheduling, budget-aware device selection, and adaptive local noise injection. Extensive evaluations are performed on three different data distribution cases of two real-world datasets, using both a networked prototype and large-scale simulations. Experimental results show that AHFL relieves the end's resource burden (w.r.t. computation time 8.58% <inline-formula><tex-math notation="LaTeX">$\downarrow$</tex-math></inline-formula> , communication time 59.35% <inline-formula><tex-math notation="LaTeX">$\downarrow$</tex-math></inline-formula> and memory consumption 43.61% <inline-formula><tex-math notation="LaTeX">$\downarrow$</tex-math></inline-formula> ) and has better accuracy (6.34% <inline-formula><tex-math notation="LaTeX">$\uparrow$</tex-math></inline-formula> ) than 3 typical baselines under the limited resource and privacy budgets. The code for our implementation is available at <uri>https://github.com/Guoyeting/AHFL</uri> .