Reviewerly/exHarmony huggingface dataset:
Enhanced Video Surveillance Systems for “Signal for Help” Detection on Edge Devices

The COVID-19 pandemic triggered a concerning rise in violence against women and children, known as The Shadow Pandemic. To address this, a Canadian foundation introduced the “Signal for Help” gesture to discreetly alert others in danger. However, the effectiveness of this approach depends on individuals recognizing and responding to the signal. In this paper, we propose an innovative solution that adopts the technology available in smart cities to detect the “Signal for Help” in real-time through surveillance footage. We developed and implemented a recognition algorithm on an affordable device that achieves accurate detection of the signal in 94 % of cases. This approach has the potential to improve the response to instances of violence, providing a reliable means of alerting authorities and support networks.
“This Forum Is NOT A DEMOCRACY”

Abstract Incels (short for “involuntarily celibate”) have recently gained notoriety for their aggressive, often violent, misogyny, yet incels were not always an antidemocratic social group. They thus pose a challenge for thinking about democracy and identity in (anonymous) digital environments: how can we create spaces for marginalized social groups while ensuring the resulting identities remain democratic? While many scholars point to technological affordances or corporate content moderation policies as providing some solutions, in this article I propose a more democratic approach. Drawing from incel wikis and archived forum posts from two early incel communities—IncelSupport and LoveShy—I argue that a community's social norms, and the moderation practices required to sustain them, are user-directed interventions that have outsized effects in shaping group identities in democratic ways.
Ultrasonic Spectral Information Promotes Specificity and Visualization of Breast Cancer in Deep Learning

Computer-aided diagnosis is important for breast ultrasound evaluation, as it can enhance diagnostic efficiency and objectivity. However, the performance of such systems, particularly deep learning methods, has been hindered by the limited resolution and interpretability of traditional B-mode ultrasound. We hypothesized that ultrasonic spectral information can serve as a representative feature for differentiating tissues. By leveraging machine learning to recognize tissue spectral patterns, we differentiated tissues in breast ultrasound with an AUCROC of 0.832 and enabled the color enhancement of the malignant probability in lesion areas. Moreover, we explored the integration of tissue spectral pattern recognition with deep learning vision models, resulting in a specificity of 0.896 and an AUCROC of 0.873 for lesion classification, representing a significant improvement.
Efficient Left Ventricle Segmentation in 3D Echocardiography using Deep nnU-Net

Left ventricle segmentation in 3D echocardiographic images has a crucial role in the analysis of cardiac structure and function and – more general – diagnosis of cardiovascular diseases. However, due to the relatively low image quality (i.e. contrast-to-noise ratio), low spatio-temporal resolution, and complexity of anatomical structures, this remains challenging. Recently, deep convolutional neural networks have been proposed for automatic segmentation in medical images, showing significant performance improvement over traditional segmentation algorithms in particular applications and might thus be able to offer a solution in 3D echocardiographic segmentation. However, annotated 3D echocardiography data as required to train deep learning models are not widely available. This paper introduces a new annotated 3D echocardiography dataset and aims to explore state-of-the-art deep learning models for volumetric left ventricle segmentation.
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning

Deep learning has shown great success in high-level image analysis problems; yet its efficacy relies on the quality and diversity of the training data. In this work, we introduce a copy-paste image augmentation for ultrasound images. The Poisson image editing technique is used to generate realistic and seamless boundary transitions around the pasted image. Results showed that the proposed image augmentation technique improves training performance in terms of higher objective metrics and more stable training results.
Inputs to optimize risk communication: testing preferences in icons for emergencies

Disaster Risk Management (DRM) introduces a paradigm change from mitigating impacts to preparedness. Communication becomes fundamental for a safe experience. Designing information has a decisive role in optimizing the experience. Following their principles, messages can be optimized for emergencies. Everyday information tools appear to be less effective during and after an emergency. Icons constitute a paradigm of visual communication, however, usually these are not evidenced in their performance. This paper presents the testing process of an open-access icon set for emergencies, specifically the preferences test. 283 responses evidence how participants associate representations with given concepts. Communication based on icons contributes to establishing preferences, constituting a support for the communication of risk. Integrating communication to DRM contributes to an effective articulation of risks. Results are discussed as three insights: (1) Familiarity, (2) Performance, (3) Knowledge for reducing risks. Information design contributes to facilitating such a process.
Digital refocusing in acoustic microscopy images using adversarial autoencoders

Scanning acoustic microscopy (SAM) is a label-free imaging technique that can visualize the surface and sub-surface structure and is extensively used in biomedical imaging, nondestructive testing, and material research. One of the major difficulties in high-frequency imaging is the ability to focus on the samples. This is because the ultrasound signals exhibit diffraction, which reduces the lateral resolution of the resulting images. For a better visual representation of the acquired images, the reduced lateral resolution in the out-of-focus region needs to be addressed. The synthetic aperture focusing technique (SAFT), which was developed in the 1970’s, is considered one of the most versatile techniques in acoustic imaging for addressing the issue of focusing. Despite its versatility, SAFT has some limitations. For instance, it can produce line artifacts in the reconstructed image and necessitates a defocusing distance between the sample and the focal point. Keeping these drawbacks in mind, we proposed a novel physics-based deep learning (DL) approach to effectively refocus the out-of-focus images (in this study, 4 mm).
Location-Independent Doppler Sensing System for Device-Free Daily Living Activity Recognition

The recent advancements in sensing technology have opened up the possibilities for various services that support daily life, such as energy-saving home appliance control. To realize such services, accurate and cost-effective daily activity recognition in a wide range is essential. To actualize such a system, it is imperative to address the following requirements: the acquisition of sensors entails very high costs (Issue 1), it is hard to achieve precise recognition for location-independent activities like reading a book (Issue 2), a burden of wearing devices from the perspective of residents (Issue 3), and the preservation of residents’ privacy is compromised by using image data from the camera (Issue 4). In this paper, we propose a method for recognizing daily living activities utilizing Doppler sensors in a relatively longer detection range than other motion detection sensors that can be used for dynamic objects. We assess the proposed system by optimizing recognition accuracy, evaluating ensemble methods, and examining sensor reduction’s impact. In the first comparison, the logistic regression achieved the highest accuracy of 65.99% in the leave-one-person-out cross-validation. The second comparison revealed an accuracy of 59.39% for the parallel activity recognition method and 57.24% for the location estimation activity recognition method. In the third comparison, logistic regression achieved a recognition accuracy of 65.26% when four sensor nodes were used: two sensors were placed on both sides of a participant, another was diagonally behind the participant, and the other was installed on the ceiling.
Toward Memory-Efficient and Interpretable Factorization Machines via Data and Model Binarization

Factorization Machines (FM) is a general predictor that can efficiently model feature interactions in linear time, and thus has been broadly used for regression, classification and ranking tasks. Subspace Encoding Factorization Machine (SEFM) is one of the recent approaches which is proposed to enhance FM’s effectiveness by explicit nonlinear feature mapping for both individual features and feature interactions through equal-width binning per input feature. SEFM, despite its effectiveness, has a major drawback of increasing the memory cost of FM by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</i> times where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</i> is the number of bins adopted for the binning. To reduce the memory cost of SEFM, we propose Binarized FM (BiFM) in which each model parameter takes only a binary value (i.e., 1 or -1) and thus can be efficiently stored using one bit. We derive an algorithm which can learn the proposed FM with binary constraints using Straight Through Estimator (STE) with Adaptive Gradient Descent (Adagrad). For performance evaluation, we compare our proposed methods with a number of baselines based on eight different classification datasets. Our experimental results demonstrated that BiFM can achieve higher accuracy than SEFM at much less memory cost. BiFM also inherits the interpretability property from SEFM, and together with adaptive data binning methods can result in a more compact and interpretable set of classification rules.
Siamese Contour Segmentation Network for Multi-State Object Tracking

Tracking multiple states of the target simultaneously is currently a research hotspot in object tracking. Existing methods obtain the initial bounding box with multi-scale search, anchor-based regression, or anchor-free regression. Then, each pixel within bounding box is classified and the mask of target is used to fit a fine bounding box for accurate tracking. However, their computation complexity is high and the accuracy of mask and fine bounding box is limited by initial bounding box. Based on SiamMask, the Siamese contour segmentation network (SiamCS) is proposed for multi-state object tracking to address these issues. This end-to-end network eliminates need to pre-define anchor based on prior knowledge, and reduces hyperparameters. With SiamCS, multi-state object tracking method formulates object tracking as region classification and contour regression to obtain bounding box, contour, and mask of target at the same time. Moreover, according to geometric meaning of definite integral and difficulty of sample, difficulty sensitive contour-intersection over union loss function is proposed to solve the problem of independent regression of contour parameters. Extensive experiments on OTB100 (92.5%Precision), UAV123 (83.5%Precision), LaSOT (64.8%AUC), TrackingNet (81.5%AUC), GOT-10K (66.3%AO), and VOT2020 (53.5%EAO) show that SiamCS outperforms many state-of-the-art trackers and achieves leading performance.
XSiteTraj: A cross-site user trajectory dataset

With the development of mobile networks, social networking plays an increasingly important role in people's daily life. User identification, which aims to match user cross-site accounts, has been becoming an important issue for user supervision and recommendation system design in social networks. At present, many different user identification methods have emerged, such as DPLink, HFUL, etc. However, compared with the continuous development of user identification methods, the open-source work of datasets is very slow, and the datasets of most of the work are not public. The shortage of datasets has greatly hindered the development of this research field. At present, the academic urgently needs a large-scale social network user linkage dataset. In this paper, we publicize a new social network user linkage dataset, XSiteTraj v1.0 [2]. This dataset has good spatio-temporal coverage, including more than 27,000 users and more than one million check-in records from all over the world crawled from Facebook, Foursquare, and Twitter. Our dataset labels the identical users from different social websites, and each check-in record includes a timestamp, point of interest (PoI), and latitude and longitude of PoI. Through our dataset, we can conduct research on user behaviour habits and apply the dataset to the experiments and evaluation of social network user identification and other algorithms.
A Comprehensive Review of Computer-Aided Models for Breast Cancer Diagnosis Using Histopathology Images

Breast cancer is the second most common cancer in women who are mainly middle-aged and older. The American Cancer Society reported that the average risk of developing breast cancer sometime in their life is about 13%, and this incident rate has increased by 0.5% per year in recent years. A biopsy is done when screening tests and imaging results show suspicious breast changes. Advancements in computer-aided system capabilities and performance have fueled research using histopathology images in cancer diagnosis. Advances in machine learning and deep neural networks have tremendously increased the number of studies developing computerized detection and classification models. The dataset-dependent nature and trial-and-error approach of the deep networks' performance produced varying results in the literature. This work comprehensively reviews the studies published between 2010 and 2022 regarding commonly used public-domain datasets and methodologies used in preprocessing, segmentation, feature engineering, machine-learning approaches, classifiers, and performance metrics.
A spanning tree algorithm with adaptive pruning with low redundancy coverage rate

In reality, when UAV is used in region reconnaissance, the shape of region is usually irregular. Selecting an algorithm to find a complete coverage path with smaller coverage redundancy rate, lower path motion complexity and theoretical energy consumption has certain research significance for the complete coverage path planning (CCPP). In existing algorithms, there is always a high redundant coverage rate under irregularly shaped regions. The motion complexity and the theoretical energy consumption of the path also increase. In this paper, a kind of spanning tree with adaptive pruning (STAP) algorithm based on the grid map is proposed to solve the above problems: on the one hand, it uses a greedy growth strategy to determine the branching growth direction, and proposes an adaptive pruning method for self-optimization of the initial path; on the other hand, a kind of nearest neighbor sub-region connection rule of adjacent paths is proposed to reduce the complexity and the coverage redundancy rate. The STAP algorithm searches for all grid centers, grows, and optimizes the spanning tree by creating paths between the centers. At the end of the optimization, it creates a complete coverage path based on the distribution of the spanning tree. Experiments show that when the STAP algorithm is applied to unobstructed regions, it plans the path with the lowest redundant coverage rate, which reaches 2.6296% and 4.9402%; when it is applied to the regions include obstacles, the advantages of the algorithm are more significant: On the one hand, the path planned by STAP has the least total length, the lowest number of turning and turning back. On the other hand, the redundant coverage rate of the path is still the lowest, reaching 4.1362% and 2.7765%, respectively. The STAP is primarily suitable for situations about CCPP under grid maps formed by irregularly shaped regions, and it holds certain significance for ground reconnaissance of UAVs.
Fairness as a Service (FaaS): verifiable and privacy-preserving fairness auditing of machine learning systems

Abstract Providing trust in machine learning (ML) systems and their fairness is a socio-technical challenge, and while the use of ML continues to rise, there is lack of adequate processes and governance practices to assure their fairness. In this paper, we propose FaaS, a novel privacy-preserving, end-to-end verifiable solution, that audits the algorithmic fairness of ML systems. FaaS offers several features, which are absent from previous designs. The FAAS protocol is model-agnostic and independent of specific fairness metrics and can be utilised as a service by multiple stakeholders. FAAS uses zero knowledge proofs to assure the well-formedness of the cryptograms and provenance in the steps of the protocol. We implement a proof of concept of the FaaS architecture and protocol using off-the-shelf hardware, software, and datasets and run experiments to demonstrate its practical feasibility and to analyse its performance and scalability. Our experiments confirm that our proposed protocol is scalable to large-scale auditing scenarios (e.g. over 1000 participants) and secure against various attack vectors.
Learning Meta-Learning (LML) dataset: Survey data of meta-learning parameters

The 'Learning Meta-Learning' dataset presented in this paper contains both categorical and continuous data of adult learners for 7 meta-learning parameters: age, gender, degree of illusion of competence, sleep duration, chronotype, experience of the imposter phenomenon, and multiple intelligences. Convenience sampling and Simple Random Sampling methods are used to structure the anonymous online survey data collection voluntarily for LML dataset creation. The responses from the 54 survey questionnaires contain raw data from 1021 current students from 11 universities in Bangladesh. The entire dataset is stored in an excel file and the entire questionnaire is accessible at (10.5281/zenodo.8112213) In this article mean and standard deviation for the participant's baseline attributes are given for scale parameters, and frequency and percentage are calculated for categorical parameters. Academic curriculum, courses as well as professional training materials can be reviewed and redesigned with a focus on the diversity of learners. How the designed courses will be learned by learners along with how they will be taught is a significant point for education in any discipline. As the survey questionnaires are set for adult learners and only current university students have participated in this survey, this dataset is appropriate for study andragogy and heutagogy but not pedagogy.
Clustering-Based Multi-Region Coverage-Path Planning of Heterogeneous UAVs

Unmanned aerial vehicles (UAVs) multi-area coverage-path planning has a broad range of applications in agricultural mapping and military reconnaissance. Compared to homogeneous UAVs, heterogeneous UAVs have higher application value due to their superior flexibility and efficiency. Nevertheless, variations in performance parameters among heterogeneous UAVs can significantly amplify computational complexity, posing challenges to solving the multi-region coverage path-planning problem. Consequently, this study studies a clustering-based method to tackle the multi-region coverage path-planning problem of heterogeneous UAVs. First, the constraints necessary during the planning process are analyzed, and a planning formula based on an integer linear programming model is established. Subsequently, this problem is decomposed into regional allocation and visiting order optimization subproblems. This study proposes a novel clustering algorithm that utilizes centroid iteration and spatiotemporal similarity to allocate regions and adopts the nearest-to-end policy to optimize the visiting order. Additionally, a distance-based bilateral shortest-selection strategy is proposed to generate region-scanning trajectories, which serve as trajectory references for real flight. Simulation results in this study prove the effective performance of the proposed clustering algorithm and region-scanning strategy.
Puppis: Hardware Accelerator of Single-Shot Multibox Detectors for Edge-Based Applications

Object detection is a popular image-processing technique, widely used in numerous applications for detecting and locating objects in images or videos. While being one of the fastest algorithms for object detection, Single-shot Multibox Detection (SSD) networks are also computationally very demanding, which limits their usage in real-time edge applications. Even though the SSD post-processing algorithm is not the most-complex segment of the overall SSD object-detection network, it is still computationally demanding and can become a bottleneck with respect to processing latency and power consumption, especially in edge applications with limited resources. When using hardware accelerators to accelerate backbone CNN processing, the SSD post-processing step implemented in software can become the bottleneck for high-end applications where high frame rates are required, as this paper shows. To overcome this problem, we propose Puppis, an architecture for the hardware acceleration of the SSD post-processing algorithm. As the experiments showed, our solution led to an average SSD post-processing speedup of 33.34-times when compared with a software implementation. Furthermore, the execution of the complete SSD network was on average 36.45-times faster than the software implementation when the proposed Puppis SSD hardware accelerator was used together with some existing CNN accelerators.
RoBERTa-Based Keyword Extraction from Small Number of Korean Documents

Keyword extraction is the task of identifying essential words in a lengthy document. This process is primarily executed through supervised keyword extraction. In instances where the dataset is limited in size, a classification-based approach is typically employed. Therefore, this paper introduces a novel keyword extractor based on a classification approach. The proposed keyword extractor comprises three key components: RoBERTa, a keyword estimator, and a decision rule. RoBERTa encodes an input document, the keyword estimator calculates the probability of each token in the document becoming a keyword, and the decision rule ultimately determines whether each token is a keyword based on these probabilities. However, training the proposed model with a small dataset presents two challenges. One problem is the case that all tokens in the documents are not a keyword, and the other problem is that a single word can be composed of keyword tokens and non-keyword tokens. Two novel heuristics are thus proposed to tackle these problems. To address these issues, two novel heuristics are proposed. These heuristics have been extensively tested through experiments, demonstrating that the proposed keyword extractor surpasses both the generation-based approach and the vanilla RoBERTa in environments with limited data. The efficacy of the heuristics is further validated through an ablation study. In summary, the proposed heuristics have proven to be effective in developing a supervised keyword extractor with a small dataset.