Reviewerly/exHarmony huggingface dataset:
Applying Transfer Learning to Traffic Surveillance Videos for Accident Detection

Automated traffic video surveillance is a crucial research domain in computer vision due to the need to enable highway safety. It is very important to detect road accidents from traffic surveillance videos in an automated manner to take necessary actions and save the lives of people and properties. Motivated by the same, this paper proposes a method to detect road accidents from traffic surveillance videos in an automated manner. Specifically, we use an object-centric accident detection model using the YOLOv2 architecture based on the transfer learning technique. The YOLOv2 model is a homogeneous convolutional architecture that makes it faster to predict bounding boxes. This work includes a brief description of the YOLOv2 architecture and how we fine-tune a 32-layer variant pre-trained on the VOC dataset to our custom accident dataset. Our experiments using a real-world anomaly detection dataset show significant results in terms of mean average precision. Moreover, our model works in real-time, achieving 60 FPS on an NVIDIA Tesla K80 GPU and ~16.67 FPS on a standard laptop with a 4GB GT GPU. Our implementation can thus provide a near real-time accident localization with 76% mAP on the road accident dataset.
AxDFM:Position Prediction System Based on the Importance of High-Order Features

The exploration and combination of high-level features is crucial for many machine learning tasks. At the same time, we cannot ignore the different importance of high-level features. In traditional machine learning predictive models, analyzing and combining the original data and manually making these features will undoubtedly increase the complexity and cost of the system. The emergence of factorization machines can use the vector product to represent the interaction of features, and automatically learn features The combination of to get high-order feature interactions not only reduces the complexity of the system, but also increases the diversity of high-order features. We refer to the depth factorization machine (xDeepFM) to generate high-level feature interactions at the display mode and vector level, and The importance of different features is dynamically learned through the squeeze-incentive (SENET) mechanism, and different weights are used for interaction.Then, use the attention mechanism to extract the importance of the obtained high-order features and assign weights, and finally get the prediction classification through the fully connected layer. We further summarized these methods into a unified model, and named the model the Advanced Attention Depth Factorization Machine (AxDFM).
Magic and Reality: what children’s drawings tell us about their perception of technology

In this paper we explore children’s perception of technology by looking at the drawings and descriptive texts submitted to the Design and Research Challenge run for the International Conference on Interaction Design and Children 2022 by 166 children from US, Japan, Portugal and Switzerland aged 7 to 11 years old. We cluster and analyse drawings as a means to elicit the perception, understanding and expectations children have of the role technology can play in supporting connectedness, the theme of the conference. We report differences and similarities across countries, age and gender, as well as discuss the dichotomy between magic and realistic proposals, as it provides an important dimension of the CCI design space. Therefore, we start by looking at the use of drawings for better understanding children’s needs and wishes and move on to explore the children’s sense of technology, to finally reflect on its implications on design.
Where we Succeed and Fail: Reflecting on the Challenge of doing Research with Children

Child Computer Interaction has become a field in its own right but it relies on its parent disciplines, HCI, CS, IxD, to train its academics. Research practice in CCI borrows heavily from HCI with an emphasis on the participation of users in research. Training academics to work with children is essential within CCI but this is seldom talked about and in many cases, students are left to learn from mistakes, especially if they are working in groups where there is little expertise in working with children. In this workshop we aim to look at what can go wrong when working with children in a range of contexts including design, evaluation, and research, in schools, homes and other venues, on many different types of projects. We will 'create' scenarios of working with participants and seek to identify potential problems and solutions with the aim of building a resource for CCI academics that helps minimize failure and maximise success.
Yolosa: Object Detection Based on 2d Local Feature Superimposed Self-Attention

We analyzed the network structure of real-time object detection models and found that the features in the feature concatenation stage are very rich. Applying an attention module here can effectively improve the detection accuracy of the model. However, the commonly used attention module or self-attention module shows poor performance in detection accuracy and inference efficiency. Therefore, we propose a novel self-attention module, called 2D local feature superimposed self-attention, for the feature concatenation stage of the neck network. This self-attention module reflects global features through local features and local receptive fields. We also propose and optimize an efficient decoupled head and AB-OTA, and achieve SOTA results. Average precisions of 49.0% (71FPS, 14ms), 46.1% (85FPS, 11.7ms), and 39.1% (107FPS, 9.3ms) were obtained for large, medium, and small-scale models built using our proposed improvements. Our models exceeded YOLOv5 by 0.8% – 3.1% in average precision.
Set-based Particle Swarm Optimization for Data Clustering

Computational intelligence approaches to data clustering have been successful in producing compact and well-separated clusters. In particular, particle swarm optimization (PSO) is deemed an effective approach to data clustering. This paper develops and evaluates a discrete-valued variation of PSO, namely the set-based PSO (SBPSO) algorithm, to cluster data. The SBPSO algorithm is evaluated on six standard data sets and nine artificially generated data sets. The clustering results of the SBPSO algorithm is compared to the performance of established clustering algorithms and a PSO clustering algorithm. It is concluded that the results of the SBPSO algorithm varies with the data set characteristics. Nonetheless, the SBPSO is deemed a successful approach to clustering data.
Eye-tracking for the study of financial decision-making: A systematic review of the literature

Eye-tracking is a technology that gives researchers an unprecedented level of access to the individual visual attention and the possibility of tracing different emotional and cognitive processes underlying one’s interaction with the environment. In recent years, increased availability of eye-tracking devices and the easier experimental tractability of data have made this technology ever-more appealing for behavioral and experimental researchers from various disciplines, finance included. With this paper, we aim to fulfill an identified need to organize and evaluate the available knowledge on the use of eye-tracking in the experimental research on financial decision-making. We perform a systematic review of the literature in these domains and analyze its structure and development (from the first published study until 2021) using a bibliometrics approach. We explore studies in proximal research fields which used eye-tracking methodology on aspects that resonate with processing of financial information, such as comprehension of graphs, effects of different data visualizations and so on. Afterwards, we organize and critically examine all contributions and chart a detailed knowledge map. Our results confirm growing interest in eye-tracking studies as well as the methodology’s potential as a tool for the understanding of different processes and aspects of individuals’ interaction with financial documents. We highlight the yet unexplored promise of the eye-tracking technology for both skill-evaluation and learning studies. Finally, we identify some of the knowledge gaps and present ideas for potential future research paths.
Intelligent Logistics Enterprise Management Based on the Internet of Things

At this stage, many large domestic logistics companies have problems such as complex processes, low efficiency, and chaotic vehicle management. This usually leads to vehicle failure, material loss, or improper installation of the company’s factory, which affects the company’s interests and damages the company’s reputation. In response to the above problems, this article proposes, designs, and implements an intelligent logistics management system based on the Internet of Things technology through the research of the Internet of Things technology at home and abroad. The specific content is as follows: one is to study the origin and current situation of the Internet of Things technology at home and abroad, to understand related theories and cutting-edge technologies of the Internet of Things; the other is to imitate intelligent logistics companies, study the processes and existing problems of the logistics link, and integrate the management concepts of the Internet of Things. A model of intelligent logistics enterprise management is established with enterprise intelligent technology; the third is to conduct an experiment on the management of an intelligent logistics enterprise and do an experimental data analysis of the management of an intelligent logistics enterprise. Experimental research shows that the data of factors affecting logistics operations in smart logistics companies are low, and smart logistics companies are operating in good condition, and the established data model is suitable for smart logistics enterprise management. The Internet of Things and intelligent logistics management are both emerging concepts, while the intelligent logistics management based on the Internet of Things is still in its infancy, and relevant scholars have little research on the intelligent logistics management based on the Internet of Things. It plays a guiding role for the intelligent logistics management of enterprises and promotes the management of intelligent logistics enterprises.
Description of digital systems by various construction schemes

The article discusses ways of describing digital systems with various schemes, provides a comparative characteristic of these schemes, indicates the advantages and disadvantages of different schemes. The four schemes for constructing digital systems described in the article are equivalent, since with the same input signal, the output signal for all circuits will be the same, although different signals will circulate inside these circuits. To build adaptive digital systems, preference should be given to state-space circuits, since in them it is easier to adjust the parameters of the digital system. When choosing between serial and parallel circuits, preference should be given to the parallel circuit if the coefficients in it are valid. With complex coefficients, the implementation of the parallel scheme becomes more complicated, therefore, in this case, one should choose a sequential scheme in the state space, all the coefficients are real.
Trajectories Without Quantum Uncertainties in Composite Systems with Disparate Energy Spectra

Methods for measurement on hybrid systems with disparate spectra realizing trajectories without quantum uncertainties are described, creating new possibilities for quantum networking and sensing applications.
EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.
Understanding Power Differentials and Cultural Differences in Co-design with Marginalized Populations

Co-design collects insights from multiple stakeholders collaboratively making it a powerful method to design with marginalized populations. In the latter context, stakeholders have varying levels of power causing asymmetry and possible suppression of one group over another. Such power differentials can hinder co-design's effectiveness. Through thirteen semi-structured interviews with co-design facilitators who have worked with marginalized communities in 43 different countries, we discovered that despite efforts to mitigate power differentials, significant disparities in educational and cultural backgrounds, language barriers, and gender imbalances prevent true collaboration. Tools for prototyping, analysis and evaluation often require literacy, advanced training, and resources. When these are inaccessible, co-design fails to materialize in the design analysis, implementation, and evaluation phases. We found this failure occurred with marginalized groups. We also found that experienced facilitators were aware of their own privilege as well as the power differentials of outside stakeholders such as donors, and they prioritized strategies to address them ahead of time.
A Lightweight Downscaled Approach to Automatic Speech Recognition for Small Indigenous Languages

Development of fully featured Automatic Speech Recognition (ASR) systems for a complete language vocabulary generally requires large data repositories, massive computing power, and a stable digital network infrastructure. These conditions are not met in the case of many indigenous languages. Based on our research for over a decade in West Africa, we present a lightweight and downscaled approach to AI-based ASR and describe a set of associated experiments. The aim is to produce a variety of limited-vocabulary ASRs as a basis for the development of practically useful (mobile and radio) voice-based information services that fit needs, preferences and knowledge of local rural communities.
Shift of User Attitudes about Anti-Asian Hate on Reddit Before and During COVID-19

Content Warning: This paper shows examples of explicit and offensive language that might be triggering. The main goal of this paper is to investigate the temporal shifts of attitudes about racism towards Asians using online social media posts. An increase in race-related crimes towards Asian communities during COVID-19 is the prime motivation of the work presented in this paper. Towards this goal, social media data from Reddit that were shared between January 2018 and December 2021 are utilized to examine the user attitudes towards anti-Asian hate along with the temporal shifts in those attitudes. Using natural language processing techniques, this study conducts a quantitative investigation on how linguistic emotions are expressed in user posts as well as the engagement received by such posts. Our data suggest that anti-Asian hate crimes or incidents have been discussed long before COVID-19, but it is during the pandemic when we noticed a sharp increase in the number of posts about this topic. Psycholinguistic emotions highlight the surge of posts from users sharing about their personal experiences during the pandemic. Topics extracted show the shift of discussions from relating anti-Asian hate crimes to different types of phobias to issues that are more related to personal encounters or private lives/opinions of users during COVID-19. Our work used "score" as a metric to measure online support, which shows an increase of support to users and an increased agreement with the posts the users are sharing on Reddit during the pandemic.
Monocular three-dimensional object detection using data augmentation and self-supervised learning in autonomous driving

Monocular three-dimensional (3D) object detection (OD) is an essential and challenging task in the domain of autonomous driving. Modern convolution neural network-based architectures for OD heavily rely on data augmentation (DA) and self-supervised learning (SSL). However, they have been relatively less explored for monocular 3D OD, especially in the field of autonomous driving. DAs for two-dimensional OD techniques do not directly extend to the 3D objects. Literature shows that this requires adaptation of the 3D geometry of the input scene and synthesis of new viewpoints. This requires accurate depth information of the scene which may not be available always. We propose augmentations for monocular 3D OD without creating view synthesis. The proposed method uses DA with SSL approach via multiobject labeling as the pretext task. We evaluate the proposed DA-SSL approach on RTM3D detection model (baseline), with and without the application of DA. The results demonstrate improvements between 2% and 3% in mAP 3D and 0.9% to 1.5% BEV scores using SSL over the baseline scores. We propose an inverse class frequency weighted (ICFW) mAP score that highlights improvements in detection for low-frequency classes in a class imbalanced datasets with long tails. We observe improvements in both ICFW mAP 3D and Bird’s Eye View (BEV) scores to take into account the class imbalance in the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) validation dataset. We achieve 4% to 5% increase in ICFW metrics with the pretext task.
The MeVer DeepFake Detection Service: Lessons Learnt from Developing and Deploying in the Wild

Enabled by recent improvements in generation methodologies, DeepFakes have become mainstream due to their increasingly better visual quality, the increase in easy-to-use generation tools and the rapid dissemination through social media. This fact poses a severe threat to our societies with the potential to erode social cohesion and influence our democracies. To mitigate the threat, numerous DeepFake detection schemes have been introduced in the literature but very few provide a web service that can be used in the wild. In this paper, we introduce the MeVer DeepFake detection service, a web service detecting deep learning manipulations in images and video. We present the design and implementation of the proposed processing pipeline that involves a model ensemble scheme, and we endow the service with a model card for transparency. Experimental results show that our service performs robustly on the three benchmark datasets while being vulnerable to Adversarial Attacks. Finally, we outline our experience and lessons learned when deploying a research system into production in the hopes that it will be useful to other academic and industry teams.
Subgraph Representation Learning for Team Mining

Team mining is concerned with the identification of a group of experts that are able to collaborate with each other in order to collectively cover a set of required skills. This problem has mainly been addressed either through graph search, which looks for subgraphs that satisfy the skill requirements or through neural architectures that learn a mapping from the skill space to the expert space. An exact graph-based solution to this problem is intractable and its heuristic variants are only able to identify sub-optimal solutions. On the other hand, neural architecture-based solutions are prone to overfitting and simplistically reduce the problem of team formation to one of expert ranking. Our work in this paper proposes an unsupervised heterogeneous skip-gram-based subgraph mining approach that can learn representations for subgraphs in a collaboration network. Unlike previous work, the subgraph representations allow our method to mine teams that have past collaborative history and collectively cover the requested desirable skills. Through our experiments, we demonstrate that our proposed approach is able to outperform a host of state-of-the-art team mining techniques from both quantitative and qualitative perspectives.
Quantum approximation of normalized Schatten norms and applications to learning

Efficient measures to determine similarity of quantum states, such as the fidelity metric, have been widely studied. In this paper, we address the problem of defining a similarity measure for quantum operations that can be \textit{efficiently estimated}. Given two quantum operations, $U_1$ and $U_2$, represented in their circuit forms, we first develop a quantum sampling circuit to estimate the normalized Schatten 2-norm of their difference ($\| U_1-U_2 \|_{S_2}$) with precision $\epsilon$, using only one clean qubit and one classical random variable. We prove a Poly$(\frac{1}{\epsilon})$ upper bound on the sample complexity, which is independent of the size of the quantum system. We then show that such a similarity metric is directly related to a functional definition of similarity of unitary operations using the conventional fidelity metric of quantum states ($F$): If $\| U_1-U_2 \|_{S_2}$ is sufficiently small (e.g. $ \leq \frac{\epsilon}{1+\sqrt{2(1/\delta - 1)}}$) then the fidelity of states obtained by processing the same randomly and uniformly picked pure state, $|\psi \rangle$, is as high as needed ($F({U}_1 |\psi \rangle, {U}_2 |\psi \rangle)\geq 1-\epsilon$) with probability exceeding $1-\delta$. We provide example applications of this efficient similarity metric estimation framework to quantum circuit learning tasks, such as finding the square root of a given unitary operation.