Reviewerly/exHarmony huggingface dataset:
Transferable Waveform-level Adversarial Attack against Speech Anti-spoofing Models

Speech anti-spoofing models protect media from malicious fake speech but are vulnerable to adversarial attacks. Studies of adversarial attacks are conducive to developing robust speech anti-spoofing systems. Existing transfer-based attack methods mainly craft adversarial speech examples at the handcrafted-feature level, which have limited attack ability against the real-world anti-spoofing systems, as these systems only have raw waveform input interfaces. In this work, we propose a waveform-level input data transformation, called the temporal smoothing method, to generate more transferable adversarial speech examples. In the optimization iterations of the adversarial perturbation, we randomly smooth input waveforms to prevent the adversarial examples from overfitting white-box surrogate models. The proposed transformation can be combined with any iterative gradient-based attack method. Extensive experiments demonstrate that our method significantly enhances the transferability of waveform-level adversarial speech examples.
Hidden Follower Detection via Refined Gaze and Walking State Estimation

Hidden following is following behavior with special intentions, and detecting hidden following behavior can prevent many criminal activities in advance. The previous method uses gaze and spacing behaviors to distinguish hidden followers from normal pedestrians. However, they express gaze behaviors in a coarse-grained way with binary values, making it difficult to accurately depict the gaze state of pedestrians. To this end, we propose the Refined Hidden Follower Detection (RHFD) model by choosing a suitable mapping function based on the principle that the closer the gaze direction is to someone, the more likely it is to gaze at someone, which converts the gaze direction into a continuous estimated gaze state representing the complex and variable gaze behavior of pedestrians. Simultaneously, we introduce variations in the magnitude and direction of pedestrian velocity to refine the representation of pedestrian walking states. Experimental results on the surveillance dataset show that RHFD outperforms state-of-the-art methods.
Video Noise Removal Using Progressive Decomposition With Conditional Invertibility

Video denoising aims at removing noise from noisy video frames and meanwhile preserving their structures and details. It is a challenging task, as both noise and video structures/details correspond to high-frequency components of a noisy video which are hard to distinguish. This paper proposes a deep video denoiser using a progressive decomposition process with conditional invertibility. Noisy video frames are first decomposed into two latent codes via a forward process of conditional invertible coupling layers, where one latent code carries the maximal information regarding the noise-free reference frame while the other encodes the information regarding noise, misalignment and content difference. The clean video is then reconstructed from the latent codes of noise-free frames using the reverse pass of the coupling layers. To improve the robustness to variant noise levels, the coupling layers are conditioned on noise level. In addition, memory units are introduced to the conditioned coupling layers to better exploit temporal correlation among frames for feature disentanglement. Experiments on two benchmark datasets have demonstrated the effectiveness of our method.
CFANet: A Cross-layer Feature Aggregation Network for Camouflaged Object Detection

Deep learning-based camouflaged object detection approaches have achieved great progress in recent years. However, it is still challenging to accurately identify the camouflaged objects from the highly similar backgrounds. In this paper, we design a novel cross-layer feature aggregation network (CFANet) for camouflaged object detection to explore how to effectively aggregate multi-level and multi-scale features generated from the backbone network by excavating the similarities and differences of features at different levels. Firstly, we design a cross-layer feature fusion (CLFF) module to generate discriminative features by fusing and refining the multi-level side-output features with similar characteristics in the same feature group. Secondly, we design a uniqueness enhancement (UE) strategy to respectively emphasize the superiority of deep features and shallow features in locating the camouflaged objects and sharpening the structure details. Extensive experiments on four benchmarks are conducted to demonstrate that the proposed CFANet network performs favorably against 11 state-of-the-art camouflaged object detection methods, demonstrating the effectiveness and superiority of our method. The code and results can be found from the link of https://github.com/ZhangQing0329/CFANet
Text-Guided Mask-Free Local Image Retouching

In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning. Most currently available text-guided methods, however, rely on object-level supervision to confine the region of interest that may be updated. This not only makes it more challenging to develop these algorithms but also limits how widely deep learning can be used for image retouching. In this paper, we offer a text-guided mask-free image retouching approach that yields consistent results to address this concern. Specifically, we propose a two-stage mask-free training paradigm tailored for text-guided image retouching tasks. In the first stage, an unified mask is proposed according to the query description, and then several candidate images are generated with the provided mask and the conditional description based on diffusion model. Extensive experiments have shown that our method can produce high-quality images based on spoken language.
Perspectives of Students and Teachers Towards Developing Speaking Skills by Using Technology Among EFL Iraqi Students

The study aims at investigating Iraqi instructors' perspectives and current techniques of using smartphones with the intention of boosting Iraqi learners' speaking skills, linking the perceptions with activities according to Iraqi teachers. The study was conducted in four Universities (University of Baghdad, Al-Mustansiriya University, Iraqia University, and Al-Ma'moun University) targeting the Department of English Language. The data were analyzed using SPSS v. 22. The findings revealed that smart devices are widely utilized in university English classes. Teachers enable students to use them freely during lectures to look up material. It is a normal practice to instruct pupils to utilize their gadgets both in classes and at home. Overall, the instructors believed the application of smartphones to be beneficial in the teaching of English, and the study includes several instances. Easy and rapid access to information, valuable learning tools, and the capacity to effortlessly produce and share data are just a few of the benefits of mobile devices in education.
An Investigation of ELT Teachers' Perceptions of Digital Technology Integration via SAMR Model in the 21st Century Education System

It is difficult for educators to teach young students to keep up with 21st-century education demands, even while the 21st century demands that learners be digitally savvy and self-driven to cope with technological innovation. Digital technology integration into the courses, however, can be thought of as the most time-appropriate and modern practice of the teaching-learning process, compared to other ways, in order to educate the students as adaptive to the needs of the 21st century. In particular, the SAMR Model is the most direct way to invest, gate teachers' perspective and technology integration levels because one of the needs of the 21st-century ELT is to stay up with the century and integrate digital sources into the lessons. This chapter intends to create a procedural connection between aspirations of the 21st century and their collaboration with the adoption of digital technology into activities. The SAMR Model and the function of teacher perceptions, as established by the literature, are also defined in the study along with some suggestions.
INTERDISCIPLINARY TOOLS FOR ACKNOWLEDGING THE ROLE OF SOCIAL ORGANIZATIONS IN ESTABLISHING THE LIFESTYLE AND BEHAVIOR OF SOCIAL OBJECTS

This article is devoted to studying the possibility of using interdisciplinary tools in sociological research. The model of V. V. Leontiev is one of the mathematical tools that make it possible to evaluate the considered set of social objects for the possibility of carrying out further sociological research with it. This tool can be used as an element of a quantitative strategy. The empirical basis of the study was the results of data monitoring carried out as part of the substantiation of the theory of the penetration of international management standards into the activities of social facilities located on the territory of the Russian Federation. The period of time covering the assessment of the behavior of social objects was from 1993 to 2021. The study shows that the key characteristics that determine the behavior of social facilities when international quality management standards are introduced are their size, type of activity, and geographical location. Factors that largely predetermine the behavior of social facilities in the context of the penetration of international quality management standards into their activities are historical, demographic and geographical. The consideration of the penetration theory is based on the hierarchy of social objects (scalar chain), which represents their interconnection and mutual influence on each other. To substantiate the possibility of classifying this tool as sociological monitoring, in the article we considered the typification of industries based on the results of their activities and the application of this typification to justify the dissemination of international quality management standards throughout Russia, based on the theory of penetration. Based on the results of using the model of V.V. Leontiev in sociological research, we identified the risks of sociological research in ongoing quantitative strategies, as well as the role of the social component in the phenomenon we studied.&#x0D; The article focuses on changing the lifestyle and behavior of social objects through organizations belonging to social sectors. Social sectors, which include educational institutions, are prevailing in changing the standards of society.
Eye Gaze Capture for Preference Tracking in a Scalable Environment

Nowadays, eye gaze tracking of real-world people to acknowledge what they are seeing in a particular page is trending; but its complexity is also growing fast, and the accuracy is not enough. In the proposed system, the image patch of the eye region is extracted from the input image using the Viola Jones algorithm for facial feature detection. Then SqueezeNet and U-Net are combined to train the model for pixel classification of iris and pupil from the eye image patch with a training dataset that contains manually labelled iris and pupil region. After extracting the iris and pupil features, the eye gaze tracking is formulated by using 2D pupil center extracted by applying Mean-Shift algorithm and 3D eyeball center. The system achieved an accuracy of 99.93% which is best comparable to the state-of-the-art methods.
Convolution Neural Network

Convolutional neural network (CNN) carries spatial information—not all nodes in one layer are fully connected to nodes in the next layer; weights are shared. The main goal of CNN is to process large image pixel matrix and try to reduce high matrix dimensions without losing information, and to simplify the network architecture with weight sharing, reducing the number of trainable parameters in the network, which helped the model to avoid overfitting and as well as to improved generalization and still to give high performance with desired accuracy. So, CNN has become dominant in various computer vision tasks and is attracting interest across a variety of domains involving image processing. This chapter focuses on the foundation of CNN, followed by architecture of CNN, activation functions, applications, and recent trends in CNN.
On the Detection of Faces With Masks Using Tiny YOLOv7 Algorithm

Today every individual is expected to wear a mask, which poses a new challenge to security and surveillance of individuals for any governing body. Though notable work has been done in the area of face mask detection, there still exists a bottleneck of fast detection. Additionally, the complexity of features, size of frames, and inhomogeneity of data poses a challenge to achieve a model with high accuracy. And law offenders are quick to exploit this opportunity to their advantage. Through this work, the aim is to propose a system that combines Tiny YOLOv7 and Jestson Nano which is able to detect faces with or without mask based on the recently introduced Tiny YOLOv7 algorithm. The proposed system was able to achieve a mAP of 55.94% and an average IoU of 53.70%. The average precision achieved for people with masks was 83.80% and 79.67% for specific detection of the mask region. The model uses a total of 5.527 BFLOPs and was able to achieve an average FPS of 71.8, which ensures a higher throughput leading to a faster model both in terms of training and detection.
Weight Uncertainty in Transformer Network for the Traveling Salesman Problem

Traveling Salesman Problem (TSP) and its variations have many applications, especially for the case of circuit design where the ordering is crucial for the final design quality. Recently the deep learning (DL) based solvers were found successful in solving TSP, and have been applied to solve practical circuit design problems. However as the problem size increases, the gap between the optimal solution and the one predicted by DL based solver increases. For practical applications where the following decision making strongly relies on the solver quality, knowing the model uncertainly is very important. In the present work, a generalized spike and slab distribution (gSaS) was introduced to model the weight uncertainty for the attention based neural network TSP solver. Instead of minimizing the variational free energy or the expected lower bound on the marginal likelihood in the traditional variational inference, we directly minimize the loss function of the original model with respected to the hyper parameters of gSaS. Numerical experiments indicate that our proposed method can predict reasonable model uncertainty without sacrificing any test accuracy.
Intent-aware Graph Neural Network for Point-of-Interest embedding and recommendation

Point of Interest (POI) recommendation algorithms can help users find the POIs that they prefer, and they can also help merchants to find potential customers. However, most existing methods still have difficulties effectively utilizing the information in users’ check-in data. Significantly, they ignore the intent behind the users’ check-in behaviors, which limits the recommendation performance. In this paper, we propose an Intent Aware Graph Neural Network-based model(IAGNN) to predict/recommend the next POI with which the target user may interact. Specifically, IAGNN first models the user’s check-in behavior sequences as graphs and utilizes the information transmission mechanism of the graph neural network (GNN) to learn the feature vector representation (embedding) of POIs. Second, we devise a hierarchical attention network for capturing users’ preferences adaptively. At the same time, we design a user intent-aware module based on disentangled representations to extract the user’s intents. Finally, the user’s preferences and their intents obtained by the user intent perception module are combined to recommend the POI for the user. Extensive evaluations are conducted on two real-world POI check-in datasets. The experimental results show that our proposed model IAGNN outperforms the baselines in terms of both recall and MRR.
The Regulation of Content Moderation

Online platforms have become a key infrastructure for creating and sharing content, thus representing a paramount context for the individual/collective exercise of fundamental rights (e.g., freedom of expression, association) and the realisation of social values (citizens’ information, education, democratic dialogue). At the same time, platforms offer new opportunities for unfair or harmful behaviours, such as the unauthorised distribution of copyrighted content, privacy violation, unlawful content distribution (e.g., hate speech, child pornography), and fake news. To prevent or at least mitigate the spread of such content, online platforms have been encouraged to resort to content moderation. This activity uses automated systems to govern content flows to ensure lawful and productive user interactions. These systems deploy state-of-the-art AI technologies (e.g., deep learning, NLP) to detect prohibited content and restrict its further dissemination. In this Chapter, we will address the use of automated systems in content moderation and the related regulatory aspects. Section 2 will provide a general overview of content moderation on online platforms, focusing mainly on automated filtering. Further, Sect. 3 will describe existing techniques for automatically filtering content. Section 4 will discuss some critical challenges in automated content moderation, namely vulnerability, failures in accuracy, subjectivity and discrimination. Furthermore, Sect. 5 will define some of the steps needed to regulate moderation. Finally, in Sect. 6, we will review existing legislation that addresses content moderation in online environments.
An Investigation Into the Use of Deep Learning to Recognize Human Activity

Human activity recognition (HAR) is a study area concerned with the voluntary detection of routine human activity using sensor-based time-series data. However, sensor-based HAR systems have higher misclassification rates for complex actions like running, jumping, wrestling, and swinging because of sensor reading errors. The HAR system's overall performance is decreased by these sensor errors, which produce the worst classification outcomes imaginable. For complex tasks, better accuracy can be attained using vision-based HAR systems. This technique builds a deep convolutional neural network, and then uses it to extract features from the input sequence in order to gather data. Then, the temporal connections between the images will be ascertained using LSTM. This model's accuracy was suggestively higher than that of other cutting-edge deep neural network models after it was effectively validated on the UCF50 dataset. The implementation of the models to maximize their efficacy has been covered in this chapter.
Quantum Computing for Feature Model Analysis

Feature modeling is a technique to model the variability of configurable systems. When working with feature models, it is possible to analyze them, for instance, by counting the number of valid configurations, searching feature model anomalies, or creating samples of configurations for testing. Classical feature model analysis techniques are based on solving algorithmic problems such as boolean satisfiability, satisfiability modulo theories, or integer linear programming. Existing analysis approaches provide satisfactory solutions for small and medium-sized problem instances, but scaling issues are observed for large-sized feature models. Quantum computers provide up to superpolynomial speedups for specific algorithmic problems and have the potential to solve those scaling issues. This paper analyzes the algorithmic techniques used in classical product line analysis and identifies potentials and challenges for quantum speedups. Our findings show that quantum algorithms like QAOA and Grover have the potential to speed up SAT and ILP-based feature model analysis techniques, but only after additional improvements in quantum hardware have been made.
Continuous Product Updates under Consideration of HW-and SW-Releases

The automotive industry is undergoing a transformation driven by rapid developments in technologies, business models and mobility concepts. As a result, customers are increasingly demanding additional, improved, or customized functionalities, services, and features of vehicles. Consequently, the number of functions, electronic control units, and software is increasing, through which modern cars are becoming complex cyber-physical systems. This transition from mechanically-based to software-intensive, intelligent systems provides extensive opportunities for enhancing vehicles but also presents organizational challenges in their planning, development, and approval. To address these challenges, the automotive industry is adopting methods such as product generation, electrics/electronics (E/E) platforms, and software product line engineering. Although these concepts are well-established and widely acknowledged in their respective research areas and various domains, there is limited practical effectiveness when it comes to implementing these concepts, especially in the context of software-driven automotive systems. Additionally, most of the requirements, such as vehicle's systems safety and security and software maintenance using over-the-air updates, are getting more important with the change of vehicles becoming digital smart devices on wheels. Our methodology starts with an investigation of existing product-structuring concepts that consider both hardware and software artifacts to derive the challenges and practical applicability to the automotive industry, as well as other cyber-physical systems, by applying a systematic mapping study. Based on the findings of the mapping study regarding challenges and issues of product-structuring concepts related to software-driven automotive systems, we analyze, discuss, and evaluate the requirements within our research group to develop specific criteria. These criteria form the basis for developing an assessment method to support the practical applicability of product-structuring concepts and decision making in managing electric/electronic platforms.
TEACHING PROFESSIONALLY ORIENTED READING STRATEGIES TO PRE-SERVICE ENGLISH TEACHERS

The purpose of our paper was to add to the available body of research regarding teaching university students professionally oriented reading, in particular teaching master’s level students, pre-service English teachers, professionally oriented reading. We found that reading for professional development does not appear to be a popular activity among the students who participated in our survey. We also found that the main reasons which hinder professionally oriented reading are lack of time and relevancy of the literature, the level of linguistic difficulty professional reading has, and lack of information about availability of professionally oriented reading materials. To alleviate these problems, we suggest teaching strategies for enhancing reading professional literature by pre-service English teachers, such as suggesting relevant readings and other professional resources; prompting annotations and reflections; inspiring connections between theory and practice; championing experimentation. We believe these strategies will work with master’s level students at universities offering a degree in teaching as well because academic subjects’ teachers and theses supervisors can act as coaches and mentors when it comes to engaging students into professional reading, suggest meaningful reading tasks for their students, and provide ongoing personalized and explicit encouragement and support. The article discusses implications for professional development, concludes about the positive impact of reading professional literature on the professional development of pre-service teachers of the English language.