Reviewerly/exHarmony huggingface dataset:
Camouflage Backdoor Attack against Pedestrian Detection

Pedestrian detection models in autonomous driving systems heavily rely on deep neural networks (DNNs) to perceive their surroundings. Recent research has unveiled the vulnerability of DNNs to backdoor attacks, in which malicious actors manipulate the system by embedding specific triggers within the training data. In this paper, we propose a tailored camouflaged backdoor attack method designed for pedestrian detection in autonomous driving systems. Our approach begins with the construction of a set of trigger-embedded images. Subsequently, we employ an image scaling function to seamlessly integrate these trigger-embedded images into the original benign images, thereby creating potentially poisoned training images. Importantly, these potentially poisoned images exhibit minimal discernible differences from the original benign images and are virtually imperceptible to the human eye. We then strategically activate these concealed backdoors in specific scenarios, causing the pedestrian detection models to make incorrect judgments. Our study demonstrates that once our attack successfully embeds the backdoor into the target model, it can deceive the model into failing to detect any pedestrians marked with our trigger patterns. Extensive evaluations conducted on a publicly available pedestrian detection dataset confirm the effectiveness and stealthiness of our camouflaged backdoor attacks.
EXPLORING THE CREATIVE POSSIBILITY OF A HYBRID DESIGN THINKING WORKSHOP THROUGH FINDING SOLUTIONS FOR CHILDREN’S SAFETY IN THE CHILDCARE ENVIRONMENT

Design thinking process has two ways of on and off-line. In the online method, it facilitates communication more freely through the setting of anonymity, while the offline method allows participants to understand the comprehensive context of the workshop. This study investigates the creative possibility of a hybrid design thinking process through a workshop using “camera-off ”: anonymous setting, “camera-on”: open setting, and instant visualization to find solutions for children’s safety in the childcare environment. In the methodology, a qualitative method of three-dimensional interviews and observations were used to collect the active participants’ opinions, and then the content analysis and the descriptive analysis were conducted. Three major childcare-related problems were noted during the workshop observations: 1) safety issues from children’s physical activities; 2) teachers’ excessive stress in childcare facilities, and 3) communication problems between parents and teachers. It was suggested that creative smart devices and artificial intelligence technologies could be helpful to relieve these problems. In insights of the hybrid design thinking workshop, the following points were noted: 1) active participants could freely share ideas in anonymous setting with “camera-off”; 2) active participants could understand various desires of diverse fields with “camera-on”, and 3) active participants could understand find core problems and solutions through instant visualization. Hybrid design thinking is an creative method for understanding problems and finding solutions.
Dynamic Evolution of Repeated Pledge in FTW from the Perspective of Blockchain Empowerment

Due to information asymmetry, finance, transportation and warehouse financing gives rise to the issue of repeated pledge, which amplifies the risk of the loan business of financial institutions. In tandem with advancements in fintech, blockchain technology plays a significant role in the supply chain finance realm, primarily because of its core characteristics of being difficult to tamper with and decentralized. Therefore, this study constructed an evolutionary game model involving financial institutions, small- and medium-sized enterprises, and third-party logistics enterprises under a blockchain-enabled model and scrutinized the repeated pledge of financing entities in the finance, transportation and warehouse financing sector from the perspective of blockchain empowerment. The results show that the platform access fee being lower than the cost of conducting a financing business and the immutable characteristics of blockchain are important reasons to promote financial institutions to choose access to blockchain. The permanent retention of performance records owing to immutable performance under the blockchain model intensifies the consequences of dishonest behavior of small- and medium-sized enterprises and third-party logistics enterprises, thus encouraging the adoption of positive financing strategies. Additionally, the additional income obtained by third-party logistics enterprises’ covering behavior surpasses the additional income obtained by the repeated pledge behavior of small- and medium-sized enterprises, which will dismantle collusion between them. This study serves as a valuable reference for decision makers in the development of supply chain finance empowered by fintech.
Integrating future trends and uncertainties in urban mobility design via data-driven personas and scenarios

Abstract Urban mobility contributes significantly to greenhouse gas emissions and comes with negative social impacts for various groups, such as limited accessibility to opportunity or basic services. Transitions towards sustainable and people-centred urban mobility systems are paramount. Yet, this is accompanied by various challenges. Complex urban systems are accompanied by high uncertainties (e.g., technological progress, demographics, climate change) which are currently not well integrated. Possible solutions originate from design, policymaking, and innovation, with a widespread disconnection due to non-compatible methods. This paper presents a method to improve the ability to design future urban mobility systems by integrating different approaches for modelling what the future could be and who could be the users. The research question is how diverse future user needs can be integrated in design processes for urban mobility systems. The proposed scenario-based design and personas allows to create data-driven proto-personas—a set of archetypical users with assigned characteristics and behaviours—test their validity, derive distributions across geographical areas, and transform them for different 2030 scenarios. This serves as input to create full personas and synthetic populations as intermediary design objects for the collaboration of designers and simulation experts. The methodology is exemplarily applied in the context of Paris. It contributes to urban mobility solution design that is more aware of future uncertainty and diverse needs of users, therefore, better capable to respond to today’s challenges. The approach is replicable with open data and accessible source code: https://github.com/TjarkGall/proto-persona-clustering .
Empathic accuracy in artificial intelligence service recovery

Purpose Artificial intelligence (AI) agents have been increasingly applied in the tourism and hospitality industry. However, AI service failure is inevitable. Thus, AI service recovery merits empirical investigation. This study aims to explore how AI empathic accuracy affects customers’ satisfaction in the context of AI service recovery. Design/methodology/approach A moderated mediation model was presented to describe the effect of empathic accuracy on customer satisfaction via four scenario-based experiments. Findings The results reveal the positive impact of AI empathic accuracy on customer satisfaction and the mediating effects of perceived agency and perceived experience. Moreover, anthropomorphism moderates the empathic accuracy effect. Originality/value This paper expanded AI service studies by exploring the significance of empathic accuracy in customer recovery satisfaction. The results provide a novel theoretical viewpoint on retaining customers following AI service failure.
The Role of Smart, Haptic Wearables in the Performance of <i>Mensura</i>

This article introduces the concept of ‘wearing-in-performance’, the phenomenon of using digital, wearable technologies in musical performance. The research uses ethnographic methods, analysing interviews and statements made by musicians in the rehearsal and performance of Mensura (2022), a work of experimental concert music composed by the author, in order to scrutinise the phenomenological role of such technologies. The paper identifies three phenomena experienced by these musicians, namely connectedness; the meditative reconfiguring of space; and an augmenting of the body. It goes on to conclude that the experience of performing with wearable, haptic technologies is a dynamic gathering of entangled and networked phenomena.
Smart education: a technology and technology resource

Smart technology is one of the most relevant technologies among information technologies in the context of updated educational content. Currently, the content of education in the pedagogical field is being modernized, this technology, which expands the innovative approach of the younger generation, increases the quality of education, improves қnowledge and sқills, provides a reliable transition to the digital age, which ensures the growth of the economy and labor productivity. Since the times require digital literacy қnowledge from everyone, I present my article aimed at forming and developing innovative sқills as the most relevant of the technologies aimed at literacy of the current information system.
The Improvement of Reading Comprehension of English Texts by Slovak Learners Via Intervention Program

Our paper is concerned with improving reading comprehension by intervention or intervention program for reading comprehension for English language. This program facilitates the individual into thinking and learning, as well as their progress at the level of cognitive and metacognitive processes, emotional and social factors. Cognitive and metacognitive processes stimulate comprehension of written texts. Emotional and social factors support self-efficiency in the process of developing comprehension. With this, we have the opportunity to improve reading comprehension in a greater manner. The research goal of our paper is to find out the effect of intervention for improving reading comprehension skills of Slovak high school students, specifically in regards to reading English texts, as well as to find out the strength of improvement in reading comprehension and the duration of this improvement. Our selected research sample consists of Slovak ESL students with the proficiency level of B1 according to CEFR. Chosen research methods were a didactic test (test of reading comprehension for English language) and experiment (the intervention itself). Findings in particular research groups support the intervention’s efficiency.
Microseismic Monitoring Signal Waveform Recognition and Classification: Review of Contemporary Techniques

Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. This paper employs a literature review approach to summarize the research progress on microseismic signal identification methods and techniques over the past decade. The advantages and limitations of commonly used identification methods are systematically analyzed and summarized. Extensive discussions have been conducted on cutting-edge machine learning models, such as convolutional neural networks (CNNs), and their applications in waveform image processing. These models exhibit the ability to automatically extract relevant features and achieve precise event classification, surpassing traditional methods. Building upon existing research, a comprehensive analysis of the strengths, weaknesses, opportunities, and threats (SWOT) of deep learning in microseismic event analysis is presented. While emphasizing the potential of deep learning techniques in microseismic event waveform image recognition and classification, we also acknowledge the future challenges associated with data availability, resource requirements, and specialized knowledge. As machine learning continues to advance, the integration of deep learning with microseismic analysis holds promise for advancing the monitoring and early warning of geological engineering disasters.
The use of M-learning to foster the development of self-regulated learning in university students: A systematic review

The rapid development of mobile technologies and their price decrease have enabled mobile devices to become ubiquitous. Both lecturers and students are integrating mobile technologies into teaching and learning. The continuous global innovation in emerging mobile technologies and their ever-increasing overlap with the lives of students and lecturers in Higher Education Institutions (HEIs) have thrust Mlearning and self-regulated learning into prominence. Accordingly, this review analyses conference proceedings and national and international journals on the potential of M-learning for fostering self-regulated learning. To select relevant sources, a systematic literature review approach was employed. This study fills gaps in existing literature and investigates how mobile devices might enhance self-regulated learning among university students. It also signals future research directions and offers implications for researchers and practitioners in HEIs.
Challenges faced by intermediate phase English First Additional Language teachers in establishing classroom libraries in Limpopo Province

Classroom libraries have an important function in the development of reading abilities for English First Additional Language (EFAL) learners in the Intermediate Phase (grades 4 to 6). This research aimed at exploring the challenges faced by the Intermediate Phase EFAL teachers in establishing classroom libraries in a rural circuit of the Limpopo Province to promote learners’ reading ability. A qualitative grounded theory approach was employed using observations and an interview schedule with semi structured open-ended questions. Nine participants were purposefully selected. The study found that there was limited relevant reading material, a lack of space for setting up a classroom library in overcrowded classrooms, teachers’ lack of knowledge on organising the classroom library books and a lack of strategies on how to control the checking in and out of books. It is recommended that schools should first allocate funds for buying books and seek donations for reading material from public libraries as well as non-governmental organisations. Inservice training should be conducted to equip teachers with the basic skills of organising and facilitating the checking out and returning of classroom library books.
Haptic feedback in a virtual crowd scenario improves the emotional response

Research has shown that incorporating haptics into virtual environments can increase sensory fidelity and provide powerful and immersive experiences. However, current studies on haptics in virtual interactions primarily focus on one-on-one scenarios, while kinesthetic haptic interactions in large virtual gatherings are underexplored. This study aims to investigate the impact of kinesthetic haptics on eliciting emotional responses within crowded virtual reality (VR) scenarios. Specifically, we examine the influence of type or quality of the haptic feedback on the perception of positive and negative emotions. We designed and developed different combinations of tactile and torque feedback devices and evaluated their effects on emotional responses. To achieve this, we explored different combinations of haptic feedback devices, including “ No Haptic ,” “ Tactile Stimulus ” delivering tactile cues, and “ Haptic Stimulus ” delivering tactile and torque cues, in combination with two immersive 360-degree video crowd scenarios, namely, “ Casual Crowd ” and “ Aggressive Crowd .” The results suggest that varying the type or quality of haptic feedback can evoke different emotional responses in crowded VR scenarios. Participants reported increased levels of nervousness with Haptic Stimulus in both virtual scenarios, while both Tactile Stimulus and Haptic Stimulus were negatively associated with pleasantness and comfort during the interaction. Additionally, we observed that participants’ sense of touch being real was enhanced in Haptic Stimulus compared to Tactile Stimulus . The “ Haptic Stimulus ” condition had the most positive influence on participants’ sense of identification with the crowd.
<b> P <sup>2</sup> ANet </b> : A Large-Scale Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos

While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging. In this work, we release yet another sports video benchmark P 2 ANet for P ing P ong- A ction detection, which consists of 2,721 video clips collected from the broadcasting videos of professional table tennis matches in World Table Tennis Championships and Olympiads. We work with a crew of table tennis professionals and referees on a specially designed annotation toolbox to obtain fine-grained action labels (in 14 classes) for every ping-pong action that appeared in the dataset, and formulate two sets of action detection problems— action localization and action recognition . We evaluate a number of commonly-seen action recognition (e.g., TSM, TSN, Video SwinTransformer, and Slowfast) and action localization models (e.g., BSN, BSN++, BMN, TCANet), using P 2 ANet for both problems, under various settings. These models can only achieve 48% area under the AR-AN curve for localization and 82% top-one accuracy for recognition since the ping-pong actions are dense with fast-moving subjects but broadcasting videos are with only 25 FPS. The results confirm that P 2 ANet is still a challenging task and can be used as a special benchmark for dense action detection from videos. We invite readers to examine our dataset by visiting the following link: https://github.com/Fred1991/P2ANET.
Non-invasive calibration for volumetric flow measurements in confined spaces

Abstract The calibration of a multi-camera system is a crucial step of volumetric flow measurements with photogrammetric methods. Conventional calibration methods are based on recording hardware targets, which are placed in the cameras fields of view. Calibrating in confined spaces with those methods is associated with an increased technical or mechanical effort. This work presents a calibration method without the use of a hardware target. Instead, crossing laser beams are introduced into the volume for creating unique calibration points. The underlying algorithms discussed in this paper for detecting the laser beams are: Ransac algorithm, Template Matching (via cross correlation) and Probabilistic Hough Transformation . The algorithms are tested with experimental data and synthetic data.
MC-Net: multi-scale contextual information aggregation network for image captioning on remote sensing images

Remote Sensing Image Captioning (RSIC) plays a crucial role in advancing semantic understanding and has increasingly become a focal point of research. Nevertheless, existing RSIC methods grapple with challenges due to the intricate multi-scale nature and multifaceted backgrounds inherent in Remote Sensing Images (RSIs). Compounding these challenges are the perceptible information disparities across diverse modalities. In response to these challenges, we propose a novel multi-scale contextual information aggregation image captioning network (MC-Net). This network incorporates an image encoder enhanced with a multi-scale feature extraction module, a feature fusion module, and a finely tuned adaptive decoder equipped with a visual-text alignment module. Notably, MC-Net possesses the capability to extract informative multiscale features, facilitated by the multilayer perceptron and transformer. We also introduce an adaptive gating mechanism during the decoding phase to ensure precise alignment between visual regions and their corresponding text descriptions. Empirical studies conducted on four publicly recognized cross-modal datasets unequivocally demonstrate the superior robustness and efficacy of MC-Net in comparison to contemporaneous RSIC methods.
Geology of the cretaceous Paraná Magmatic Province at the central portion of Torres Syncline, Southern Brazil

Geological maps of tropical areas demand special challenges due to weathering conditions, mainly where relief has low variability. Most part of Paraná Magmatic Province is included in this scenario. An exception to this situation is the southeastern portion of the province, in the state of Rio Grande do Sul, Brazil. The stratigraphic disposition of volcanic units and the characteristics of erosion of each one caused the exposition of four identified plateaus, in which outcrop, from bottom to top: (1) basalts from Torres/Vale do Sol formations; (2) Caxias do Sul dacite; (3) Barros Cassal sequence; (4) Santa Maria rhyolite. The contacts were traced using SRTM images (through altitude and slope maps), integrating previously published semi- to detailed maps and geochemical database from our group. The resulting map (1:250,000) contributes to the volcano-stratigraphic knowledge of this huge province, and allows us to suggest the review of silicic units, initially called Palmas Formation.
Data-to-text generation using conditional generative adversarial with enhanced transformer

Abstract In this paper, we propose an enhanced version of the vanilla transformer for data-to-text generation and then use it as the generator of a conditional generative adversarial model to improve the semantic quality and diversity of output sentences. Specifically, by adding a diagonal mask matrix to the attention scores of the encoder and using the history of the attention weights in the decoder, this enhanced version of the vanilla transformer prevents semantic defects in the output text. Also, using this enhanced transformer along with a triplet network, respectively, as the generator and discriminator of conditional generative adversarial network, diversity and semantic quality of sentences are guaranteed. To prove the effectiveness of the proposed model, called conditional generative adversarial with enhanced transformer (CGA-ET), we performed experiments on three different datasets and observed that our proposed model is able to achieve better results than the baselines models in terms of BLEU, METEOR, NIST, ROUGE-L, CIDEr, BERTScore, and SER automatic evaluation metrics as well as human evaluation.
Aerial tracking of camouflaged people in woodlands

With the remarkable advances of unmanned aerial vehicles (UAVs) and machine vision, aerial tracking has attracted wide attention from scholars. Previous tracking methods were mostly implemented in clean and well-lit environments, making it challenging to track camouflaged people rapidly and accurately in woodlands. We develop a framework for camouflaged people aerial tracking (CPAT) based on transformer. Specifically, a camouflaged people discovery strategy is proposed to rapidly generate training samples from the unlabeled videos captured by the UAV. Dynamic programming is also employed to filter noises to generate smooth candidate frames. To exploit multilevel feature information, a transformer fusion framework is designed to integrate shallow spatial information and in-depth semantic features. For reducing computing consumption, the spatial attention reduction mechanism is embedded in the multihead attention for fast tracking. Further, we build a dataset for evaluating the effect of camouflaged people tracking called Cam235, which consists of 85 manually labeled test sequences and more than 100k frames of the unlabeled training set. Exhaustive experiments on Cam235-test and popular tracking datasets prove that the CPAT is superior to other trackers for practical application. Under the most challenging condition of camouflaged people tracking, the CPAT achieves the precision of 67.9%, surpassing the state-of-the-art trackers by large margins.