Reviewerly/exHarmony huggingface dataset:
Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching Between Parts and Words

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here.
PACO: Parts and Attributes of Common Objects

Object models are gradually progressing from predicting just category labels to providing detailed descriptions of object instances. This motivates the need for large datasets which go beyond traditional object masks and provide richer annotations such as part masks and attributes. Hence, we introduce PACO: Parts and Attributes of Common Objects. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Eg04D) datasets. We provide 641K part masks an-notated across 260K object boxes, with roughly half of them exhaustively annotated with attributes as well. We design evaluation metrics and provide benchmark results for three tasks on the dataset: part mask segmentation, object and part attribute prediction and zero-shot instance detection. Dataset, models, and code are open-sourced at https://github.com/jacebookresearch/paco.
Ultrahigh Resolution Image/Video Matting with Spatio-Temporal Sparsity

Commodity ultrahigh definition (UHD) displays are becoming more affordable which demand imaging in ultrahigh resolution (UHR). This paper proposes SparseMat, a computationally efficient approach for UHR image/video matting. Note that it is infeasible to directly process UHR images at full resolution in one shot using existing matting algorithms without running out of memory on consumer-level computational platforms, e.g., Nvidia 1080Ti with 11G memory, while patch-based approaches can introduce unsightly artifacts due to patch partitioning. Instead, our method resorts to spatial and temporal sparsity for addressing general UHR matting. When processing videos, huge computation redundancy can be reduced by exploiting spatial and temporal sparsity. In this paper, we show how to effectively detect spatio-temporal sparsity, which serves as a gate to activate input pixels for the matting model. Under the guidance of such sparsity, our method with sparse high-resolution module (SHM) can avoid patch-based inference while memory efficient for full-resolution matte refinement. Extensive experiments demonstrate that SparseMat can effectively and efficiently generate high-quality alpha matte for UHR images and videos at the original high resolution in a single pass. Project page is in https://github.com/nowsyn/SparseMat.git.
KIAAA: An AI Assistant for Teaching Programming in the Field of Automation

Especially in highly interdisciplinary fields such as automation engineering, contemporary programming education with tailored assignments and individual feedback is a major challenge for educational institutions due to the increasing number of students per teacher and the ever-increasing demand for computer science professionals. To address this gap, we present ”KIAAA” an AI Assistant for Automation Engineering Teaching, a work-in-progress approach for an integrated, customized, and AI-based learning support system for automation and programming courses based on instructor-defined course objectives. Thereby in the KIAAA system, the individual knowledge level of the students is determined and individually tailored virtual learning scenarios are generated based on the knowledge and learning profile of the students. These are iteratively adapted based on the answers given. To achieve this, KIAAA uses several AI components, a hybrid rule-based scenario generation component, a Help-DKT-based cognitive model, and a solution assessor that uses a combination of traditional code analysis methods and AI-based analyses methods for automated programming task assessment. These components are the main parts of KIAAA to generate customized programming scenarios as well as visualization and simulation based on a modern game and physics engine.
Global Path Planning for UUV Based on Grey Wolf-Particle Swarm Optimization

The improved particle swarm optimization (PSO) is proposed for the three-dimensional space path planning of unmanned underwater vehicle (UUV) to solve the problem that the classical PSO is easy to fall into the local optimal solution. Firstly, the elevation environment model is established according to the UUV navigation environment, and the fitness evaluation function is formulated by comprehensively considering the path length, threat factors, UUV performance constraints and other conditions. Secondly, the inertial weight of the particle swarm algorithm introduces the beta distribution based on the cosine dynamic adjustment. Thirdly, adopt the reverse learning mechanism for the optimal particle in the algorithm iteration. Finally, the encirclement strategy of the grey wolf algorithm optimizes the iterative update formula. By designing two application scenarios and comparing GWO-PSO with PSO, grey wolf optimizer (GWO) and GA-PSO, the simulation results show that GWO-PSO has stronger global search ability and higher optimization accuracy.
Adaptive Zone-aware Hierarchical Planner for Vision-Language Navigation

The task of Vision-Language Navigation (VLN) is for an embodied agent to reach the global goal according to the instruction. Essentially, during navigation, a series of sub-goals need to be adaptively set and achieved, which is naturally a hierarchical navigation process. However, previous methods leverage a single-step planning scheme, i.e., directly performing navigation action at each step, which is unsuitable for such a hierarchical navigation process. In this paper, we propose an Adaptive Zone-aware Hierarchical Planner (AZHP) to explicitly divides the navigation process into two heterogeneous phases, i.e., sub-goal setting via zone partition/selection (high-level action) and sub-goal executing (low-level action), for hierarchical planning. Specifically, AZHP asynchronously performs two levels of action via the designed State-Switcher Module (SSM). For high-level action, we devise a Scene-aware adaptive Zone Partition (SZP) method to adaptively divide the whole navigation area into different zones on-the-fly. Then the Goal-oriented Zone Selection (GZS) method is proposed to select a proper zone for the current sub-goal. For low-level action, the agent conducts navigation-decision multi-steps in the selected zone. Moreover, we design a Hierarchical RL (HRL) strategy and auxiliary losses with curriculum learning to train the AZHP framework, which provides effective supervision signals for each stage. Extensive experiments demonstrate the superiority of our proposed method, which achieves state-of-the-art performance on three VLN benchmarks (REVERIE, SOON, R2R).
Utilizing Virtual Reality and Online Gaming for the Construction and Application of Distance Physical Education Teaching Window in Emergency Situations

The contemporary education community has been trying to use video games or game elements in teaching in recent years.The pioneers in this field have tried to improve the quality of education by means of educational games or gamified teaching, but it has been difficult to be widely promoted for various reasons.In recent years, attempts have found that although there are certain advantages over educational video games and gamified teaching, like the former, there are still considerable difficulties in popularizing them in practice.Therefore, this paper proposes a learning structure design based on the big data video game task mode, hoping to make the learning process really arouse the learners' interest in learning, achieve good learning results in a relaxed and focused learning atmosphere, and achieve unsupervised high efficiency.Self-learning.Finally, the F1 value of simple teaching resources is increased by 8.6% on average compared with the other four algorithms, and it can reach 0.888 when the test set is 10%; when recommending complex teaching resources, the F1 value is increased by an average of 5.6% compared with the other four algorithms.It can reach 0.958 at 10% of the test set.The above data show that the CUPMF method can effectively improve the recommendation accuracy and improve the recommendation effect.
A Novel Battery Thermal Management System based on Capillary Driven Evaporative Cooling

In this paper we present an experimental and a numerical study on a novel battery thermal management system (BTMS) based on capillary driven evaporative cooling (CDEC).The experimental study was conducted using Cu foam and Novec 7000 to determine the cooling performance of this system when cooled through a single surface of the battery at three different heating inputs: 10W, 20W and 30W.The system was further expanded to incorporate cooling from the two larger surfaces of the battery using a numerical model.Results from the experimental study showed that the maximum temperature of battery can be maintained below 50℃ for a heat input of 30W, when the cell is cooled using a single surface.This was further reduced to 40℃ when cooled from both surfaces.Furthermore, these temperatures were achieved through partial wetting of the battery surface due to a capillary rise of about 11% of the total battery height.This clearly demonstrates the superior cooling capabilities of this novel BTMS.However, the maximum temperature different recorded in the battery at a heat input of 30W was 5.47℃ which was a result of the uneven cooling rates in the wetted and nonwetted regions of the battery due to partial capillary rise.
‘Why aren’t you married? The same reason you are not dead, it’s not my time’: Social Media Use of Hate Speech on Female Celebrities in Ghana

This study is aimed at analysing the rhetoric of hate speech and the degree of pervasiveness in discriminating against the female celebrity?s identity on social media which is often centred on the question: „why aren?t you married or why haven?t you still given birth?? The paper sought to examine the nature of social media posts of five female celebrities who had experienced the phenomenon of flaming through hate speech and explore the adoptive strategies of these female celebrities to ward off perpetrators of flames. Data was cyber-ethnographically gathered from the Twitter and Facebook pages of the female celebrities and thematically analysed using Willard?s Indicators of Cyberbullying and the tenets of Suler?s Online Disinhibition Effect. The findings revealed that the female celebrities experienced direct flames manifested in comments of aggression including dissing, hurtful jocular messages, invectives, snide comments, obsessive and malicious stalking, and profanity. A flamer of an attack attains a pseudo- celebrification status as other celebrity friends or fans of the female celebrity join in a particular hate speech episode as bystander reporters to either provide support to the victim or reinforce the attack. The data also revealed that the female celebrities mainly used instant blocking and the adoption of an aggressive persona as a strategy to rebut their abusers through clap backs, invectives, and evocation of death or curses as a way of warding off online abusers. The study thus contributes to the literature on gender stereotyping through flaming and recommends female celebrities employ the services of social media managers as gatekeepers for their accounts. Also, efforts at stringent laws of civility should be intensified in protecting the image of social media users in an era of social media pervasiveness.
Multi-UAV Cooperative and Continuous Path Planning for High-Resolution 3D Scene Reconstruction

Unmanned aerial vehicles (UAVs) are extensively employed for urban image captures and the reconstruction of large-scale 3D models due to their affordability and versatility. However, most commercial flight software lack support for the adaptive capture of multi-view images. Furthermore, the limited performance and battery capacity of a single UAV hinder efficient image capturing of large-scale scenes. To address these challenges, this paper presents a novel method for multi-UAV continuous trajectory planning aimed at the image captures and reconstructions of a scene. Our primary contribution lies in the development of a path planning framework rooted in task and search principles. Within this framework, we initially ascertain optimal task locations for capturing images by assessing scene reconstructability, thereby enhancing the overall quality of reconstructions. Furthermore, we curtail energy costs of trajectories by allocating task sequences, characterized by minimal corners and lengths, among multiple UAVs. Ultimately, we integrate considerations of energy costs, safety, and reconstructability into a unified optimization process, facilitating the search for optimal paths for multiple UAVs. Empirical evaluations demonstrate the efficacy of our approach in facilitating collaborative full-scene image captures by multiple UAVs, achieving low energy costs while attaining high-quality 3D reconstructions.
Intelligent Musical Instruments: Challenges for the Composer/Performer

The field called New Interfaces for Musical Expression (NIME) has arisen to address questions of Human-Computer Interaction in the context of musical performance.The interface is a gesture sensor whose output is mapped by software to actuate sound generators.As we introduce more and more complex software to interpret and mediate the musical gestures of the performer, it may become more difficult for the observer to interpret what the performer is doing.Thus a guiding principle of our research is to retain expressive musical interaction with NIME when making software and hardware design decisions.The research outcome is a musical performance where the observer perceives an intuitive relationship between the musical gestures and the musical sounds, even though the observer may not discern the exact mapping between gesture and sound.We demonstrate some musical applications for our research, following the development of the radiodruminterface and the Mechdrum sound generator.
GAN-Based Fine-Grained Feature Modeling For Zero-Shot Voice Cloning

With the continuous development of deep learning and speech signal processing, speech synthesis technology has greatly improved in naturalness and comprehensibility, and many application technologies such as artificial intelligence voice assistant and personalized navigation have been widely used in real life, and the demand for personalized speech synthesis is increasing.Personalized speech synthesis requires models that can achieve speech timbre migration, also known as speech reproduction, with only a small number of target speaker speech samples.However, since human speech is highly expressive and contains rich information, including speaker identity information, prosody, rhythm, emotion and other factors, the limited speech data will lead to poor similarity and rhythmic performance of the model-generated speech, and the model needs to be fine-tuned to improve the quality of the synthesized speech.Therefore, personalized speech synthesis with few samples is a very challenging task.To achieve the goal of speech cloning, this paper proposes a personalized speech synthesis method based on FastSpeech2.By using fine-grained feature modeling module containing prosody extractor and prosody predictor, and a training strategy based on Generative adversarial network (GAN) and meta-learning, it is realized that personalized speech with high similarity and naturalness can be generated with a very short reference audio.The subjective and objective experiments also demonstrate that the model proposed in this paper can achieve high quality speech replication without fine-tuning the model under a few or even a single reference audio of the target speaker.
Inverse-tone-mapped HDR video quality assessment: A new dataset and benchmark

The accurate quality assessment for inverse-tone-mapped videos (ITMVs) has become increasingly important due to the ongoing rapid development of inverse tone mapping (ITM) algorithms. We observed that most existing video quality assessment (VQA) datasets and objective VQA methods are based on two underlying assumptions: the videos are in standard dynamic range (SDR) domain and the distortions are introduced in the video degradation process. However, ITMVs are videos in high dynamic range (HDR) domain, and are accompanied by distortions introduced during the enhancement process, which are distinct from those in degradation process. We then pose the question: Can existing VQA methods objectively assess the quality of ITMVs? To address this question and foster the advancement of VQA methods, we contribute the first publicly available dataset specifically tailored for assessing the quality of ITMVs. The dataset is called “VQA4ITM”, which is an acronym for “Video Quality Assessment for Inverse Tone Mapping”. Specifically, 30 distortion-free, professional 4K Rec. 2020 10 bit 50fps HDR source videos and 12 ITM methods were carefully selected, and a total of 360 ITMVs were generated in the end. A subjective experiment was conducted in a professional HDR viewing environment and 6,098 valid quality scores were finally gathered to calculate the mean opinion score (MOS) of each ITMV. Furthermore, we perform benchmark experiments on the VQA4ITM dataset, revealing that existing VQA methods are unable to fairly assess the quality of ITMVs. The inadequate performance of existing VQA methods on the VQA4ITM dataset highlights the challenging nature of our dataset, as well as the imperative need for the development of more effective VQA algorithms in the context of ITM. To facilitate future research, the VQA4ITM dataset will be made publicly accessible.
Study From Home: An Analysis of Learning Difficulties of Biology in Junior High School Students During Online Learning

This research aims to discover students' learning difficulties during online learning, especially in science and biology subjects. This study used a descriptive method with a survey technique on the eighth-grade students at SMPN Siak Hulu. The research sample was obtained using a multilevel sampling technique with 191 students. The research instrument used a validated online learning difficulties questionnaire comprising 36 items. The data collection technique was done by distributing online questionnaires via Google Forms and then analyzed descriptively. The results showed that the level of student learning difficulties during online learning was in the medium category, with a percentage of 69.27. The conclusion obtained from the results of this study is that class VIII students of SMPN Siak Hulu experience learning difficulties during online learning in biology science subjects, especially at the level of understanding the material.ABSTRAK: Tujuan dari penelitian ini adalah untuk mengetahui kesulitan belajar siswa selama pembelajaran online khususnya pada mata pelajaran IPA biologi. Penelitian ini menggunakan metode deskriptif dengan teknik survei pada siswa kelas VIII di SMPN Siak Hulu. Sampel penelitian diperoleh menggunakan teknik sampling bertingkat yang berjumlah 191 orang. Instrumen penelitian menggunakan angket kesulitan belajar online yang telah divalidasi dan terdiri dari 36 item. Teknik pengumpulan data dilakukan dengan menyebarkan angket online melalui google form kemudian dianalisis secara deskriptif. Hasil penelitian menunjukkan tingkat kesulitan belajar siswa selama pembelajaran online berada pada kategori sedang dengan persentase sebesar 69.27. Kesimpulan yang diperoleh dari hasil penelitian ini bahwa siswa kelas VIII SMPN Siak Hulu mengalami kesulitan belajar selama pembelajaran online pada mata pelajaran IPA biologi terutama pada tingkat pemahaman materi.
X-Secure T-Private Linear Computation With Graph Based Replicated Storage

The problem of X-secure T-private linear computation with graph based replicated storage (GXSTPLC) is to enable the user to retrieve a linear combination of messages privately from a set of N distributed servers where every message is only allowed to replicate among a subset of servers subject to an X-security constraint, i.e., any groups of up to X colluding servers must reveal nothing about the messages. Besides, any groups of up to T servers must reveal no information about the coefficients of the linear combination retrieved by the user. In this paper, inspired by a Vandermonde decomposition of Cauchy matrices, we propose an achievability scheme for GXSTPLC that achieves the rate of (ρ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</inf> −X −T)/N if every message is replicated at least ρ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</inf> times and ρ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</inf> > X + T, which coincides with a lower bound of the rate of X-secure T-private information retrieval with graph based replicated storage (GXSTPIR) by Jia and Jafar. Moreover, the asymptotic capacity of GXSTPLC is partially settled, including the setting where the storage forms a symmetric pattern.
Privacy-preserving Adversarial Facial Features

Face recognition service providers protect face privacy by extracting compact and discriminative facial features (representations) from images, and storing the facial features for real-time recognition. However, such features can still be exploited to recover the appearance of the original face by building a reconstruction network. Although sev-eral privacy-preserving methods have been proposed, the enhancement offace privacy protection is at the expense of accuracy degradation. In this paper, we propose an adver-sarial features-based face privacy protection (AdvFace) approach to generate privacy-preserving adversarial features, which can disrupt the mapping from adversarial features to facial images to defend against reconstruction attacks. To this end, we design a shadow model which simulates the attackers' behavior to capture the mapping function from facial features to images and generate adversarial la-tent noise to disrupt the mapping. The adversarial features rather than the original features are stored in the server's database to prevent leaked features from exposing facial information. Moreover, the AdvFace requires no changes to the face recognition network and can be implemented as a privacy-enhancing plugin in deployed face recognition systems. Extensive experimental results demonstrate that Adv Face outperforms the state-of-the-art face privacy-preserving methods in defending against reconstruction at-tacks while maintaining face recognition accuracy.