Reviewerly/exHarmony huggingface dataset:
Self-Distillation Dual-Memory Online Hashing with Hash Centers for Streaming Data Retrieval

With the continuous generation of massive amounts of multimedia data nowadays, hashing has demonstrated significant potentials for large-scale search. To handle the emerging needs for streaming data retrieval, online hashing is drawing more and more attention. For online scenario, data distribution may change and concept drifts may occur as new data is continuously added to the database. Inevitably, hashing models may lose or disrupt the previously obtained knowledge when learning from new information, which is called the problem of catastrophic forgetting. In this paper, we propose a new online hashing method called Self-distillation Dual-memory Online Hashing with Hash Centers, which is abbreviated to SDOH-HC, to overcome this challenge. Specifically, SDOH-HC contains replay and distillation modules. For replay, a dual-memory mechanism is proposed which involves hash centers and exemplars. For knowledge distillation, we let hash centers distill information from themselves, i.e., the version of last round. Additionally, a new objective function is further built on above modules and is solved discretely to learn hash codes. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method.
Multi-view Graph Clustering via Efficient Global-Local Spectral Embedding Fusion

With the proliferation of multimedia applications, data is frequently derived from multiple sources, leading to the accelerated advancement of multi-view clustering (MVC) methods. In this paper, we propose a novel MVC method, termed GLSEF, to handle the inconsistency existing in multiple spectral embeddings. To this end, GLSEF contains a two-level learning mechanism. Specifically, on the global level, GLSEF considers the diversity of features and selectively assigns smooth weights to partial more discriminative features that are conducive to clustering. On the local level, GLSEF resorts to the Grassmann manifold to maintain spatial and topological information and local structure in each view, thereby enhancing its suitability and accuracy for clustering. Moreover, unlike most previous methods that learn a low-dimension embedding and perform the k-means algorithm to obtain the final cluster labels, GLSEF directly acquires the discrete indicator matrix to prevent potential information loss during post-processing. To address the optimization involved in GLSEF, we present an efficient alternating optimization algorithm accompanied by convergence and time complexity analyses. Extensive empirical results on nine real-world datasets demonstrate the effectiveness and efficiency of GLSEF compared to existing state-of-the-art MVC methods.
Mass transfer during hydrothermal bleaching alteration of red beds in the Lanping Basin, SW China: Implications for regional Cu(–Co) and Pb-Zn mineralization

The Lanping Basin in SW China hosts many hydrothermal Cu(–Co) and Pb-Zn deposits. Widespread bleaching alterations of red beds in the basin record mass transfer during the hydrothermal fluid migration and may provide useful information about metal sources for the deposits. Based on fluid inclusion study, mineral and elemental mapping of thin sections, and whole-rock major and trace element analyses, this study investigates fluid properties and mass transfer during the hydrothermal bleaching alterations at T1 and T2 two places where Jurassic red siltstones have been bleached in association with sulfides-free hydrothermal quartz(-siderite) veins. Mineral compositions and fluid inclusion microthermometry indicate that both T1 and T2 fluids are CO2-free, weakly acidic to neutral, S-poor, hydrocarbon-bearing reduced fluids, but the former is higher than the latter in the homogenization temperatures (188 – 265 ℃ versus 136 – 184 ℃) and salinities (17 – 36 wt% NaCl eq. versus < 11 wt.%NaCl eq.). These reduced fluids caused the bleaching alterations due to the loss of Fe3+ (in terms of hematite) from the red siltstones. The T1 fluid is comparable to the vein-type Cu-Co and Pb-Zn ore fluids in the Baiyangping district. Enrichment of Cu-Co and depletion of Pb-Zn in the bleached rocks relative to the protolith at T1 suggest that the two pairs of elements are less possibly derived from the same source rocks. Red beds probably provided Pb and Zn, while other rock types, such as mafic volcanic rocks, were likely the sources of Cu and Co. As such, various degrees of contributions from different source rock types during the ore fluid migration may determine the variable (Cu-Co)/(Pb-Zn) ratios observed in the vein-type Cu-Co and Pb-Zn deposits. The T2 fluid is similar to the ore fluids of some vein-type Cu deposits along the western margin of the Lanping Basin. Considering that the T2 hydrothermal veins are free of sulfides and the fluid inclusions from the vein-type Cu deposits in this region either contain CO2 or do not contain CO2, the abundance of reduced sulfur rather than CO2 determines whether the reduced fluids in this area are capable of causing Cu mineralization. Different from T1, Mg and TFe2O3 were introduced into the bleached rocks at T2 site. This possibly resulted from interaction between the migration fluids and widespread mafic volcanic rocks in this region. Enrichment degree of Cu is apparently greater than those of Co, Zn, and Pb in the bleached rocks, which is consistent with the Cu-only mineralization for the vein-type Cu deposits in this area.
Panoptic Driving Perception Using Deep Learning Techniques

The technology of autonomous driving allows vehicles to operate independently without the intervention of a human driver. This system relies on sensors and algorithms to execute driving functions, such as braking, steering and accelerating, by interpreting the vehicle’s environment and making decisions accordingly. A critical component of the autonomous driving system is the panoptic driving perception system, which offers a comprehensive understanding of the surrounding environment to the vehicle, leading to advancements in autonomous driving technology. We introduce a panoptic driving perception network that performs drivable area segmentation, traffic object detection, lane detection and distance computation concurrently. The model includes three decoders for specialized tasks and an encoder for feature extraction. The model has delivered exceptional results on the demanding BDD100K dataset by achieving state-of-the-art performance in speed and accuracy for all four tasks, making it the first study to process these four simultaneous visual perception tasks while maintaining high precision. Traditionally, Ultrasonic sensors like HC-sr04 have been used to measure the distance of objects. However, this approach may not be practical when designing embedded devices requiring both object detection (via a camera) and distance measurement capabilities. Fortunately, there is an alternative approach to calculate the distance of an object from the camera by using the depth information utilized for object detection through bounding boxes.
SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge

Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts, leveraging vast amounts of multimodal data available on social media platforms. Studying and investigating social media popularity becomes central to various online applications and requires novel methods of comprehensive analysis, multimodal comprehension, and accurate prediction.
Multiply-and-Fire: An Event-Driven Sparse Neural Network Accelerator

Deep neural network inference has become a vital workload for many systems from edge-based computing to data centers. To reduce the performance and power requirements for deep neural networks (DNNs) running on these systems, pruning is commonly used as a way to maintain most of the accuracy of the system while significantly reducing the workload requirements. Unfortunately, accelerators designed for unstructured pruning typically employ expensive methods to either determine non-zero activation-weight pairings or reorder computation. These methods require additional storage and memory accesses compared to the more regular data access patterns seen in structurally pruned models. However, even existing works that focus on the more regular access patterns seen in structured pruning continue to suffer from inefficient designs, which either ignore or expensively handle activation sparsity leading to low performance. To address these inefficiencies, we leverage structured pruning and propose the multiply-and-fire (MnF) technique, which aims to solve these problems in three ways: (a) the use of a novel event-driven dataflow that naturally exploits activation sparsity without complex, high-overhead logic; (b) an optimized dataflow takes an activation-centric approach, which aims to maximize the reuse of activation data in computation and ensures the data are only fetched once from off-chip global and on-chip local memory; and (c) based on the proposed event-driven dataflow, we develop an energy-efficient, high-performance sparsity-aware DNN accelerator. Our results show that our MnF accelerator achieves a significant improvement across a number of modern benchmarks and presents a new direction to enable highly efficient AI inference for both CNN and MLP workloads. Overall, this work achieves a geometric mean of 11.2× higher energy efficiency and 1.41× speedup compared to a state-of-the-art sparsity-aware accelerator.
Irradiance and cloud optical properties from solar photovoltaic systems

Abstract. Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. The method is tested on data from two measurement campaigns that took place in the Allgäu region in Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 min resolution along with a non-linear photovoltaic module temperature model, global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 5.79 W m−2 (7.35 W m−2) under clear (cloudy) skies, averaged over the two campaigns, whereas for the retrieval using coarser 15 min power data with a linear temperature model the mean bias error is 5.88 and 41.87 W m−2 under clear and cloudy skies, respectively. During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a 1D radiative transfer simulation, and the results are compared to both satellite retrievals and data from the Consortium for Small-scale Modelling (COSMO) weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken-cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work.
Event-Based Pupil Tracking Using Bright and Dark Pupil Effect

Real-time high-speed gaze estimation can enable next-generation gaze-based interaction. The event camera, which captures intensity variations at high frequency, has been employed to this end. However, pupil tracking based only on events is difficult because events are sparse and limited. We propose high-speed pupil tracking using an event camera based on the bright and dark pupil effect. Two illumination sources generate events in the pupil area, and the pupil center is determined in real time at over 2000 Hz without requiring complete image from the events. We implemented gaze target estimation using smooth-pursuit eye movements and confirmed high-speed pupil tracking that may reduce the delay in gaze-based interaction.
Advanced Blockchain Applications in Cryptocurrency and Various Sectors

Blockchain innovation has essentially progressed in later a long time, advertising implies to perform secure and open exchanges without the utilize of agents. The paper offers a intensive examination of the blockchain technology's conceivable outcomes within the cryptocurrency and other related businesses. It looks at blockchain's potential for advancement and modern employments, as well as how cryptocurrencies are made utilizing it. The think about covers the advancement of unused cryptocurrencies, which are more secure, straightforward, and straightforward to exchange than customary currencies and are developing increasingly well known. It too looks at how blockchain innovation is being utilized to change healthcare information security and supply chain administration frameworks. It considers the improvements in blockchain innovation, such as the creation of keen contracts, which can computerize resource exchanges and dispense with the require for brokers. The investigate report offers a exhaustive examination of blockchain technology's potential, illustrating its capacity to upend set up arrange and change how we do trade within the future.
A Machine Learning Approach for Voice Modelling and Identification

The study of nonlinear and dynamic systems, as well as the field of practical systems, both make system identification extremely important. Because the vast majority of practical systems do not have any previous information on their behaviour, mathematical modelling is essential. To address this issue of system identification, the authors in this work suggest a Deep-Extreme Learning Machine (DELM) model trained on discrete wavelet transform coefficients. A voice model is considered, similar to the Autoregressive model (AR), for identification. The goal is to create a voice model using machine learning techniques and its identification with level-3 Haar DWT coefficients of the voice signal. The results of the comparison are presented in the result section. The mean square error (MSE) is the metric that is used to assess performance. The proposed DELM model outperforms the models developed with RNN, Bi-LSTM, and LPC-RBF. The findings of this work are able to improve prediction accuracy while reducing convergence time.
When Perceptual Authentication Hashing Meets Neural Architecture Search

In recent years, many perceptual authentication hashing schemes have been proposed, especially for image content authentication. However, most of the schemes directly use the dataset of image processing during model training and evaluation, which is actually unreasonable due to the task difference. In this paper, we first propose a specialized dataset for perceptual authentication hashing of images (PAHI), and the image content-preserving manipulations used in this dataset are richer and more in line with realistic scenarios. Then, in order to achieve satisfactory perceptual robustness and discrimination capability of PAHI, we exploit the continuous neural architecture search (NAS) on the channel number and stack depth of the ConvNeXt architecture, and obtain two PAHI architectures i.e., NASRes and NASCoNt. The former has better overall performance, while the latter is better for some special manipulations such as image cropping and background overlap. Experimental results demonstrate that our architectures both can achieve competitive results compared with SOTA schemes, and the AUC areas are increased by 1.6 (NASCoNt) and 1.7 (NASRes), respectively.
CropCap: Embedding Visual Cross-Partition Dependency for Image Captioning

Transformer-based approaches to image captioning have shown great success by utilizing long-term dependency for visual embedding. However, their coarse long-term dependency, using the multi-head self-attention mechanism to capture the contextual interactions between the visual tokens on the time step and (or) embedded dimension, fail to distinguish fine-grained features of local partition. In this case, some similar features are captured, which leads to feature redundancy that decreases the performance. To respond to this issue, this paper proposes a novel image captioner embedding visual cross-partition dependency, dubbed CropCap. Specifically, the visual sequence generated from the Swin Transformer-based pre-embedding network is fed into the proposed cross-partition dependency module to refinedly model the interaction between partial representations on both the time step and embedded dimension. Furthermore, we formulaically reason the proposed cross-partition dependency, and theoretically prove its correctness. Extensive comparisons on the benchmark MS-COCO dataset demonstrated the effectiveness addressing the information redundancy issue, and verified the superior performance of our method.
Reinforcement Graph Clustering with Unknown Cluster Number

Deep graph clustering, which aims to group nodes into disjoint clusters by neural networks in an unsupervised manner, has attracted great attention in recent years. Although the performance has been largely improved, the excellent performance of the existing methods heavily relies on an accurately predefined cluster number, which is not always available in the real-world scenario. To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC). In our proposed method, cluster number determination and unsupervised representation learning are unified into a uniform framework by the reinforcement learning mechanism. Concretely, the discriminative node representations are first learned with the contrastive pretext task. Then, to capture the clustering state accurately with both local and global information in the graph, both node and cluster states are considered. Subsequently, at each state, the qualities of different cluster numbers are evaluated by the quality network, and the greedy action is executed to determine the cluster number. In order to conduct feedback actions, the clustering-oriented reward function is proposed to enhance the cohesion of the same clusters and separate the different clusters. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method. The source code of RGC is shared at https://github.com/yueliu1999/RGC and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github.
Explicit Model Predictive Control Design based on Constrained Zonotope Propagation

Zonotopes have become a widely used tool in control design, demonstrating their effectiveness in various applications ranging from state estimation to explicit model predictive control. However, the traditional use of zonotopes is limited by a set certain geometric shapes that restrict the range of state sets that can be considered. This paper proposes a novel approach to control design using constrained zonotopes that can encode arbitrary polytopes. We present a propagation scheme and an affine approximation of the exact control law for this representation, and discuss issues related to center identification for constrained zonotopes. A fast method for detecting set membership is also proposed based on lifting the constrained zonotopes into a higher dimensional space. The proposed method extends the range of shapes that can be considered in control design and offers a promising alternative to the limitations of regular zonotopes. It has the potential for wide-ranging impact in various areas of control, providing a valuable tool for solving complex control problems.
Speaker Anonymity and Voice Conversion Vulnerability: A Speaker Recognition Analysis

Anonymized voice data is essential for maintaining privacy in voice-based exchanges of sensitive information, particularly in healthcare. However, voice conversion methods prioritize target voice identity over completely obscuring the source speaker’s voice features. Recent advancements in speaker recognition systems have increased their ability to detect subtle residual voice features of the source speaker in voice-converted samples with greater precision and accuracy, posing potential risks to voice anonymity. Balancing speaker anonymity and recognition accuracy is a persistent challenge in voice-based applications, where maintaining voice anonymity and correctly identifying the speaker are critical but vulnerable. Violating voice anonymity can result in privacy and security threats. In this work, we examine multiple voice conversion and speaker recognition systems to explore the threats to voice anonymity. Our findings demonstrate the significant risk of identifying source speakers from converted voice samples. We discovered that voice anonymity is more vulnerable to breaking with one-to-one conversions compared to many-to-many and any-to-any conversions. The likelihood of identifying the original speaker from anonymized speech data depends on target voice features, voice conversion techniques, and speaker recognition methods.
Palette-PrintAR: an augmented reality fluidic design tool for multicolor resin 3D printing

poster Share on Palette-PrintAR: an augmented reality fluidic design tool for multicolor resin 3D printing Authors: Gabriel Lipkowitz Mechanical Engineering, Stanford University, United States Mechanical Engineering, Stanford University, United States 0000-0003-4869-6804View Profile , Eric Shaqfeh Mechanical Engineering, Stanford University, United States Mechanical Engineering, Stanford University, United States 0000-0001-8241-2946View Profile , Joseph Desimone Chemical Engineering, Stanford University, United States Chemical Engineering, Stanford University, United States 0000-0002-1764-8634View Profile Authors Info & Claims UIST '23 Adjunct: Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and TechnologyOctober 2023Article No.: 47Pages 1–3https://doi.org/10.1145/3586182.3616684Published:29 October 2023Publication History 0citation97DownloadsMetricsTotal Citations0Total Downloads97Last 12 Months97Last 6 weeks83 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Knowledge Compass: A Question Answering System Guiding Students with Follow-Up Question Recommendations

Pedagogical question-answering (QA) systems have been utilized for providing individual support in online learning courses. However, existing systems often neglect the education practice of guiding and encouraging students to think of relevant questions for deeper and more comprehensive learning. To address this gap, we introduce Knowledge Compass, an interactive QA system. The system can recommend follow-up questions that provide potential further explorations of the topics students ask about. Additionally, the system applies a course outline visualization and a set of interactive features for students to track the relationship between their questions and the course content.