Reviewerly/exHarmony huggingface dataset:
Adaptive Graph Representation for Clustering

Many graph construction methods cannot consider both local and global data structures in the construction of initial graph. Meanwhile, redundant features or even outliers and data with important characteristics are addressed equally in the graph optimization process. These lead to the learned representation graph may not capture the optimal structure. This paper proposes a novel graph learning method, called ACLWN, to overcome these problems. ACLWN is composed of an adaptive representation graph construction model named ARG, and an adaptive weighted sparse representation graph learning model named AWSG. In ARG, manifold learning and sparse representation are employed to capture the local structure of data. In AWSG, an adaptive weighted matrix is proposed to strengthen the important features and improve the robustness of the low-dimensional representation graph. Moreover, constraints such as non-negative low-rank, sparsity and distance regularization terms are imposed to capture the local and global structures of data. Comprehensive experimental results show that our method outperforms the compared state-of-the-art methods. The low-dimensional representation graph constructed by ACLWN is more suitable for clustering.
FEDARGOS-V1: A Monitoring Architecture for Federated Cloud Computing Infrastructures

Resource management in cloud infrastructure is one of the key elements of quality of services provided by the cloud service providers. Resource management has its taxonomy, which includes discovery of resources, selection of resources, allocation of resources, pricing of resources, disaster management, and monitoring of resources. Specifically, monitoring provides the means of knowing the status and availability of the physical resources and services within the cloud infrastructure. This results in making “monitoring of resources” one of the key aspects of the cloud resource management taxonomy. However, managing the resources in a secure and scalable manner is not easy, particularly when considering a federated cloud environment. A federated cloud is used and shared by many multi-cloud tenants and at various cloud software stack levels. As a result, there is a need to reconcile all the tenants’ diverse monitoring requirements. To cover all aspects relating to the monitoring of resources in a federated cloud environment, we present the FEDerated Architecture for Resource manaGement and mOnitoring in cloudS Version 1.0 (FEDARGOS-V1), a cloud resource monitoring architecture for federated cloud infrastructures. The architecture focuses mainly on the ability to access information while monitoring services for early identification of resource constraints within short time intervals in federated cloud platforms. The monitoring architecture was deployed in a real-time OpenStack-based FEDerated GENomic (FEDGEN) cloud testbed. We present experimental results in order to evaluate our design and compare it both qualitatively and quantitatively to a number of existing Cloud monitoring systems that are similar to ours. The architecture provided here can be deployed in private or public federated cloud infrastructures for faster and more scalable resource monitoring.
Examining Actual Effects of a Tangible Tool on Children's Collaboration

Tangible user interfaces create a novel collaborative environment for children to interact with physical objects with augmented computing technology jointly. However, most previous studies only did short-term experiments. Moreover, we still need to examine the actual effects of tangible tools on children's collaboration. Thus, we designed, developed, and evaluated a tangible collaborative tool called SpellBoard. We conducted user studies for two weeks to see the actual effects. We dug out three key findings: 1) Interactive constraints can be specially designed to allow children to coordinate their collaborative actions; 2) From an actual usage effect, we need to redesign SpellBoard to consider children's cognitive engagement and interdependency in collaborative activities; 3) Children need time to understand the tangible design fully. Thus, we need a frame-work to add new learning content to sustain their engagements. Our findings could improve the future tangible design and make it have a good long-term impact on children's collaborative learning.
An Enhanced EWMA for Alert Reduction and Situation Awareness in Industrial Control Networks

Intrusion detection systems (IDSs) are widely deployed in the industrial control systems to protect network security. IDSs typically generate a huge number of alerts, which are time-consuming for system operators to process. Most of the alerts are individually insignificant false alarms. However, it is not the best solution to discard these alerts, as they can still provide useful information about network situation. Based on the study of characteristics of alerts in the industrial control systems, we adopt an enhanced method of exponentially weighted moving average (EWMA) control charts to help operators in processing alerts. We classify all detection signatures as regular and irregular according to their frequencies, set multiple control limits to detect anomalies, and monitor regular signatures for network security situational awareness. Extensive experiments have been performed using real-world alert data. Simulation results demonstrate that the proposed enhanced EWMA method can greatly reduce the volume of alerts to be processed while reserving significant abnormal information.
Secure Non-interactive Simulation from Arbitrary Joint Distributions

Secure non-interactive simulation (SNIS), introduced in EUROCRYPT 2022, is the information-theoretic analog of pseudo-correlation generators. SNIS allows parties, starting with samples of a source correlated private randomness (correlation), to non-interactively and securely transform them into samples from a different correlation. This work studies SNIS of binary symmetric or erasure correlations from any arbitrary source correlation. In this context, our work presents: These results collectively yield the fascinating instances of computer-assisted search for secure computation protocols that identify ingenious protocols that are more efficient than all known constructions. Our work generalizes the algebraization of the simulation-based definition of SNIS as an approximate eigenvector problem. The following technical contributions are the underpinnings of the results above. Our technical analysis relies on Fourier analysis over large alphabets with arbitrary measure, the orthogonal Efron-Stein decomposition, and junta theorems. Our technical approach motivates the new problem of "security-preserving dimension reduction" in harmonic analysis, which may be of independent interest.
NeXT: Towards High Quality Neural Radiance Fields via Multi-skip Transformer

Neural Radiance Fields (NeRF) methods show impressive performance for novel view synthesis by representing a scene via a neural network. However, most existing NeRF based methods, including its variants, treat each sample point individually as input, while ignoring the inherent relationships between adjacent sample points from the corresponding rays, thus hindering the reconstruction performance. To address this issue, we explore a brand new scheme, namely NeXT, introducing a multi-skip transformer to capture the rich relationships between various sample points in a ray-level query. Specifically, ray tokenization is proposed to represent each ray as a sequence of point embeddings which is taken as input of our proposed NeXT. In this way, relationships between sample points are captured via the built-in self-attention mechanism to promote the reconstruction. Besides, our proposed NeXT can be easily combined with other NeRF based methods to improve their rendering quality. Extensive experiments conducted on three datasets demonstrate that NeXT significantly outperforms all previous state-of-the-art work by a large margin. In particular, the proposed NeXT surpasses the strong NeRF baseline by 2.74 dB of PSNR on Blender dataset. The code is available at https://github.com/Crishawy/NeXT .
Human Sign Language Recognition Using Deep Learning

Disability of deaf and dumb is one of the major significant problems in the human world. The development of technologies for recognition of deaf and dumb impaired peoples is done by several methodologies. One of the most significant components in helping sign language users to communicate with the rest of society is recognizing sign language. Machine learning and image categorization can help computers in recognizing sign language, which can subsequently be deciphered by others. In this paper, convolutional neural networks were used to recognize sign language motions by recognizing the symbolic hand gestures of deaf and dumb impaired peoples. Deaf and dumb persons acquire sign language, which is largely unknown to normal people. This makes it challenging for regular people to communicate with disabled people who are deaf and dumb. The main focus of our paper is to make predictions of hand gestures from deaf and dumb peoples. Our paper recognizes different types of hand gestures as input and make predictions with accuracy level based on pre-trained dataset models by using Convolutional Neural Network (CNN) Algorithm.
Face Mask Detection using an Automated Hybrid Deep Learning Method in the COVID Scenario

Currently, in light of the health catastrophe due to the COVID-19 which has been unfolding all over the world. Wearing a defensive mask has ended up a substitute normal. Face recognition technology is most commonly implemented for surveillance and other applications. Traditional machine learning classifiers as well as deep transfer learning classifiers have been used to accomplish the face mask detection mechanism. In this paper, two hybrid deep learning models MobileNetV2-SVM and MobilNetV2-KNN has been proposed for the task of face mask detection. The models involve two processes: feature extraction and classification. For initialization, the MobileNetV2 pre-trained weights from ImageNet were employed, and during training, data augmentation and resampling were applied. By integrating the model with an SVM classifier and a KNN classifier, the model is further refined, creating hybrid models that are effective in terms of processing. The Kaggle dataset of 45000 images (22582 images are masked and 23423 images that are unmasked) of the proposed model/system is trained using MobilenetV2 and classified using SVM and K-NN algorithm in different models. Various machine learning frameworks were used like pandas, TensorFlow, Keras and NumPy. The accuracy achieved by the SVM model is 98.17% and 95.22% accuracy are achieved by using the K-NN classifier.
ASD-ISSPA:Adaptive Stochastic Droppath and Interactive Slow Semi-Polarized Attention based on FPN for Object Detection

Feature Pyramid Network (FPN) has been widely used to combine features at different scales for extracting semantically strong representations in object detection. However, most of the previous approaches only focus on the original image resolution to allocate pyramidal layers without considering the image size change in the processing network. In this work, an algorithm is proposed to determine the feature layer based on the ratio of the target box area to the original image area, which improves the network adaptability to the scale change. Further-more, a novel Adaptive Stochastic Droppath and Interactive Slow Semi-Polarized Attention (ASD-ISSPA) is proposed to retain the valuable information in the feature maps of FPN. ASD-ISSPA can be split into two parts, Adaptive Stochastic Droppath Attention (ASDA) and Interactive Slow Semi-Polarized Attention (ISSPA). The ASDA module acts on the bottom-up pathway in the pyramid to collect the loss information after convolution, while the ISSPA module deals with the top-down path in the pyramid to capture the semantics before upsampling and the interaction details after upsampling. The integrated two-path attention mechanism effectively regains the loss information of the convolution and removes the redundancy of the deconvolution so that our model can improve the category prediction accuracy and reduce the discrepancy between the predicted and ground-truth boxes. Compared with existing methods, the proposed attention extract network, called ASD-ISSPA, achieves competitive results on the PASCAL VOC dataset.
Gated Relational Encoder-Decoder Model for Target-Oriented Opinion Word Extraction

Target-Oriented Opinion Word Extraction (TOWE) is a challenging information extraction task that aims to find the opinion words corresponding to given opinion targets in text.To solve TOWE, it is important to consider the surrounding words of opinion words as well as the opinion targets.Although most existing works have captured the opinion target using Deep Neural Networks (DNNs), they cannot effectively utilize the local context, i.e. relationship among surrounding words of opinion words.In this work, we propose a novel and powerful model for TOWE, Gated Relational target-aware Encoder and local context-aware Decoder (GRED), which dynamically leverages the information of the opinion target and the local context.Intuitively, the target-aware encoder catches the opinion target information, and the local context-aware decoder obtains the local context information from the relationship among surrounding words.Then, GRED employs a gate mechanism to dynamically aggregate the outputs of the encoder and the decoder.In addition, we adopt a pretrained language model Bidirectional and Auto-Regressive Transformer (BART), as the structure of GRED to improve the implicit language knowledge.Extensive experiments on four benchmark datasets show that GRED surpasses all the baseline models and achieves state-ofthe-art performance.Furthermore, our in-depth analysis demonstrates that GRED properly leverages the information of the opinion target and the local context for extracting the opinion words.
Contrastive Learning for Diverse Disentangled Foreground Generation

We introduce a new method for diverse foreground generation with explicit control over various factors. Existing image inpainting based foreground generation methods often struggle to generate diverse results and rarely allow users to explicitly control specific factors of variation (e.g., varying the facial identity or expression for face inpainting results). We leverage contrastive learning with latent codes to generate diverse foreground results for the same masked input. Specifically, we define two sets of latent codes, where one controls a pre-defined factor ("known"), and the other controls the remaining factors ("unknown"). The sampled latent codes from the two sets jointly bi-modulate the convolution kernels to guide the generator to synthesize diverse results. Experiments demonstrate the superiority of our method over state-of-the-arts in result diversity and generation controllability.
You Only Look &amp; Listen Once: Towards Fast and Accurate Visual Grounding

Visual Grounding (VG) aims to locate the most relevant region in an image, based on a flexible natural language query but not a pre-defined label, thus it can be a useful technique in practice. Most methods in VG operate in a two-stage manner, wherein the first stage an object detector is adopted to generate a set of object proposals from the input image and the second stage is simply formulated as a cross-modal matching problem. There might be hundreds of proposals produced in the first stage that need to be compared in the second stage, which is infeasible for real-time VG applications, and the performance of the second stage may be affected by the first stage. In this paper, we propose a much more elegant one-stage detection based method that joints the region proposal and matching stage as a single detection network. The detection is conditioned on the input query with a stack of novel Relation-to-Attention modules that transform the image-to-query relationship to a relation map, which is used to predict the bounding box directly without proposing large numbers of useless region proposals. During the inference, our approach is about 20 x ~ 30 x faster than previous methods and, remarkably, it achieves comparable performance on several benchmark datasets.
On Guiding Visual Attention with Language Specification

While real world challenges typically define visual categories with language words or phrases, most visual classification methods define categories with numerical indices. However, the language specification of the classes provides an especially useful prior for biased and noisy datasets, where it can help disambiguate what features are task-relevant. Recently, large-scale multimodal models have been shown to recognize a wide variety of high-level concepts from a language specification even without additional image training data, but they are often unable to distinguish classes for more fine-grained tasks. CNNs, in contrast, can extract subtle image features that are required for fine-grained discrimination, but will overfit to any bias or noise in datasets. Our insight is to use high-level language specification as advice for constraining the classification evidence to task-relevant features, instead of distractors. To do this, we ground task-relevant words or phrases with attention maps from a pretrained large-scale model. We then use this grounding to supervise a classifier's spatial attention away from distracting context. We show that supervising spatial attention in this way improves performance on classification tasks with biased and noisy data, including ~3 −15% worst-group accuracy improvements and ~41-45% relative improvements on fairness metrics.
Confidence Evaluation for Machine Learning Schemes in Vehicular Sensor Networks

In this paper, we study a cooperative perception scheme in a vehicular sensor network, attempting to fuse the semantic information provided by different sensors at multiple vehicles, so as to expand the vehicle’s perception range, eliminate blind spots, improve the ability to handle environmental interference and enhance the accuracy and robustness of the perception results. The key to guide the fusion process is the evaluation of the confidence levels of the outputs provided by various machine learning schemes implemented at individual sensors in the vehicular sensor network. We first propose an evaluation criterion termed as Environmental Sensitivity (ES), which is used to measure the sensitivity of the network to environmental changes. Based on the ES, we further evaluate the confidence of the perception output of neural networks and quantify the confidence level considering the abnormal level of the input data, the general performance of the perception algorithm, the detection performance and the ES of the network. Semantic information fusion algorithm is then developed based upon the confidence levels. Experiment results are provided to validate the proposed fusion method in various scenarios.
Introduction to the theory of stochastic processes: Studying the basic concepts

Models of stochastic processes describe many phenomena in nature, technology, and economics. However, STEM and economics students usually do not have enough time to study this topic. When developing a course on stochastic processes, a teacher faces some challenges. The task is to present sophisticated educational material, combining the mathematical severity of presentation and ease of understanding. Throughout several lessons, students should get a holistic understanding of the subject so that they can correctly and meaningfully apply models of stochastic processes in the field of their professional activities. The complicated nature of stochastic processes models requires understanding their multidimensionality, facilitated by various representation methods: formulas, tables, graphs, and computer simulation. The article discusses applying these techniques on the example of the formation of the concepts "stochastic process" "mathematical expectation and variance of a stochastic process."
Efficient Multi-Task Learning via Iterated Single-Task Transfer

In order to be effective general purpose machines in real world environments, robots not only will need to adapt their existing manipulation skills to new circumstances, they will need to acquire entirely new skills on-the-fly. One approach to achieving this capability is via Multi-task Reinforcement Learning (MTRL). Most recent work in MTRL trains a single policy to solve all tasks at once. In this work, we investigate the feasibility of instead training separate policies for each task, and only transferring from a task once the policy for it has finished training. We describe a method of finding near optimal sequences of transfers to perform in this setting, and use it to show that performing the optimal sequence of transfer is competitive with other MTRL methods on the Meta World MT10 benchmark. Lastly, we describe a method for finding nearly optimal transfer sequences during training that is able to improve on training each task from scratch.
A Compiler for Transparent Namespace-Based Access Control for the Zeno Architecture

With memory safety and security issues continuing to plague modern systems, security is rapidly becoming a first class priority in new architectures and competes directly with performance and power efficiency. The capability-based architecture model provides a promising solution to many memory vulnerabilities by replacing plain addresses with capabilities, i.e., addresses and related metadata. A key advantage of the capability model is compatibility with existing code bases. Capabilities can be implemented transparently to a programmer, i.e., without source code changes. Capabilities leverage semantics in source code to describe access permissions but require customized compilers to translate the semantics to their binary equivalent.In this work, we introduce a complete capabilityaware compiler toolchain for such secure architectures. We illustrate the compiler construction with a RISC-V capability-based architecture, called Zeno. As a securityfocused, large-scale, global shared memory architecture, Zeno implements a Namespace-based capability model for accesses. Namespace IDs (NSID) are encoded with an extended addressing model to associate them with access permission metadata elsewhere in the system. The NSID extended addressing model requires custom compiler support to fully leverage the protections offered by Namespaces. The Zeno compiler produces code transparently to the programmer that is aware of Namespaces and maintains their integrity. The Zeno assembler enables custom Zeno instructions which support secure memory operations. Our results show that our custom toolchain moderately increases the binary size compared to nonZeno compilation. We find the minimal overhead incurred by the additional NSID management instructions to be an acceptable trade-off for the memory safety and security offered by Zeno Namespaces.
Multi-agent Weapon Target Assignment and Cooperative Path Planning Based on QS-RRT Algorithm

In this paper, based on the typical combat scenario where multiple UAVs strike multiple targets, the model of weapon firepower allocation is firstly modeled to obtain the corresponding objective function, and then the improved distributed auction algorithm is used to solve the objective function to obtain the multi-target allocation result. Planning layer first analyze the traditional RRT algorithm, and gets the quick and smooth convergence RRT (QS-RRT) on the basis of improved algorithm, the algorithm on the basis of adding the synergy of constraints, and makes the collaborative planning path compared with non-cooperative path planning, the planning path length in the keep basically remain unchanged, on the basis of our UAV for the same target can arriving together, The difference of arrival time is greatly reduced, and the subsequent coordinated strike mission can be completed more efficiently in the combat scenario.