Reviewerly/exHarmony huggingface dataset:
Efficient Certificateless Ring Signcryption Scheme with Constant Ciphertext Length on Blockchain

Ring signcryption schemes have been proposed on blockchain, but compared with ring signcryption using public key infrastructure (PKI), certificateless can simplify the certificate management process. At the same time, the length of the ciphertext increases with the size of the ring is also a pressing challenge to be solved. In this paper, we propose a new certificateless ring signcryption scheme with constant ciphertext length and prove the security under the random oracle model. Compared with other schemes, the computational overhead of this scheme is smaller and more efficient.
Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

The a priori probability of a dataset is usually used as a baseline for comparing a particular algorithm’s accuracy in a given binary classification task. ZeroR is the simplest algorithm for this, predicting the majority class for all examples. However, this is an extremely simple approach that has no predictive power and does not describe other dataset features that could lead to a more demanding baseline. In this paper, we present the Extended A Priori Probability (EAPP), a novel semi-supervised baseline metric for binary classification tasks that considers not only the a priori probability but also some possible bias present in the dataset as well as other features that could provide a relatively trivial separability of the target classes. The approach is based on the area under the ROC curve (AUC ROC), known to be quite insensitive to class imbalance. The procedure involves multiobjective feature extraction and a clustering stage in the input space with autoencoders and a subsequent combinatory weighted assignation from clusters to classes depending on the distance to nearest clusters for each class. Class labels are then assigned to establish the combination that maximizes AUC ROC for each number of clusters considered. To avoid overfit in the combined feature extraction and clustering method, a cross-validation scheme is performed in each case. EAPP is defined for different numbers of clusters, starting from the inverse of the minority class proportion, which is useful for a fair comparison among diversely imbalanced datasets. A high EAPP usually relates to an easy binary classification task, but it also may be due to a significant coarse-grained bias in the dataset, when the task is previously known to be difficult. This metric represents a baseline beyond the a priori probability to assess the actual capabilities of binary classification models.
No Budget, No Barriers

This chapter provides a roadmap for Twitter use by academic libraries at a branch, remote, or satellite campus. These smaller libraries, operating at a distance from the center of their institution, often face challenges relating to their budget, resources, and status. The use of Twitter can empower a branch to serve their unique populations more effectively, promote engagement within the local community, and establish a presence for the branch within the institution. The authors provide strategies and suggestions for managing Twitter based upon their own experiences while managing the Southampton Campus Library of Stony Brook University Twitter account.
Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics

Language modality within the vision language pre-training framework is innately discretized, endowing each word in the language vocabulary a semantic meaning. In contrast, visual modality is inherently continuous and high-dimensional, which potentially prohibits the alignment as well as fusion between vision and language modalities. We therefore propose to "discretize" the visual representation by joint learning a codebook that imbues each visual token a semantic. We then utilize these discretized visual semantics as self-supervised ground-truths for building our Masked Image Modeling objective, a counterpart of Masked Language Modeling which proves successful for language models. To optimize the codebook, we extend the formulation of VQ-VAE which gives a theoretic guarantee. Experiments validate the effectiveness of our approach across common vision-language benchmarks.
Efficient Meta-Learning for Continual Learning with Taylor Expansion Approximation

Continual learning aims to alleviate catastrophic forgetting when handling consecutive tasks under non-stationary distributions. Gradient-based meta-learning algorithms have shown the capability to implicitly solve the transfer-interference trade-off problem between different examples. However, they still suffer from the catastrophic forgetting problem in the setting of continual learning, since the past data of previous tasks are no longer available. In this work, we propose a novel efficient meta-learning algorithm for solving the online continual learning problem, where the regularization terms and learning rates are adapted to the Taylor approximation of the parameter's importance to mitigate forgetting. The proposed method expresses the gradient of the meta-loss in closed-form and thus avoid computing second-order derivative which is computationally inhibitable. We also use Proximal Gradient Descent to further improve computational efficiency and accuracy. Experiments on diverse benchmarks show that our method achieves better or on-par performance and much higher efficiency compared to the state-of-the-art approaches.
Image Processing

Computer graphics are stored and processed as either vector or raster data. Most of the data encountered in the previous chapter were vector data, including points, lines, and polygons. Examples of vector data include drainage networks, the outlines of geologic units, sampling locations, and topographic contours.
A Framework for Evaluating MRC Approaches with Unanswerable Questions

Machine reading comprehension (MRC) is a challenging task in natural language processing that demonstrates the language understanding of the machine. An approach to tackle this challenge requires the machine to answer the question about the given context when needed and abstain from answering when there is no answer. Recent works attempted to solve this challenge with various comprehensive neural network architectures for sequences such as SAN, U-Net, EQuANt, and others that were trained on the SQuAD 2.0 dataset containing unanswerable questions. However, the robustness of these approaches has not been evaluated. In this paper, we propose a data augmentation approach that converts answerable questions to unanswerable questions in the SQuAD 2.0 dataset by altering the entities in the question to its antonym from ConceptNet which is a semantic network. The augmented data is, then, fitted into the U-Net question answering model to evaluate the robustness of the model.
Zero-Shot Image Dehazing Using Pseudo Atmospheric Light Image

Abundant training data for deep neural networks improve the performance of single image dehazing (SID) substantially, however they suffer from the domain-shift problem of the discrepancy between the training set and the test set. In this paper, we propose a zero-shot SID method to overcome the domain-shift problem in a self-supervised learning manner. We employ two generator networks to estimate the transmission and the original scene radiance, respectively, from an input hazy image. We also synthesize the pseudo atmospheric light image (PALI) to train the transmission generator to assign zero transmission values to PALI. Since the pseudo-sky patches and the dense-hazy region rarely have the structural textures, the network learns the dense-hazy property from the PALI in a self-supervision learning manner. The experimental results show that the proposed method faithfully restores the scene radiance image, and the PALI loss is effective to train the deep neural network.
Leachable Component Clustering

Clustering attempts to partition data instances into several distinctive groups, while the similarities among data belonging to the common partition can be principally reserved. Furthermore, incomplete data frequently occurs in many real-world applications, and brings perverse influence on pattern analysis. As a consequence, the specific solutions to data imputation and handling are developed to conduct the missing values of data, and independent stage of knowledge exploitation is absorbed for information understanding. In this work, a novel approach to clustering of incomplete data, termed leachable component clustering, is proposed. Rather than existing methods, the proposed method handles data imputation with Bayes alignment, and collects the lost patterns in theory. Due to the simple numeric computation of equations, the proposed method can learn optimized partitions while the calculation efficiency is held. Experiments on several artificial incomplete data sets demonstrate that, the proposed method is able to present superior performance compared with other state-of-the-art algorithms.
Hand-object Interaction based Semi-automatic Objects Annotation for Human Activity Datasets

Objects annotation is a prerequisite for learning-based detectors to locate and extract object semantic information from the scene. This provides a significant contribution to human activity recognition (HAR), which is a core study in computer vision and assistive robotics area. The current dominant HAR approaches focus on the end-to-end model using RGB images or optical flow stream as input rather than using semantic human-object interaction information. This is mainly due to the lacking of object annotations in the current HAR datasets. To this end, we propose a novel hand-object interaction based approach to semi-automatic annotating objects in videos. The proposed approach annotates the objects by mapping the trajectories of the hands to the trajectories of the object bounding boxes, and smoothen the track with the Kalman filter. Unlike existing methods, our approach requires only a few clicks for annotations without fine-tuning any object detection models after annotating objects manually. Our experimental results on the Bimanual Actions Dataset achieve 78.81% accuracy compared with the ground truth objects annotation at an intersection over union (IoU) of 0.5. This outperforms the state-of-the-art tracker by 7.15%, and reduces manual annotation workload by 92.86%.
Deep Learning-Based Recognition of Human Training States During Sit-to-Stand Transfer

The monitoring methods used in traditional lower extremity rehabilitation require complex preparation of the user, which is not only tedious but also prone to secondary injuries. To address this problem, this paper establishes a deep learning algorithm for image recognition to classify and recognize the state features of the human body in the process of sit-stand transfer in the motion phase, firstly, the state images are preprocessed to improve the computing efficiency; then the data are broadened to improve the accuracy of recognition; then the image recognition is used to extract features and train the classification for each motion phase state image. Finally, the results obtained by using the optical motion capture system as a control test verified that the image recognition accuracy can reach 99.5%, meeting the purpose of monitoring the human condition during training.
Generating Natural Language Explanations for Black-Box Predictions

As the use of black-box models in our daily lives increases so does the need to explain its predictions. This creates a need for a system which can generate explanations for input-prediction pairs of any given black-box. Firstly, we highlight the challenges to be resolved to make such a system and present insights into why the given task is difficult. Then, we address some of the challenges thereby designing and implementing an explanation generation system. We draw inspiration from state-of-the-art techniques and models to create multiple variants of the proposed solution. Finally, we test and compare the variants over multiple datasets. We also briefly discuss how the unresolved challenges may be addressed. These endeavors act as stepping stones, taking us forward to build the desired explanation system.
MATGAN: Unified GANs for Multimodal Attribute Transfer by Coarse-to-Fine Disentangling Representations

Image attribute transfer aims to change an image to a target one with desired attributes. There are mainly two challenges for this task: multi-domain transfer and attribute-level multimodality. The first means editing multiple attributes using a single model and the second means diverse appearances for the target attribute. Existing methods cannot address the two problems simultaneously. Moreover, many works focus on image-level multimodality rather than attribute-level. In this paper, we propose a novel coarse-to-fine disentangling representation framework MATGAN to achieve Multimodal Attribute Transfer. In the coarse disentangling stage, we propose to embed images onto a content space and an attribute space for image-level multimodality. In the fine disentangling stage, we further disentangle the attribute space to bind with each attribute for attribute-level multimodal and multi-domain transfer. Extensive experiments demonstrate the effectiveness of our approach.
Attack on the Common Prime Version of Murru and Saettone’s RSA Cryptosystem

In this paper, we study the security bounds of d for the Common Prime version of Murru and Saettone’s RSA cyptosystem. We show that this variant of RSA can be broken if $$d<N^{\frac{3}{2}-\frac{\alpha }{2}+\epsilon }$$ , where $$\alpha =\log _Ne$$ , and $$\epsilon $$ is a small constant. By using Jochemsz and May’s extended strategy, we improve this bound to $$\delta <\min \{1,\frac{7-2\sqrt{3\alpha +1}}{3}+\epsilon \}$$ . Notice that if e is a full size exponent, the bound for d turns to be $$d<N^{0.5695+\epsilon }$$ . Compared with the bound of d in the classical Common Prime RSA cryptosystem, that is $$d<N^{\frac{1}{4}(4+4\gamma -\sqrt{13+20\gamma +4\gamma ^2})}$$ , where $$\gamma =\log _Ng<\frac{1}{2}$$ , and $$g=gcd(\frac{p-1}{2},\frac{q-1}{2})$$ , Murru and Saettone’s variant should be used with more care. Our algorithms apply Coppersmith’s method for solving trivariate polynomial equations.
A High-Resolution Human Pose Estimation Method with Coordinate Attention

Human pose estimation is an important element in video and image analysis. However, existing methods have some problems such as less accurate predicted position and over-lapping key points. To address these problems, we construct a high-resolution human pose estimation method based on Coordinate Attention (CA) mechanism that combines coordinate perception. The method constructs a coordinate attention bottleneck module (CABottleneck) and a coordinate attention module (CABlock) based on the attention mechanism, which allow the model to obtain richer coordinate and orientation information in feature extraction, and then combine the downsampling operation to convert the original image into four different sizes of feature maps, and then fuse these feature maps in multivariate resolution. Then, we use the deconvolution module to obtain the high-resolution feature maps, and finally derive the positions of each key point to achieve the estimation of human pose. The experimental results show that the accuracy of the method in this paper is better than that of various methods such as HigherHRNet when using similar number of parameters. In the CrowdPose test dataset, the accuracy of both single-scale and multi-scale exceeds 72%, which is 6.6% better than HigherHRNet; 5% better than DEKR. In the COCO2017 val dataset, our method maintains a high performance and reduces the repetition rate of human key points.
Collision and Rollover-Free g<sup>2</sup> Path Planning for Mobile Manipulation

This paper presents a path planning refinement technique that allows the efficient collision and rollover-free motion planning for mobile manipulator robots working on rough terrain. First, the necessary theoretical background on a mobile manipulator's kinematics and dynamic stability measure is introduced. Then, after the brief introduction of the sampling-based path planning problem, the additional refinement stage and its problem formulation will be introduced. Within the refinement stage, the novel Bézier control point addition method is introduced to allow for fast, collision and rollover-free path smoothing using curvature-continuous parametrized curves. Analytical proofs and simulated comparisons are provided in the paper to show effectiveness. The beneficial effect of the refined path on trajectory planning will also be demonstrated through simulation.
Online Optimized Product Quantization for ANN Queries over Dynamic Database using SVD-Updating

Approximate nearest neighbor (ANN) search allows us to perform similarity search over massive vectors with less memory and computation. Optimized Product Quantization (OPQ) is one of the state-of-the-art methods for ANN where data vectors are represented as combinations of codewords by taking into account the data distribution. However, it suffers from degradation in accuracy when the database is frequently updated with incoming data whose distribution is different. An existing work, Online OPQ, addressed this problem, but the computational cost is high because it requires to perform of costly singular value decomposition for updating the codewords. To this problem, we propose a method for updating the rotation matrix using SVD-Updating, which can dynamically update the singular matrix using low-rank approximation. Using SVD-Updating, instead of performing multiple singular value decomposition on a high-rank matrix, we can update the rotation matrix by performing only one singular value decomposition on a low-rank matrix. In the experiments, we prove that the proposed method shows a better trade-off between update time and retrieval accuracy than the comparative methods.
Part-Based Multi-Scale Attention Network for Text-Based Person Search

Text-based person search aims to retrieve the target person in an image gallery based on textual descriptions. Solving such a fine-grained cross-modal retrieval problem is very challenging due to differences between modalities. Moreover, the inter-class variance of both person images and descriptions is small, and more semantic information is needed to assist in aligning visual and textual representations at different scales. In this paper, we propose a Part-based Multi-Scale Attention Network (PMAN) capable of extracting visual semantic features from different scales and matching them with textual features. We initially extract visual and textual features using ResNet and BERT, respectively. Multi-scale visual semantics is then acquired based on local feature maps of different scales. Our proposed method learns representations for both modalities simultaneously based mainly on Bottleneck Transformer with self-attention mechanism. A multi-scale cross-modal matching strategy is introduced to narrow the gap between modalities from multiple scales. Extensive experimental results show that our method outperforms the state-of-the-art methods on CUHK-PEDES datasets.
Demonstrating quest

Machine learning models are everywhere now; but only few of them are transparent in how they work. To remedy this, local explanations aim to show users how and why learned models produce a certain output for a given input (data sample). However, most existing approaches are oriented around images or text data and, thus, cannot leverage the structure and properties of tabular data. Therefore, we demonstrate Quest, a new framework for generating explanations that are a better fit for tabular data. The main idea is to create explanations in the form of relational predicates (called queries hereafter) that approximate the behavior of a classifier around the given sample. For this demo, we use Quest on different synthetic and real-world tabular data sets and pair it with a user interface intended to be used during model development by a data scientist working on classification models.
Holistic Approach for Representation of Interaction Scenarios in Semantically Integrated Conceptual Modelling

One of the problems with conventional conceptual modelling methods is that they do not take into account certain important semantic interdependency types between the static and dynamic aspects. Integrity of dimensions is crucial for successful reasoning and solving problems that occur in conceptual modelling. Typically, conceptual modelling methods project various aspects of information systems using different graphical representations. Therefore, to reach semantic integration of various architectural aspects is very difficult. This paper presents semantically integrated conceptual modelling method. This method enables stability and flexibility of the diagrams that are very important for managing constant changes of organizational and technical requirements. It shows how alternative actions introduced into different scenarios. This is also important for controlling semantic integrity and for maintaining holistic representations of different aspects. Holistic modelling approach enables reasoning about system architecture across organizational and technical system boundaries. On a simple hotel reservation system scenario, it is demonstrated how different actions can be decomposed into more primitive underlying interaction loops. Integrated conceptual modelling method is important for evaluation of expressive power of conceptual modelling languages.
Improving the Ability of Robots to Navigate Through Crowded Environments Safely using Deep Reinforcement Learning

Autonomous robot navigation in unpredictable and crowded environments requires a guarantee of safety and a stronger ability to pass through a narrow passage. However, it’s challenging to plan safe, dynamically-feasible trajectories in real-time. Previous approaches, such as Reachability-based Trajectory Design (RTD), focus on safety guarantee, but the lack of online strategy always makes the robot fail to pass through a narrow passage. This paper proposes to learn a policy that guides the robot to make successful plans using deep Reinforcement Learning (RL). We train a deep network based on the RTD method to create cost functions in real-time. The created cost function is expected to help the online planner optimize the robot’s feasible trajectory, satisfying its kino-dynamics model and collision avoidance constraints. In crowded simulated environments, our approach substantially improves the planning success rate compared to RTD and some other methods.
PENGEMBANGAN LEMBAR KERJA PESERTA DIDIK BERBASIS DISCOVERY LEARNING UNTUK PESERTA DIDIK KELAS VII SMP

The purpose of this research is to develop a valid and practical student worksheet based on Discovery Learning on the topic of Statistics. This research used the Plomp model that consists of three phase, which are preliminary research, prototyping stage, and assessment phase. The preliminary research phase consists of needs, student, curiculum, and concept analysis. The prototyping phase is the phase to develop student worksheet based on Discovery Learning on the topic of Statistics. At the development stage through formative evaluation in the form of self evaluation and expert review to get a valid product. The last phase is the assessment phase, with the aim of determining the practicality category of the student worksheet through the one to one and small group evaluation. The results Showed that the student worksheet had a validity level of 85,63% with a very valid category and a practicality level of 95,22% with a very practical category. Keywords – Discovery Learning, Student Worksheet, Statistics.