Reviewerly/exHarmony huggingface dataset:
TPSAD: Learning to Detect and Localize Anomalies With Thin Plate Spline Transformation

We present a self-supervised learning approach with a novel proxy task, based on thin-plate spline transformation, for detecting and localizing anomalies in images. The self-supervised model, referred as TPSAD, is firstly optimized to classify normal examples from the artificially anomalous ones which are created by a new data augmentation technique that applies random thin-plate spline transformation to a patch of an image, selected by the Canny edge detector. Then, the last layer representations of the model are utilized for detecting anomalies with the Gaussian density estimator technique, while the middle layer representations are used for localizing anomalies. By assessing the proposed method on the MVTec dataset, we discover that not only can it detect anomalous images and localize irregularities properly, but also it is computationally efficient in both training and testing stages, compared to previous methods. Moreover, the method is robust to images containing unaligned objects due to the usage of the Canny edge algorithm in proxy task learning. Lastly, high performance in addition to low computational cost makes our method a good candidate for image anomaly detection in industrial applications.
A comparison of temporal aggregators for speaker verification

Speaker verification is the task of examining a speech signal to authenticate the claimed identity of a speaker as true or false. In order to deal with utterances having different lengths, and to accumulate information along the time dimension, different temporal aggregators have been proposed inside speaker verification pipelines. In this paper we investigate the behavior of five different temporal aggregators in the state of art, namely Temporal Average Pooling (TAP), Global Statistical Pooling (GSP), Self-Attentive Pooling (SAP), Attentive Statistical Pooling (ASP), and Vector of Locally Aggregated Descriptors (VLAD) at varying lengths of the two utterances. Starting from a speaker verification method in the state of the art, the experimental results on the VoxCeleb2 dataset show that there is a sweet spot for utterance length where speaker verification performance is higher independently from the temporal aggregator used.
An Interactive Short Answer Grading System Based on Active Learning Models

Grading automation can improve learning experience with quick around-the-clock feedback and superior grading consistency. Obtaining annotated data for training short answer grading models is costly. Active learning has been proven an effective approach to build accurate models with few annotated data. This paper presents an active learning approach of short answer grading that comprises of a few novelties. The first is a specialized active learning formulation adapted to short answer grading principles. The second is a proposal to exploit human expertise in fine-tuning several active learning model parameters for adaptation to the specifics of each grading task. The third is an interactive short answer grading system that is designed for building better quality grading model by informing users with data visualizations. The prototype presented in the paper should provide a useful conceptual demonstration for real-life deployment of active learning for short answer grading and further research in an enhanced interactive form of active learning.
SEMANTIC ANALYSIS OF THE CATEGORY «INNOVATIONS IN AGRICULTURAL BUSINESS»

Introduction. Sustainable development of the domestic economy is possible only under the condition of an innovative way of its development, in this regard, one of the serious tasks is to identify promising directions for the development of socio-economic systems. This article discusses the tools for determining the strategic directions of socio-economic and innovative development. The purpose of this study is to develop a methodological approach to determining promising directions for the development of socio-economic systems using big data technology. Methodology. The work uses the tools of system analysis, semantic and content analysis. Results. Based on the conducted benchmarking, trends in the development of approaches to determining innovative directions of economic systems development are analyzed, a content analysis of the terms of the semantic field "innovations in agricultural business" is carried out, an approach to searching and processing big data and determining strategic directions for the development of economic systems is tested. Conclusions. The proposed methodology, combining expert methods with Data Mining technologies and Big Data analysis, will allow us to accurately determine the current trends in the development of the processes under study and identify the vectors of their innovative development.
Results on order with respect to incidence topology-modelling of path graph

Material Science is a field that applies many aspects of Mathematics, including topology and graph theory. In this paper, the order of elements of a topological space with topology as incidence topology generated by Path graph has been studied. Various results based on connectedness and separation axioms of the incidence topology generated by path graph have been discussed. Moreover, order of these nodes as well as order of edges with respect to the incidence topology have been established and generalized for these graphs and sufficient examples are illustrated to support the results.
An Adaptive BSCO Algorithm of Solid Color Optimization for 3D Reconstruction System with PIFuHD

PIFuHD can generate high-resolution model in the process of human 3D reconstruction. However, PIFuHD will produce debris outside the human body when the image background is more complex. In order to solve this problem, this paper develops an adaptive BSCO algorithm for the background of human body and image of human body in 3D reconstruction system. The BSCO algorithm is divided into four steps in processing. First, BSCO algorithm uses Go-selfies to separate the background. Second, BSCO algorithm converges the RGB of all pixels of the character into a set. Third, BSCO algorithm finds the greatest difference from the set through HSV conversion. Fourth, BSCO algorithm weighs the set and then calculates the RGB score. The highest score of RGB is used as the RGB of the background after solid color optimization. The experimental results show that the proposed method improves the reconstruction effect of PIFuHD.
Dual-Branch Multiscale Channel Fusion Unfolding Network for Optical Remote Sensing Image Super-Resolution

Single image super-resolution technology is critical in remote sensing fields because it can effectively improve the details of target images. However, the application of deep learning is limited due to the lack of interpretability and the need for many parameters. This letter proposes an interpretable dual-branch multi-scale channel fusion unfolding network (DMUNet) for optical remote sensing image (ORSI) super-resolution. We design an unfolding network with double branches, each optimized with different strategies. Two branches focus on texture and edge reconstruction, respectively. This unfolding network follows the iteration process of the alternating direction method of multipliers (ADMM) and can learn the hyper-parameters adaptively. The functions of the two branches can complement each other. Further, to better fuse the feature maps of the two branches, a multi-scale fusion module is proposed. This module can effectively fuse information between different branches, scales, and channels. It is noted that it only requires a little computation cost. Experiments on two public ORSI datasets demonstrate that our method can achieve significant performance in both quantitative evaluation and visual results.
Provable-Correct Partitioning Approach for Continuous-Observation POMDPs With Special Observation Distributions

The partially observable Markov decision process with continuous observations has emerged as a popular model for system modeling and sequential decision-making for many real-world problems. The main challenge induced by continuous observations is its high computational complexity in the planning process because it is impossible to enumerate all observations in a continuous space. In this letter, we propose a static observation space partitioning approach to solve a continuous-observation POMDP approximately. Although observation space partitioning approaches have been investigated in the literature, a formal analysis of the partitioning effect on the system performance is still missing. We aim to fill this gap by providing a formal analysis of the approximation error. For this, the belief update function is shown to be Lipschitz continuous for the observation when the observation function satisfies certain properties. With this property, we formally prove that the approximation error of each value iteration is bounded. Meanwhile, we show that the proposed approach can be integrated into the heuristic search value iteration algorithm with performance guarantees. Finally, the advantage of using the static partitioning approach rather than the Monte Carlo sampling approach is validated by experimental results.
Research on gesture recognition based on YOLOv5

In recent years, with the rapid development of 5G, artificial intelligence, and Internet of Things technologies, smart digital home have entered thousands of households, which made our lives colorful. As a kind of human-computer interaction (HCI), gesture has the characteristics of strong information expression ability and transmission function, non-contact, and simple and easy to understand, which has been widely used in robot control, virtual reality, intelligent driving, and somatosensory games. The initial interaction method of gesture was through external hardware such as digital gloves, biological myoelectricity, and Kinect depth equipment, and then gradually developed into a method based on computer vision algorithms. The rapid development of deep learning in recent years has greatly improved the performance of image recognition and provided a new direction for gesture recognition. The deep learning method based on convolutional neural network can automatically extract image features, and is less affected by image background factors, which greatly improves the recognition accuracy. With the development of target detection technology, some algorithms have been increasing the depth of the network to obtain higher recognition accuracy, which will lead to excessive computing resource and high hardware requirements. Complex neural networks slow down the model recognition rate and are difficult to apply to resource-constrained mobile and embedded devices. This paper introduces the overall structure of the YOLOv5 network. The model is trained by YOLOv5s network and validated by real gesture. Finally, a gesture recognition software demo is made by PyQT5. The experimental results show that gesture recognition based on YOLOv5 can achieve better recognition accuracy.
A Simple Approach and Benchmark for 21,000-Category Object Detection

Current object detection systems and benchmarks typically handle a limited number of categories, up to about a thousand categories. This paper scales the number of categories for object detection systems and benchmarks up to 21,000, by leveraging existing object detection and image classification data. Unlike previous efforts that usually transfer knowledge from base detectors to image classification data, we propose to rely more on a reverse information flow from a base image classifier to object detection data. In this framework, the large-vocabulary classification capability is first learnt thoroughly using only the image classification data. In this step, the image classification problem is reformulated as a special configuration of object detection that treats the entire image as a special RoI. Then, a simple multi-task learning approach is used to join the image classification and object detection data, with the backbone and the RoI classification branch shared between two tasks. This two-stage approach, though very simple without a sophisticated process such as multi-instance learning (MIL) to generate pseudo labels for object proposals on the image classification data, performs rather strongly that it surpasses previous large-vocabulary object detection systems on a standard evaluation protocol of tailored LVIS. Considering that the tailored LVIS evaluation only accounts for a few hundred novel object categories, we present a new evaluation benchmark that assesses the detection of all 21,841 object classes in the ImageNet-21K dataset. The baseline approach and evaluation benchmark will be publicly available at https://github.com/SwinTransformer/Simple-21K-Detection. We hope these would ease future research on large-vocabulary object detection.
DAReN: A Collaborative Approach Towards Visual Reasoning And Disentangling

Computational learning approaches to solving visual reasoning tests, such as Raven’s Progressive Matrices (RPM), critically depend on the ability to identify the visual concepts used in the test (i.e., the representation) as well as the latent rules based on those concepts (i.e., the reasoning). However, learning of representation and reasoning is a challenging and ill-posed task, often approached in a stage-wise manner (first representation, then reasoning). In this work, we propose an end-to-end joint representation-reasoning learning framework, which leverages a weak form of inductive bias to improve both tasks together. Specifically, we introduce a general generative graphical model for RPMs, GM-RPM, and apply it to solve the reasoning test. We accomplish this using a novel learning framework Disentangling based Abstract Reasoning Network (DAReN) based on the principles of GM-RPM. We perform an empirical evaluation of DAReN over several benchmark datasets. DAReN shows consistent improvement over state-of-the-art (SOTA) models on both the reasoning and the disentanglement tasks. This demonstrates the strong correlation between disentangled latent representation and the ability to solve abstract visual reasoning tasks.
Pointshift: Point-Wise Shift MLP for Pixel-Level Cloud Type Classification in Meteorological Satellite Imagery

The deep neural network has recently achieved promising results on cloud type classification, which gets rid of the hand-crafted features and plays an essential role in climate change analysis. Previous methods perform context reasoning with single-scale representation at the centre of the network, which is challenging to capture sufficient contextual information. In this paper, we propose a point-wise shift multi-layer perceptron (MLP) for pixel-level cloud type classification, termed PointShift, which effectively models point-wise and multi-scale neighbour information. We design a shift operation to compose a multi-scale receptive field in a non-parametric manner. Besides, we introduce split attention to improve the interaction of feature channels. Extensive experiments on the Himawari-8 image dataset demonstrate that our proposed architecture achieves the best mIoU of 71.06% and a competitive trade-off between efficiency and performance.
BLS12-381 Pairing Implementation with RAM Footprint Smaller than 4KB

Advanced cryptographic technologies such as identity-based encryption, broadcast encryption, and aggregate signature are expected to be utilized for smart security management of IoT (Internet of Things) systems. Most advanced cryptography is based on complicated algebraic operations, namely bilinear pairings defined over elliptic curve groups. BLS12-381 pairing is the most promising one that satisfies a sufficient security level, and it is of great interest whether this pairing can be implemented on resource-restricted IoT end nodes. This paper derives Techniques to implement BLS12-381 pairing with less than 4 KB of RAM and evaluates its performance on two resource-restricted microcontrollers, Arduino Nano Every and Arduino Nano 33 IoT.
Smarter Movement Control Application

The coronavirus disease 2019 (Covid-19) has devastated the entire globe in a short period of time and was declared a global pandemic by the World Health Organization (WHO) on March 11th, 2020. It rapidly increased in fatality rate and has become an international public health crisis, culminating in social and economic calamity. However, mobile applications are being introduced globally to minimize the coronavirus’s continuous spread by tracing people’s circulation or mobility using digital software and smartphones. There is no digitally equipped tool that monitors the movement of the people, particularly in the public places. This work on Smarter Movement Control Application (SMCApp) aims to develop methods that will assist Malaysian people to move around with the aid of mobile tracing application. Therefore, the SMCApp was designed and developed as a mobile application software which stands to improve compliance with the mandated SOP measures across the country, as well as to provide digital support to those who wish to travel to various parts of the country. As a result, it is concluded that the usage of Smarter Movement Control Application (SMCApp) is hoped to bring about safe and effective movement of people throughout these two regions, as it will constantly alert individuals of any suspicious close-contact or the state of the location. Once the application is in effect, with at least 90% of users installing the App on their smartphones, it is projected to increase tranquility and elevate compliance with SOP measures.
Pay-as-you-Train: Efficient ways of Serverless Training

Serverless (FaaS) architecture is emerging as a paradigm of choice for many application types, including event triggered, query processing, and machine learning (ML). The use of serverless platforms for ML inference is well known, but its applicability for model training is still under exploration. This paper presents an efficient “pay-as-you-train” methodology for training large deep learning models using serverless cloud services for compute and data management. Serverless compute (such as AWS Lambda) and serverless data management systems (such as AWS key-value store DynamoDB) impose restrictions on the computing time and size of the allowed data objects respectively. We present a novel approach for training deep learning models, which overcomes the limitations imposed by the underlying serverless platforms. We also present an analytical model to study the performance and cost involved in training using different data management services (such as AWS object storage S3, in-memory Memcached, and DynamoDB) as a communication channel with serverless platforms. Additionally, we compare the performance and cost of these services available on cloud. Our optimization techniques improve the performance and hence the cost of training by a factor of 1.2x to 5.5x with these services.
How to Sample a Discrete Gaussian (and more) from a Random Oracle

The random oracle methodology is central to the design of many practical cryptosystems. A common challenge faced in several systems is the need to have a random oracle that outputs from a structured distribution $$\mathcal {D} $$ , even though most heuristic implementations such as SHA-3 are best suited for outputting bitstrings. Our work explores the problem of sampling from discrete Gaussian (and related) distributions in a manner that they can be programmed into random oracles. We make the following contributions:
Machine Reading Comprehension for Continuous Questions via Multi-Task Learning

Machine Reading Comprehension for Continuous questions (Cq-MRC) is an important branch of MRC, which requires to answer a series of follow-up questions with the given passages. Questions in Cq-MRC are usually incomplete and may co-reference to previous ones. To tackle these challenges more efficiently, we propose a deep neural model via multi-task learning, in which key-entity prediction is regarded as the auxiliary task to help the model focus on significant entities which may be ignored and in turn improve the performance of the main task, answer prediction. Two subtasks share the same representation vector and feature encoder and are trained jointly, so that they can learn from each other. In addition, we design a static weight distribution approach to assign the optimal weight to subtasks. Experiments conducted on a massive MRC dataset, Les MMRC 2.0, demonstrate the efficiency and effectiveness of our model.
Vibration-Based Uncertainty Estimation for Learning from Limited Supervision

We investigate the problem of estimating uncertainty for training data, so that deep neural networks can make use of the results for learning from limited supervision. However, both prediction probability and entropy estimate uncertainty from the instantaneous information. In this paper, we present a novel approach that measures uncertainty from the vibration of sequential data, e.g., the output probability during the training procedure. The key observation is that, a training sample that suffers heavier vibration often offers richer information when it is manually labeled. Motivated by Bayesian theory, we sample the sequences from the latter part of training. We make use of the Fourier Transformation to measure the extent of vibration, deriving a powerful tool that can be used for semi-supervised, active learning, and one-bit supervision. Experiments on the CIFAR10, CIFAR100, mini-ImageNet and ImageNet datasets validate the effectiveness of our approach.
Relevant Knowledge Use During Collaborative Explanation Activities: Investigation by Laboratory Experiment and Computer Simulation Using ACT-R

This study investigated the nature of knowledge use during collaborative explanation activities through a laboratory experiment and a computer simulation. We focused on how relevant knowledge is used in individual learning and how the use of such individual knowledge, influenced by cognitive bias (e.g., misconceptions), affects explanation activities and group activities in developing a concept map. Our results show that learners used relevant knowledge retrieved from memory during group activities, but some learners changed their strategy to use different knowledge during explanation activities. We further examined the cognitive process by developing an Adaptive Control of Thought-Rational (ACT-R) model. We focused on the knowledge retrieval process of self/other’s knowledge brought about by discrepancies in perspectives. The simulation results showed that the opportunity to search for knowledge brought a chance to retrieve relevant knowledge for generating adequate explanations in collaboration exercises.