Reviewerly/exHarmony huggingface dataset:
Uncertainty Estimation for Efficient Monocular Depth Perception

In monocular depth perception, the ground truths always contain wrong depth values. Network performance suffers when such data are used for training. To this end, a modified uncertainty loss is proposed to monocular depth estimation to alleviate this issue. The epistemic uncertainty is calculated in logarithm space, while the aleatoric uncertainty is unchanged. The experimental results demonstrate that our method outperforms the previous state-of-the-art, yielding the highest performance on the NYU-Depth-v2 dataset in all metrics. Besides, the uncertainty maps help evaluate the area&#x0027;s estimation quality qualitatively,
Few-shot Learning for Human Activity Recognition Based on CSI

Human Activity Recognition(HAR) based on Channel State Information(Csi)plays an increasingly impor-tant role in human-computer interaction. Traditional research requires a large amount of activity sample to train network model. However, collecting a great many of data causes waste of time and manpower. Some models can well identify the categories of activities in this scene, but when another scene is tested, the identification accuracy of the model will be reduced. Therefore it needs to re-collect data to retrain the model. We proposed a method which can transfer the knowledge learned from a scenario to a new scenario. It can also facilitate the model&#x0027;s knowledge learning from the source domain and quickly generalize to new tasks that contain only a small number of samples. Through this method, the model that maintains high accuracy and scalability to identify new category, we added attention mechanism can automatically extract features that are useful to the model and ignore some noise that negatively affects the model, meanwhile improve system stability and the effectiveness of activity recognition. We also performed the scaling and shifting(SS) transformation on the network, which could reduce the parameters of the model, improve the training speed, and avoid overfitting.
Connecting Data to Mechanisms with Meta Structural Causal Model

Recent years have seen impressive progress in theoretical and algorithmic developments of causal inference across various disciplines in science and engineering. However, there are still some unresolved theoretical problems, especially for cyclic causal relationships. In this article, we propose a meta structure causal model(meta-SCM) framework inspired by understanding causality as information transfer. A key feature of our framework is the introduction of the concept of active mechanisms to connect data and the collection of underlying causal mechanisms. We show that the meta-SCM provides a novel approach to address the theoretical complications for modeling cyclic causal relations. In addition, we propose a sufficient activated mechanisms assumption, and explain its relationship with existing hypotheses in causal inference and learning. Finally, we conclude the main idea of the meta-SCM framework with an emphasis on its theoretical and conceptual novelty.
Performance Assessment of 25 Global Horizontal Irradiance Clear Sky Models in Riyadh

Estimations of clear-sky Global Horizontal Irradiance (GHI) are essential to assess the solar resources. These clear-sky models are the basis when studying any solar project. However, the accuracy of these models differs significantly, that is mostly related to the geographical location of the site under test, as well as the input variables to the model and the goodness of the model itself. In this work, we focus on studying different clear-sky models, where we examine 25 popular clear-sky GHI models using data from two sites in Riyadh, Saudi Arabia. A dataset that covers the period of 7 years was used, with an hourly readings. Since the validation of GHI models requires comparison of the model results with measured values during clear-sky periods, Polo's algorithm was used to determine clear days. Clear-sky models' performance is evaluated using the Combined Performance Index (CPI), which combines Root Mean Square Error (RMSE), Kolmogorov-Smirnov test Integral (KSI) and OVER parameter into single statistical indicator. Results show that 7 models have less than 50% Combined Performance Index (CPI), where MAC, and REST2 clear-sky models has the best performance with less than 25% CPI. Overall, the top performing models are the ones that include the climatic variables, which indicate the importance of considering the climatic and geographical conditions when selecting a model.
An Efficient Model for Dynamic Video Summarization based-on Deep Visual Features

Digital video consists of frames that are presented at variant frame rates such as 15, 24, 30, 60 fps. There is a saying “A picture is worth a thousand words.” Pertaining to Digital Video the saying is “a video represents a million of those words strung together”. A digital video’s core components are frames. Video summarization is the procedure of distilling an original video to build a short raw video that keeps the important stories/content without sacrificing too much information. In this paper, a proposed model is offered for enhancement of dynamic video summarization based on deep learning producing a video skimming summary by selecting keyframes that have importance score met the specification. The task of assigning importance scores to certain frames in a video is critical for summarization, but it is also tough. Proposed model assigns a probability to each video frame, indicating how important it is to be selected, and then selects frames based on the expected values, creating video summaries. Using InceptionV3, ResNet50 pretrained models, it early fuses two feature sets for object-based and place-based in order to predict importance scores. The performance of proposed model has been compared to state-of-the-art models on TVSum dataset. Experimental evaluation show that proposed model performs better in terms of F-score to assess the effectiveness of the proposed model.
Learning Algorithm in Two-Stage Selective Prediction

Data gathered from real-world applications often suffer from corruption. The low-quality data will hinder the performance of the learning system in terms of classification accuracy, model building time, and interpretability of the classifier. Selective prediction, also known as prediction with a reject option, is to reduce the error rate by abstaining from prediction under uncertainty while keeping coverage as high as possible. Deep Neural Network (DNN) has a high capacity for fitting large-scale data. If DNNs can leverage the trade-off coverage by selective prediction, then the performance can potentially be improved. However, the current DNN embedded with the reject option requires the knowledge of the rejection threshold, and the searching of threshold is inefficient in large-scale applications. Besides, the abstention of prediction on partial datasets increases the model bias and might not be optimal. To resolve these problems, we propose innovative threshold learning algorithms integrated with the selective prediction that can estimate the intrinsic rejection rate of the dataset. Correspondingly, we provide a rigorous framework to generalize the estimation of data corruption rate. To leverage the advantage of multiple learning algorithms, we extend our learning algorithms to a hierarchical two-stage system. Our methods have the advantage of being flexible with any neural network architecture. The empirical results show that our algorithms can achieve state-of-the-art performance in challenging real-world datasets in both classification and regression problems.
Implementation Of Fuzzy Logic For Determining The Level Score Historical Character Recognition Puzzle Educational Game

The development of information technology is very rapid at this time, more or less has affected the whole community. This research makes educational games with learning materials to know historical figures by arranging puzzle pieces. In this game, players will arrange puzzles according to their level, namely easy, medium and difficult. If the player can finish the game well, he will applied to the game which is useful for setting the game score that will be given to players who successfully complete the puzzle pieces. With fuzzy logic is expected to improve the quality of user interaction with game play
Automatic Related Work Section in Scientific Article: Research Trends and Future Directions

Related work plays an important role in the scientific article to track the history of topic evolution and demonstrate the improvement of the proposed method. Writing related work tasks could be arduous for young researchers and scholars. Some studies have attempted to design systems to ease this work besides improving the quality of related work produced. Initially, the research focused on extractive approaches by exploiting the heuristic method, machine learning, or graph-based methods. Recently, deep learning and its variants have become more popular because they can handle abstractive approaches. We propose five research questions that direct discussion and analysis of research in related work summarization. We look for answers to this research question in scientific articles on ScienceDirect, Web of Science, or the Google Scholar Database. This research presents a widespread survey about the trends in related work tasks: related work structure, important aspects, scientific corpora standards, prior proposed methods, baseline systems comparison, evaluation metrics, and challenges in the future. The results of this study provide an overview of research trends in related work summarization, as well as describe research gaps and future research that can still be developed.
Bayesian Optimization Based on Pseudo Labels

The performance of a machine learning or deep learning algorithm is heavily influenced by its hyperparameters. The selection of the hyperparameters is of great significance. To automatically find a superior-performing set of hyperparameters, Bayesian optimization is a common and effective hyperparameter optimization method. And an early stopping strategy is usually employed in the optimization algorithm to improve efficiency. The early stopped trials cannot run to the end, so their final performance metrics are unavailable. Therefore, the existing Bayesian optimization algorithms fail to use the trials terminated early as samples for modeling. This may result in less information participating in the modeling, which leads to high model uncertainty. In this paper, we propose Bayesian optimization based on pseudo labels (BOPL). We apply the extrapolation of learning curves as the early stopping strategy and the pseudo labels obtainment method. We use the pseudo labels of all trials to model the surrogate model in Bayesian optimization, thereby avoiding the waste of information contained in the early stopped trials. Experiments on the ResNet-18 on the CIFAR-100 dataset show that the proposed BOPL consistently outperforms vanilla Bayesian and Bayesian with early stopping. It proves the effectiveness of the proposed method, which finds better-performing hyperparameters at a faster rate. The proposed method is versatile, conceptually simple, and easy to implement.
Joint Event Extraction Based on CNN-BiGRU and Attention Mechanism

Biomedical event extraction is a very challenging task of information extraction, which plays a key role in medical research, disease analysis and other applications. At present, the task of biomedical event extraction mainly consists of two steps: trigger identification and argument classification. Most research methods use a pipelining approach to accomplish two sub-tasks in stages, which leads to cascading errors. Therefore, a joint event extraction method based on CNN-BiGRU and attention mechanism is proposed, which can extract deeper and more comprehensive features more effectively to complete the task. Firstly, the word vector representation obtained by pretraining language model is combined with part-of-speech vector and position vector. Then input them into Convolutional Neural Network (CNN) and Bi-directional Gated Recurrent Unit (BiGRU) respectively to obtain the local and global feature representations of sentences. Finally, the attention mechanism is used to integrate these two feature representations and jointly deal with these two subtasks. Experiments on MLEE data sets show that the proposed method is superior to the previously proposed biological event extraction method and can effectively extract biomedical events.
Line Average Pooling: A Better Way to Handle Feature Maps on CNN for Skin Cancer Classification

This paper proposes a new pooling method called line average pooling (LAP), which operates between the convolution layer and the final output layer, replacing the traditional mapping method, such as Flatten and global average pooling (GAP). LAP effectively reduces the total number of parameters of the model, thereby preventing overfitting effectively while retaining more features from high-level feature maps. Additionally, it increases the fitting speed of the model. We selected the ISIC skin cancer dataset, then examined the performances of three pooling methods: LAP, GAP and Flatten, on a customized CNN model. In addition, we analyzed the fitting degree when the epoch was 100. The experimental results show that, the degree of overfitting using LAP is greatly reduced when compared with Flatten. Compared with GAP, LAP is better and faster in extracting features and fitting the training data. Both GAP and LAP demonstrate good generalization abilities, reaching 87.56&#x0025; and 88.11&#x0025; respectively. With proper means of additional regularization, LAP can even perform better than GAP.
Review of Multi-Object Tracking Based on Deep Learning

As a research hotspot and difficulty in the field of computer vision, multi-object tracking technology has received wide attention from researchers. In recent years, the performance of object detection algorithms has been improved due to the rise of deep learning methods, promoting the rapid development of multi-object tracking technology. This paper begins with a brief overview of object tracking. Then, the challenges of multi-object tracking are presented. According to the algorithm framework, multi-object tracking algorithms based on deep learning can be divided into two major groups: detection-based tracking algorithms and joint detection tracking algorithms. In the following we describe the principle and the specific implementation of several algorithms respectively. Next, we discuss the running results of the algorithms on MOT16 and MOT17 datasets. Finally, a summary and an outlook are given.
Secure Internet of Thing using Blockchain Technology

With their vast range of uses, Internet of things (IoT) and its applications are becoming more popular. However, the security and privacy of the IoT-based applications remains a worry. The majority of the privacy and security issues of the interconnected, diverse resource-constrained IoT devices are unable to be solved efficiently by traditional security protocols. Blockchain technology has emerged as a distributed ledger technology or data structure that can manage many intelligent IoT transactions and ensure greater security to data management. In this paper, we propose a light-weight authentication system based Blockchain approach that provides network and communication security. Due to Blockchain decentralized nature and transparency, the proposed approach ensures high-level data security, improves system operation efficiency and system performances.
Artistic Text Effect Transfer with Conditonal Generative Adversarial Network

With the wider application of artistic text in real scenes, the artistic text effect transfer has become an increasingly important topic in the field of computer vision. Existing methods either maintain poor structural information or poor effect information, both of which can seriously deteriorate the final result. We propose a conditional generative adversarial network to solve this problem, by alternately training the generator and the discriminator. The structure of the generator is Unet, which is an encoder-decoder with skip connections between the corresponding mirrored layers. We inject the conditional information of the reference effect image into the generator using a simple but effective concatenate strategy. The discriminator, which is conditioned on the generator&#x0027;s input to determine how true its output is. Ideally, the discriminator acts as a loss function and guides the optimization of the generator. Qualitative and quantitative results are performed on TE141k dataset, which demonstrate the proposed network outperforms the existing SOTA models and has good visual results.
Hybrid Security Enhancement of ECC with Side Channel And Sign Fault Attack Countermeasures

The research presents the elliptic curve algorithm in addition to measures and countermeasures for Sign Change Fault attacks (SCF) and side-channel attacks (SCA) on the elliptic curve algorithm (ECC). The attack and countermeasures are studied in the ambience of exact projective coordinates and ECC on the main fields. The combination between countermeasures both SCF Attacks and SCA on ECC is envisioned to potency the device to discharge objects on a curve that is coded weakly. The research presents the enhancement of security of hybrid-ECC for defending against types of attacks. In elliptic curve cryptography, side-channel attacks take place when data is unintentionally leaked while being processed. Calculating n*P, where n is a positive integer and P is a point on the elliptic curve E, is a crucial operation. The binary algorithm's implementations may show whether P + Q is calculated for P=6*Q or P=Q, depending on the circumstances. There are many solutions that have been proposed for this issue. Here, we outline a generic method for creating a variety of representations of the points on E with characteristic p $\ge$ 5, each with a consistent application of P+Q. For each n*P computation, the parametrization can be modified effectively for free. It works with all elliptic curves. Hence, all others ECC included in the current and future standards for p $\ge$ 5 may be used with them. In characteristic p $\ge$ 5.
Influence Functions for Interpretable link prediction in Knowledge Graphs for Intelligent Environments

Knowledge graphs are large, graph-structured databases used in many use-case scenarios such as Intelligent Environments. Many Artificial Intelligent latent feature models are used to infer new facts in Knowledge Graphs. Despite their success, the lack of interpretability remains a challenge to overcome. This paper applies influence functions to obtain the most significant facts when predicting new knowledge and allows users to understand these models. However, Influence Functions do not scale well. We present an efficient method to scale up influence functions to large Knowledge Graphs to overcome such an issue. It drastically reduces the number of training samples when computing influences and uses fast curvature matrix-vector products to linearize the computation steps required for the inverse Hessian. We conduct experiments on different sized Knowledge Graphs demonstrating the scalability of our approach and its effectiveness in measuring the most influential facts. Our method provides an intuitive understanding of link prediction behaviour in Knowledge Graphs and Intelligent Environments.
Smartwatch Multifactor Authentication for Keyboard based Methods

A method for leveraging a smartwatch as an additional form of user authentication has been proposed in this work. Both inherent and subtle intentional wrist gestures are examined as means of appropriately identifying a user. This system leverages a custom-developed Android Wear OS application to record sensor data while typing the username-password on the keyboard. A total of 49 accelerometer samples were collected from three users and performed 3,614 different genuine and impostor comparison tests. Experimental setups include several hostile scenarios where username-password is considered compromised. To thwart such impostor attacks, the proposed method, as a standalone multifactor authentication, achieves Equal Error Rate (EER) values as low as 0% and an average of 18.7% for the observed three axes of accelerometer data.
High-performance Active Domain Adaption with Fewer Manual Labeling

Domain adaption has been found to suffer from a potentially severe degradation in migration performance when facing source and target domains with insufficient shared representations. Active learning is introduced to avoid the problem by providing partial target domain data labels. However, the addition of active learning inevitably brings the problem of high manual labeling costs. This paper proposes an active generation domain adaption method (AGDA). Only the first round of data labeling requires manual participation. The target domain classifier will undertake the labeling task from the second round. The labeled data will be selected and used to generate a large amount of labeled fake target domain data to train the target domain classifier. The fewer manual labeling is made possible by the HcTop data selection method proposed in this paper, which selects the batch of samples predicted by the classifier with the highest confidence. The HcTop method can select the &#x201C;confusing&#x201D; data valuable for migration. Experiments demonstrate that the AGDA method can achieve higher migration performance with less manual labeling in the case of insufficient shared representations.
Research on The Development of Commercial Digital Economy Based on Information Driven

With the help of the &#x201C;information technology and business&#x201D; model, this paper summarizes and innovates the information kinetic energy theory by using the scientific experiment of information energy and relevant professional technology theory. According to the three-tier technical logic of the new generation of information technology, this paper solves the new kinetic energy logic of traditional business economy and reading the information kinetic energy driving the development of commercial digital economy from three levels.
Stacked Generative Adversarial Networks for Image Generation based on U-Net discriminator

Although Generative Adversarial Networks (GANs) are powerful generative models and have shown remarkable success in various tasks recently but suffers from generating high-quality images. In this paper, we proposed a U-Net-based discriminator structure on the network structure of the two-stage Stacked Generative Adversarial Networks (StackGAN++), aiming to generate high-resolution images with actual shapes and textures. To gain more insight from limited datasets, we focused on improving the discriminator&#x0027;s ability to discriminate the real from the fake. The discriminator based on U-Net architecture allows providing details per-pixels and global feedback to the generator to maintain the global coherence of synthetic images and the realistic of local shape and textures. In addition, for the problem that the training effect on a small number of sample datasets is not ide-al, we further improve the quality of the generated samples by transfer learning of model parameters. Compared with the StackGAN++ baseline, experiments show that we have significantly improved the IS and FID evaluation indicators of the ImageNet subset.