Reviewerly/exHarmony huggingface dataset:
Real-Time Domestic Garbage Detection Method Based on Improved YOLOv5

With the substantial improvement of people’s living standards, the amount of domestic garbage is increasing rapidly, and intelligent waste classification has become an urgent need in modern society. In this paper, we propose a real-time garbage detection model based on the improved YOLOv5 (you only look once version 5) algorithm. Firstly, mosaic data enhancement is introduced to enrich the background of the detection object and improve the robustness of the network. Secondly, Distance-IOU Non-Maximum Suppression is used to replace the traditional Non-Maximum Suppression to improve the suppression effect of prediction boxes. Finally, the network is further optimized from the aspect of activation function. The experimental results show that among the four versions of YOLOv5, their mean average precision(mAP) all reach more than 84%, The improved YOLOv5x has the best recognition effect, whose mAP reaches 89.4%, which is 2.1% higher than that of YOLOv5x and 5.3% higher than that of YOLOv5s.
Neural Encoding and Decoding With a Flow-Based Invertible Generative Model

Recent studies on visual neural encoding and decoding have made significant progress, benefiting from the latest advances in deep neural networks having powerful representations. However, two challenges remain. First, the current decoding algorithms based on deep generative models always struggle with information losses, which may cause blurry reconstruction. Second, most studies model the neural encoding and decoding processes separately, neglecting the inherent dual relationship between the two tasks. In this article, we propose a novel neural encoding and decoding method with a two-stage flow-based invertible generative (FLIG) model to tackle the above issues. First, a convolutional autoencoder (CAE) is trained to bridge the stimuli space and the feature space. Second, an adversarial cross-modal normalizing flow is trained to build up a bijective transformation between image features and neural signals, with local and global constraints imposed on the latent space to render cross-modal alignment. The method eventually achieves bidirectional generation of visual stimuli and neural responses with a combination of the flow-based generator and the autoencoder. The FLIG model can minimize information losses and unify neural encoding and decoding into a single framework. Experimental results on different neural signals containing spike signals and functional magnetic resonance imaging demonstrate that our model achieves the best comprehensive performance among the comparison models.
An Obstacle Avoidance and Trajectory Tracking Algorithm for Redundant Manipulator End

An obstacle avoidance algorithm was suggested in this study to handle the redundant manipulator’s end trajectory planning problem, with the accuracy of the end trajectory guaranteed to be within the projected error. To accomplish this goal, the calculation method of vector pseudo distance is used instead of the traditional Euclidean distance to express the minimum proximity between the robot arm and the obstacle. This method can express the constraint conditions between the obstacle and the robot arm more clearly and improve the obstacle avoidance efficiency. The virtual repulsion force is generated to make the end of the manipulator far away from the obstacle through the relationship between the velocity vector and the angle vector of the end motion, and the end recovers to the desired trajectory quickly and stably after avoiding the obstacle through the adjustment of the tangent velocity function. The error adjustment coefficient e K is intended to communicate back the actual trajectory of the end obstacle avoidance to the tracking function in real time, and to adaptively change the velocity and acceleration of the end-executor to reduce the end trajectory tracking error. The simulation results show that when an envelope body obstacle appears unexpectedly on the end executor’s expected mission locus, if the expected distance between the endeffector r and the obstacle is greater than or equal to 0.02m, the end of the actuator can avoid the obstacle and return to the expected track quickly and smoothly.
Creativity Under COVID-19: How Technology Has Enhanced and Promoted Student Engagement Online

In 2020, the sudden outburst of COVID-19 changed teaching and learning drastically. Teachers and students worldwide had to adapt to hybrid teaching and learning within an extremely short period of time. This study focuses on how the sudden and involuntary changes in pedagogies brought about by the onset of COVID-19 impacted students’ class engagement in a general education (GE) course delivered online. The study also examines how features such as chatrooms and share-screen in the selected video-conferencing tool enhanced the hybrid mode of teaching and learning in the GE course in question. This study is one of the few studies with a focus on teaching and learning at the tertiary level in the context of creativity during COVID-19, and it aims to demonstrate how technology has facilitated and enhanced online student engagement in the said context.
Transforming into Smart Business: The Role of Artificial Intelligence

Artificial Intelligence (AI) is the peak of the fourth industrial revolution, since it is implemented in various business activities such as HR, supply chain management, manufacturing, e-Commerce, marketing, and corporate finance. Several AI tools were presented in this chapter to shed the light on how such technologies can be implemented to improve organizational predictive capability and boost business performance. Thus, organizations are urged to implement AI systems to overcome forceful threats, as AI augments BI to achieve competitive advantages. Unescapably, AI transformation requires changes in both the individual and the collective mindsets in organizations. This chapter proposes an AI business model, named SHAM, to guide organizations in emerging countries towards AI-driven business transformation. Since the current study is descriptive in nature, the SHAM model was built based on an extensive literature review using the Comprehensive Literature Review Meta-Framework of the seven step model (CLR Meta-Framework). The SHAM model adapts the Sense-Think-Act paradigm which is a broad roadmap in robotics research to fit the business context. The Sense step is to conceptualize the organizational direction towards the new AI-driven entity; the Think step is to build a solid strategy for the AI transformation; and the Act step is to implement the strategy to reach the desired smart business. The model is conceived to empower organizations to transform into smart businesses to achieve competitive advantage, adaptation to the environment, and success.
Benchmarking Conventional Outlier Detection Methods

Nowadays, businesses in many industries face an increasing flow of data and information. Data are at the core of the decision-making process, hence it is vital to ensure that the data are of high quality and no noise is present. Outlier detection methods are aimed to find unusual patterns in data and find their applications in many practical domains. These methods employ different techniques, ranging from pure statistical tools to deep learning models that have gained popularity in recent years. Moreover, one of the most popular outlier detection techniques are machine learning models. They have several characteristics which affect the potential of their usefulness in real-life scenarios. The goal of this paper is to add to the existing body of research on outlier detection by comparing the isolation forest, DBSCAN and LOF techniques. Thus, we investigate the research question: which ones of these outlier detection models perform best in practical business applications. To this end, three models are built on 12 datasets and compared using 5 performance metrics. The final comparison of the models is based on the McNemar's test, as well as on ranks per performance measure and on average. Three main conclusions can be made from the benchmarking study. First, the models considered in this research disagree differently, i.e. their type I and type II errors are not similar. Second, considering the time, AUPRC and sensitivity metrics, the iForest model is ranked the highest. Hence, the iForest model is the best in the cases when time performance is a key consideration as well as when the opportunity costs of not detecting an outlier are high. Third, the DBSCAN model obtains the highest ranking along the F1 score and precision dimensions. That allows us to conclude that if raising many false alarms is not an important concern, the DBSCAN model is the best to employ.
DSMA: Reference-Based Image Super-Resolution Method Based on Dual-View Supervised Learning and Multi-Attention Mechanism

Reference image based super-resolution methods (RefSR) have made rapid and remarkable progress in the field of image super-resolution (SR) in recent years by introducing additional high-resolution (HR) images to enhance the recovery of low-resolution (LR) images. The existing RefSR methods can rely on implicit correspondence matching to transfer the HR texture from the reference image (Ref) to compensate for the information loss in the input image. However, the differences between low-resolution input images and high-resolution reference images still affects the effective utilization of Ref images, so it is an important challenge to make full use of the information in Ref images to improve the SR performance. In this paper, we propose an image super-resolution method based on dual-view supervised learning and multi-attention mechanism (DSMA). It enhances the learning of important detail features of Ref images and weakens the interference of noisy information by introducing the multi-attention mechanism, while employing dual-view supervision to motivate the network to learn more accurate feature representations. Quantitative and qualitative experiments on these benchmarks, i.e., CUFED5, Urban100 and Manga109, show that DSMA outperforms the state-of-the-art baselines with significant improvements.
A Comparison of Fuzzy and ACO-Based Fuzzy for Classification of Bio-Medical Database

This paper deals with fuzzy evolutionary algorithms which is the fast developing area in the field of machine learning and artificial intelligence. In today’s world, lots and lots of data are available for various real-life problems, where finding the relevant information from huge quantities become a challenge to the human society. This leads the way for data mining which plays an important role to extract the relevant information from vast amount of data. In this paper, deadly diseases such as lung cancer and breast cancer are considered for classification. Also, the concept of ant colony optimization (ACO) is used for variable selection, and fuzzy concept is used for classification. Based on the activities of the real ant systems, ACO is developed by Marco Dorigo in the year 1992, and it is utilized in solving many complex problems of optimization. It is one of the types of evolutionary computations. Its main aim is to identify the shortest distance between the source of food and its nest. The behavior of ants that deposits their incense on the top layer of ground to make a desired path so that other members of the colony should follow the path. Using the concept of fuzzy unordered rule induction algorithm (FURIA), classification has been done, and the results are compared with and without hybridization. Lung cancer dataset consists of 57 variables. Fuzzy gives classification accuracy as 75% with total variables. By ACO, 57 variables are reduced to five variables, and fuzzy gives classification after reduction of variables is 84.37%. Similarly, breast cancer consists of ten variables, and fuzzy classification gives accuracy as 73.07% with total variables. ACO reduced ten variables to three variables, and fuzzy gives classification after reduction of variables is 75.17%. Hence, the hybridization concept consumed less time and also less cost. By finding the relevant variables, identifying the disease at earlier stage becomes easier, and curing the disease becomes faster.
The Forensicability of Operation Detection in Image Operation Chain

A large number of forensics research focus on operation detection to reveal the evidence of forgery action in the digital image. In the early works, analyst firstly model the probability distribution of single operation, and design the forensic tools based on feature extraction and machine learning based classifier. With increasing dimension of the feature and facing multiple operations detection scenario, the physical meaning of the feature gradually become ambiguous. Especially, since deep learning algorithm was used in forensic research, the automatic feature selection and making decision with high performance of classification conceals the intrinsic forensic clues. In this paper, we explore the availability of feature for operation detection in the operation chain, so called forensicability. An anti-forensic attack algorithm is introduced to formulate the impact on the feature due to the following operation. We propose two measurements: attack angle and scale, mutual information scale to indicate the forensic feature variation after the image manipulated by the following operation. The uncoupled relationship can be revealed by our methods. In the experiments, four operation chains involving ten operations are considered as the case study. The results are encouraging and improve the explanation of the forensics method based on high dimensional features.
A High Performance SIKE Accelerator With High Frequency and Low Area-Time Product

Post-quantum cryptography (PQC) has emerged as a quantum-resilient class of cryptography that will be able to withstand attacks from quantum computers. Among the PQC family, the isogeny-based scheme, i.e., Supersingular Isogeny Key Encapsulation (SIKE) protocol, an alternative candidate in Round 3 of the National Institute of Standards and Technology (NIST), has the advantage of a shorter public-key length. However, longer computational time and larger area overhead are the main constraints for its practical applications. In this brief, we proposed a SIKE accelerator with optimized multiplier and adder designs achieving the lowest area-time (AT) product with high operating frequency. The proposed SIKE accelerator for four different security levels has the highest frequency of 303.0-322.5 MHz with 4.0-21.0% improved AT in comparison to the state-of-the-art designs.
Global Representation Guided Adaptive Fusion Network for Stable Video Crowd Counting

Modern crowd counting methods in natural scenes, even when video datasets are available, are mostly based on images. Because of background interference or occlusion in the scene, these methods can easily lead to mutations and instability in density prediction. There has been minimal research on how to exploit the inherent consistency among adjacent frames to achieve high estimation accuracy of video sequences. In this study, we explore the long-term global temporal consistency in the video sequence and propose a novel Global Representation Guided Adaptive Fusion Network (GRGAF) for video crowd counting. The primary aim is to establish a long-term temporal representation among consecutive frames to guide the density estimation of local frames, which can alleviate the prediction instability caused by background noise and occlusions in crowd scenes. Moreover, in order to further enforce the temporal consistency, we apply the generative adversarial learning scheme and design a global-local joint loss, which can make the estimated density maps more temporally coherent. Extensive experiments on four challenging video-based crowd counting datasets (FDST, DroneCrowd, MALL and UCSD) demonstrate that our method makes effective use of spatio-temporal information of video and outperforms the other state-of-the-art approach.
Performance Analysis of kNN-Based Image Demosaicing for Variable Window Sizes

Image demosaicing plays a prominent role when it comes to healthcare, forensics, and other imaging fields. The representation of an image in terms of numbers which can be understood and dealt with computers is termed as digital image. The process of recreating an entire colored image from incomplete color sample outputs linked to an image sensor is termed as demosaicing. The proposed work highlights the application of kNN algorithm for different window sizes. The comparison deduced is based on the evaluation metrics, viz. SNR, PSNR to detect the best window size for a set of images as they help in deciding the visualization of an image.
Subject Line Personalization Techniques and Their Influence in the E-Mail Marketing Open Rate

E-mailing is an effective and close form of communication between company and customers. However, due to the massive number of e-mails sent by almost all brands, companies have faced recently low open rates on their e-mail marketing campaigns. It is important to understand the most effective techniques that capitalize e-mail marketing. Considering the importance of customization in direct marketing, this study explores the impact of e-mail marketing customization in subject line. Four A/B tests were used regarding the subject line of a specific brand campaign. Four tactics were used: include the first name of the reader to the subject line; include the first and last name of the reader; add emojis to the subject line; opting for a subject line in form of a question; and lastly, the default subject line without any of the previous variables. The study considered a sample of 102.574 individuals of a company’s e-mail marketing database. The variables of subject line were analyzed according to the layout and segmentation, as well as open rates and clicks. The results showed that e-mails containing the variable with the first and last name had the highest open rate, specifically 50,07%. Although A/B tests should consider the specific database of the company under analysis and generalization should be seen with caution, the study enhances the importance of e-mail personalization in the subject line.
On Sidechain-Assisted Transaction Service Management for Internet of Things: A Random Contract Approach

With the evolution of industrial technologies, the Internet of Things and blockchain show enormous potential. Different institutes with heterogeneous devices indicate diversity in their private information, including service demand and risk-bearing capability. Low efficiency and rising transaction prices of the public blockchain have been significant barriers to these potential service purchasers. The sidechain is recognized as one of the scalability solutions to a public chain. However, it is challenging to design the economic incentive mechanism and study the transaction fee pricing for transaction services on a sidechain due to the uncertainty of the network's performance and the miner's effort. Considering the asymmetric information (i.e., anonymous private information) and uncertainty, we propose a random-contract-based scheme to maximize the sidechain service provider's revenue and assign the service buyers the feasible service price under the framework of a sidechain linked to the public blockchain. Furthermore, we show that random contracts outperform the deterministic contracts under the increasing absolute risk-aversion assumption. We investigate service pricing in various scenarios using Geometric Brownian Motion. The simulation findings reveal that random contracts can increase the sidechain's revenue by 24.70%, compared with deterministic contracts. The efficient service payments have been reduced by 44.65%, compared with the main chain's cost.
Whole-Slide Imaging: Updates and Applications in Papillary Thyroid Carcinoma

Whole-slide imaging (WSI) has wide spectrum of application in histopathology, especially in the study of cancer including papillary thyroid carcinoma. The main applications of WSI system include research, teaching, and assessment and recently pathology practices. The other major advantages of WSI over histological sections on glass slides are easier storage and sharing of information as well as adaptation of use in artificial intelligence. The applications of WSI depend on factors such as volume of services requiring WSI, physical factors (computer server, bandwidth limitation of networks, storages requirements for data), adaption of the WSI images with the laboratory workflow, personnel (IT expert, pathologist, technicians) adaptation to the WSI workflow, validation studies, ethics, and cost efficiency of the application(s).
Benchmarking Neural Networks-Based Approaches for Predicting Visual Perception of User Interfaces

Deep Learning techniques have become the mainstream and unquestioned standard in many fields, e.g. convolutional neural networks (CNN) for image analysis and recognition tasks. As testing and validation of graphical user interfaces (GUIs) is increasingly relying on computer vision, CNN models that predict such subjective and informal dimensions of user experience as aesthetic or complexity perception start to achieve decent accuracy. They however require huge amounts of human-labeled training data, which are costly or unavailable in the field of Human-Computer Interaction (HCI). More traditional approaches rely on manually engineered features that are extracted from UI images with domain-specific algorithms and are used in "traditional" Machine Learning models, such as feedforward artificial neural networks (ANN) that generally need fewer data. In our paper, we compare the prediction quality of CNN (a modified GoogLeNet architecture) and ANN models to predict visual perception per Aesthetics, Complexity, and Orderliness scales for about 2700 web UIs assessed by 137 users. Our results suggest that the ANN architecture produces smaller Mean Squared Error (MSE) for the training dataset size (N) available in our study, but that CNN should become superior with N > 2912. We also propose the regression model that can help HCI researchers to foretell MSE in their ML experiments.
What Is Data Governance?

If you ask ten data managers what Data Governance actually is or how you define Data Governance, you will usually get ten different answers. This is because every Data Governance implementation, like every company, is unique. This makes it all the more important to create a common basis at the beginning, from which you can then derive the right decisions and course settings for customized Data Governance in your company at a later stage. To this end, we start with a simple definition and then, as the complexity increases, we introduce all aspects and fields of action of Data Governance step by step in an understandable and practical manner. Experience has shown that many people find the topic of Data Governance very abstract and difficult to grasp at first. The aim of this chapter is to help you to transform this abstract vagueness of Data Governance as a concept into your own clear understanding of the practical processes and activities behind the term Data Governance.