Reviewerly/exHarmony huggingface dataset:
Memory-Replay Knowledge Distillation

Knowledge Distillation (KD), which transfers the knowledge from a teacher to a student network by penalizing their Kullback–Leibler (KL) divergence, is a widely used tool for Deep Neural Network (DNN) compression in intelligent sensor systems. Traditional KD uses pre-trained teacher, while self-KD distills its own knowledge to achieve better performance. The role of the teacher in self-KD is usually played by multi-branch peers or the identical sample with different augmentation. However, the mentioned self-KD methods above have their limitation for widespread use. The former needs to redesign the DNN for different tasks, and the latter relies on the effectiveness of the augmentation method. To avoid the limitation above, we propose a new self-KD method, Memory-replay Knowledge Distillation (MrKD), that uses the historical models as teachers. Firstly, we propose a novel self-KD training method that penalizes the KD loss between the current model’s output distributions and its backup outputs on the training trajectory. This strategy can regularize the model with its historical output distribution space to stabilize the learning. Secondly, a simple Fully Connected Network (FCN) is applied to ensemble the historical teacher’s output for a better guidance. Finally, to ensure the teacher outputs offer the right class as ground truth, we correct the teacher logit output by the Knowledge Adjustment (KA) method. Experiments on the image (dataset CIFAR-100, CIFAR-10, and CINIC-10) and audio (dataset DCASE) classification tasks show that MrKD improves single model training and working efficiently across different datasets. In contrast to the existing fancy self-KD methods with various external knowledge, the effectiveness of MrKD sheds light on the usually abandoned historical models during the training trajectory.
University Student Surveys Using Chatbots: Artificial Intelligence Conversational Agents

Predefined web surveys are often used to collect course evaluations from students in higher education institutions. These institutions use the evaluations to adjust their courses’ pedagogical standards and lecture style to cope with an increasingly uncertain and complex world. Many limitations to using web surveys have been reported such as low response rates and low-quality responses to open questions. To overcome these limitations, artificial intelligence conversational agents (CAs) or ‘chatbots’ are used to play the interviewer role, facilitating the enhancement of the quality of responses. This is accomplished by mimicking human-human conversations; by asking questions in a friendly, casual way and pursuing high-quality responses. This study aims to explore the opportunities and the obstacles of using CAs in collecting course evaluations in three European universities (UK, Spain and Croatia) and one Centre of excellence in Cyprus. The transcripts collected have been analyzed using statistical data analysis methods and qualitative data analysis techniques. Our findings reveal that the use of CAs in collecting course feedback from students has a positive impact on response quality and can boost students’ enjoyment levels. Furthermore, gender differences and student age have been identified as important factors that can influence the depth of the conversation with the CA.
Back from the dead (again): The specter of the Fairness Doctrine and its lesson for social media regulation

Abstract In the United States, debates about political bias in the content curation and moderation practices of social media platforms have spilled over into the policy realm, rekindling conversations about the Fairness Doctrine and its potential utility in possible regulatory approaches to social media. This article revisits the history of the Fairness Doctrine and uses this history as a lens for critically examining current proposals for integrating Fairness Doctrine‐like principles into a regulatory framework for social media. In addressing this topic, the first section of this article provides a brief overview of the history of the Fairness Doctrine and how the Doctrine has informed (and misinformed) subsequent media policy debates in the years since its elimination. The second section describes how the Fairness Doctrine is being brought to bear in the contemporary debates around social media regulation. The third section offers a critical analysis of the applicability of the Fairness Doctrine to the social media context.
MRANet: Multi-atrous residual attention Network for stereo image super-resolution

In recent years, stereo cameras have been widely used in various fields. Due to the limited resolution of real equipments, stereo image super-resolution (SR) is a very important and hot topic. Recent studies have shown that deep network structures can directly affect feature expression and extraction and thus influence the final results. In this paper, we propose a multi-atrous residual attention stereo super-resolution network (MRANet) with parallax extraction and strong discriminative ability. Specifically, we propose a multi-scale atrous residual attention (MARA) block to obtain receptive fields of different scales through a multi-scale atrous convolution and then combine them with attention mechanisms to extract more diverse and meaningful information. Moreover, we propose a stereo feature fusion unit for stereo parallax extraction and single viewpoint feature refinement and integration. Experiments on benchmark datasets show that MRANet achieves state-of-the-art performance in terms of quantitative metrics and visual quality compared with several SR methods.
Cumulative Quality Modeling for HTTP Adaptive Streaming

HTTP Adaptive Streaming has become the de facto choice for multimedia delivery. However, the quality of adaptive video streaming may fluctuate strongly during a session due to throughput fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this article, we propose a model to estimate the cumulative quality for HTTP Adaptive Streaming. In the model, a sliding window of video segments is employed as the basic building block. Through statistical analysis using a subjective dataset, we identify four important components of the cumulative quality model, namely the minimum window quality, the last window quality, the maximum window quality, and the average window quality. Experiment results show that the proposed model achieves high prediction performance and outperforms related quality models. In addition, another advantage of the proposed model is its simplicity and effectiveness for deployment in real-time estimation. Our subjective dataset as well as the source code of the proposed model have been made publicly available at https://sites.google.com/site/huyenthithanhtran1191/cqmdatabase .
Pre-Processing techniques of Text Mining using Computational Linguistics and Python Libraries

One of the foremost requirements of modern man is undoubtedly the possibility to access the desired information with minimal effort. And it is an undeniable fact that the amount of storing information digitally is increasing minute by minute. As people are moving to a digital world of information or knowledge. Whenever one wishes to collect some information, the first task is to identify the relevant points from the vastness of digitally stored data. The upsurge and flourishing of Text Mining paved the way for a new beginning in the area of Information Extraction (IE) and Information Retrieval (IR). As the term suggests, it is to mine relevant information from the text document. The text document could be un-structured or semi-structured. There exist different approaches and methods for text mining and most of these techniques are computational linguistics with python library related functions. The entitled paper attempts to provide a deliberation on the existing various concepts, techniques, pre-processing steps, applications and issues of text mining.
An Effective e-Commerce Recommender System Based on Trust and Semantic Information

Abstract Electronic commerce has been growing gradually over the last decade as a new driver of the retail industry. In fact, the growth of e-Commerce has caused a significant rise in the number of choices of products and services offered on the Internet. This is where recommender systems come into play by providing meaningful recommendations to consumers based on their needs and interests effectively. However, recommender systems are still vulnerable to the scenarios of sparse rating data and cold start users and items. To develop an effective e-Commerce recommender system that addresses these limitations, we propose a Trust-Semantic enhanced Multi-Criteria CF (TSeMCCF) approach that exploits the trust relations and multi-criteria ratings of users, and the semantic relations of items within the CF framework to achieve effective results when sufficient rating data are not available. The experimental results have shown that the proposed approach outperforms other benchmark recommendation approaches with regard to recommendation accuracy and coverage.
PreSizE: Predicting Size in E-Commerce using Transformers

Recent advances in the e-commerce fashion industry have led to an exploration of novel ways to enhance buyer experience via improved personalization. Predicting a proper size for an item to recommend is an important personalization challenge, and is being studied in this work. Earlier works in this field either focused on modeling explicit buyer fitment feedback or modeling of only a single aspect of the problem (e.g., specific category, brand, etc.). More recent works proposed richer models, either content-based or sequence-based, better accounting for content-based aspects of the problem or better modeling the buyer's online journey. However, both these approaches fail in certain scenarios: either when encountering unseen items (sequence-based models) or when encountering new users (content-based models).
Conclusive local interpretation rules for random forests

In critical situations involving discrimination, gender inequality, economic damage, and even the possibility of casualties, machine learning models must be able to provide clear interpretations of their decisions. Otherwise, their obscure decision-making processes can lead to socioethical issues as they interfere with people’s lives. Random forest algorithms excel in the aforementioned sectors, where their ability to explain themselves is an obvious requirement. In this paper, we present LionForests, which relies on a preliminary work of ours. LionForests is a random forest-specific interpretation technique that provides rules as explanations. It applies to binary classification tasks up to multi-class classification and regression tasks, while a stable theoretical background supports it. A time and scalability analysis suggests that LionForests is much faster than our preliminary work and is also applicable to large datasets. Experimentation, including a comparison with state-of-the-art techniques, demonstrate the efficacy of our contribution. LionForests outperformed the other techniques in terms of precision, variance, and response time, but fell short in terms of rule length and coverage. Finally, we highlight conclusiveness, a unique property of LionForests that provides interpretation validity and distinguishes it from previous techniques.
Real Time Path Planning of Robot using Deep Reinforcement Learning

This paper considers finding a path in real time for a robot from the given initial position to the goal position. The environment is assumed to be mapped (known completely) and the resulting path should avoid all the obstacles, both static and dynamic in the mapped environment. The robot’s (agent) dynamics is assumed to be discrete LTI with process noise and is controlled with a finite set of inputs. An MDP formulation and a solution based on Deep Reinforcement Learning framework are presented for the problem. Numerical experiments are performed for the proposed method using Deep Q-Network algorithm and the results are compared with the state of the art sampling based path planning algorithms for both static and dynamic environments. It is shown that even though the proposed algorithm provides a sub-optimal path, the computational time is shown to be significantly faster compared to the traditional methods of path planning.
Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM

In allusion to the shortcomings of traditional facial expression recognition (FER) that only uses a single feature and the recognition rate is not high, a FER method based on fusion of transformed multilevel features and improved weighted voting SVM (FTMS) is proposed. The algorithm combines the transformed traditional shallow features and convolutional neural network (CNN) deep semantic features and uses an improved weighted voting method to make a comprehensive decision on the results of the four trained SVM classifiers to obtain the final recognition result. The shallow features include local Gabor features, LBP features, and joint geometric features designed in this study, which are composed of distance and deformation characteristics. The deep feature of CNN is the multilayer feature fusion of CNN proposed in this study. This study also proposes to use a better performance SVM classifier with CNN to replace Softmax since the poor distinction between facial expressions. Experiments on the FERPlus database show that the recognition rate of this method is 17.2% higher than that of the traditional CNN, which proves the effectiveness of the fusion of the multilayer convolutional layer features and SVM. FTMS-based facial expression recognition experiments are carried out on the JAFFE and CK+ datasets. Experimental results show that, compared with the single feature, the proposed algorithm has higher recognition rate and robustness and makes full use of the advantages and characteristics of different features.
Identification of continuous stirred tank reactor based on PCA-interval type-2 fuzzy logic system method

For unknown model of Continuous Stirred Tank Reactor (CSTR), a model identification based on the combination of principal component analysis (PCA) and a type-2 non-singular interval type-2 fuzzy logic system (Type-2 NSFLS-2) is proposed method. The PCA method can reduce the dimensionality of the high-dimensional input of the model. On this basis, type-2 NSFLS-2 identification model is established, and the backpropagation (BP) algorithm is used to update tthe parameters of antecedent and consequent membership function respectively. The method in this paper is applied to the example of CSTR, and it is compared with other types of FLS method and support vector machine (SVM) under the same conditions. The experimental results show that different types of interval type-2 FLS methods can achieve higher identification accuracy, among which Type-2 NSFLS-2 has higher identification accuracy.
An improved small target detection method based on Yolo V3

Aiming at the efficiency and accuracy of small target detection in current traffic flow, this paper proposes an improved Yolo V3 method and applies it to small target detection. The method is to first optimize the network structure of Yolo V3, and add a new small target-friendly 4-fold down-sampling residual between the second residual block and the third residual block of Darknet-53 Block, improve the detection accuracy of small targets; perform 2-fold up sampling on the 8-fold down-sampling feature map output by the original network, and perform the 2-fold up-sampling feature map with the feature map output by the newly added third residual block Splicing, build a feature fusion target detection layer whose output is 4 times down sampling. The improved Yolo V3 algorithm is compared with the unimproved algorithm, and it is concluded that the improved algorithm can significantly improve the recall rate of small target detection and the average detection accuracy.
Deep Learning Approach Combining Lightweight CNN Architecture with Transfer Learning: An Automatic Approach for the Detection and Recognition of Bangladeshi Banknotes

Automatic detection and recognition of banknotes can be a very useful technology for people with visual difficulties and also for the banks itself by providing efficient management for handling different paper currencies. Lightweight models can easily be integrated into any handy IoT based gadgets/devices. This article presents our experiments on several state-of-the-art deep learning methods based on Lightweight Convolutional Neural Network architectures combining with transfer learning. ResNet152v2, MobileNet, and NASNetMobile were used as the base models with two different datasets containing Bangladeshi banknote images. The Bangla Currency dataset has 8000 Bangladeshi banknote images where the Bangla Money dataset consists of 1970 images. The performances of the models were measured using both the datasets and the combination of the two datasets. In order to achieve maximum efficiency, we used various augmentations, hyperparameter tuning, and optimizations techniques. We have achieved maximum test accuracy of 98.88&#x0025; on 8000 images dataset using MobileNet, 100&#x0025; on the 1970 images dataset using NASNetMobile, and 97.77&#x0025; on the combined dataset (9970 images) using MobileNet.
Multimodal Activation: Awakening Dialog Robots without Wake Words

When talking to the dialog robots, users have to activate the robot first from the standby mode with special wake words, such as "Hey Siri", which is apparently not user-friendly. The latest generation of dialog robots have been equipped with advanced sensors, like the camera, enabling multimodal activation. In this work, we work towards awaking the robot without wake words. To accomplish this task, we present a Multimodal Activation Scheme (MAS), consisting of two key components: audio-visual consistency detection and semantic talking intention inference. The first one is devised to measure the consistency between the audio and visual modalities in order to figure out weather the heard speech comes from the detected user in front of the camera. Towards this end, two heterogeneous CNN-based networks are introduced to convolutionalize the fine-grained facial landmark features and the MFCC audio features, respectively. The second one is to infer the semantic talking intention of the recorded speech, where the transcript of the speech is recognized and matrix factorization is utilized to uncover the latent human-robot talking topics. We ultimately devise different fusion strategies to unify these two components. To evaluate MAS, we construct a dataset containing 12,741 short videos recorded by 194 invited volunteers. Extensive experiments demonstrate the effectiveness of our scheme.
Advanced Ellipse Fitting Algorithm Based on ADMM and Hybrid BFGS Method

Quick and accurate ellipse detection is essential for computer vision, automatic manufacturing, and other fields. In order to obtain more accurate ellipse parameters, the least-squares support vector regression is employed to establish the objective function and further convert the ellipse fitting problem into an optimization problem. This article proposes a hybrid BFGS algorithm integrated with an alternating direction method of multipliers (ADMM) to reduce the number of iterations and improve the algorithm convergence speed. The effectiveness and accuracy of the proposed algorithm are then verified in a series of numerical simulations by using the actual data of crystal images taken by charge-coupled device (CCD) cameras within the Czochralski silicon growth. Finally, the effectiveness is verified by comparing Hough transform (HT), maximum correntropy criterion (MCC), sparsity-based method (SBM), and other methods.
A Reversible Data Hiding Algorithm Based on Image Camouflage and Bit-Plane Compression

Reversible data hiding in encrypted image (RDHEI) is a widely used technique for privacy protection, which has been developed in many applications that require high confidentiality, authentication and integrity. Proposed RDHEI methods do not allow high embedding rate while ensuring losslessly recover the original image. Moreover, the ciphertext form of encrypted image in RDHEI framework is easy to cause the attention of attackers. This paper proposes a reversible data hiding algorithm based on image camouflage encryption and bit plane compression. A camouflage encryption algorithm is used to transform a secret image into another meaningful target image, which can cover both secret image and encryption behavior based on “plaintext to plaintext” transformation. An edge optimization method based on prediction algorithm is designed to improve the image camouflage encryption quality. The reversible data hiding based bit-plane level compression, which can improve the redundancy of the bit plane by Gray coding, is used to embed watermark in the camouflage image. The experimental results also show the superior performance of the method in terms of embedding capacity and image quality.
Data denoising and compression of intelligent transportation system based on two‐dimensional discrete wavelet transform

Summary Two‐dimensional discrete wavelet transform is an important tool for digital image analysis. It is widely used in the field of image editing, such as image coding and compression and digital image processing. The realization of effective two‐dimensional discrete wavelet transform has important realities in data or image processing. This paper mainly introduces the research of intelligent transportation system data denoising and compression based on two‐dimensional discrete wavelet transform and intends to provide solutions to the problem of data overload in the process of intelligent transportation system data collection, transmission, and storage. This paper proposes the construction of two‐dimensional discrete wavelet transform, including separation calculation method and non‐separation calculation method, and proposes the construction of time–space data model of intelligent transportation system, including support vector machine (SVM), K nearest neighbor algorithm, and deep neural network algorithm. It is proposed to construct two‐dimensional wavelet transform to denoise and compress data of intelligent transportation systems. The experimental results in this paper show that the signal after denoising by two‐dimensional discrete wavelet transform is smoother, with a maximum difference of 0.57, and the denoising effect is better.
Interactive Layout Transfer

During the design of graphical user interfaces (GUIs), one typical objective is to ensure compliance with pertinent style guides, ongoing design practices, and design systems. However, designing compliant layouts is challenging, time-consuming, and can distract creative thinking in design. This paper presents a method for interactive layout transfer, where the layout of a source design – typically an initial rough working draft – is transferred automatically using a selected reference/template layout while complying with relevant guidelines. Our integer programming (IP) method extends previous work in two ways: first, by showing how to transform a rough draft into the final target layout using a reference template and, second, by extending IP-based approaches to adhere to guidelines. We demonstrate how to integrate the method into a real-time interactive GUI sketching tool. Evaluation results are presented from a case study and from an online experiment where the perceived quality of layouts was assessed.