Reviewerly/exHarmony huggingface dataset:
Distill-DBDGAN: Knowledge Distillation and Adversarial Learning Framework for Defocus Blur Detection

Defocus blur detection (DBD) aims to segment the blurred regions from a given image affected by defocus blur. It is a crucial pre-processing step for various computer vision tasks. With the increasing popularity of small mobile devices, there is a need for a computationally efficient method to detect defocus blur accurately. We propose an efficient defocus blur detection method that estimates the probability of each pixel being focused or blurred in resource-constraint devices. Despite remarkable advances made by the recent deep learning-based methods, they still suffer from several challenges such as background clutter, scale sensitivity, indistinguishable low-contrast focused regions from out-of-focus blur, and especially high computational cost and memory requirement. To address the first three challenges, we develop a novel deep network that efficiently detects blur map from the input blurred image. Specifically, we integrate multi-scale features in the deep network to resolve the scale ambiguities and simultaneously modeled the non-local structural correlations in the high-level blur features. To handle the last two issues, we eventually frame our DBD algorithm to perform knowledge distillation by transferring information from the larger teacher network to a compact student network. All the networks are adversarially trained in an end-to-end manner to enforce higher order consistencies between the output and the target distributions. Experimental results demonstrate the state-of-the-art performance of the larger teacher network, while our proposed lightweight DBD model imitates the output of the teacher network without significant loss in accuracy. The codes, pre-trained model weights, and the results will be made publicly available.
Lo-fi Today

This article investigates two current incarnations of ‘lo-fi’ music and questions the extent to which these subgenres are actually low in fidelity. In essence, mainstream ‘hi-fi’ productions use similar effects, such as filtering to sound like a radio or adding noise to sound like a vinyl record. To understand lo-fi today, this article explores music by a lo-fi hip-hop producer and a lo-fi ambient producer, drawing upon the analytical methods of Alan Moore and Dennis Smalley. First to be discussed is Glimlip, one of the many anonymous producers behind the popular Lofi Girl YouTube streams. The next discussed is Amulets, an ambient musician known for using hacked and looped cassette tapes. Analyses of their music demonstrate a level of care in production that goes against the idea that lo-fi is primitive or naive.
‘We Cross Examine with Old Sonic DNA’: King Britt and Tara Rodgers in conversation on Blacktronika, music technology and pedagogy

Composers, producers and educators King Britt and Tara Rodgers discuss music technology history and pedagogy in the context of King Britt’s Blacktronika course at the University of California San Diego, which researches and celebrates Black artists and other artists of colour who are pioneers of electronic music genres. Conducted over email in June 2021, this interview also touches on King Britt’s studio practice and evolution as an artist and educator, as well as on the social and political roots of contemporary electronic music genres.
Lattice‐based batch authentication scheme with dynamic identity revocation in VANET

Aggregate signatures allow someone to aggregate multiple signatures into one signature, which is suitable for resource-constrained and computationally inefficient environments. Identify-based aggregate signature can solve the storage problem of public key certificates while achieving efficient signature verification. However, in most of the identity-based aggregate signature schemes, the user identity revocation process is time-consuming and cannot resist quantum attacks. To solve above problems, this paper proposes a lattice-based aggregate signature scheme with dynamic identity revocation by combining lattice-based cryptography and an aggregate signature scheme. The security of the proposed lattice-based aggregate signature scheme with dynamic identity revocation has been proved in the random oracle model. In addition, the verification efficiency of the aggregate signature has been improved compared with multiple different signatures. Much of the data transfer in Vehicular Ad Hoc Network (VANET) is carried out wirelessly, which makes VANET vulnerable to identity spoofing attacks. Identity authentication technology can prevent attackers from impersonating legitimate users, thus ensuring the security of VANET. Based on the proposed lattice-based aggregate signature scheme with dynamic identity revocation, this paper proposes a lattice-based batch authentication scheme with dynamic identity revocation in VANET. Through the proposed batch authentication scheme, we can effectively resist the impersonation attack of VANET in the quantum computer environment, and the efficiency of authentication is improved.
Intelligent Scene Text Recognition in Streaming Videos

The Scene text Recognition is a problem of extracting textual information from images and videos and plays an increasingly important roles in the era of consuming visual media through social networks. Besides, The execution speed of the models is also shown based on fbs to compare the performance between methods in real-time services occasion. We believe that by conducting a detailed examination of using many modern Deep Learning architectures including SAST, EAST, Rosetta on the Vietnamese scene text dataset also called Vintext developed by the VinAI organization, we may contribute considerable knowledge improving the quality of Vietnamese deep learning field.
Deep Convolutional Support Vector Machines for Human Activity Recognition

Over the past few decades, the Internet of Things has enabled advancements and innovations to develop modern health care applications. Human Activity Recognition (HAR) is new trend using data generated from the wearable devices to monitor health. The activity monitoring applications require high accuracy of the activity classification that is a challenging time series classification task. In this context, Deep Convolutional Support Vector Machine (DCSVM) model is proposed to perform human activity recognition. This model trains all 1D convolution layers through a support vector machine. The classifying results show that the our proposal improves generalization performance on HAR dataset without preprocessing data.
Aspect-Based Sentiment Analysis with Deep Learning: A Multidomain and Multitask Approach

Sentiment analysis aids in obtaining the opinion of the users towards a particular product, service or policy. Focusing on classifying the sentiment that corresponds to each aspect of the entity in the document will help to identify the sentiment more clearly. This is also the mission of aspect-based sentiment analysis (ABSA). The vast majority of prior studies in ABSA have implemented single-task execution models on single-domain datasets. This is inconvenient when it is necessary to perform the full range of tasks in ABSA and on domain-independent datasets. In this paper, we offer to operate the advanced arrangement of deep learning techniques for multidomain and multitask approach in ABSA. The main tasks in ABSA: aspect extraction, category identification, sentiment classification and domain classification are all finished by an integration framework of Convolutional Neural Network (CNN), Bidirectional Independent Long Short Term Memory (BiIndyLSTM) and Attention mechanism. In addition, we use a POS tag layer combined with GloVe in word embedding layer to get the morphological attributes of each token word from review sentences. Through the experimenting process in the Laptop_Restaurant_Hotel multidomain dataset, we found that our proposed model has achieved high precision in multitasking ABSA. With this approach, we hope our proposed model will lay the foundation for ensuring flexibility and multiutility compared to previous opinion analysis models.
Role of the Curator in the Educational process of University Students

The work is devoted to the study of the role of the curator of an academic group in the educational process of students of a higher educational institution. The author’s sociological research was carried out, which made it possible to evaluate the activities of the curator in organizing the interaction of students within the study group, with teachers, structural units of the university. The issues of the curator’s assistance in solving students’ problems are considered. The satisfaction of students with the work of the curator is analyzed.
Investment Model Based on LSTM Network Forecasting and Portfolio Investment

In recent years, Bitcoin has attracted more and more investors' attention. Different from traditional financial assets, the price of Bitcoin is extremely volatile. Therefore, investors need to choose an appropriate asset portfolio to hedge the risk of bitcoin with traditional financial assets. Long Short Term Memory (LSTM) networks are the most advanced sequential learning methods in deep learning of time series prediction, which can accurately predict the prices of financial assets and thus provide traders with the most profitable asset portfolio. In this paper, LSTM-CNN asset price prediction models are developed with data of gold and bitcoin in 2016-2021 as research samples to obtain return maximizing trading strategy. The contribution of this study is to apply deep learning to financial asset price prediction to improve the prediction accuracy and use it as a basis for asset allocation.
Engaging Undergraduate Students in an Introductory A.I. Course through a Knowledge-Based Chatbot Workshop

In this paper we share interim results of an ongoing mixed method evaluative study of 43 students enrolled in an elective course “Doing Business with A.I.” at the Lee Kong Chian School of Business (LKCSB), Singapore Management University. A key component of the course design is an experiential chatbot workshop that provides non-STEM students with an opportunity to acquire basic skills to build a chatbot prototype using the ‘Dialogflow’ program. The workshop and the experiential learning activity were designed to impart students with relevant knowledge and skills such as conversation and user-centric design know how and know why that are transferrable to other situational contexts beyond the course. Based on ongoing class surveys and qualitative interviews with students, we are trying to corroborate a conceptual model developed from learning theories and models related to technology mediated learning (TML) aimed at measuring the effects of a hands-on knowledge-based chatbot workshop designed by the authors on students’ engagement and motivation as drivers of acquiring AI-related competencies such as natural language processing skills (NLP). One important didactical aspect during the design and roll-out of the chatbot workshop is that novice learners with no or very little knowledge about A.I. recognize and create the important linkage between knowledge inputs and outputs of NLP-powered conversational agents (chatbots) so that user queries are effectively addressed. The knowledge-based chatbot workshop design as described in the paper provides useful practical information for instructors interested in designing educational chatbot prototypes for effective digital teaching and learning in a business school (higher education) context that can be transferred to other organizational units beyond the university (e.g. quality customer service) in order to make learners future-ready.
Dissecting the Security and Usability Alignment in the Industry

Security and usability are two important characteristics often in conflict with each other. This paper presents challenges related to alignment between security and usability in the industry. The challenges were identified after analyzing the data from 12 semi-structured interviews. There were nine different challenges in industrial practices which were identified after the interviews, moreover, two recommendations for future solutions were also identified. The paper also presents a framework for addressing the identified challenges within the industry context. The framework presented in the paper has been tailored for the agile development context and aims at identifying minimal trade-offs between security and usability.
Estimating 3D body mesh without SMPL annotations via alternating successive convex approximation

This paper focused on extracting effective human shape and pose information from solely joint annotations. With few training datasets and without using SMPL annotations from MoSh, we proposed a method based on alternating successive convex approximation (ASCA) to estimate the 3D human shape and pose. Previous methods tended to utilize a large number of mixed 2D and 3D datasets for training. These methods extract shape and pose information from SMPL annotations containing ground-truth shape and pose annotations. It is challenging to learn useful information of human shape and pose from solely joint annotations independently because 2D and 3D joint positions can be expressed as a non-convex function of coupled SMPL shape and pose parameters. The proposed method decouples the function into joint-shape and joint-pose functions making the training focus on shape and pose separately during each procedure. A minimum number of datasets are used to train the proposed method (InstaVariety and MPI-INF-3DHP) compare to previous methods. We make a comparison with other methods that have been trained with SMPL annotations. The result shows that the proposed method is competitive with other algorithms trained with additional SMPL annotations. What is more, when ground-truth SMPL annotations exist, ASAC can equally extract useful shape and pose information from image sequences. The result trained with a 3DPW training set outperforms most current video-based algorithms.
A Nonsynaptic Memory Based Neural Network for Hand-Written Digit Classification Using an Explainable Feature Extraction Method

Deep learning methods have been developed for handwritten digit classification. However, these methods work as ‘black-boxes’ and need large training data. In this study, an explainable feature extraction method is developed for handwritten digit classification. The features of the digit image include horizontal, vertical, and orthogonal lines as well as full or semi-circles. In our proposed method, such features are extracted using 10 neurons as computational units. Specifically, the neurons store the features through network training and store them inside the neurons in a non-synaptic memory manner. Following that, the trained neurons are used for the retrieval of information from test images to assign them to digit categories. Our method shows an accuracy of 75 % accuracy using 0.016 % of the training data and achieves a high accuracy of 86 % using one epoch of whole training data of the MNIST dataset. To the best of our knowledge, this is the first model that stores information inside a few single neurons (i.e., non-synaptic memory) instead of storing the information in synapses of connected feed-forward layers. Due to enabling single neurons to compute individually, it is expected that such a class of neural networks can be combined with synaptic memory architectures that we expect to show higher performance compared to traditional neural networks.
Experience Report: A Distributed, Hybrid Course to Teach Global Software Engineering Course During a Global Pandemic

In an age of geographically distributed software development teams, international communication skills are becoming ever more important to IT students. During the Covid-19 pandemic, contact and travel restrictions have made it almost impossible for students to gain international experience by spending a semester in a foreign country. One possible solution is to conduct distributed courses in software engineering as a virtual cooperation between two universities. Experiences gained during a hybrid, distributed course on global software engineering are presented. The challenges encountered when coordinating a course that takes place simultaneously in two countries are reported. Feedback from students and professors are discussed and recommendations for future work are derived from these lessons learned.
Implementation of Machine Learning-Aided Speech Analysis for Speaker Accent Identification Applied to Audio Forensics

Accent recognition as a subset of speech recognition is crucial in audio forensics as it provides the authenticity of the speech that can be presented to the judicature as evidence. The challenge here is to design a speaker accent identifying system that can provide significant information to match with the standards of the court of justice. This process requires robust descriptors to represent speech signal with good discrimination ability. This chapter proposes to use Mel Frequency Cepstral Coefficients to identify and represent the human utterances by transforming the frequency from normal scale to Mel scale. The work utilized support vector machine, k nearest neighbors, XG boost, linear discriminant analysis, quadratic discriminant analysis, and decision tree algorithms to recognize the accent of the speaker. The experiment conducted on the accent recognition dataset demonstrated the ability of Mel Frequency Cepstral Coefficients and kNN classifier in identifying and discriminating six accents belonging to different speakers with better accuracy.
AMRank: An adversarial Markov ranking model combining short- and long-term returns

Learning to rank (LTR) is a method of ranking search results using machine learning techniques. Currently, the reinforcement-learning-based ranking models have achieved some success in LTR task. However, these models have disadvantages like high variance gradient estimates and train inefficiency, which bring great challenges to the convergence and accuracy of the ranking model. Combining short- and long-term returns, this paper proposes AMRank, an adversarial Markov ranking model, which is based on reinforcement learning and formalizes the ranking task as a Markov decision process. To address the aforementioned weaknesses, in AMRank, we present a sequence discriminator to output a long-term return with a smaller variance and conduct single step updates, and use a document discriminator to yield a short-term return. The two discriminators are trained simultaneously before the decision is made. In the training process, the policy network is applied as a generator to sample candidate documents and get negative samples. At the beginning of the decision, the discriminator outputs the returns based on the environment state and the policy, and finally updates the parameters of the policy network using the policy gradient method. Experimental results on three LETOR benchmark datasets, OHUSMED, MQ2007 and MQ2008, demonstrate that the proposed AMRank outperforms the baseline models in document ranking task.
Local variational Probabilistic Minimax Active Learning

In the last decade, many excellent active learning methods have been proposed whose algorithms generally deliver an acceptable performance. However, many of these methods rely on measures other than the risk of the classifier. Recently, researchers have proposed Probabilistic Minimax Active Learning (PMAL). PMAL is optimal because, after adding the result of a query, it minimizes the upper bound of the risk of the classifier. Unfortunately, the exact computation of PMAL’s objective function is intractable for likelihood functions suitable for classification problems, such as logistic regression. Employing a variational approach, the present study approximates the PMAL objective when logistic regression is the likelihood function. Experiments show that the proposed algorithm effectively asks queries, which results in superior performance to that of the state-of-the-art.
Computer-assisted i‘raab of Arabic sentences for teaching grammar to students

The end-case analysis of Arabic sentences is one of the keys to their meaning. This process is called i‘raab, a daunting task for the students. The outcome of the analysis is twofold: (a) placing a proper diacritical marking on the end-cases of individual words, and (b) providing a logical justification. Our objective is to generate a full i‘raab of the sentences, which could be incorporated in a computer-assisted Arabic grammar learning environment, teaching and helping the students with the i‘raab process. Our system comprises four components that work together to generate a proper i‘raab for the sentence. We devise an enhanced context-free grammar (eCFG) that covers all the cases and rules taught in Saudi schools’ grammar textbooks (grades 7–12). Moreover, our eCFG grammar eliminates the need for specialized grammars, e.g., head-driven phrase structure grammar and link grammar, to resolve complex cases involving dependencies. Furthermore, we utilize ontology to determine the correct semantics. The system was tested on 300 sentences varying in complexity from the Arabic grammar textbooks used in the schools. Our system achieved an overall accuracy of 88.33%.
Data Mining-Based Tracking Method for Multisource Target Data of Heterogeneous Networks

In order to solve the problem that the target is easily lost in the process of multisource target data fusion tracking, a multisource target data fusion tracking method based on data mining is proposed. Multisource target data fusion tracking belongs to location level fusion. Firstly, a hybrid heterogeneous network fusion model is established, and then, data features are extracted, and a fusion source big data acquisition algorithm is designed based on compressed sensing to complete data preprocessing to reduce the amount of data acquisition. Based on data mining association multisource fusion target, get the relationship between each measurement and target, and build multisource target data fusion tracking model to ensure the stable state of fusion results. It shows that the proposed method can save the tracking time and improve the tracking accuracy compared with the methods based on NNDA and PDA, which is more conducive to the real-time tracking of multisource targets.
Improved U-Net-Like Network for Visual Saliency Detection Based on Pyramid Feature Attention

As a widely used technology, visual saliency detection has attracted a lot of attention in the past decades. Although a large number of methods, especially fully convolutional neural network- (FCN-) based approaches, have been proposed and achieved remarkable performance, it is still of great value to extend representative architecture to visual saliency detection task. In this paper, we propose an improved U-Net-like network, pyramid feature attention-based U-Net-like (PFAU-Net) for visual saliency detection problem. The main improvements of the proposed model include that in order to enable the network to extract features with more representation ability, we introduce a context-aware feature extraction (CFE) module and a channel attention module into the U-shaped backbone to obtain valuable multiscale features, and a feature pyramid path is also utilized in the decoder part of the network to take advantages of multilevel information. Moreover, we construct the loss function using three terms including pixel-level cross-entropy, image-level intersection over union (IoU), and a structural similarity term, which aim to make the model learn more saliency related knowledge. To verify the effectiveness of the proposed model, we conduct extensive experiments on six widely used public datasets, and the experimental results indicate that (1) our improved model can significantly improve the performance of the backbone network on all test datasets, and (2) our proposed model can outperform comparison FCN-based networks and nonneural network approaches. Both objective and qualitative evaluations verify the effectiveness of our proposed model.