Reviewerly/exHarmony huggingface dataset:
Entropy-Driven Sampling and Training Scheme for Conditional Diffusion Generation

Denoising Diffusion Probabilistic Model (DDPM) is able to make flexible conditional image generation from prior noise to real data, by introducing an independent noise-aware classifier to provide conditional gradient guidance at each time step of denoising process. However, due to the ability of the classifier to easily discriminate an incompletely generated image only with high-level structure, the gradient, which is a kind of class information guidance, tends to vanish early, leading to the collapse from conditional generation process into the unconditional process. To address this problem, we propose two simple but effective approaches from two perspectives. For sampling procedure, we introduce the entropy of predicted distribution as the measure of guidance vanishing level and propose an entropy-aware scaling method to adaptively recover the conditional semantic guidance. For the training stage, we propose the entropy-aware optimization objectives to alleviate the overconfident prediction for noisy data. On ImageNet1000 256 $$\times $$ 256, with our proposed sampling scheme and trained classifier, the pretrained conditional and unconditional DDPM model can achieve 10.89% (4.59 to 4.09) and 43.5% (12 to 6.78) FID improvement, respectively. Code is available at https://github.com/ZGCTroy/ED-DPM .
Providing Authentication and Privacy for University Certificates using Smart Contracts in Blockchain Technology

Traditional ways of distributing and verifying academic certificates are not efficient.Certificates are distributed as hard copy.Verifying the integrity of the certificate is a time and resource consuming process.As a result, forged certificates have become common.It is very difficult to differentiate between a real and a forged certificate.Through our system, we intend to make the certificate generation, distribution, and verification process seamless.Any student can enter his or her personal details, academic coursework details, and the university code, and thus submit a certificate request to the university.University admins can verify the certificate requests, and approve or reject the requests as per their policy.Any student or third party could verify the integrity of the certificate by entering the details of the certificate under scrutiny into the system.
Identifying the Best Clear Sky Model for the Delhi-NCR Region

While intermittent nature of solar irradiance values is primarily attributed to clouds, its equally important to analyze the solar irradiance variance throughout the day in clear sky conditions. Although a vast amount of research has been conducted so far and a numerous clear sky models have been proposed in the literature, none of them holds true temporally <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> nd/or spatially across the earth. Accordingly, this paper proposes a generic framework to identify the most appropriate clear sky model for the location of interest, i.e. Delhi-NCR in this case. The paper further highlights the importance of incorporating seasonal and temporal variations while determining the suitable clear sky model.
Sentiment Analysis on Student Feedback Using RNN

Almost all organizations collect feedback to improve the quality of service provided. Frequent and ongoing feedback analysis is inevitable for the development of any organization. But handling this huge amount of data is time-consuming and not economical. In the present feedback system, qualitative analysis of data is not possible. It may require huge manpower to analyze the feedback. Also, the currently used grading system is not useful in conveying the exact emotions of students. To overcome this, a system is proposed that gives summarised feedback and works on sentiment analysis. The summarised feedback contains deep key phrases. Key phrases can provide highly condensed and valuable information that helps in acquiring the main idea of the content. It attempts to capture the deep semantic meaning of text content. Here, the Recurrent Neural Network (RNN) algorithm is used for keyphrase extraction and sentiment analysis
How Efficient are Replay Attacks against Vote Privacy? A Formal Quantitative Analysis

Replay attacks are among the most well-known attacks against vote privacy. Many e-voting systems have been proven vulnerable to replay attacks, including systems like Helios that are used in real practical elections.Despite their popularity, it is commonly believed that replay attacks are inefficient but the actual threat that they pose to vote privacy has never been studied formally. Therefore, in this paper, we precisely analyze for the first time how efficient replay attacks really are.We study this question from commonly used and complementary perspectives on vote privacy, showing as an independent contribution that a simple extension of a popular game-based privacy definition corresponds to a strong entropy-based notion.Our results demonstrate that replay attacks can be devastating for a voter’s privacy even when an adversary’s resources are very limited. We illustrate our formal findings by applying them to a number of real-world elections, showing that a modest number of replays can result in significant privacy loss. Overall, our work reveals that, contrary to a common belief, replay attacks can be very efficient and must therefore be considered a serious threat.
Sketch-based City Generation Using Procedural Modeling and Generative Model

In this study, we propose an efficient city-generation method based on user sketches that combine a deep generative model with the procedural modeling approach. The proposed framework adopts the deep learning-based network of conditional generative adversarial networks. For the data training, we randomly generated three-dimensional cities from perlin noise. The contours of the cities were extracted by the holistically-nested edge detection approach. The proposed method is a deep learning model that uses paired data of cities generated by a procedural model, along with the corresponding hand-drawn style sketches.
Deep Semantic Ranking Hashing Based on Self-Attention for Medical Image Retrieval

With the rapid progress of medical image technology, medical image retrieval has attracted wide attention in medical data processing fields. Deep hashing methods have been proven effective for massive medical image retrieval. However, existing medical image retrieval methods ignore lesion context and category-level semantics, so it is difficult to correctly correspond to the context information and category of the lesion, resulting in poor performance. This paper addresses this dilemma with a novel Deep Semantic Ranking Hashing Based on Self-Attention (DSHA) approach. We first divide the medical triplet into smaller patches and send them to the multi-head self-attention module, which can more effectively encode the context information of the lesion and the interaction among patches. Meanwhile, the weight-sharing triplet networks are used to learn hash codes, which are forced to be semantically aligned. The proposed semantic enhancement loss effectively enhances the category-level semantics of hash codes. In addition, we added a semantic ranking penalty loss to optimize the retrieval accuracy. Extensive experiments on diverse medical image datasets prove that our DSHA method achieves remarkable results compared with the state-of-the-art medical image retrieval methods.
Alignahead: Online Cross-Layer Knowledge Extraction on Graph Neural Networks

Existing knowledge distillation methods on graph neural networks (GNNs) are almost offline, where the student model extracts knowledge from a powerful teacher model to improve its performance. However, a pre-trained teacher model is not always accessible due to training cost, privacy, etc. In this paper, we propose a novel online knowledge distillation framework to resolve this problem. Specifically, each student GNN model learns the extracted local structure from another simultaneously trained counterpart in an alternating training procedure. We further develop a cross-layer distillation strategy by aligning ahead one student layer with the layer in different depth of another student model, which theoretically makes the structure information spread over all layers. Experimental results on five datasets including PPI, Coauthor-CS/Physics and Amazon-Computer/Photo demonstrate that the student performance is consistently boosted in our collaborative training framework without the supervision of a pre-trained teacher model. In addition, we also find that our alignahead technique can accelerate the model convergence speed and its effectiveness can be generally improved by increasing the student numbers in training. Code is available: https://github.com/GuoJY-eatsTG/Alignahead
Deep learning and Threshold Probability for Out of Scope Intent Detection in Task Oriented Chatbot

A lot of research on task-oriented chatbots has been carried out to improve research on intent recognition. However, there are not many studies that discuss out-of-scope. This study introduces the use of threshold for classification results. The use of threshold improves recall performance of OOS detection above baseline for BiLSTM but reduces recall inside scope for all models. Directly applying multiclass classification with combined class of inside scope and out of scope also showed higher recall than baseline.
Reduce Computing Complexity of Deep Neural Networks Through Weight Scaling

Large deep neural network (DNN) models are computation and memory intensive, which limits their deployment especially on edge devices. Therefore, pruning, quantization, data sparsity and data reuse have been applied to DNNs to reduce memory and computation complexity at the expense of some accuracy loss. The reduction in the bit-precision results in loss of information, and the aggressive bit-width reduction could result in noticeable accuracy loss. This paper introduces Scaling-Weight-based Convolution (SWC) technique to reduce the DNN model size and the complexity and number of arithmetic operations. This is achieved by, using a small set of high-precision weights (maximum absolute weight “MAW”) and a large set of low-precision weights (Scaling weights “SWs”). This results in decreasing the model size with minimum loss in accuracy compared to simply reducing the precision. Moreover, a scaling and quantized network-acceleration processor (SQNAP) is proposed based on the SWC method to achieve high-speed and low-power with reduced memory accesses. The proposed SWC eliminate >90% of the multiplications in the network. Moreover, the less important SWs are pruned, which has a small portion of the MAW. Retraining is applied in order to maintain accuracy. Full analysis for MNIST, Fashion MNIST, Cifar 10 and Cifar 100 datasets is presented for image recognition, where different DNN models are used including LeNet, ResNet, AlexNet and VGG 16.
Secure Medical Data Sharing For Healthcare System

A new generation of advanced information technologies are used nowadays by healthcare systems to provide access to affordable and high-quality healthcare services. However, such services, generally require a large amount of data that needs to be stored, shared and secured efficiently. Therefore, a secure distributed data storing and sharing mechanism is proposed in this paper for healthcare systems. By using a specifically designed data splitting and encryption approach, the proposed sharing mechanism ensures that any user in the network can recuperate the data only if it gets the approval of a prefixed number of trusted nodes. This ensure that although the data is distributed among different network nodes, it becomes useless without the collection of the necessary data parts and approvals. In order to enhance the data sharing robustness to failures while minimizing the transmission delays, and satisfying the network constraints, an iterative algorithm is proposed to optimize the selection of the nodes that should participate in the data storage process. The simulation results confirm the efficiency of the proposed approaches to efficiently and securely store and share the data with the other legitimate nodes.
Bengali Speech Recognition: An Overview

This study outlines the notable efforts of creating of automatic speech recognition (ASR) system in Bengali. It describes data from the Bengali language's existing voice corpus and the major reports that have contributed to the recent research scenario. It provides an overview of dataset or corpus that has been created for bengali ASR, challenge faced to create bengali ASR as well as techniques used to build Bengali ASR system. ASR techniques for the Bengali language have made significant progress in recent years. Our article contains studies from 2016 through 2020. We examined the results of these investigations, as well as the strategies used to accomplish this goal, for Automated voice recognition. We have examined these publications to obtain a feel of the present state of Bengali ASR. We have observed a dearth of sufficient datasets among these researchers, which is important for any automated system. Due to the language's abundance of consonant clusters, the Machine Learning (ML) system has difficulty interpreting Bengali words. As a result of these modifications, the system now confronts a new set of difficulties in terms of effectiveness and efficiency. Additionally, numerous words have nearly identical pronunciations. These are only some of the issues that the papers we examined face. This research makes use of a variety of techniques, including linear prediction coding, Mel Frequency Cepstral Coefficient, Hidden Markov Model, Neural Network, and Fuzzy logic. Bengali ASR will require further investigation shortly. While recent research is encouraging, ASR of other languages, such as English, is far from perfect and efficient.
Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift

We introduce LIMES, a new method for learning with non-stationary streaming data, inspired by the recent success of meta-learning. The main idea is not to attempt to learn a single classifier that would have to work well across all occurring data distributions, nor many separate classifiers, but to exploit a hybrid strategy: we learn a single set of model parameters from which a specific classifier for any specific data distribution is derived via classifier adaptation. Assuming a multiclass classification setting with class-prior shift, the adaptation step can be performed analytically with only the classifier’s bias terms being affected. Another contribution of our work is an extrapolation step that predicts suitable adaptation parameters for future time steps based on the previous data. In combination, we obtain a lightweight procedure for learning from streaming data with varying class distribution that adds no trainable parameters and almost no memory or computational overhead compared to training a single model. Experiments on a set of exemplary tasks using Twitter data show that LIMES achieves higher accuracy than alternative approaches, especially with respect to the relevant real-world metric of lowest within-day accuracy.
SGLCMR: Self-supervised Graph Learning of Generalized Representations for Cross-Market Recommendation

Cross-Market Recommendation (CMR) has been proposed to improve item recommendation performance in the data-insufficient markets by leveraging knowledge learned from other data-sufficient markets. CMR can be regarded as a subtask of Cross-Domain Recommendation (CDR), which overlaps in items and differs in users. Usually, there are a lot of overlapped items in different markets that make the knowledge transfer across markets feasible and valuable. Most existing graph-based methods construct a cross-domain graph by collecting interactions from different domains and then devise some kind of graph neural network (GNN) to learn graph representations for each node. However, we think these methods suffer from two limitations: (1) graph representations are vulnerable to noisy interactions, especially interactions collected from different domains that make the problem more serious; (2) graph representations are biased because they have been more influenced by data-sufficient domains, making graph representations less generalized. In this paper, we propose a novel framework, Self-supervised Graph Learning of Generalized Representations for Cross-Market Recommendation (SGLCMR), to address the foregoing limitations, and we treat different markets as different domains. First, we construct the single-market and cross-market graph to model market-specific and market-generalized features respectively. Second, we adopt Light Graph Convolution (LGC) to aggregate neighbors without loss of generality. Then, we devise two graph-structured data augmentation operators for self-supervised graph learning, i.e., Market-unaware Dropout and Market-aware Dropout, aiming to solve the limitations that graph representations are vulnerable and biased. Finally, we employ a multi-task learning strategy to optimize our model. Extensive experiments on seven pairs of real-world datasets show that our proposed SGLCMR is highly superior to the state-of-the-art CMR methods.
DeepDream Algorithm for Data Augmentation in a Neural Network Ensemble Applied to Multiclass Image Classification

This paper presents the application of Deep-Dream algorithm for data augmentation applied to images. This algorithm analyzes the image by a trained neural network and hides main features of the image. Importance and place of the features is estimated based on neural network layers output values. The new neural network trained on the processed data are forced to search and train a new set of features in the data, since the known features have been hidden. Trained on original and processed data neural networks are used in an ensemble. Experiments were conducted with a balanced images dataset with 5000 images in 10 classes. Experiment was conducted with a neural network based on InceptionV3 architecture in two variations: with non pretrained weights and with pretrained weights. The neural network received a 256 × 256 pixel image as input. Training was conducted using categorical cross entropy loss function, accuracy metric, Adam optimizer with a learning rate of 0.0001. The improvements of the algorithm are almost insignificant when classifying an ensemble of neural network models for a small number of classes, but the impact of the algorithm increases as the number of classes increases. For binary classification there may be no improvement in ensemble accuracy, but when the number of classes increases and becomes more than 5, the influence of the algorithm on the accuracy of the final ensemble increases. The improvement in ensemble accuracy can be 0.5–4%, depending on the initial training conditions without the use of other types of data processing and augmentation.
Robust Learning Against Label Noise Based on Activation Trend Tracking

Defect detection of industrial products plays an important role in industrial production. Deep learning methods have been introduced to replace manual labor and save costs. Training deep neural networks requires large datasets with precisely annotated labels. However, it is challenging to obtain such clean datasets, especially in industrial scenes. For instance, in tire industry, classification of tire defects is ambiguous and thus labeling a big and clean dataset for defect detection is almost impossible. Therefore, training robust models with datasets containing noisy labels has received increasing attention. Different from existing methods, we conduct research on the learning process of such datasets from a new perspective, by analyzing the channel-wise activation intensity of training samples. Deep models exhibit a two-phase learning process when training with noisy datasets, which is quite different from training with clean labels. This motivates us to propose a novel method called Activation Trend Tracking based Robust Learning. During the training process, activation intensity is monitored and used to adjust the loss function to avoid overfitting on noisy labels. To illustrate the effectiveness of the proposed method, empirical analysis and robustness verification are presented through extensive experiments. Experimental results on synthetic and tire datasets demonstrate that our approach is tolerant to high proportions of noisy labels and is superior to other existing methods, exhibiting a good prospect for industrial application.
Parsimonious Finite Mixtures of Matrix-Variate Regressions

Over the years, there has been an increased interest in the analysis of matrix-variate data. In the model-based clustering literature, finite mixtures of matrix-variate regressions have been recently introduced. However, a serious concern about this model is the excessive number of parameters associated with the two covariance matrices, related to the responses, for each mixture component. To attain parsimony, the well-known eigen-decomposition is applied to the covariance matrices, yielding a family of 98 different parsimonious mixture models. Parameter estimation, under the maximum likelihood paradigm, is carried out via an expectation-conditional maximization (ECM) algorithm. Our family of models is applied to real data with the aim to assess their clustering performance and for analyzing their behavior with respect to other parsimonious mixture models.
Master Multiple Real-Time Strategy Games with a Unified Learning Model Using Multi-agent Reinforcement Learning

General artificial intelligence requires an intelligent agent to understand or learn any intellectual tasks like a human being. Diverse and complex real-time strategy (RTS) game for artificial intelligence research is a promising stepping stone to achieve the goal. In the last decade, the strongest agents have either simplified the key elements of the game, or used expert rules with human knowledge, or focused on a specific environment. In this paper, we propose a unified learning model that can master various environments in RTS game without human knowledge. We use a multi-agent reinforcement learning algorithm that uses data from agents in a diverse league played on multiple maps to train the deep neural network model. We evaluate our model in microRTS, a simple real-time strategy game. The results show that the agent is competitive against the strong benchmarks in different environments.