Reviewerly/exHarmony huggingface dataset:
Transferability of Pretrained Convolutional Neural Networks for Breast Cancer Detection

Breast cancer is the most common cancer in the world. In breast cancer, the invasive ducal carcinoma (IDC) is the most common breast cancer. For the past few years, more and more machine learning models have been used in medical treatment as an auxiliary tool for doctors. In this work, the machine learning model mainly including Convolutional Neural Network (CNN) and transfer learning was employed to identify breast cancer images to test if the histopathological breast sections are IDC. Since the process of diagnosing IDC by naked eyes is tedious, and time-costy, it could be helpful to develop a model to let machine classify the pathological section, such that the process would be more efficient. While based on previous study, it has shown that deep learning methods could achieve high performance, but it remains to be a question, that if the pre-trained models trained on datasets with no weights on medical images could be helpful while applying on tasks such as this study. The goal is to explore how transfer learning performs compare to other methods. In previous breast cancer recognition work, the SVM model did a pretty good job, so, in this work, a comparison will be done among CNN, transfer learning and SVM. In this study, methods such as cross validation, feature scaling, Support Vector Machine (SVM), CNN and transfer learning were applied to find which one can Figure out the types of pathological sections in the best way; different approaches to evaluate the performance of SVM, CNN and transfer learning model were applied, and find the best one to classify breast cancer pathological sections; the best model were selected regarding to these standards. Finally, it turns out that pre-trained model DenseNet201 for transfer learning works very well. This suggests that transfer learning can achieve good performance in this case by utilizing the weight of ImageNet.
FACS-GCN: Fairness-Aware Cost-Sensitive Boosting of Graph Convolutional Networks

Graph neural networks (GNNs) have emerged as a powerful tool for modeling graph data due to their ability to learn a concise representation of the data by integrating the node attributes and link information in a principled fashion. However, despite their promise, there are several practical challenges that must be overcome to effectively use them for node classification problems. In particular, current approaches are vulnerable to different kinds of biases inherent in the graph data. First, if the class distribution is imbalanced, then the GNNs' loss function is biased towards classifying the majority class correctly rather than the minority class, which hurts the performance of the latter class. Second, due to homophily effect, the learned representation and subsequent downstream tasks may favor certain demographic groups over others when applied to social network data. To mitigate such biases, we propose a novel framework called Fairness-Aware Cost Sensitive Graph Convolutional Network (FACS-GCN) for classifying nodes in networks with skewed class distributions. Our approach combines a cost-sensitive exponential loss with an adversarial learning component to alleviate the ill-effects of both biases. The framework employs a stagewise additive modeling approach to ensure there is no significant loss in accuracy when imparting fairness into the GNN. Experimental results on 6 benchmark graph data demonstrate the effectiveness of FACS-GCN against comparable baseline methods in terms of promoting fairness while maintaining a high model accuracy on the majority of the datasets.
A Secure Blockchain-based Pharmaceutical Supply Chain Management System: Traceability and Detection of Counterfeit Covid-19 Vaccines

Despite the network and internet technologies development, cases of counterfeit medicine still exist and an unreliable pharmaceutical supply chain infrastructure is one of the key factors behind drug counterfeiting. Before reaching the patient, medicines are transferred from suppliers to wholesalers, distributors, and pharmacists. Currently, information is not exchanged between supply chain management systems. Therefore, there is no visibility on the drug supply chain. Drug counterfeiting issue has become more important during the side spread of coronavirus disease due to the high popularity of this disease’s vaccine. Even though this vaccine can help millions of people to eliminate coronavirus, the fake vaccine might be killable for them. This paper is proposing a solution by using blockchain technology for developing a secure pharmaceutical supply chain management system. This technology can add visibility, traceability, security, and transparency to the supply chain system. It is considered that to store the transactions, a permissioned blockchain will be used for this system, and only trusted parties will be permissioned to push data to the blockchain. By the end of this paper, a secure blockchain-based drug supply chain management system will be proposed by the researcher.
Intelligent surveillance system (Iss) using hardware software co-design

In the recent years ISS has received growing attention in the market due to the rise in the demand of safety and security. The demand of ISS is increasing due to its ability to automatically analyze images, videos, audios or any other types of raw data without or with very less human involvement. The developments of sensory devices, machine learning and computer vision in the recent years have an important role in enabling such intelligent systems for surveillance. The proposed one has to develop an embedded surveillance system for video analysis from image processing and feature extraction, to midlevel event detection to high-level image processing, reasoning and scene understanding with hardware-software co-design and discuss some possible sensor modalities and their fusion with visible cameras, infrared camera, thermal camera etc.
Recommendation Algorithm based on Relative Features

Ratings and reviews on e-commerce platforms often contain a lot of useful information. In recent years, researchers aim to mine more information from ratings and reviews to improve recommendation performance. However, researchers have not fully considered the effect of mining the relationship between comments on feature representation. On the basis of previous research, this paper proposes a deep recommendation model based on relative features. We mine the relative attention between comments, calculate the relative features of users and products based on the attention, and use the relative features to complete recommendation. Experiments show that the effect of the model proposed in this paper is better than the previous models.
A Novel Approach for Vietnamese Speech Recognition Using Conformer

Research on speech recognition has existed for a long time, but there is very little research on applying deep learning to Vietnamese language speech recognition. In this paper, we solve the Vietnamese speech recognition problem by deep learning speech recognition frameworks including CTC and Joint CTC/Attention combined with encoder architectures Conformer. Experimental results achieved moderate accuracy using over 115 h of training data of VLSP and Vivos. Compared with the other models, the training results show that the Conformer model trained on CTC achieved good results with a WER value of 20%. Training on big data gives remarkable results and is the basis for us to continue improving the model and increasing accuracy in the future.
Visual Tracking With Reinforced Template Updating and Redetection Discriminator

Though many deep-learning-based trackers for visual object tracking have achieved state-of-the-art performance on multiple benchmarks, they still suffer from significant variations in object appearance and loss of the object. To capture variations of the object appearance, this article proposes a template matching network for object tracking, where deep reinforcement learning is introduced to learn how to update the template. Specifically, the template updating problem is modeled to a Markov decision process where the proximal policy optimization (PPO) algorithm is applied to learn the policy of updating the current template. The resultant template updating policy not only considers the variations of the object but also estimates the influence of current updating for the following frames. To further handle the sudden loss of the object, a two-class redetection discriminator is proposed to conclude whether the object is lost or not. If the object is believed to be lost, a global redetection will be launched to locate the target. Experimentally, the proposed method is compared with some representative methods on dataset OTB2015, and experimental results show that our method can get competitive performance on both accuracy and frame speed.
Improved YOLO Pedestrian Detection Algorithm Based on Attention Mechanism

Pedestrian detection has a wide range of application scenarios in the fields of intelligent monitoring and security, but there is a problem that it cannot achieve sufficient accuracy at sufficient speed. In this context, this paper proposes an improved YOLOv3 pedestrian detection method using combined attention mechanism. Firstly, K-means clustering algorithm is used to extract the prior box of INRIA dataset, so as to obtain the prior boxes more suitable for INRIA dataset. Secondly, SE block is added to the three effective feature layers derived from the backbone Darknet53, which brings a small number of parameters, but can add weight to the features of each channel. Thus removing the side effects of ineffective or weak feature layers. The addition of SE block can effectively improve the accuracy with a small number of parameters. Finally, on the INRIA dataset, the improved YOLOv3 algorithm improves mAP by 3.15% compared with YOLOv3, and is also higher than the similar two-stage algorithm.
Multi-branch Network with Circle Loss Using Voice Conversion and Channel Robust Data Augmentation for Synthetic Speech Detection

Synthesized speech in internet and telephone communications is often difficult to detect by traditional systems due to channel coding. Moreover, traditional systems limited by training data tend to perform poorly on specific synthetic attacks. Accordingly, we propose a new data augmentation strategy that training the voice conversion system without out-of-set data to synthesize specific attack data and performing single-channel data augmentation for both training and evaluation data. Further, we use multi-branching networks and introduce circle loss to improve system performance. The effectiveness of our approach is validated on the ASVspoof 2019 and 2021 LA database.
K-Means cluster optimization for potentiality student grouping using elbow method

The grouping of potential students conducts to determine the student's interest and increase the student's academic performance. The K-Means algorithm could do collection or clusterization. This study aims to implement one of the Machine Learning algorithms, K-Means, to classify the potential of interest grouping of Informatics Engineering student's batch 2019 at the Universitas Muhammadiyah Purwokerto. The process of categorization was based on average course values, which are a part of student specializations, namely 1) Intelligent Systems (IS), 2) Software Engineering (SE), 3) Computer Networks (CN), and 4) Multimedia (MM), as well as student's GPA data (semester 1 to semester 4). Moreover, this research involves the Elbow method for determining the number of optimal clusters and Sum of Squared Errors (SSE) as a cluster validation technique. From the Elbow process, Within Cluster Sum of Squares (WCSS) significantly decreases when K is significantly upwards from 2 to 3, and the SSE maximum rate of change is 71.29 %. Therefore, the optimal cluster is 3. With K-Means clustering results, the majority of the students (62 or 41.05 %) are assigned to the Intelligent System group, the second majority (59 or 39.07 %) to the Multimedia group. At the same time, a cluster of Computer Networks was the group with the fewest members.
Bangla Radical Text Categorization Using Lightweight Convolutional-LSTM Framework

In the era of the fourth industrial revolution, with the advent of internet technology, human life is becoming more and more dependent on online media for social interaction and making important decisions in their lives. Opinions expressed on social media can either be positive or negative which can trigger radicalization in society. This paper mainly focuses on detecting radical text in Bangla from the opinions of people conveyed on social media. Radical Long Short Term Memory (RAD-LSTM) Network, a novel technique, is proposed in this paper to solve the issue. Our technique consists of a one-dimensional convolutional layer of 64 units and only 10 units of LSTM cells. We carried out our experiments using Bangla-Aggressive-Text, a publicly available Bangla dataset. We compared the performance of our architecture with many state-of-the-art techniques and found satisfactory performance in detecting radical speech in Bangla. Despite having the lightweight characteristics, an adequate performance provided by our proposed technique by acquiring 93% accuracy.
Domain-Robust Pre-Training Method for the Sensor-Based Human Activity Recognition

Transfer learning improves problem-solving efficiency by transferring the learned knowledge from the source domain to the target domain. In transfer learning, using a large amount of data for pre-training is beneficial to improve the robustness of the model. Data differ significantly when the domain changes in Sensor-Based human activity recognition (HAR). Currently, in HAR, data usage is relatively independent, lacking source domains with massive data and rich labels. This paper proposes a new pre-training method using multiple domain datasets to construct a domain-robust pre-training model. We divide the pre-training dataset into basic and complex activities scenarios by considering the difference in activity classification. We evaluate the classification scenarios that are most beneficial for sensor-based HAR based on the constituted dataset and using deep convolutional networks. We show that our method verified the influence of the source domain on transfer learning in sensor-based HAR. By constructing a sizeable correlated source domain, our method can enhance the generalization ability of the network model. This paper also demonstrated that large-scale and basic activity classification datasets can be better used as pre-training models to participate in HAR classification tasks.
A Framework Model for Integrating Social Media, the Web, and Proprietary Services Into YouTube Video Classification Process

Online video streaming has gained ubiquity in disparate educational, governmental, and corporate environments. The ubiquity of these videos elicits new challenges in video classification that is used to promote relative videos and block unwanted content. These challenges include the incorporation of contextual information, rapid development of the ad-hoc query modules, and keeping pace with contemporary contextual information. In this article, the authors present a framework model for incorporating contextual information into video classification information. To illustrate the model, the authors propose a framework which comprises video classification, web search engines, social media platforms, and third-party classification modules. The modules enable the framework's flexibility and adaptivity to different contextual environment—educational, governmental, and corporate. Additionally, the model emphasizes standardized module interface to enable the framework's extensibility and rapid development of future modules.
Efficient Clutter Suppression by SOM-SMOTE Random Forest

Radar clutters have complex distribution in space, time and frequency domains especially under complex environment. In the complex geographical environment, radar will produce a large number of clutter residues. Therefore, we designed a clutter suppression method based on intelligent classification. This method constructs high-dimensional feature vectors of radar echo signals, and uses SOM-SMOTE Random Forest (SOM-SMOTE-RF) to identify clutter. In order to solve the problem of data set imbalance, we propose SOM-SMOTE method, which combines self-organizing mapping (SOM) with synthetic minority oversampling (SMOTE). The actual data shows that SOM-SMOTE-RF clutter recognition rate ≥92%.
Text driven virtual speakers

Online courses have had exponential growth during COVID-19 pandemic, and video lectures are also important for lifelong learning. However, lecturers experience a number of challenges in creating video lectures, related to both speech recording (microphone and noise; diction, articulation and intonation) and video recording (camera and light; consistency in appearance). It is particularly difficult to modify and update recorded content. The paper presents a solution for these problems based on the application of artificial intelligence in creating virtual speakers based on TTS synthesis and Wav2Lip GAN trained on a custom data set. A pilot project which included the evaluation and testing of the developed system by dozens of teachers will be presented in detail. The use of TTS overcomes the problems in achieving speaker consistency by providing high quality speech in different languages, while the attention and motivation of students is improved by using animated virtual speakers.
An Integrated Semi-Supervised Learning Framework for Image Compression Using DCT, Huffman Encoding, and LZW Coding

The application of compression of the data to digital files is called image compression. The encoding of images tackles the question of reducing the volume of data needed for digital image representation. The storage space for image loading is also limited. The compression of the picture can be lossy or lossless. This text aims to use different strategies including Discrete Cosine Transformation, LZW Coding, and Huffman Encoding to implement simple JPEG compression. Digital picture data is transformed from spatial domain to frequency domain by the Digested Cosine Transformations (DCT). The compression methods used in this article do not impact data loss in the field of picture transparency. We use JPEG for this reason. JPEG is a format for still compression frames based on the Discrete Cosine Transform and is also appropriate for most compression applications. In a python framework, the project has been tested and a compressed picture was finally created. We were able to achieve 96.11%, 46.61%, and 67.91% compression in DCT, LZW, and Huffman, respectively.
Case-Based Reasoning for Home Health Care planning considering unexpected events

In recent years, Home Health Care (HHC) has gained popularity in different countries around the world (e.g. France, US, Germany, etc.). The HHC consists in providing medical services to patients at home. During the HHC service, caregivers' planning may be disrupted by some unexpected events (e.g. urgent request, caregiver absence, traffic congestion, etc.), which makes HHC activities infeasible. This paper addresses the daily HHC routing and scheduling problem by considering unpredicted events. To solve this problem, we propose a Case-Based Reasoning (CBR) methodology. Our purpose is to create the HHC case base which contains the knowledge about the perturbation.
Split Learning Based on Self-supervised Learning

Vertical federated learning is a type of federated learning which aims to achieve feature fusion of different participants, and the data possessed at distinct participants usually contain different features. In vertical federated learning, distributed deep learning schemes represented by Split learning have received extensive attention. However, Split learning has problems such as label leakage due to frequent gradient interactions. Aiming to solving the above problems, and considering that there are many unused non-overlapping data in the vertical federated learning participants, we propose Split learning based on Self-supervised learning (self-split learning). We split the model into presentation layers and inference layers. The participants first perform self-supervised learning based on the idea of autoencoder on non-overlapping data. After the training of the presentation layers is completed, the server aggregates the overlapping data encoded by each participant, and completes the training of the inference layers independently. By truncating the gradient propagation between the server and the participants, the scheme proposed in this paper effectively solves a series of problems caused by gradient leakage, enables privacy protection. At the same time, our method only requires participants to upload once, which reduces communication overhead and alleviates disconnection, etc. question. We conducted experiments on the financial risk control dataset, and the experiments proved that our algorithm is competitive in performance with current mainstream vertical federated learning algorithms.
Chirp-Based Over-the-Air Computation for Long-Range Federated Edge Learning

In this study, we propose circularly-shifted chirp (CSC)-based majority vote (MV) (CSC-MV), a power-efficient over-the-air computation (OAC) scheme, to achieve long-range federated edge learning (FEEL). The proposed approach maps the votes (i.e., the sign of the local gradients) from the edge devices (EDs) to the linear CSCs constructed with a discrete Fourier transform-spread orthogonal frequency division multiplexing (DFT-s-OFDM) transmitter. At the edge server (ES), the MV is calculated with an energy detector. We compare our proposed scheme with one-bit broadband digital aggregation (OBDA) and show that the output-power back-off (OBO) requirement of the transmitters with an adjacent-channel-leakage ratio (ACLR) constraint for CSC-MV is lower than the one with OBDA. For example, with an ACLR constraint of −22 dB, CSC-MV can have an OBO requirement of 6-7 dB less than the one with OBDA. When the power amplifier (PA) non-linearity is considered, we demonstrate that CSC-MV outperforms OBDA in terms of test accuracy for both homogeneous and heterogeneous data distributions, without using channel state information (CSI) at the ES and EDs.
Balancing Three Important Goals for Runtimes - Isolation, High Performance, and Resource Efficiency

Much of today’s software executes inside of Runtimes, which are software platforms that manage and run code submitted by other developers. Isolation, high performance and resource efficiency are three important goals for a Runtime. However, existing Runtimes cannot meet all of these goals simultaneously. In my thesis, I will explore the trade-offs between these goals and how to provide autonomous system support to maximize them in three important environments.
Adversarial Stacking Ensemble for Facial Landmark Tracking

Current approaches for facial landmark tracking predict facial landmarks through either a single tracker or an ensemble of trackers. However, the conventional ensemble is not designed for facial landmark tracking and can not capture spatial and temporal patterns of facial landmarks efficiently. In this paper, we propose to extend the conventional stacking with an adversarial training strategy to better suit the facial landmark tracking task. Specifically, the meta learner attempts to distinguish the predictions from the base learners with the ground truths, while the base learners attempt to confuse the meta learner by predicting landmarks close to the ground truths. The adversary between the two levels of learners forces them to fully capture the inherent spatial and temporal patterns of facial landmarks. Moreover, to promote the diversity of different base trackers, we design two classification tasks at both the feature level and prediction level. Experimental results on the 300VW dataset and the TF dataset demonstrate the effectiveness of our method, and we achieve state-of-the-art performances on both datasets.