Reviewerly/exHarmony huggingface dataset:
ECG Baseline Estimation and Denoising With Group Sparse Regularization

Baseline wander (BW) and electrocardiogram (ECG) noise reduction play an important role in ECG data analysis and disease diagnosis.This article introduces a sparse optimization method, which takes into account the group sparse characteristics of the signal, and combines low-pass filter to denoise the ECG signal and estimate the baseline.Derived from the classic total variation (TV) denoising method, a denoising method considering the structural characteristics of ECG signals is proposed.This method uses a band matrix to represent the sparse optimization problem, and adopts majorization-minimization (MM) algorithm to optimize the solution of the convergence problem.Through data comparison and detailed analysis, we first compares the method with two TV denoising methods.Then, the proposed method is validated in the MIT-BIH arrhythmia database of ECG signals, and compared with nonlocal means (NLM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods.The simulation experiment results show that the proposed algorithm has lower root mean square error (RMSE) and higher signal-to-noise ratio improvement (SNR_imp).
New polyp image classification technique using transfer learning of network-in-network structure in endoscopic images

While colorectal cancer is known to occur in the gastrointestinal tract. It is the third most common form of cancer of 27 major types of cancer in South Korea and worldwide. Colorectal polyps are known to increase the potential of developing colorectal cancer. Detected polyps need to be resected to reduce the risk of developing cancer. This research improved the performance of polyp classification through the fine-tuning of Network-in-Network (NIN) after applying a pre-trained model of the ImageNet database. Random shuffling is performed 20 times on 1000 colonoscopy images. Each set of data are divided into 800 images of training data and 200 images of test data. An accuracy evaluation is performed on 200 images of test data in 20 experiments. Three compared methods were constructed from AlexNet by transferring the weights trained by three different state-of-the-art databases. A normal AlexNet based method without transfer learning was also compared. The accuracy of the proposed method was higher in statistical significance than the accuracy of four other state-of-the-art methods, and showed an 18.9% improvement over the normal AlexNet based method. The area under the curve was approximately 0.930 ± 0.020, and the recall rate was 0.929 ± 0.029. An automatic algorithm can assist endoscopists in identifying polyps that are adenomatous by considering a high recall rate and accuracy. This system can enable the timely resection of polyps at an early stage.
DeepOpt

Scheduling computations in each layer of a convolutional neural network on a deep learning (DL) accelerator involves a large number of choices, each of which involves a different set of memory reuse and memory access patterns. Since memory transactions are the primary bottleneck in DL acceleration, these choices can strongly impact the energy and throughput of the accelerator. This work proposes an optimization framework, DeepOpt, for general ASIC-based systolic hardware accelerators for layer-specific and hardware-specific scheduling strategy for each layer of a CNN to optimize energy and latency. Optimal hardware allocation significantly reduces execution cost as compared to generic static hardware resource allocation, e.g., improvements of up to 50x in the energy-delay product for VGG-16 and 41x for GoogleNet-v1.
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency

We offer a theoretical characterization of off-policy evaluation (OPE) in reinforcement learning using function approximation for marginal importance weights and $q$-functions when these are estimated using recent minimax methods. Under various combinations of realizability and completeness assumptions, we show that the minimax approach enables us to achieve a fast rate of convergence for weights and quality functions, characterized by the critical inequality \citep{bartlett2005}. Based on this result, we analyze convergence rates for OPE. In particular, we introduce novel alternative completeness conditions under which OPE is feasible and we present the first finite-sample result with first-order efficiency in non-tabular environments, i.e., having the minimal coefficient in the leading term.
Packet-level and IEEE 802.11 MAC frame-level network traffic traces data of the D-Link IoT devices

With the growth of wireless network technology-based devices, identifying the communication behaviour of wireless connectivity enabled devices, e.g. Internet of Things (IoT) devices, is one of the vital aspects, in managing and securing IoT networks. Initially, devices use frames to connect to the access point on the local area network and then, use packets of typical communication protocols through the access point to communicate over the Internet. Toward this goal, network packet and IEEE 802.11 media access control (MAC) frame analysis may assist in managing IoT networks efficiently, and allow investigation of inclusive behaviour of IoT devices. This paper presents network traffic traces data of D-Link IoT devices from packet and frame levels. Data collection experiment has been conducted in the Network Systems and Signal Processing (NSSP) laboratory at Universiti Brunei Darussalam (UBD). All the required devices, such as IoT devices, workstation, smartphone, laptop, USB Ethernet adapter, and USB WiFi adapter, have been configured accordingly, to capture and store network traffic traces of the 14 IoT devices in the laboratory. These IoT devices were from the same manufacture (D-Link) with different types, such as camera, home-hub, door-window sensor, and smart-plug.
Artificial Intelligence: Learning and Limitations

Artificial Intelligence, IA, is a new technology with enormous potential to change the world forever as we know it. It finds applications in many fields of human activity, including services, industry, education, social networks, transportation, among others. However, there is little discussion about the accuracy and reliability of such technology, which has been used in situations where human life depends on its decision-making process, which is the result of its training, one of the stages of development. It is known that the learning process of an Artificial Intelligence, which can use the Artificial Neural Networks technology, presents an error of the predicted value in relation to the real value, which can compromise its application, being more critical in situations where the user's security is a major issue. In this article, we discuss the main technologies used in AI, their development history, considerations about Artificial Neural Networks and the failures arising from the training and hardware processes used. Three types of errors are discussed: The Adversarial Examples, the Soft Errors and the Errors due the lack of Appropriate Training. A case study associated with the third type of error is discussed and actions based on Design of Experiments are proposed. The objective is to change the way the AI models are trained, to add some rare conditions, and to improve their ability to forecast with greater accuracy in any situation
Joint Modulation Format Identification and OSNR Monitoring Using Cascaded Neural Network With Transfer Learning

We propose a cascaded neural network (NN) to simultaneously identify the modulation formats and monitor the optical-signal-to-noise ratio (OSNR). In the second-level network, it is a single deep NN (DNN) rather than multiple sub-networks, which makes the architecture more compact and can save the resource for real implementation. However, since the data set is constituted from all modulation formats, the universality can be guaranteed but not for the accuracy and the complexity. To accelerate the estimation process and improve the accuracy, we introduce the transfer learning (TL) and reconstruct the data set with a part from all of the modulation formats for universality and another part from a specific modulation format for TL to pursue higher accuracy. In the experiment, we compare the proposed cascaded single neural network (CSNN) with or without TL, cascaded multiple neural networks (CMNN) and adaptive multi-task learning (MTL) for MFI and OSNR monitoring. In the first-level NN, all of the three schemes can achieve the accuracy of MFI as 100%. In the second-level NN, the CSNN with TL (TL-CSNN) can significantly improve the training speed and decline the RMSE of 0.19 dB compared with CSNN without TL. The TL-CSNN also has faster convergence speed and is more stable compared with CMNN and adaptive MTL.
A Novel Aggregated Multipath Extreme Gradient Boosting Approach for Radar Emitter Classification

Radar emitter classification (REC) plays an increasingly significant role in the electronic reconnaissance system. Due to there are many unreliable factors in traditional feature-based methods, this article focuses on improving the accuracy and stability of REC based on multipaths of original radar signals. A novel aggregated multipath extreme gradient boosting (AMP-XGBoost) is therefore put forward. Multi-path, including multi-scale, multi-domain transformations and their concatenations, is developed to exploit more information of the original signal, which contributes to providing more distinguishable features. XGBoost is used to automatically extract the features contained in these paths and complete recognition. Finally, multiple paths are sifted and aggregated according to certain weights to obtain a higher accuracy. Experiments are carried out based on signals from 6 different intra-pulse modulation mode radars with the same radio frequency (RF) range. It is demonstrated that the proposed method has higher REC accuracy than the methods only based on a single scale or domain. In the meantime, experimental results under different low SNRs show that the proposed method has higher stability. Finally, experiments based on the measured aviation radar data proves the superiority of the proposed method.
Off-line Handwritten Signature Verification System: Artificial Neural Network Approach

Nowadays, it is evident that signature is commonly used for personal verification, this justifies the necessity for an Automatic Verification System (AVS). Based on the application, verification could either be achieved Offline or Online. An online system uses the signature’s dynamic information; such information is captured at the instant the signature is generated. An offline system, on the other hand, uses an image (the signature is scanned). In this paper, some set of simple shaped geometric features are used in achieving offline Verification of signatures. These features include Baseline Slant Angle (BSA), Aspect Ratio (AR), and Normalized Area (NA), Center of Gravity as well as the line’s Slope that joins the Center of Gravities of the signature’s image two splits. Before the features extraction, a signature preprocessing is necessary to segregate its parts as well as to eliminate any available spurious noise. Primarily, System training is achieved via a signature record which was acquired from personalities whose signatures had to be validated through the system. An average signature is acquired for each subject as a result of incorporating the aforementioned features which were derived from a sample set of the subject’s true signatures. Therefore, a signature functions as the prototype for authentication against a requested test signature. The similarity measure within the feature space between the two signatures is determined by Euclidian distance. If the Euclidian distance is lower than a set threshold (i.e. analogous to the minimum acceptable degree of similarity), the test signature is certified as that of the claiming subject otherwise detected as a forgery. Details on the stated features, pre-processing, implementation, and the results are presented in this work.
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models

Model parallelism has become a necessity for training modern large-scale deep language models. In this work, we identify a new and orthogonal dimension from existing model parallel approaches: it is possible to perform pipeline parallelism within a single training sequence for Transformer-based language models thanks to its autoregressive property. This enables a more fine-grained pipeline compared with previous work. With this key idea, we design TeraPipe, a high-performance token-level pipeline parallel algorithm for synchronous model-parallel training of Transformer-based language models. We develop a novel dynamic programming-based algorithm to calculate the optimal pipelining execution scheme given a specific model and cluster configuration. We show that TeraPipe can speed up the training by 5.0x for the largest GPT-3 model with 175 billion parameters on an AWS cluster with 48 p3.16xlarge instances compared with state-of-the-art model-parallel methods. The code for reproduction can be found at https://github.com/zhuohan123/terapipe
An Overview of Recommender Systems and Machine Learning in Feature Modeling and Configuration

Recommender systems support decisions in various domains ranging from simple items such as books and movies to more complex items such as financial services, telecommunication equipment, and software systems. In this context, recommendations are determined, for example, on the basis of analyzing the preferences of similar users. In contrast to simple items which can be enumerated in an item catalog, complex items have to be represented on the basis of variability models (e.g., feature models) since a complete enumeration of all possible configurations is infeasible and would trigger significant performance issues. In this paper, we give an overview of a potential new line of research which is related to the application of recommender systems and machine learning techniques in feature modeling and configuration. In this context, we give examples of the application of recommender systems and machine learning and discuss future research issues.
Towards a Collaborative Model to Assist People with Disabilities and the Elderly People in Smart Assistive Cities

People with disabilities and the elderly face difficulties to fulfill their accessibility needs in their daily life routines, mainly when they have an emergency demanding speedy and specific assistance. This article proposes the SafeFollowing, a collaborative model to help people with disabilities and the elderly in smart assistive cities. The main contribution of this work is the specification of a decentralized model of ubiquitous accessibility, which involves public agents and volunteers to attend the users’ requests. SafeFollowing also allows the use of police vehicles mapping, in order to provide a specific follow-up in adverse situations of daily life. The model was validated through experiments in real case scenarios by 14 public agents and 11 elderly and people with disabilities. The results showed that 100% of evaluators (users and agents) stated that the model was useful and 82% of users and 100% of agents considered that SafeFollowing was easy to use. The experiments also allowed the learning of 5 relevant lessons on technological and usability aspects of SafeFollowing that are recorded in this article.
Research on Innovation and Entrepreneurship Education and practice in Shandong Province based on the Internet

Abstract Information technology innovation is the inevitable trend of the development of the times. ince the cloud computing, mobile technology, Internet of Things technology and big data was put forward, developed and applied in the new era, new development and construction paths have been explored in various fields. As the main place of social talent input, university educational institutions are constantly exploring new educational paths. Through the integration of Information technology and traditional education, we have effectively promoted the transformation and upgrading of professional and theoretical education. This paper first analyzes the necessity of carrying out innovation and entrepreneurship education for college students under the background of “Internet +”. Then, taking Shandong universities as an example, this paper investigates the actual situation of innovation and entrepreneurship education by questionnaires. Finally, this paper puts forward the strategies and ways to promote the development of innovation and entrepreneurship education and practice.
Memory Optimized Deep Learning based Face Recognization

Machine learning powerful deep learning model is difficult to apply on a low segment embedded system or low segment mobile due to memory-constrained and battery-constrained platforms.This is a major problem, deep learning models have not been yet deployed on very low segment embedded systems such as smartphones (low price), smart wearables, traffic signals, and embedded systems.We are going to present a new way of doing Face Recognition with CNN to solve this problem in this paper.With the help of this, we can easily deploy a trained CNN based model on memory constrained platform such as low segments embedded system or low memory mobile devices/smart wearables, etc.We present a novel and "very efficient" Network Architecture in this paper that includes MobileNetV2 with center loss and training tricks for "deep face recognition" for an embedded system that is memory constrained.In our tests, we ran our proposed Network Architecture on the network at some very memory constrained systems (as low as 2 MB).The result shows that when it is runs on a very low memory segment platform as compared to standard CNN based models, It produces very good results on various face verification datasets and model size less than 1MB for a memory-limited embedded device
On Statistical Bias In Active Learning: How and When to Fix It

Active learning is a powerful tool when labelling data is expensive, but it introduces a bias because the training data no longer follows the population distribution. We formalize this bias and investigate the situations in which it can be harmful and sometimes even helpful. We further introduce novel corrective weights to remove bias when doing so is beneficial. Through this, our work not only provides a useful mechanism that can improve the active learning approach, but also an explanation for the empirical successes of various existing approaches which ignore this bias. In particular, we show that this bias can be actively helpful when training overparameterized models---like neural networks---with relatively modest dataset sizes.
Fast Self-Supervised Clustering With Anchor Graph

Benefit from avoiding the utilization of labeled samples, which are usually insufficient in the real world, unsupervised learning has been regarded as a speedy and powerful strategy on clustering tasks. However, clustering directly from primal data sets leads to high computational cost, which limits its application on large-scale and high-dimensional problems. Recently, anchor-based theories are proposed to partly mitigate this problem and field naturally sparse affinity matrix, while it is still a challenge to get excellent performance along with high efficiency. To dispose of this issue, we first presented a fast semisupervised framework (FSSF) combined with a balanced K -means-based hierarchical K -means (BKHK) method and the bipartite graph theory. Thereafter, we proposed a fast self-supervised clustering method involved in this crucial semisupervised framework, in which all labels are inferred from a constructed bipartite graph with exactly k connected components. The proposed method remarkably accelerates the general semisupervised learning through the anchor and consists of four significant parts: 1) obtaining the anchor set as interim through BKHK algorithm; 2) constructing the bipartite graph; 3) solving the self-supervised problem to construct a typical probability model with FSSF; and 4) selecting the most representative points regarding anchors from BKHK as an interim and conducting label propagation. The experimental results on toy examples and benchmark data sets have demonstrated that the proposed method outperforms other approaches.
Training data augmentation for deep learning radio frequency systems

Applications of machine learning are subject to three major components that contribute to the final performance metrics. Within the category of neural networks, and deep learning specifically, the first two are the architecture for the model being trained and the training approach used. This work focuses on the third component, the data used during training. The primary questions that arise are ``what is in the data'' and ``what within the data matters?'' Looking into the Radio Frequency Machine Learning (RFML) field of Automatic Modulation Classification (AMC) as an example of a tool used for situational awareness, the use of synthetic, captured, and augmented data are examined and compared to provide insights about the quantity and quality of the available data necessary to achieve desired performance levels. There are three questions discussed within this work: (1) how useful a synthetically trained system is expected to be when deployed without considering the environment within the synthesis, (2) how can augmentation be leveraged within the RFML domain, and lastly, (3) what impact knowledge of degradations to the signal caused by the transmission channel contributes to the performance of a system. In general, the examined data types each have useful contributions to a final application, but captured data germane to the intended use case will always provide more significant information and enable the greatest performance. Despite the benefit of captured data, the difficulties and costs that arise from live collection often make the quantity of data needed to achieve peak performance impractical. This paper helps quantify the balance between real and synthetic data, offering concrete examples where training data is parametrically varied in size and source.
Radar HRRP Target Recognition Based on Dynamic Learning with Limited Training Data

For high-resolution range profile (HRRP)-based radar automatic target recognition (RATR), adequate training data are required to characterize a target signature effectively and get good recognition performance. However, collecting enough training data involving HRRP samples from each target orientation is hard. To tackle the HRRP-based RATR task with limited training data, a novel dynamic learning strategy is proposed based on the single-hidden layer feedforward network (SLFN) with an assistant classifier. In the offline training phase, the training data are used for pretraining the SLFN using a reduced kernel extreme learning machine (RKELM). In the online classification phase, the collected test data are first labeled by fusing the recognition results of the current SLFN and assistant classifier. Then the test samples with reliable pseudolabels are used as additional training data to update the parameters of SLFN with the online sequential RKELM (OS-RKELM). Moreover, to improve the accuracy of label estimation for test data, a novel semi-supervised learning method named constraint propagation-based label propagation (CPLP) was developed as an assistant classifier. The proposed method dynamically accumulates knowledge from training and test data through online learning, thereby reinforcing performance of the RATR system with limited training data. Experiments conducted on the simulated HRRP data from 10 civilian vehicles and real HRRP data from three military vehicles demonstrated the effectiveness of the proposed method when the training data are limited.