Reviewerly/exHarmony huggingface dataset:
Learn-to-adapt: Concept drift adaptation for hybrid multiple streams

Existing concept drift adaptation (CDA) methods aim to continually update outdated classifiers in a single-labeled stream scenario. However, real-world data streams are massive, with hybrids of labeled and unlabeled streams. In this paper, we discuss CDA in multiple data streams that may contain unlabeled drifting streams. To address this realistic and complex problem, we rethink the concept drift problem by adopting a meta-learning approach and introduce a Learn-to-Adapt framework (L2A). The L2A framework simultaneously 1) makes adaptations for drifting labeled streams, and 2) leverages knowledge from labeled drifting streams to make adaptations for unlabeled stream prediction. In L2A, a meta-representor with an adapter in the meta-training stage is designed to learn the invariant representations for drifting streams, enabling the model to quickly produce a good generalization of new concepts with limited training samples. In the online stage, the meta-representor will be adapted continually under the control of the adapter and will contribute to adapting the classifiers for unlabeled drifting stream prediction. Compared to existing CDA methods which mostly only adapt the classifiers, L2A adapts the feature extractor and classifier in a feedback process, which is advanced in dealing with more complex and high-dimensional data streams.
Retrieval-Enhanced Machine Learning

Although information access systems have long supported people in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to include task-driven machines, such as machine learning models. In this way, the core principles of indexing, representation, retrieval, and ranking can be applied and extended to substantially improve model generalization, scalability, robustness, and interpretability. We describe a generic retrieval-enhanced machine learning (REML) framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. The REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
Safe intuitionistic fuzzy twin support vector machine for semi-supervised learning

Learning unlabeled samples without deteriorating performance is a challenge in semi-supervised learning. In this paper, we propose a safe intuitionistic fuzzy twin support vector machine (SIFTSVM) for semi-supervised learning. In our SIFTSVM, whether an unlabeled sample should be learned by a twin support vector machine is determined by its plane intuitionistic fuzzy number. The unlabeled samples are learned gradually according to the current decision environment, which is safer and more precise than learning all of the unlabeled samples simultaneously. Interestingly, the iterative algorithm of our SIFTSVM obtains a solution to a mixed integer programming problem whose global solution corresponds to a classifier by learning the unlabeled samples with implicit labels. Experimental results on several synthetic datasets confirm the safety of our SIFTSVM for learning unlabeled samples, and the results on 56 groups of benchmark datasets demonstrate that our SIFTSVM outperforms the state-of-the-art semi-supervised classifiers on most groups.
Wireless modulation classification based on Radon transform and convolutional neural networks

Abstract Convolutional Neural Networks (CNNs) are efficient tools for pattern recognition applications. They have found applications in wireless communication systems such as modulation classification from constellation diagrams. Unfortunately, noisy channels may render the constellation points deformed and scattered, which makes the classification a difficult task. This paper presents an efficient modulation classification algorithm based on CNNs. Constellation diagrams are generated for each modulation type and used for training and testing of the CNNs. The proposed work depends on the application of Radon Transform (RT) to generate more representative patterns for the constellation diagrams to be used for training and testing. The RT has a good ability to represent discrete points in the spatial domain as curved lines. Several pre-trained networks including AlexNet, VGG-16, and VGG-19 are used as classifiers for modulation type from the spatial-domain constellation diagrams or their RTs. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different Signal-to-Noise Ratios (SNRs) and fading channel conditions.
On-Board Ship Detection in SAR Images Based on L-YOLO

Ship detection in Synthetic Aperture Radar (SAR) images is an essential but challenging task. Considering the high-delay between spaceborne SAR system and ground system communication, it is difficult for ground system to achieve realtime SAR data processing. It brings the difficulty for obtaining real-time SAR ship detection. To solve this problem, a novel onboard SAR ship detection method based on the Lightweight You Only Look Once (L-YOLO) algorithm is proposed. Different from the original You Only Look Once version 5 (YOLOv5) algorithm, L-YOLO utilizes lite convolution block instead of original convolution block to compact the network by the means of introducing low-cost linear operations. To further compensate the accuracy, we apply a cost-free K-means algorithm to re-cluster the priori boxes of the dataset. For the following algorithm validation, we also transplant L-YOLO to Nvidia Jetson TX2 to validate the practicability of the scheme. Experimental results on Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) show that our method can achieve &#x007E; 73% mean Average Precision (mAP) with a compact network architecture, where the Floating point Operations (FLOPs) are half (from 16.3G to 8.1G) of YOLOv5.
Occlusion-aware spatial attention transformer for occluded object recognition

Object classification under partial occlusion has been challenging for deep convolutional neural networks due to their innate locality in extracting features. We propose an Occlusion-aware Spatial Attention Transformer (OSAT) architecture based on Vision Transformer (ViT), CutMix augmentation, and Occlusion Mask Predictor (OMP) to solve the occlusion problem. ViT mainly utilizes the self-attention mechanism, which enables the model to capture spatially distant information. In addition, for occluded image augmentation, we combine CutMix augmentation with ViT. OMP is used as a multi-task learning method and for spatial attention on non-occluded region. Our proposed OSAT achieves state-of-the-art performance on occluded vehicle classification datasets from PASCAL3D+ and MS-COCO. Moreover, additional experiments show that OMP outperforms previous approach in occluder localization both quantitatively and qualitatively. According to our ablation studies, ViT is effective at analyzing occluded objects, and our approach of CutMix augmentation and OMP led to further improvements.
Tell Me More: Automating Emojis Classification for Better Accessibility and Emotional Context Recognition

Users of web or chat social networks typically use emojis (e.g., smilies, memes, hearts) to convey in their textual interactions the emotions underlying the context of the communication, aiming for better interpretability, especially for short polysemous phrases. Semantic-based context recognition tools, employed in any chat or social network, can directly comprehend text-based emoticons (i.e., emojis created from a combination of symbols and characters) and translate them into audio information (e.g., text-to-speech readers for individuals with vision impairment). On the other hand, for a comprehensive understanding of the semantic context, image-based emojis require image-recognition algorithms. This study aims to explore and compare different classification methods for pictograms, applied to emojis collected from Internet sources. Each emoji is labeled according to the basic Ekman model of six emotional states. The first step involves extraction of emoji features through convolutional neural networks, which are then used to train conventional supervised machine learning classifiers for purposes of comparison. The second experimental step broadens the comparison to deep learning networks. The results reveal that both the conventional and deep learning classification approaches accomplish the goal effectively, with deep transfer learning exhibiting a highly satisfactory performance, as expected.
LAP: Latency-aware automated pruning with dynamic-based filter selection

Model pruning is widely used to compress and accelerate convolutional neural networks (CNNs). Conventional pruning techniques only focus on how to remove more parameters while ensuring model accuracy. This work not only covers the optimization of model accuracy, but also optimizes the model latency during pruning. When there are multiple optimization objectives, the difficulty of algorithm design increases exponentially. So latency sensitivity is proposed to effectively guide the determination of layer sparsity in this paper. We present the latency-aware automated pruning (LAP) framework which leverages the reinforcement learning to automatically determine the layer sparsity. Latency sensitivity is used as a prior knowledge and involved into the exploration loop. Rather than relying on a single reward signal such as validation accuracy or floating-point operations (FLOPs), our agent receives the feedback on the accuracy error and latency sensitivity. We also provide a novel filter selection algorithm to accurately distinguish important filters within a layer based on their dynamic changes. Compared to the state-of-the-art compression policies, our framework demonstrated superior performances for VGGNet, ResNet, and MobileNet on CIFAR-10, ImageNet, and Food-101. Our LAP allowed the inference latency of MobileNet-V1 to achieve approximately 1.64 times speedup on the Titan RTX GPU, with no loss of ImageNet Top-1 accuracy. It significantly improved the pareto optimal curve on the accuracy and latency trade-off.
Graph convolutional network with sample and feature weights for Alzheimer’s disease diagnosis

Either traditional learning methods or deep learning methods have been widely applied for the early Alzheimer’s disease (AD) diagnosis, but these methods often suffer from the issue of training set bias and have no interpretability. To address these issues, this paper proposes a two-phase framework to iteratively assign weights to samples and features. Specifically, the first phase automatically distinguishes clean samples from training samples. Training samples are regarded as noisy data and thus should be assigned different weights for penalty, while clean samples are of high quality and thus are used to learn the feature weights. In the second phase, our method iteratively assigns sample weights to the training samples and feature weights to the clean samples. Moreover, their updates are iterative so that the proposed framework deals with the training set bias issue as well as contains interpretability on both samples and features. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that our method achieves the best classification performance in terms of binary classification tasks and has better interpretability, compared to the state-of-the-art methods.
Finding Software Ways to Reduce the Error for the Solar Power Plant Simulation Model

The paper deals with the accuracy of the proposed imitational model of solar power plant (SPP) in its operating conditions at modeling in well-known software packages. The necessity of solving this question is dictated by the fact that the inaccuracy of simulation programs allows one to argue that the improvement of the simulation model cannot be performed correctly due to calculation errors in the program itself. The results of simulation of electric power generation by SPP in Matlab package are presented. It was found out, which of the packages meets the set requirements more exactly (by calculation of error of deviation of mathematical modeling and experimental data) and from the point of view of the method incorporated in the program algorithms. Experimental data were compared with the results of simulation modeling.
Secure Model for IoT Healthcare System under Encrypted Blockchain Framework

With the advancements in the new technologies like IoT, the healthcare sector is also growing rapidly. The core concept of introducing IoT in the healthcare centers is to make it remotely accessible, so that communication between doctor and patient can be easy, and diagnosis of any disease can be remotely done through the Internet in case of emergency. The major objective of this study is to develop a security framework for the healthcare industry that can encrypt secret data on cloud servers using block-based data encoding. Since any information that is available on the Internet is vulnerable to various attacks, and the medical records of the patients carry sensitive information that must not be accessible to any unauthorized or unauthentic person, in this context, the concept of Blockchain (BC) technology was introduced in the healthcare systems. In the past few years, a number of BC based healthcare models were proposed in the system; however, these systems are decentralized in nature, which is a feature of Blockchain technology, but in certain applications, especially in healthcare, this feature gave rise to a problem termed as disease overlapping when the count of chains gets increased. To solve this issue, a BC based framework is used in this work that has a feature of creating a separate block in the chain every time whenever any alteration in information about patient’s health, any allergies, new symptoms, medications, etc. is updated. In addition to this, securing the sensitive information of patients is taken into consideration by developing an improved and enhanced multilayer Blockchain based IoT data security approach that not only protects the data, but also builds the trust between patients/user and healthcare service providers. The main focused area of this research article is to propose a security framework for healthcare domain that can encrypt the confidential data over cloud server under block-based data encoding. In the proposed approach, 128-bit AES key is generated in order to support the SHA-256 hashing algorithm. Individual user’s data is subdivided to blocks and encrypted to secure it from unauthorized access. An interface-based system is designed to present the real-world usage of the proposed model in healthcare application. Finally, the effectiveness of the proposed approach is validated in the MATLAB software in terms of MAE, RMSE, MSE, encryption time, and decryption time. The RSA and DSA are the most trending encryption algorithms. These algorithms are crucial components of Blockchain technology, implemented in the proposed work, and later on, their performances are also compared with each other to check their effectiveness. The simulation results demonstrated that the proposed encrypting algorithm surpasses the other encryption approaches such as RSA and DSA in all factors to prove its supremacy. The experimental results show that the encryption and decryption time are lowest in the proposed framework; therefore, its performance is more effective and robust. It is also more effective and resilient.
Understanding and handling alert storm for online service systems

Alert is a kind of key data source in monitoring system for online service systems, which is used to record the anomalies in service components and report to engineers. In general, the occurrence of a service failure tends to be along with a large number of alerts, which is called alert storm. However, alert storm brings great challenges to diagnose the failure, because it is time-consuming and tedious for engineers to investigate such an overwhelming number of alerts manually. To help understand alert storm in practice, we conduct the first empirical study of alert storm based on large-scale real-world alert data and gain some valuable insights. Based on the findings obtained from the study, we propose a novel approach to handling alert storm. Specifically, this approach includes alert storm detection which aims to identify alert storm accurately, and alert storm summary which aims to recommend a small set of representative alerts to engineers for failure diagnosis. Our experimental study on real-world dataset demonstrates that our alert storm detection can achieve high F1-score (larger than 0.9). Besides, our alert storm summary can reduce the number of alerts that need to be examined by more than 98% and discover representative alerts accurately. We have successfully applied our approach to the service maintenance of a large commercial bank (China EverBright Bank), and we also share our success stories and lessons learned in industry.
Virtual Empathy

Abstract This article offers a theoretical response to the recent interest in virtual reality (VR) as a technology for enhancing empathy. It argues that the version of empathy envisioned by the champions of the technology—one in which care for the other is a function of being able to collapse the other’s experience into one’s own—runs counter to the very concept of empathy, as originally construed, which preserves otherness in its alterity. The point is not simply to invoke a historical concept of empathy to contrast with the VR version, but to emphasize its enduring salience in response to recent technological developments. There is little doubt regarding the need for empathy, in the sense explored in this article, as we confront the pathologies of political fragmentation and social polarization facilitated by the current iteration of social media. However, this article argues that the VR version is more likely to exacerbate than redress these concerns.
The Space Bender: Supporting Natural Walking via Overt Manipulation of the Virtual Environment

Manipulating the appearance of a Virtual Environment to enable natural walking has so far focused on modifications that are intended to be unnoticed by users.In our research, we took a radically different approach by embracing the overt nature of the change.To explore this method, we designed the Space Bender, a natural walking technique for room-scale VR.It builds on the idea of overtly manipulating the Virtual Environment by "bending" the geometry whenever the user comes in proximity of a physical boundary.Our aim was to evaluate the feasibility of this approach in terms of performance and subjective feedback.We compared the Space Bender to two other similarly situated techniques: Stop and Reset and Teleportation, in a task requiring participants to traverse a 100 m path.Results show that the Space Bender was significantly faster than Stop and Reset, and preferred to the Teleportation technique, highlighting the potential of overt manipulation to facilitate natural walking.
Real-Time FPGA Implementation of Parallel Connected Component Labelling for a 4K Video Stream

Abstract In this paper, a hardware implementation in reconfigurable logic of a single-pass connected component labelling (CCL) and connected component analysis (CCA) module is presented. The main novelty of the design is the support of a video stream in 2 and 4 pixel per clock format (2 and 4 ppc) and real-time processing of 4K/UHD video stream (3840 x 2160 pixels) at 60 frames per second. We discuss several approaches to the issue and present in detail the selected ones. The proposed module was verified in an exemplary application – skin colour areas segmentation – on the ZCU 102 and ZCU 104 evaluation boards equipped with Xilinx Zynq UltraScale+ MPSoC devices.
A-star algorithm for expanding the number of search directions in path planning

In order to solve the problem that the traditional A-star algorithm (square A-star algorithm has 8 search directions, regular hexagon A-star algorithm has 6 search directions) has many inflection points and long path, an improved A-star algorithm is proposed to expand the number of search directions. Firstly, the grid method is used to model the spatial environment that needs path planning. The improved A-star algorithm improves the search direction to 12 search directions. Because the improved A-star algorithm has more search directions and more choices than the traditional A-star algorithm, the total length of the planned path is significantly smaller than the traditional A-star algorithm, compared with the traditional square A-star algorithm and the traditional hexagon A-star algorithm, the total length of the path is reduced by 8.87% and 3.65% respectively. And because the search direction of the improved A-star algorithm is more, the planned path is smoother and the inflection points in the path are reduced.
Visualizations

Matplotlib is a library package for plotting graphs of 2D and 3D. Modules inside matplotlib like pyplot and pylab make the plotting of the graphs easier (Learning TensorFlow [Authors: Tom Hope, Yehezkel S. Resheff&Itay Lieder]). Pyplot and pylab provide features to control line styles, font, formatting axes, etc. (Deep Learning Pipeline: Building A Deep Learning Model With TensorFlow [Authors: Hisham El-Amir, Mahmoud Hamdy]). Overall, matplotlib supports a wide variety of graphs and graphs like histograms, bar charts, power spectra, polar graph, etc. (TensorFlow for Machine Intelligence_ A Hands-On Introduction to Learning Algorithms [Authors: Sam Abrahams, DanijarHafner, Erik Erwitt, Ariel Scarpinelli]). Matplotlib and numpy provide a best environment for handling and visualizing the data.
Feature Selection Using Artificial Immune Network: An Approach for Software Defect Prediction

Software Defect Prediction (SDP) is a dynamic research field in the software industry.A quality software product results in customer satisfaction.However, the higher the number of user requirements, the more complex will be the software, with a correspondingly higher probability of failure.SDP is a challenging task requiring smart algorithms that can estimate the quality of a software component before it is handed over to the end-user.In this paper, we propose a hybrid approach to address this particular issue.Our approach combines the feature selection capability of the Optimized Artificial Immune Networks (Opt-aiNet) algorithm with benchmark machine-learning classifiers for the better detection of bugs in software modules.Our proposed methodology was tested and validated using 5 open-source National Aeronautics and Space Administration (NASA) data sets from the PROMISE repository: CM1, KC2, JM1, KC1 and PC1.Results were reported in terms of accuracy level and of an AUC with highest accuracy, namely, 94.82%.The results of our experiments indicate that the detection capability of benchmark classifiers can be improved by incorporating Opt-aiNet as a feature selection (FS) method.
muRedder

The experience of sound may be seen as fleeting or ephemeral, as it naturally disperses through space in waveforms unless recorded by media. We designed muRedder to reinstate the ephemerality of sound by shredding a song ticket that embeds a sound source while playing the song simultaneously. In this study, we explored ordinary music listening activities by turning intangible music content into tangible artefacts, making the music unable to be replayed, and representing the sound-fading process by shredding the ticket. We conducted a field study with 10 participants over seven days. The results showed that muRedder enabled users to focus solely on the music content and to actively find times to enjoy the music. We also found that limitedness of the media draws prudent decision in selecting music. By showing the process of consuming the invisible auditory content in a way that is tangibly perceivable, our findings imply new value for slow consumption of digital content and musical participation in public spaces.