Reviewerly/exHarmony huggingface dataset:
Hybrid Genetic Algorithm and Modified-Particle Swarm Optimization Algorithm (GA-MPSO) for Predicting Scheduling Virtual Machines in Educational Cloud Platforms

Cloud computing is expanding gradually as the number of educational applications is rapidly increasing. To get Educational cloud services, internet connectivity is predominantly important and Cloud Environment uses one of the basic technology to manage the Physical servers effectively ie; Virtualization Technology. In Cloud Computing, the data centers host numerous Virtual Machines (VMs) on top of the Servers. Due to the rapid growth of Educational platforms, the workload of the VM is computationally getting increased. In the Cloud Educational platforms, to execute the jobs IT resources are provisioned over the network. Since the data generated from the client-side is dynamic in nature, it is difficult to allocate the computational resources efficiently. So to enhance the energy efficiency and to provide the resources in an optimized way, a VM Scheduling mechanism with Hybrid Genetic Algorithm-Modified Particle Swarm Optimization (GA-MPSO) is proposed in this work to achieve QoS parameters like reduced Energy consumption, SLA violation, and cost reduction over the heterogeneous environments. The Hybrid G-MPSO develops the optimal range and improves the best range of scheduling the Virtual resources to VMs from Physical Machines (PMs). The proposed approach, when compared to other VM scheduling algorithms, it intensifies the energy consumption to 105KWH, SLA violation rate of 0.08%, reduces the migrations count to 2122, and consumes the overall cost of 2567.68$. The different scheduling methods for VMs are evaluated against the results, which show that the Hybrid GA-MPSO method is far better than the existing algorithms.
Structured Pruning Learns Compact and Accurate Models

The growing size of neural language models has led to increased attention in model compression.The two predominant approaches are pruning, which gradually removes weights from a pre-trained model, and distillation, which trains a smaller compact model to match a larger one.Pruning methods can significantly reduce the model size but hardly achieve large speedups as distillation.However, distillation methods require large amounts of unlabeled data and are expensive to train.In this work, we propose a task-specific structured pruning method CoFi 1 (Coarse-and Fine-grained Pruning), which delivers highly parallelizable subnetworks and matches the distillation methods in both accuracy and latency, without resorting to any unlabeled data.Our key insight is to jointly prune coarse-grained (e.g., layers) and fine-grained (e.g., heads and hidden units) modules, which controls the pruning decision of each parameter with masks of different granularity.We also devise a layerwise distillation strategy to transfer knowledge from unpruned to pruned models during optimization.Our experiments on GLUE and SQuAD datasets show that CoFi yields models with over 10× speedups with a small accuracy drop, showing its effectiveness and efficiency compared to previous pruning and distillation approaches. 2
IBM Quantum Platforms: A Quantum Battery Perspective

We characterize for the first time the performances of IBM quantum chips as quantum batteries, specifically addressing the single-qubit Armonk processor. By exploiting the Pulse access enabled to some of the IBM Quantum processors via the Qiskit package, we investigate advantages and limitations of different profiles for classical drives used to charge these miniaturized batteries, establishing the optimal compromise between charging time and stored energy. Moreover, we consider the role played by various possible initial conditions on the functioning of the quantum batteries. As main result of our analysis, we observe that unavoidable errors occurring in the initialization phase of the qubit, which can be detrimental for quantum computing applications, only marginally affects energy transfer and storage. This can lead counter-intuitively to improvements of the performances. This is a strong indication of the fact that IBM quantum devices are already in the proper range of parameters to be considered as good and stable quantum batteries, comparable to state of the art devices recently discussed in literature.
Neural Contextual Anomaly Detection for Time Series

We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is applicable to both univariate and multivariate time series. This is achieved by combining recent developments in representation learning for multivariate time series, with techniques for deep anomaly detection originally developed for computer vision that we tailor to the time series setting. Our window-based approach facilitates learning the boundary between normal and anomalous classes by injecting generic synthetic anomalies into the available data. NCAD can effectively take advantage of domain knowledge and of any available training labels. We demonstrate empirically on standard benchmark datasets that our approach obtains a state-of-the-art performance in the supervised, semi-supervised, and unsupervised settings.
AXM-Net: Implicit Cross-Modal Feature Alignment for Person Re-identification

Cross-modal person re-identification (Re-ID) is critical for modern video surveillance systems. The key challenge is to align cross-modality representations conforming to semantic information present for a person and ignore background information. This work presents a novel convolutional neural network (CNN) based architecture designed to learn semantically aligned cross-modal visual and textual representations. The underlying building block, named AXM-Block, is a unified multi-layer network that dynamically exploits the multi-scale knowledge from both modalities and re-calibrates each modality according to shared semantics. To complement the convolutional design, contextual attention is applied in the text branch to manipulate long-term dependencies. Moreover, we propose a unique design to enhance visual part-based feature coherence and locality information. Our framework is novel in its ability to implicitly learn aligned semantics between modalities during the feature learning stage. The unified feature learning effectively utilizes textual data as a super-annotation signal for visual representation learning and automatically rejects irrelevant information. The entire AXM-Net is trained end-to-end on CUHK-PEDES data. We report results on two tasks, person search and cross-modal Re-ID. The AXM-Net outperforms the current state-of-the-art (SOTA) methods and achieves 64.44% Rank@1 on the CUHK-PEDES test set. It also outperforms by &gt;10% for cross-viewpoint text-to-image Re-ID scenarios on CrossRe-ID and CUHK-SYSU datasets.
An Improved Yolov5 for Multi-Rotor UAV Detection

Multi-rotor drones have a wide range of applications in practical scenarios; however, the use of multi-rotor drones for illegal acts is also on the rise, in order to improve the recognition accuracy of multi-rotor drones. A new multi-rotor drone detection algorithm is proposed. Firstly, the Yolov5 backbone is replaced with Efficientlite, thus reducing the number of parameters in the model. Secondly, adaptively spatial feature fusion is injected into the head of the baseline model to facilitate the fusion of feature maps with different spatial resolutions, in order to balance the accuracy loss caused by the lightweight of the model backbone. Finally, a constraint of angle is introduced into the original regression loss function to avoid the mismatch between the prediction frame and the real frame orientation during the training process in order to improve the speed of network convergence. Experiments show that the improved Yolov5s exhibits better detection performance, which provides a superior method for detecting multi-rotor UAVs in real-world scenarios.
Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks

Silicon photonics is promising for artificial neural networks computing owing to its superior interconnect bandwidth, low energy consumption and scalable fabrication. However, the lack of silicon-integrated and monitorable optical neurons limits its revolution in large-scale artificial neural networks. Here, we highlight nonlinear germanium-silicon photodiodes to construct on-chip optical neurons and a self-monitored all-optical neural network. With specifically engineered optical-to-optical and optical-to-electrical responses, the proposed neuron merges the all-optical activation and non-intrusive monitoring functions in a compact footprint of 4.3 × 8 μm2. Experimentally, a scalable three-layer photonic neural network enables in situ training and learning in object classification and semantic segmentation tasks. The performance of this neuron implemented in a deep-scale neural network is further confirmed via handwriting recognition, achieving a high accuracy of 97.3%. We believe this work will enable future large-scale photonic intelligent processors with more functionalities but simplified architecture.
YOLO-LRDD: a lightweight method for road damage detection based on improved YOLOv5s

Abstract In computer vision, timely and accurate execution of object identification tasks is critical. However, present road damage detection approaches based on deep learning suffer from complex models and computationally time-consuming issues. To address these issues, we present a lightweight model for road damage identification by enhancing the YOLOv5s approach. The resulting algorithm, YOLO-LRDD, provides a good balance of detection precision and speed. First, we propose the novel backbone network Shuffle-ECANet by adding an ECA attention module into the lightweight model ShuffleNetV2. Second, to ensure reliable detection, we employ BiFPN rather than the original feature pyramid network since it improves the network's capacity to describe features. Moreover, in the model training phase, localization loss is modified to Focal-EIOU in order to get higher-quality anchor box. Lastly, we augment the well-known RDD2020 dataset with many samples of Chinese road scenes and compare YOLO-LRDD against several state-of-the-art object detection techniques. The smaller model of our YOLO-LRDD offers superior performance in terms of accuracy and efficiency, as determined by our experiments. Compared to YOLOv5s in particular, YOLO-LRDD improves single image recognition speed by 22.3% and reduces model size by 28.8% while maintaining comparable accuracy. In addition, it is easier to implant in mobile devices because its model is smaller and lighter than those of the other approaches.
On the feasibility of Federated Learning towards on-demand client deployment at the edge

Nowadays, researchers are investing their time and devoting their efforts in developing and motivating the 6G vision and resources that are not available in 5G. Edge computing and autonomous vehicular driving applications are more enhanced under the 6G services that are provided to successfully operate tasks. The huge volume of data resulting from such applications can be a plus in the AI and Machine Learning (ML) world. Traditional ML models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated Learning (FL) plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients in real-time wherever and whenever needed. In fact, some mobile and vehicular devices are not available to serve as clients in the FL due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in FL offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using any type of client devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the deployment of clients whenever and wherever needed. • A novel on-demand client deployment in Federated learning. • Targeting the problem of clients availability in pre-configured FL areas. • Targeting clients that do not have the needed capabilities for learning. • An efficient orchestration and deployment of ML services on newly formed client devices.
Solving quasi-free and quadratic Lindblad master equations for open fermionic and bosonic systems

The dynamics of Markovian open quantum systems are described by Lindblad master equations. For fermionic and bosonic systems that are quasi-free, i.e., with Hamiltonians that are quadratic in the ladder operators and Lindblad operators that are linear in the ladder operators, we derive the equation of motion for the covariance matrix. This determines the evolution of Gaussian initial states and the steady states, which are also Gaussian. Using ladder super-operators (a.k.a. third quantization), we show how the Liouvillian can be transformed to a many-body Jordan normal form which also reveals the full many-body spectrum. Extending previous work by Prosen and Seligman, we treat fermionic and bosonic systems on equal footing with Majorana operators, shorten and complete some derivations, also address the odd-parity sector for fermions, give a criterion for the existence of bosonic steady states, cover non-diagonalizable Liouvillians also for bosons, and include quadratic systems. In extension of the quasi-free open systems, quadratic open systems comprise additional Hermitian Lindblad operators that are quadratic in the ladder operators. While Gaussian states may then evolve into non-Gaussian states, the Liouvillian can still be transformed to a useful block-triangular form, and the equations of motion for $k$-point Green's functions form a closed hierarchy. Based on this formalism, results on criticality and dissipative phase transitions in such models are discussed in a companion paper [arXiv:2204.05346].
A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia

The procedure to diagnose anemia is time-consuming and resource-intensive due to the existence of a multitude of symptoms that can be felt physically or seen visually. Anemia also has several forms, which can be distinguished based on several characteristics. It is possible to diagnose anemia through a quick, affordable, and easily accessible laboratory test known as the complete blood count (CBC), but the method cannot directly identify different kinds of anemia. Therefore, further tests are required to establish a gold standard for the type of anemia in a patient. These tests are uncommon in settings that offer healthcare on a smaller scale because they require expensive equipment. Moreover, it is also difficult to discern between beta thalassemia trait (BTT), iron deficiency anemia (IDA), hemoglobin E (HbE), and combination anemias despite the presence of multiple red blood cell (RBC) formulas and indices with differing optimal cutoff values. This is due to the existence of several varieties of anemia in individuals, making it difficult to distinguish between BTT, IDA, HbE, and combinations. Therefore, a more precise and automated prediction model is proposed to distinguish these four types to accelerate the identification process for doctors. Historical data were retrieved from the Laboratory of the Department of Clinical Pathology and Laboratory Medicine, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia for this purpose. Furthermore, the model was developed using the algorithm for the extreme learning machine (ELM). This was followed by the measurement of the performance using the confusion matrix and 190 data representing the four classes, and the results showed 99.21% accuracy, 98.44% sensitivity, 99.30% precision, and an F1 score of 98.84%.
Precise Facial Landmark Detection by Reference Heatmap Transformer

Most facial landmark detection methods predict landmarks by mapping the input facial appearance features to landmark heatmaps and have achieved promising results. However, when the face image is suffering from large poses, heavy occlusions and complicated illuminations, they cannot learn discriminative feature representations and effective facial shape constraints, nor can they accurately predict the value of each element in the landmark heatmap, limiting their detection accuracy. To address this problem, we propose a novel Reference Heatmap Transformer (RHT) by introducing reference heatmap information for more precise facial landmark detection. The proposed RHT consists of a Soft Transformation Module (STM) and a Hard Transformation Module (HTM), which can cooperate with each other to encourage the accurate transformation of the reference heatmap information and facial shape constraints. Then, a Multi-Scale Feature Fusion Module (MSFFM) is proposed to fuse the transformed heatmap features and the semantic features learned from the original face images to enhance feature representations for producing more accurate target heatmaps. To the best of our knowledge, this is the first study to explore how to enhance facial landmark detection by transforming the reference heatmap information. The experimental results from challenging benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art methods in the literature.
Cluster-Guided Contrastive Graph Clustering Network

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms. The code of CCGC is available at https://github.com/xihongyang1999/CCGC on Github.
A Crowd-Powered Socially Embedded Search Engine

People have always asked questions of their friends, but now, with social media, they can broadcast their questions to their entire social network. In this paper we study the replies received via Twitter question asking, and use what we learn to create a system that augments naturally occurring “friendsourced” answers with crowdsourced answers. By analyzing of thousands of public Twitter questions and answers, we build a picture of which questions receive answers and the content of their answers. Because many questions seek subjective responses but go unanswered, we use crowdsourcing to augment the Twitter question asking experience. We deploy a system that uses the crowd to identify question tweets, create candidate replies, and vote on the best reply from among different crowd- and friend-generated answers. We find that crowdsourced answers are similar in nature and quality to friendsourced answers, and that almost a third of all question askers provided unsolicited positive feedback upon receiving answers from this novel information agent.
Your Age Is No Secret: Inferring Microbloggers' Ages via Content and Interaction Analysis

Microblogging systems such as Twitter have seen explosive use in public and private sectors. The age information of microbloggers can be very useful for many applications such as viral marketing and social studies/surveys. Current microblogging systems, however, have very sparse age information. In this paper, we present MAIF, a novel framework that explores public content and interaction information in microblogging systems to explore the hidden ages of microbloggers. We thoroughly evaluate the accuracy of MAIF with a real-world dataset with 54,879 Twitter users. Our results show that MAIF can achieve up to 81.38% inference accuracy and outperforms the state of the art by 9.15%. We also discuss some countermeasures to alleviate the possible privacy concerns caused by MAIF.
Optimizing Autoencoders for Learning Deep Representations From Health Data

Analyzing patients' health data using machine learning techniques can improve both patient outcomes and hospital operations. However, heterogeneous patient data (e.g., vital signs) and inefficient feature learning methods affect the implementation of machine learning-based patient data analysis. In this paper, we present a novel unsupervised deep learning-based feature learning (DFL) framework to automatically learn compact representations from patient health data for efficient clinical decision making. Real-world pneumonia patient data from the National University Hospital in Singapore are collected and analyzed to evaluate the performance of DFL. Furthermore, publicly available electroencephalogram data are extracted from the UCI Machine Learning Repository to test and support our findings. Using both data sets, we compare the performance of DFL to that of several popular feature learning methods and demonstrate its advantages.