Reviewerly/exHarmony huggingface dataset:
An improved deep echo state network inspired by tissue-like P system forecasting for non-stationary time series

Abstract As a recurrent neural network, ESN has attracted wide attention because of its simple training process and unique reservoir structure, and has been applied to time series prediction and other fields. However, ESN also has some shortcomings, such as the optimization of reservoir and collinearity. Many researchers try to optimize the structure and performance of deep ESN by constructing deep ESN. However, with the increase of the number of network layers, the problem of low computing efficiency also follows. In this paper, we combined membrane computing and neural network to build an improved deep echo state network inspired by tissue-like P system. Through analysis and comparison with other classical models, we found that the model proposed in this paper has achieved great success both in predicting accuracy and operation efficiency.
Implementing Affordance-Based Design Review Method Using Virtual Reality in Architectural Design Studio

In recent years, researchers in the architecture field have explored VR technology for various purposes in the architectural design process. Current efforts have supported the growing knowledge of VR utilization, specifically for the architectural design review process. In this study, we adapt the affordance concept from the ecological psychology study to add further evidence on VR utilization for the design review process. This study aims to develop an affordance-based design review method in architectural design by utilizing immersive VR technology. It sits at the crossroads of architecture design, virtual reality, and affordance research. This project aimed to create an affordance-based review method framework, a VR system to support the method, test both the method and the VR system, and assess the VR system’s effectiveness as a companion system for the affordance-based design method process. The study was conducted in the scope of architectural education settings only. It used a third-year architectural design studio course as a case. This study confirms that the affordance-based design review method using virtual reality helps students improve their design work. The method reveals the presence of positive and negative affordances in their work. It also shows the differences between a student and supervisor in perceiving the affordances for reviewing design works.
A Decision Model for Designing NLP Applications

Among the NLP models’ usages, some applications provide multiple output options, and some offer only a single result to the end-users. However, there is little research about which situations providing multiple outputs from NLP models will benefit the user experience. Therefore, in this position paper, we summarize the progress of NLP applications, which shows parallel outputs from the NLP model at once to users. Then a decision model is presented that can assist in deciding whether a given condition is suitable to show multiple outputs at once from the NLP model. We hope developers and UX designers can examine the decision model and create an easy-to-use interface that can present numerous results from the NLP model at once. Moreover, we hope future researchers can reference the decision model from this paper to explore the potential of other NLP models’ usage that can show parallel outputs at once to create a more satisfactory user experience.
Accelerating and pruning CNNs for semantic segmentation on FPGA

Semantic segmentation is one of the popular tasks in computer vision, providing pixel-wise annotations for scene understanding. However, segmentation-based convolutional neural networks require tremendous computational power. In this work, a fully-pipelined hardware accelerator with support for dilated convolution is introduced, which cuts down the redundant zero multiplications. Furthermore, we propose a genetic algorithm based automated channel pruning technique to jointly optimize computational complexity and model accuracy. Finally, hardware heuristics and an accurate model of the custom accelerator design enable a hardware-aware pruning framework. We achieve 2.44X lower latency with minimal degradation in semantic prediction quality (−1.98 pp lower mean intersection over union) compared to the baseline DeepLabV3+ model, evaluated on an Arria-10 FPGA. The binary files of the FPGA design, baseline and pruned models can be found in github.com/pierpaolomori/SemanticSegmentationFPGA
SALO

The attention mechanisms of transformers effectively extract pertinent information from the input sequence. However, the quadratic complexity of self-attention w.r.t the sequence length incurs heavy computational and memory burdens, especially for tasks with long sequences. Existing accelerators face performance degradation in these tasks. To this end, we propose SALO to enable hybrid sparse attention mechanisms for long sequences. SALO contains a data scheduler to map hybrid sparse attention patterns onto hardware and a spatial accelerator to perform the efficient attention computation. We show that SALO achieves 17.66x and 89.33x speedup on average compared to GPU and CPU implementations, respectively, on typical workloads, i.e., Longformer and ViL.
Brain-inspired chaotic backpropagation for MLP

Backpropagation (BP) algorithm is one of the most basic learning algorithms in deep learning. Although BP has been widely used, it still suffers from the problem of easily falling into the local minima due to its gradient dynamics. Inspired by the fact that the learning of real brains may exploit chaotic dynamics, we propose the chaotic backpropagation (CBP) algorithm by integrating the intrinsic chaos of real neurons into BP. By validating on multiple datasets (e.g. cifar10), we show that, for multilayer perception (MLP), CBP has significantly better abilities than those of BP and its variants in terms of optimization and generalization from both computational and theoretical viewpoints. Actually, CBP can be regarded as a general form of BP with global searching ability inspired by the chaotic learning process in the brain. Therefore, CBP not only has the potential of complementing or replacing BP in deep learning practice, but also provides a new way for understanding the learning process of the real brain.
Two-stage unsupervised facial image quality measurement

The performance of face recognition methods predominantly depends on the quality of facial images. Therefore, by filtering low-quality facial images, face recognizers can be effectively implemented. Moreover, the factors causing degradation in the quality of facial images include the face acquisition environment (e.g., illumination, motion blur, and resolution) and facial characteristics (e.g., pose and occlusion). Therefore, in this study, we propose a novel two-stage unsupervised facial image quality measure (TUFIQM) algorithm to evaluate the degradations caused by the acquisition environment and face factors. The proposed TUFIQM consists of an environmental degradation estimation module (EDEM) and a face factor degradation measurement module (FFDMM). Specifically, the EDEM introduces meta-learning to learn prior knowledge from the subjective quality assessment of natural scene images; subsequently, it employs the acquired prior knowledge to estimate the face quality degradation caused by the face acquisition environment. The design principle of the EDEM is based on the similar effect demonstrated by the acquisition environment on the quality of facial and natural scene images. Considering that face factor degradation leads to loss of highly identifiable high-frequency facial features, the FFDMM quantifies the amount of high-frequency information in visible face areas to represent the degradation in quality due to varying facial factors. The EDEM and FFDMM were combined in this study to achieve the proposed TUFIQM. Experiments on public datasets showed that the proposed TUFIQM algorithm is better than other state-of-the-art image quality measurement methods devised for natural scene and facial images.
TryItOn: A Virtual Dressing Room with Motion Tracking and Physically Based Garment Simulation

The interest in the online shopping field has increased considerably nowadays. This has led to emerging technologies being applied in this context. One of the sectors to which particular attention has been paid is the clothing sale. The main reason comes from the problem of physically trying on clothes to choose the correct size. This issue can be addressed using methodologies based on virtual reality and Human-Computer Interaction (HCI). This paper presents a Virtual Dressing Room (VDR) application named TryItOn that allows the user to try on digital clothes. The proposed solution offers an immersive and realistic experience by combining a high degree of photorealism, the presence of an avatar with accurate measurements, the natural interaction with the environment through movements of hands or the entire body, and the use of a real-time physical simulation of the garment.
A multi-robot cooperative exploration algorithm considering working efficiency and working load

The cooperative exploration in unknown environment is a tough task for the multi-robot system. The imbalance of individual workload caused by the weak autonomous cooperation ability will affect the working efficiency of the multi-robot system. In this paper, a two-objective cooperative exploration algorithm (TOCEA) is proposed, where the working efficiency and working load at the individual and system levels are both considered. There are three parts in the proposed algorithm, namely, frontier detection, target point selection and exploration decision. First, based on the analysis of the frontier characteristics, the accumulation of local frontiers is used to replace the method of traversal search, which greatly improves the exploration efficiency. Second, each robot plays as an equal role to select their candidate target points from the individual level, and uses the maximum area criterion to select the final target points. Finally, the robot target points are clustered to reduce the repeated exploration of the robots, which is essential for reduce the path length and exploration time. Significantly, the whole exploration process is completely based on the autonomous cooperation of the robots. The experimental results performed on three different platforms illustrate that the TOCEA shows improvements in terms of working efficiency, cooperation performance and applicable fields.
Dimensionally Reduction based Machine Learning Approaches for Code smells Detection

Code smells refer to the lack of the software quality, such as difficulty in understandability and changeability. In this research work, we proposed a technique to detect the CSs using three machine learning algorithms. For this purpose, we used two class level and two method level datasets. In this research work, a principle component analysis (PCA) based dimensionality reduction technique is applied to select different components from each datasets. Four PCA based machine learning algorithms are applied, namely Principal component analysis based Logistic regression (PCA_LR), Principal component analysis based Random forest (PCA_RF), Principal component analysis based K-nearest neighbor (PCA_KNN), and Principal component analysis based Decision tree (PCA_DT). The 10-fold cross-validation is used to validate the model accuracy. In this research work, we found that the PCA_LR model gives the best accuracy, 99.97%, for the data class dataset, while the worst accuracy, 77.38%, was found by the PCA_KNN model for the long parameter list dataset.
COCO-Net: A Dual-Supervised Network With Unified ROI-Loss for Low-Resolution Ship Detection From Optical Satellite Image Sequences

Low-resolution ship detection from optical satellite image sequences is critical in high-orbit remote sensing satellite applications. However, it is still a difficult problem due to the following challenges: 1) the size of the ship is tiny in the low-resolution image; 2) the ship target is dim and the contrast with the background is low; 3) the interference of cloud and fog covering is complex and changeable. For these reasons, the targets are easily lost during the detection. In fact, the Clearer the Objects against to the background, the more Confident the Observers can detect it. In light of these considerations, we propose a COCO-Net to detect the small dynamic objects on low-resolution images in this paper. First, the multi-frame images are associated by introducing motion information as an effective compensation for small object features. Second, an integrated dual-supervised network that processes single-level tasks hierarchically is presented to adaptively enhance the input data quality of object detection without being limited by diverse scene disturbances. Third, a unified ROI-loss scheme that modulates the loss function of the first component by introducing ROI-masks from the second component is utilized to make the first component also work for object detection. In addition, we construct a new dataset for the small dynamic object detection based on the GaoFen-4 satellite imagery. Comprehensive experiments on a self-assembled dataset from the GaoFen-4 satellite show the superior performance of the proposed method compared to state-of-the-art object detectors.
Multivariate Goppa Codes

In this paper, we introduce multivariate Goppa codes, which contain, as a particular case, the well-known classical Goppa codes. We provide a parity check matrix for a multivariate Goppa code in terms of a tensor product of generalized Reed-Solomon codes. We prove that multivariate Goppa codes are subfield subcodes of augmented Cartesian codes. By showing how this new family of codes relates to a tensor product of generalized Reed-Solomon codes and augmented codes, we obtain information about the parameters, subcodes, duals, and hulls of multivariate Goppa codes. We see that in some instances, the hulls of multivariate Goppa codes (resp., tensor product of generalized Reed-Solomon codes) are also multivariate Goppa codes (resp. tensor product of generalized Reed-Solomon codes). We utilize the multivariate Goppa codes to obtain entanglement-assisted quantum error-correcting codes and to build families of long LCD, self-dual, or self-orthogonal codes.
5G-NR Latency Field Performance for Immersive Live Video

Low latency and high reliability are major superiority of 5G New Radio (5G-NR) on top of enhanced mobile broadband. 5G end-to-end network architecture supports flexible routing of traffic from remote radio and distributed units to various user traffic destinations. This combined with emerging Mobile Edge Computing (MEC) enables immersive live video experiences with much lower latency and higher reliability than that of enhanced mobile broadband services. Those enhancements are important for various vertical sectors, such as Vehicle-to-Everything (V2X), entertainment, healthcare, industrial IoT and metaverse. In this paper, 5G-NR latency performances are studied systematically for the first time in a commercial network under adverse radio fading environments. Both the latency and the latency variation increase with a higher video data rate and Block Error Rate. 5G-NR mobility and handover performance are evaluated for a ubiquitous consistent latency. End-to-end average latency of 4.5 to 15.5 ms are demonstrated in a wide area network. The impact of devices and Connected Mode Discontinuous Reception (cDRX) on latency are studied. The solution to tradeoff latency with battery life are proposed. Future work and various means to enhance immersive video experience are explored.
Traffic-Aware Multi-View Video Stream Adaptation for Teleoperated Driving

Remote control of an autonomous vehicle by a human operator requires low delay video transmission to resolve complex situations and ensure safety. The remote operator perceives the current traffic scenario via video streams from multiple cameras. To provide the operator with the best possible scene understanding while matching the available network resources, the video streams need to be automatically adapted. In this paper, we propose a traffic-aware multi-view video stream adaptation scheme. We estimate the importance of each camera view based on the vehicle’s real-time movement in traffic. The resulting prioritization together with the total available transmission rate determines a specific bit-budget for each camera view. We optimize the video quality of each individual video stream for the given bit-budget using a quality-of-experience-driven multi-dimensional adaptation scheme. Additionally, we apply a region-of-interest mask to the rear-facing camera views. The mask removes less important areas from the image which reduces the required bitrate. All modules are implemented to extend the existing TELECARLA framework. We evaluate the proposed traffic-aware adaptation scheme in a user study. We observe a high correlation between the proposed view prioritization module and the subjective ratings obtained in the user study. The region-of-interest masking achieves Bjøntegaard Delta Rate savings of at least 19.8% compared to streaming the full camera view. The overall system improves the VMAF score by 1.86 per camera when considering the importance of the individual camera views as rated by the users. This demonstrates the potential of an individual adaptation for each camera view optimized for the current traffic situation.
Optimization of UAV Cooperative Path Planning Mathematical Model Based on Personalized Multigroup Sparrow Search Algorithm in Complex Environment

Sparrow search algorithm has the problem of redundancy of convergence speed due to its fast convergence speed, and it is easy to fall into local optimum in multimodal environment. To solve the above problem, this paper presents a personalized multipopulation sparrow search algorithm (MPSSA). By introducing multiple population mechanisms to reduce the probability of falling into the local optimum due to single-population search, by using a personalized subpopulation strategy to improve the personalized differences of subpopulations and balance the exploratory ability of algorithm development, then by using weighted center-of-gravity communication strategy to improve the quality of communication between populations, and finally by using dimension by dimension dynamic reverse learning to improve the accuracy of search. The superiority of MPSSA is validated by comparing the benchmark function and CEC2017. Finally, the algorithm solves the problem of poor quality due to the dimension increase of the UAV cooperative track. MPSSA helps the UAV to quickly plan a better and stable track group to ensure the UAV to complete the cooperative task safely and stably.
Information Security Risk Assessment Using Situational Awareness Frameworks and Application Tools

This paper describes the development of situational awareness models and applications to assess cybersecurity risks based on Annex ISO 27001:2013. The risk assessment method used is the direct testing method, namely audit, exercise and penetration testing. The risk assessment of this study is classified into three levels, namely high, medium and low. A high-risk value is an unacceptable risk value. Meanwhile, low and medium risk values can be categorized as acceptable risk values. The results of a network security case study with security performance index indicators based on the percentage of compliance with ISO 27001:2013 annex controls and the value of the risk level of the findings of the three test methods showed that testing with the audit method was 38.29% with a moderate and high-risk level. While the test results with the tabletop exercise method are 75% with low and moderate risk levels. On the other hand, the results with the penetration test method are 16.66%, with moderate and high-risk levels. Test results with unacceptable risk values or high-risk corrective actions are taken through an application. Finally, corrective actions have been verified to prove there is an increase in cyber resilience and security.
Evaluating Geochemical Discriminants in Archean Gold Deposits: A Superior Province Perspective with an Emphasis on the Abitibi Greenstone Belt

Abstract Discriminating Archean Au deposit types and related ore-forming processes is challenging but paramount for increasing Au exploration success. This study tests the validity of applying geochemical data generated from conventional bulk versus modern in situ methods as discriminants for classifying Au deposits in the Archean Swayze greenstone belt with further comparison to other deposits in the contiguous Abitibi greenstone belt and Red Lake area (Superior Province, Canada). The study used five well-characterized Au settings, based on new mapping, as a basis for evaluating in situ (δ18Oquartz, δ33, 34Ssulfide, laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analysis of pyrite, electron microprobe analysis of gold), and whole-rock geochemical datasets to resolve whether intrusion-related Au deposits can be discriminated from orogenic-type Au deposits. Results show that the in situ methods provide insight into processes related to Au mineralization, both primary and subsequent remobilization and upgrading, and define elemental and isotopic correlations that cannot be resolved using conventional bulk methods. For example, when comparing the whole-rock to laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) trace metal data, only Ag, Te, and Hg show a consistent positive correlation with Au across deposit types in both data sets. Furthermore, the wholerock datasets combined with in situ isotopic analysis suggest the Archean sanukitoid-associated Au deposits represent a distinct group of intrusion-related deposits with mineralization characterized by low δ34Spyrite (&amp;lt;–5 to –25‰), inferred high fO2, an Hg-Te signature, and hosted in intrusions of &amp;lt;2690 Ma that predate shearing. The data and interpretations presented herein provide a baseline that can be widely utilized in future studies of Au deposits.
Fast incremental learning by transfer learning and hierarchical sequencing

In this paper we address the Class Incremental Learning (CIL) problem, characterized by sequences of data batches in which examples of different classes occur at different times. From a theoretical point of view, we propose a new approach that we call hierarchical sequencing and prove that any CIL task can be sequenced into simple incremental classification tasks by means of the hierarchical sequencing. From a practical point of view, we propose the HILAND method for image classification, which combines the hierarchical sequencing with transfer learning. In our experiments, the HILAND method has obtained state-of-the-art results for the CIL problem, but with far less training effort through transfer learning. • We prove that any class incremental learning (CIL) task can be sequenced into simpler tasks by means of the so-called hierarchical sequencing. • We propose a fast CIL method for image classification, which combines transfer learning with hierarchical sequencing. • Our method attains state-of-the-art results with far less training effort.