Reviewerly/exHarmony huggingface dataset:
Phenomenology and Architecture: Examining Embodied Experience and Graphic Representations of the Built Environment

Abstract How do we reconcile the way we represent architectural designs with a real, embodied, experience of a built space? Merleau-Ponty suggests that “The Umwelt marks the difference between the world such as it exists in itself, and the world of a living being. It is an intermediary reality between the world as it exists for an absolute observer and a purely subjective domain.” Yet there is a third condition: the anticipation of the world as it might exist, as visualized in drawings. Architectural drawings are understood as referential and non-autonomous; they are representations of architectural space and form. Yet all modalities of two-dimensional spatial representation are abstractions of physical space and form, and are therefore subject to interpretation. This chapter investigates the relationship between physiological responses in the embodied experience of a built space with the responses evoked when looking at various modes of architectural representation.
Online Classification of Network Traffic Based on Granular Computing

At Presently, it is still a great challenge to achieve online classification of traffic flows due to the highly varying network environments, e.g., unpredictable new traffic classes, network noise, and congestion. Traditional classification methods work well in stable network environments, but may not exhibit their performance in dynamic environments. To address online classification issues, a granular computing-based classification model (GCCM) is developed, where the spatial and temporal flow granules are defined to make GCCM robust against variations and less sensitive to noise, and the correlations among flow granules are explored to establish the granular relation matrix (GRM). The inherent burst features between packets indicated by GRM prompt GCCM to achieve fine classification in unstable network environments. GCCM analyzes the burst features of packets without inspecting the payload information, and thus can be used to classify encrypted traffic as well as unencrypted traffic at a fast speed. In addition, the GCCM model, depending on difference measurement <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D(\cdot)$ </tex-math></inline-formula> , is a threshold-based classification, and therefore can be used to distinguish between time-varying classes. The validity of GCCM for online traffic classification is examined through theoretical results. The experimental evaluation of classification for fine and varied classes under dynamic network environments with noise and congestion also demonstrates its superiority in terms of classification accuracy and real-time performance with the state-of-the-art.
Collaborative Byzantine Resilient Federated Learning

Federated learning (FL) enables an effective and private distributed learning process. However, it is vulnerable against several types of attacks, such as Byzantine behaviors. The first purpose of this work is to demonstrate mathematically that traditional arithmetic-averaging model-combining approach will ultimately diverge to an unstable solution in the presence of Byzantine agents. This article also proposes a low-complexity, decentralized Byzantine resilient training mechanism. The proposed technique identifies and isolates hostile nodes rather than just mitigating their impact on the global model. In addition, the suggested approach may be used alone or in conjunction with other protection techniques to provide an additional layer of security in the event of misdetection. The suggested solution is decentralized, allowing all participating nodes to jointly identify harmful individuals using a novel cross-check mechanism. To prevent biased assessments, the identification procedure is done blindly and is incorporated into the regular training process. A smart activation mechanism based on flag activation is also proposed to reduce the network overhead. Finally, general mathematical proofs combined with extensive experimental results applied in a healthcare electrocardiogram (ECG) monitoring scenario show that the proposed techniques are very efficient at accurately predicting heart problems.
Siamese Network Tracking Based on Feature Enhancement

Most popular Siamese network trackers implement similarity learning by convolutional intercorrelation using the output features of the last layer of the feature extraction network. The extracted image features, although having high semantic information, lack edge as well as some detail information, which is not conducive to meet some challenges in tracking tasks. In this paper, a simple Siamese network tracking based on feature enhancement is proposed to address the above problems. The method applies the output features of the last three layers of the feature extraction network together, which are used to supplement some detailed information and to augment the target information features by a self-attentive mechanism. At the same time, a classification enhancement module is designed in the back-end classification section to assist in classifying target information more accurately. Finally, this method is experimented on four challenging datasets, namely GOT-10k, UAV123, OTB100 and LaSOT, and the experiments show that the proposed method in this paper outperforms some existing state-of-the-art target trackers. And the method in this paper weighs the tracking accuracy and speed in the model structure design, which both improves the tracker tracking performance and can run at a speed of about 61 fps on such large data as GOT-10k.
CLSR: Cross-Layer Interaction Pyramid Super-Resolution Network

Convolutional Neural Network (CNN) achieves impressive success in image super-resolution (SR), where global context interaction is critical for reconstructing reliable edge and texture details. However, most CNN-based SR models focus on modeling the global contextual information within a single feature map by using attention mechanisms, and ignore the dependencies among hierarchical features, resulting in blurred or even distorted detail restoration, especially for SR tasks with large scaling factors (i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 8$ </tex-math></inline-formula> ). To tackle the above issue, we propose a Cross-Layer interaction pyramid Super-Resolution (CLSR) network that reconstructs the desired SR images progressively in a coarse-to-fine fashion. Specifically, we propose a novel Cross-Layer Non-Local attention (CLNL) for accurate detail restoration. Through explicitly modeling the long-range feature-wise similarities within and between layers, the proposed CLNL is able to discriminatively explore complementary patches from hierarchical features to reconstruct the target LR patches. Then, to further strengthen the information interaction among hierarchical features at different scales, we propose a novel Gradient Consistency-Aware learning framework (GCA) by constructing a closed loop (LR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rightarrow $ </tex-math></inline-formula> HR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rightarrow $ </tex-math></inline-formula> LR) on the gradient space. The proposed GCA is able to effectively capture the interdependence between LR and HR gradient maps to guide our CLSR for reliable detail restoration. Extensive experiments validate that our CLSR outperforms the state-of-the-art methods in terms of both reconstruction accuracy and visual quality.
Multi-Objective Optimization for Bandwidth-Limited Federated Learning in Wireless Edge Systems

This paper studies a bandwidth-limited federated learning (FL) system where the access point is a central server for aggregation and the energy-constrained user equipemnts (UEs) with limited computation capabilities (e.g., Internet of Things devices) perform local training.Limited by the bandwidth in wireless edge systems, only a part of UEs can participate in each FL training round.Selecting different UEs could affect the FL performance, and selected UEs need to allocate their computing resource effectively.In wireless edge FL systems, simultaneously accelerating FL training and reducing computingcommunication energy consumption are of importance.To this end, we formulate a multi-objective optimization problem (MOP).In MOP, the model training convergence is difficult to calculate accurately.Meanwhile, MOP is a combinatorial optimization problem, with the high-dimension mix-integer variables, which is proved to be NP-hard.To address these challenges, a multi-objective evolutionary algorithm for the bandwidth-limited FL system (MOEA-FL) is proposed to obtain a Pareto optimal solution set.In MOEA-FL, an age-of-update-loss method is first proposed to transform the original global loss function into a convergence reference function.Then, MOEA-FL divides MOP into N single objective subproblems by the Tchebycheff approach and optimizes the subproblems simultaneously by evolving a population.Extensive experiments have been carried out on MNIST dataset and a medical case called TissueMNIST dataset for both the i.i.d and non-i.i.d data setting.Experimental results demonstrate that MOEA-FL performs better than other algorithms and verify the robustness and scalability of MOEA-FL.
Input Validation Vulnerabilities in Web Applications: Systematic Review, Classification, and Analysis of the Current State-of-the-Art

In recent years, huge increase in attacks and data breaches is noticed.Most of the attacks are performed and focused on the vulnerabilities related to web applications.Hence, nowadays the mitigation of application vulnerabilities is an ignited research area.Thus, due to the potential high severity impacts of web application, many different approaches have been proposed in the past decades to mitigate the damages of application vulnerabilities.Static and dynamic analysis are the two main techniques used.In this paper, a new classification for web application input validation vulnerabilities is proffered.In addition, various techniques/tools that are used to detect them are analyzed and evaluated to apprehend their strengths and weaknesses.Thus, this paper provides both technical as well as literature countermeasures to input validation vulnerabilities.Moreover, various statistical distributions of the reviewed techniques were manifested and scrutinize in different aspects to reveal the perception of the prevailing techniques and the gaps in the literature.In addition, the most widespread metrics are also propounded.
A Multi-Objective Based Scheduling Framework for Effective Resource Utilization in Cloud Computing

Cloud computing is a promising platform for running massive workflow applications based on a pay-per-use model.In cloud computing, the reduction of energy consumption and providing security to workflow scheduling are the key research areas.The primary focus of the existing algorithms, viz., particle swarm optimization (PSO), crow Search optimization (CSO) and Other non-metaheuristic algorithms like Round Robbin(RR), SJF, Min-Min, Min-Max etc., is based on the execution time and cost of the workflow applications as a budget constraint.However, these algorithms failed to adequately determine energy consumption, resource utilization, and security in workflow scheduling.To address this issue, a multi-objective scheduling framework is proposed.In this paper, the framework performs dynamic workflow scheduling using universal unique identification-Blake (UUID-Blake), Manhattan Distance-Partition around algorithm (MD-PAM), Linear Scaling-Crow Search Optimization (LS-CSO), Anova-Recurrent Neural Network.The implementation of this framework was achieved in three phases (Phase 1, Phase 2, and Phase 3).Phase 1 is about user registration and authentication using UUID-Blake, which enhances security by allowing legitimate users into the cloud environment.Phase 2 deals with clustering and resource monitoring using MD-PAM and A-RNN, to reduce makespan the similar tasks are clustered using task length and maximize the resource utilization by predicting the resource availability.Phase 3 deals with the scheduling of dynamic workflows using LS-CSO by selecting suitable virtual machines.We have considered the heterogeneous computing scheduling problem (HCSP) and grid workload archive (GWA)-T-12 Bitbrains datasets for comparing our proposed framework with existing works.Based on the result analysis, the proposed LS-SCO outperformed when compared with the algorithms CSO,PSO and RR has achieved better performance.
EnsMOD: A Software Program for Omics Sample Outlier Detection

Detection of omics sample outliers is important for preventing erroneous biological conclusions, developing robust experimental protocols, and discovering rare biological states. Two recent publications describe robust algorithms for detecting transcriptomic sample outliers, but neither algorithm had been incorporated into a software tool for scientists. Here we describe Ensemble Methods for Outlier Detection (EnsMOD) which incorporates both algorithms. EnsMOD calculates how closely the quantitation variation follows a normal distribution, plots the density curves of each sample to visualize anomalies, performs hierarchical cluster analyses to calculate how closely the samples cluster with each other, and performs robust principal component analyses to statistically test if any sample is an outlier. The probabilistic threshold parameters can be easily adjusted to tighten or loosen the outlier detection stringency. EnsMOD can be used to analyze any omics dataset with normally distributed variance. Here it was used to analyze a simulated proteomics dataset, a multiomic (proteome and transcriptome) dataset, a single-cell proteomics dataset, and a phosphoproteomics dataset. EnsMOD successfully identified all of the simulated outliers, and subsequent removal of a detected outlier improved data quality for downstream statistical analyses.
Key Technologies of Face Sensor Recognition Entry System for New Energy Vehicles Based on Particle Swarm Neural Network

In order to solve the problem of high dimensionality and low recognition rate caused by complex calculation in face recognition, the author proposes a face recognition algorithm based on weighted DWT and DCT based on particle swarm neural network applied to new energy vehicles. The algorithm first decomposes the face image with wavelet transform, removes the influence of the diagonal component, the weighted low-frequency and high-frequency discrete cosine transform coefficients are extracted as feature vectors, and finally, the particle swarm optimization BP neural network is used for classification and identification. Experimental results show that when the wavelet weights take <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>a</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>0.9</mn> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi>a</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>0.05</mn> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msub> <mrow> <mi>a</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>=</mo> <mn>0.05</mn> </math> , the recognition rate reaches the highest. Regardless of whether the low-frequency component continues to increase or decrease, and the high-frequency component continues to decrease or increase, the recognition rate will decrease. When the eigenvector dimension is around 60, the recognition rate difference between the weighted wavelet algorithm and the general low-frequency wavelet algorithm reaches the maximum. The recognition rate of the proposed algorithm is much higher than the other two traditional algorithms. Conclusion. The effectiveness and feasibility of the algorithm are verified on the ORL face database.
NEW QUANTUM AND LCD CODES FROM CYCLIC CODES OVER A FINITE NON-CHAIN RING

In this work, we study cyclic codes of length n over a finite commutative non-chain ring ℛ=Fq[u,v]/〈u2−γu,v2−ϵv,uv−vu〉 where γ,ϵ∈Fq* and we find new and better quantum error-correcting codes than previously known quantum error correcting codes. Then certain constraints are imposed on the generator polynomials of cyclic codes, so these codes become linear complementary dual codes (in short LCD codes). We then verify that the Gray image of linear complementary dual codes of length n over ℛ is a linear complementary dual code of length 4n over Fq by establishing a Gray map.
Inq-ITS: Creating Rigorous Assessment and Real-Time Support of Science Learning

This article is an example of how theoretical frameworks about how people learn science were used in combination with computational techniques to develop authentic assessments and intelligent tutoring for science.
Secure Share: Optimal Blokchain Integration in IoT Systems

ABSTRACTBlockchain is an inherently secure technology, and incorporating it into IoT, will assist in enhancing IoT security. The proposed work focuses on establishing the blockchain’s viability for application in the Internet of Things to solve the trust issue. Due to the trust factor, publicly available IoT datasets are limited. First, various techniques proposed by researchers to adopt blockchain in IoT are discussed with their limitations. To overcome these limitations, the proposed work identified the optimal layer in a simplified three-tiered IoT architecture for incorporating blockchain to keep this integration lightweight. The challenges of storing all IoT data on the blockchain ledger are discussed. Finally, a blockchain-based secure data-sharing model is developed to trace the integrity of shared IoT data among its various IoT system stakeholders. The healthcare domain is being used to demonstrate the value of blockchain in IoT. Using blockchain for tracing the integrity of IoT data rather than storing all IoT data is an attempt to reduce the cost and complexity associated with Blockchain and IoT integration. It enables a trusted environment and promotes more availability of IoT datasets to the stakeholders.KEYWORDS: IoTblockchainIoT-securityIoT-healthcareIoT-data sharing Disclosure statementNo potential conflict of interest was reported by the author(s).
Understanding the Diffusion of Business Analysis Responsibilities

Business analysis is core to the information systems (IS) discipline. Historically, those performing business analysis held the “Business Analyst” job title; however, in recent years evidence suggests that business analysis is now a job responsibility diffused across a wide variety of roles. It is vital that the IS community understand more about the diffusion of responsibility for business analysis across a variety of organizational roles. This research seeks to provide such an understanding by analyzing job postings referencing business analysis responsibilities. Over 231,000 unique job postings were analyzed to identify the most common job titles associated with business analysis, the hard and soft skills associated with them, and how the job titles compared in terms of skill requirements. The study provides insights to support both academia and industry in understanding business analysis as it relates to IT job titles and roles.
YOLOOD: an arbitrary-oriented flexible flat cable detection method in robotic assembly

Flexible flat cable (FFC) detection is the premise of robot 3C assembly and is challenging because FFCs are often non-axis aligned with arbitrary orientations having cluttered surroundings. However, to date, the traditional robotic object detection methods mainly regress the object horizontal bounding box, in which the size and aspect ratios do not reflect the actual shape of the target object and hardly separate the FFCs in dense. In this paper, rotated object detection was introduced into FFC detection, and a YOLO-based arbitrary-oriented FFC detection method named YOLOOD was proposed. Firstly, oriented bounding boxes are used to reflect the object's physical size and angle information and better separate the FFCs from the dense background. Secondly, the circular smooth label angular classification algorithm is adopted to obtain the angle information of FFCs. Finally, the head point regression branch is introduced to distinguish between the head and the tail of the FFC and expand the range of FFC detection angle to $$\left[ 0^{\circ }, 360^{\circ }\right) $$ . The proposed YOLOOD can reach the detection performance with an average precision of 90.82% and a detection speed of 112 FPS on an FFC dataset. Meanwhile, an actual FFC grasping experiment demonstrated the proposed YOLOOD's effectiveness and feasibility in practical assembly scenarios. In conclusion, this paper innovatively introduces rotating object detection into robot object detection, and the proposed YOLOOD solves the problem of detecting and locating non-axis aligned FFCs and has particular significance for robot 3C assembly.
Coherent activation of a steerability-breaking channel

The inevitable noise within one-sided device-independent quantum information tasks may completely break any steering resources. These noisy effects are characterized by the steerability breaking (SB) channels. In this work, we demonstrate the use of a control system that can superpose $N$ copies of the given SB channel to activate SB channels such that the channel becomes a non-SB one. In particular, we analytically and numerically show that the SB depolarizing and amplitude-damping channel can be coherently activated when $N\ensuremath{\ge}2$. In addition, for both cases, the channels can preserve more steerability when increasing $N$. Finally, we propose circuit models to implement our proposed approaches and present the simulation results.