Reviewerly/exHarmony huggingface dataset:
Study of Adoption and Absorption of Emerging Technologies for Smart Supply Chain Management

The world has been witnessing the advent of a wide range of emerging technologies like machine learning, big data analytics, artificial intelligence, blockchain technology, internet of things, and others. In supply chain management (SCM) functions, these technologies have increasingly been playing a decisive role in improving the overall success of an organization. So, the purpose of this article was to understand the strategic imperative of adopting smart SCM utilizing these emerging technologies by Indian firms. Thus, the context was of an emerging economy like India from a dynamic capabilities' perspective. The authors undertook a qualitative research with a positivist approach by undertaking expert interviews with 14 respondents. The respondents were interviewed with a semi-structured open-ended questionnaire. The responses were content analyzed for themes. The SCM experts were engaged in the adoption of emerging technologies to transform SCM. The adoption of emerging technologies helped SCM functions of Indian firms to cater to the increased expectations of customers.
Characterizing Structural Regularities of Labeled Data in Overparameterized Models

Humans are accustomed to environments that contain both regularities and exceptions. For example, at most gas stations, one pays prior to pumping, but the occasional rural station does not accept payment in advance. Likewise, deep neural networks can generalize across instances that share common patterns or structures yet have the capacity to memorize rare or irregular forms. We analyze how individual instances are treated by a model via a consistency score. The score characterizes the expected accuracy for a held-out instance given training sets of varying size sampled from the data distribution. We obtain empirical estimates of this score for individual instances in multiple data-sets, and we show that the score identifies out-of-distribution and mislabeled examples at one end of the continuum and strongly regular examples at the other end. We identify computationally inexpensive proxies to the consistency score using statistics collected during training. We apply the score toward understanding the dynamics of representation learning and to filter outliers during training, and we discuss other potential applications including curriculum learning and active data collection.
A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm

Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP-hard, meta-heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy-aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works.
Rethinking Spatial Dimensions of Vision Transformers

Vision Transformer (ViT) extends the application range of transformers from language processing to computer vision tasks as being an alternative architecture against the existing convolutional neural networks (CNN). Since the transformer-based architecture has been innovative for computer vision modeling, the design convention towards an effective architecture has been less studied yet. From the successful design principles of CNN, we investigate the role of spatial dimension conversion and its effectiveness on transformer-based architecture. We particularly attend to the dimension reduction principle of CNNs; as the depth increases, a conventional CNN increases channel dimension and decreases spatial dimensions. We empirically show that such a spatial dimension reduction is beneficial to a transformer architecture as well, and propose a novel Pooling-based Vision Transformer (PiT) upon the original ViT model. We show that PiT achieves the improved model capability and generalization performance against ViT. Throughout the extensive experiments, we further show PiT outperforms the baseline on several tasks such as image classification, object detection, and robustness evaluation. Source codes and ImageNet models are available at this https URL
Knowledge-Intensive Language Understanding for Explainable AI

AI systems have seen significant adoption in various domains. At the same time, further adoption in some domains is hindered by the inability to fully trust an AI system that it will not harm a human. Besides, fairness, privacy, transparency, and explainability are vital to developing trust in AI systems. As stated in Describing Trustworthy AI,aa.https://www.ibm.com/watson/trustworthy-ai. “Trust comes through understanding. How AI-led decisions are made and what determining factors were included are crucial to understand.” The subarea of explaining AI systems has come to be known as XAI. Multiple aspects of an AI system can be explained; these include biases that the data might have, lack of data points in a particular region of the example space, fairness of gathering the data, feature importances, etc. However, besides these, it is critical to have human-centered explanations directly related to decision-making, similar to how a domain expert makes decisions based on “domain knowledge,” including well-established, peer-validated explicit guidelines. To understand and validate an AI system's outcomes (such as classification, recommendations, predictions) that lead to developing trust in the AI system, it is necessary to involve explicit domain knowledge that humans understand and use. Contemporary XAI methods are yet addressed explanations that enable decision-making similar to an expert. Figure 1 shows the stages of adoption of an AI system into the real world.
Limitations in Quantum Computing from Resource Constraints

Fault-tolerant schemes can use error correction to make a quantum computation arbitrarily accurate, provided that errors per physical component are smaller than a certain threshold and independent of the computer size. However, in current experiments, physical-resource limitations such as energy, volume, or available bandwidth induce error rates that typically grow as the computer grows. We analyse how error correction performs under such constraints and show that the amount of error correction can be optimized, leading to a maximum attainable computational accuracy. We find this maximum for generic situations where noise is scale dependent. By inverting the logic, we provide experimenters with a tool for finding the minimum resources required to run an algorithm with a given computational accuracy. When combined with a full-stack quantum computing model, this provides the basis for energetic estimates of future large-scale quantum computers.Received 17 December 2020Accepted 13 October 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040335Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationQuantum error correctionQuantum information architectures & platformsInterdisciplinary PhysicsQuantum Information
iVisit-Collaborate: Collaborative problem-solving in multiuser 360-degree panoramic site visits

Collaborative problem-solving skills are required from learners in the 21st century, particularly in construction graduates upon entering the workforce. However, challenges associated with spatiotemporal contexts of construction sites and resource limitations of educational programs reduce the opportunities available for students to practice these skills. This study focuses on using 360-degree panoramic virtual environments enhanced with virtual humans to produce realistic site visits for practicing collaborative problem-solving opportunities (iVisit-Collaborate). The goal of this phenomenological research study is to understand the collaborative problem-solving process of student dyads within iVisit-Collaborate. The findings suggest that student collaborative behaviors did not directly result in successful problem-solving. A hierarchical relationship between student problem-solving and collaborative behaviors was observed, indicating that problem-solving behaviors must take place to enable the occurrence of collaborative behaviors. Moreover, it was also observed that instructor scaffolding helped reduce the degrees of freedom in complex problem-solving activities. Finally, it was observed that student discussion engagement supported the creation and maintenance of shared mental models.
3D Vehicle Detection Using Multi-Level Fusion From Point Clouds and Images

3D vehicle detectors based on point clouds generally have higher detection performance than detectors based on multi-sensors. However, with the lack of texture information, point-based methods get many missing detection of occluded and distant vehicles, and false detection with high-confidence of similarly shaped objects, which is a potential threat to traffic safety. Therefore, in the long run, fusion-based methods have more potential. This paper presents a multi-level fusion network for 3D vehicle detection from point clouds and images. The fusion network includes three stages: data-level fusion of point clouds and images, feature-level fusion of voxel and Bird’s Eye View (BEV) in the point cloud branch, and feature-level fusion of point clouds and images. Besides, a novel coarse-fine detection header is proposed, which simulates the two-stage detectors, generating coarse proposals on the encoder, and refining them on the decoder. Extensive experiments show that the proposed network has better detection performance on occluded and distant vehicles, and reduces the false detection of similarly shaped objects, proving its superiority over some state-of-the-art detectors on the challenging KITTI benchmark. Ablation studies have also demonstrated the effectiveness of each designed module.
IoT and 5G technology in agriculture

This article examines the development of agriculture through digitalization and robotization within Industry 4.0. To open this topic, the current state of agriculture is considered, such information as: about yield, soil, data on fertilization. As well as 5G communication technologies, in particular, the placement of sensors in the fields was investigated using the Cooja simulation environment.
Bi-LSTM based deep learning method for 5G signal detection and channel estimation

&lt;abstract&gt; &lt;p&gt;The advanced neural network methods solve significant signal estimation and channel characterization difficulties in the next-generation 5G wireless communication systems. The number of transmitted signal copies received through multiple paths at the receiver leads to delay spread, which intern causes interference in communication. These adverse effects of the interference can be mitigated with the orthogonal frequency division modulation (OFDM) technique. Furthermore, the proper signal detection methods optimal channel estimation enhances the performance of the multicarrier wireless communication system. In this paper, bi-directional long short-term memory (Bi-LSTM) based deep learning method is implemented to estimate the channel in different multipath scenarios. The impact of the pilots and cyclic prefix on the performance of Bi LSTM algorithm is analyzed. It is evident from the symbol-error rate (SER) results that the Bi-LSTM algorithm performs better than the state of art channel estimation methods known as the Minimum Mean Square and Error (MMSE) estimation method.&lt;/p&gt; &lt;/abstract&gt;
Content-adaptive image compression and encryption via optimized compressive sensing with double random phase encoding driven by chaos

Abstract With the advancement of multimedia technology and coming of big data era, the size of image data is significantly increased. However, the traditional image encryption methods cannot solve the emerging problems of efficient compression. To settle with this challenge, an effective content-adaptive image compression and encryption method based on compressive sensing and double random phase encoding (DRPE) is proposed in this paper. The original image is converted to one low-frequency part and three high-frequency parts by DWT and then permutated by sorting-based chaotic sequences. Afterward, a novel measurement matrix optimization algorithm based on adaptive step size is presented to measure the high-frequency components. To enhance the security of the scheme, the DRPE, quantization, and diffusion are successively performed on the complex matrix composed of the shuffled low-frequency component and three measurement value matrices to obtain the cipher image. Logistic-Sine chaotic system is utilized to produce the chaotic keystreams for the encryption process, and its system parameter and initial value are determined by the information entropy of the plain image and external key parameters, so that the proposed cipher can withstand known-plaintext and chosen-plaintext attacks effectively. Numerical experiments demonstrate the effectiveness of the proposed image compression and encryption algorithm.
Blockchain evaluation framework for supply chain management: a decision-making approach

The purpose of this research is to evaluate the application of Blockchain technology for Supply Chain Management. The research approach includes a Multi-Criteria Decision-Making framework based on Fuzzy Analytical Hierarchy Process. The framework analyses a set of Blockchain implementation criteria and their importance ranking for achieving Supply Chain Management. The findings suggest that, to implement Blockchain technology for Supply Chain Management, the most important criterion is Traceability followed by Security and Smart Contact. Decision makers can make use of the developed framework in the supply chain section to evaluate readiness of Blockchain technology and its benefits for Supply Chain frameworks. This will lead to effective allocations for resources and budgets in their planning for various Blockchain deployment.
Joint Scheduling and Resource Allocation for Hierarchical Federated Edge Learning

The concept of hierarchical federated edge learning (H-FEEL) has been recently proposed as an enhancement of federated learning model. Such a system generally consists of three entities, i.e., the server, helpers, and clients, in which each helper collects the trained gradients from clients nearby, aggregates them, and sends the result to the server for global model update. Due to limited communication resources, only a portion of helpers can be scheduled to upload their aggregated gradients in each round of the model training. And that necessitates a well-designed scheme for the joint helper scheduling and communication resources allocation. In this paper, we develop a training algorithm for the H-FEEL system which involves local gradient computing, weighted gradient uploading, and machine learning model updating phases. By characterizing these phases mathematically and analyzing one-round convergence bound of the training algorithm, we formulate an optimization problem to achieve the scheduling and resource allocation scheme. The problem simultaneously captures the uncertainty of the wireless channel and the importance of the weighted gradient. To solve the problem, we first transform it into an equivalent problem and then decompose the transformed problem into two subproblems: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bit and sub-channel allocation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">helper scheduling</i> , which are mixed integer nonlinear programming and continuous nonlinear problems, respectively. For the first subproblem, we obtain an optimal solution of exponential complexity and a suboptimal solution that has polynomial complexity. For the second subproblem, we obtain a closed-form optimal solution in a special case and a suboptimal solution in the general case. The efficacy of our scheme is amply demonstrated via simulations and the analytical framework is shown to provide valuable design insights for the practical implementation of the H-FEEL system.
Linear Cost-sensitive Max-margin Embedded Feature Selection for SVM

• An efficient embedded support vector machine feature selection model. • Cost-sensitive selection of features considering feature relevance and redundancy. • Feature relevance is influenced by the cost sensitivity of support vector machines. The information needed for a certain machine application can be often obtained from a subset of the available features. Strongly relevant features should be retained to achieve desirable model performance. This research focuses on selecting relevant independent features for Support Vector Machine (SVM) classifiers in a cost-sensitive manner. A review of recent literature about feature selection for SVM revealed a lack of linear programming embedded SVM feature selection models. Most reviewed models were mixed-integer linear or nonlinear. Further, the review highlighted a lack of cost-sensitive SVM feature selection models. Cost sensitivity improves the generalization of SVM feature selection models, making them applicable to various cost-of-error situations. It also helps with handling imbalanced data. This research introduces an SVM-based filter method named Knapsack Max-Margin Feature Selection (KS-MMFS), which is a proposed linearization of the quadratic Max-Margin Feature Selection (MMFS) model. MMFS provides explicit estimates of feature importance in terms of relevance and redundancy. KS-MMFS was then used to develop a linear cost-sensitive SVM embedded feature selection model. The proposed model was tested on a group of 11 benchmark datasets and compared to relevant models from the literature. The results and analysis showed that different cost sensitivity (i.e., sensitivity–specificity tradeoff) requirements influence the features selected. The analysis demonstrated the competitive performance of the proposed model compared with relevant models. The model achieved an average improvement of 31.8% on classification performance with a 22.4% average reduction in solution time. The results and analysis in this research demonstrated the competitive performance of the proposed model as an efficient cost-sensitive embedded feature selection method.
Real-Time Human Pose Detection and Recognition Using MediaPipe

Significance of human action recognition has increased manifolds due to its wide-scale application in the field of public security, gaming, etc., due to the introduction of various new technologies. We propose a framework that detects human action under different conditions and viewing angles that enable the identification of divergent patterns based on different spatiotemporal trajectories. In this paper, we use new technology such as MediaPipe Holistic which provides pose, face, and hand landmark detection models which parses the frames obtained through real-time device feed using OpenCV through our MediaPipe Holistic model and provide a total of 501 landmarks which is exported as coordinates to a CSV file upon which we train a custom multi-class classification model to understand the relationship between the class and coordinates to classify and detect custom body language pose. The machine learning classification algorithms implemented in this paper are random forest, linear regression, ridge classifier, and gradient boosting classifier.
QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers

The ability to have the same experience for different user groups (i.e., accessibility) is one of the most important characteristics of Web-based systems. The same is true for Knowledge Graph Question Answering (KGQA) systems that provide the access to Semantic Web data via natural language interface. While following our research agenda on the multilingual aspect of accessibility of KGQA systems, we identified several ongoing challenges. One of them is the lack of multilingual KGQA benchmarks. In this work, we extend one of the most popular KGQA benchmarks - QALD-9 by introducing high-quality questions' translations to 8 languages provided by native speakers, and transferring the SPARQL queries of QALD-9 from DBpedia to Wikidata, s.t., the usability and relevance of the dataset is strongly increased. Five of the languages - Armenian, Ukrainian, Lithuanian, Bashkir and Belarusian - to our best knowledge were never considered in KGQA research community before. The latter two of the languages are considered as “endangered” by UNESCO. We call the extended dataset QALD-9-plus and made it available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Figshare: https://doi.org/10.6084/m9.figshare.16864273. GitHub: https://github.com/Perevalov/qald_9_plus.
Decision support system for diagnosing diseases in dogs from behaviors using text mining technique

This research aims to develop a decision system for diagnosing diseases in dogs from behavior with text mining techniques. To help dog owners to diagnose the disease from the behavior of the pet dog in order to be able to recognize the disease that may occur or already occur of the pet dog Measure the accuracy of the data and satisfaction in using the system by using questionnaires as tools Evaluation of accuracy of data with 80 percent accuracy and accuracy in diagnosis is 80 percent accurate
Graph4Web: A relation-aware graph attention network for web service classification

Software reuse is a popular way to utilize existing software components to ensure the quality of newly developed software in service-oriented architecture. However, how to find a suitable web service from existing repositories to meet requirements is still an open issue. Among others, web service classification is one of the most essential and effective means for web service recommendation. Previous studies have concerned this problem, but a critical issue, i.e., the semantic and syntactic information for the web service, is often ignored. To address such an issue, in this work, we propose Graph4Web, which uses a relation-aware graph attention network for web service classification. Specifically, we first parse the web service description sequence into the dependency graph and initialize the embedding vector of each node in the graph by tuning the pre-trained BERT model. We further propose a relation-aware graph attention layer to learn and update the node embedding vector by aggregating the information of neighborhood nodes and the distinct types of relationships between nodes. In addition, we introduce the self-attention mechanism to acquire the high-level global representation for web service classification. Various experiments demonstrate that Graph4Web has better classification performance compared with seven baseline methods with three indicators.