Reviewerly/exHarmony huggingface dataset:
Integrating the Challenges of Cloud Computing in Supply Chain Management

Cloud Computing is an advanced technology in todays’ scenario. The purpose of this paper is to identify and evaluate the challenges of cloud computing adoption in the SMEs of the supply chain management. SMEs supply chain management play a vital role in achieving operational excellence and in achieving its objectives. The research will give cloud service providers knowledge about the various challenges that the firms are facing while adopting this technology in their systems. Literature review was utilized to identify the components of challenges for the cloud computing adoption. Data was collected utilizing questionnaire method from various firms across the nation. Factor analysis was utilized for the analysis. The major influencing factors that was identified while adopting cloud computing were lock-in, loss of governance, compliance challenges, malicious insiders, resource and service management, service level agreement, data loss or leakage, legal jurisdiction, data privacy and protection, licensing risk.
DisasterGAN: Generative Adversarial Networks for Remote Sensing Disaster Image Generation

Rapid progress on disaster detection and assessment has been achieved with the development of deep-learning techniques and the wide applications of remote sensing images. However, it is still a great challenge to train an accurate and robust disaster detection network due to the class imbalance of existing data sets and the lack of training data. This paper aims at synthesizing disaster remote sensing images with multiple disaster types and different building damage with generative adversarial networks (GANs), making up for the shortcomings of the existing data sets. However, existing models are inefficient in multi-disaster image translation due to the diversity of disaster and inevitably change building-irrelevant regions caused by directly operating on the whole image. Thus, we propose two models: disaster translation GAN can generate disaster images for multiple disaster types using only a single model, which uses an attribute to represent disaster types and a reconstruction process to further ensure the effect of the generator; damaged building generation GAN is a mask-guided image generation model, which can only alter the attribute-specific region while keeping the attribute-irrelevant region unchanged. Qualitative and quantitative experiments demonstrate the validity of the proposed methods. Further experimental results on the damaged building assessment model show the effectiveness of the proposed models and the superiority compared with other data augmentation methods.
Online knowledge distillation with elastic peer

Knowledge distillation is a highly effective method for transferring knowledge from a cumbersome teacher network to a lightweight student network. However, teacher networks are not always available. An alternative method called online knowledge distillation, which applies a group of peer networks to learn collaboratively with each other, has been proposed previously. In this study, we revisit online knowledge distillation and find that the existing training strategy limits the diversity among peer networks. Thus, online knowledge distillation cannot achieve its full potential. To address this, a novel online knowledge distillation with elastic peer (KDEP) strategy is introduced here. The entire training process is divided into two phases by KDEP. In each phase, a specific training strategy is applied to adjust the diversity to an appropriate degree. Extensive experiments have been conducted on individual benchmarks, including CIFAR-100, CINIC-10, Tiny ImageNet, and Caltech-UCSD Birds. The results demonstrate the superiority of KDEP. For example, when the peer networks are ShuffleNetV2-1.0 and ShuffleNetV2-0.5, the target peer network ShuffleNetV2-0.5 achieves 57.00% top-1 accuracy on Tiny ImageNet via KDEP.
MDS symbol-pair codes from repeated-root cyclic codes

Symbol-pair codes are proposed to protect against pair-errors in symbol-pair read channels. The research of symbol-pair codes with the largest possible minimum symbol-pair distance is of great significance since such codes have the best error-correcting capability. A symbol-pair code meeting the Singleton bound is called a maximum distance separable (MDS) symbol-pair code. In this paper, two new classes of MDS symbol-pair codes are proposed by utilizing repeated-root cyclic codes over finite fields with odd characteristic. It should be noted that these codes have minimum symbol-pair distance ten or twelve.
An Enhanced Lightweight Speck System for Cloud-Based Smart Healthcare

In the realm of information and communication sciences, the Internet of Things (IoT) is a new technology with sensors in the healthcare sector. Sensors are critical IoT devices that receive and send crucial bodily characteristics like blood pressure, temperature, heart rate, and breathing rate to and from cloud repositories for healthcare specialists. As a result of technical advancements, the usage of these devices, referred to as smart sensors, is becoming acceptable in smart healthcare for illness diagnosis and treatment. Data generated from these devices is huge and intrinsically tied to every sphere of daily life including healthcare domain. This information must be safeguarded and processed in a safe location. The term “cloud computing” refers to the type of innovation that is employed to safe keep such tremendous volume of information. As a result, it has become critical to protect healthcare data from hackers in order to maintain its protection, privacy, confidentiality, integrity, and its processing mode. This research suggested a New Lightweight Speck Cryptographic Algorithm to Improve High - Performance Computing Security for Healthcare Data. In contrasted to the cryptographic methods commonly employed in cloud computing, the investigational results of the proposed methodology showed a high level of security and an evident improvement in terms of the time it takes to encrypt data and the security obtainable.
Using Augmented Reality as a Powerful and Innovative Technology to Increase Enthusiasm and Enhance Student Learning in Higher Education Chemistry Courses

The promotion of spatial skills is essential in chemistry education. However, the process of acquiring these skills can be monotonous if learning is limited to the memorization of Newman projections or 3D molecular kits. Existing approaches to learning using visualizing tools require physical models which limit learning activities to within the classroom. Augmented reality (AR) in chemistry education allows students to see actual compound representation in a 3D environment, inspect compounds from multiple viewpoints, and control compounds interaction in real-time in any location. This facilitates the understanding of the spatial relations between compounds. We developed a methodology to use and assess an AR program to teach chemistry to associate degree science students. Figures of small organic molecules together with customized AR cards were used to let students appreciate the complexity of a 3D compound structure by viewing and rotating the depicted compounds. The effectiveness of learning chemistry using AR technology was evaluated. Quantitative questionnaire feedback results from students showed that 87% found that using AR technology for chemistry subjects was an effective teaching method that enhanced their learning, and students were satisfied with the AR educational app and the AR materials used. In a pre- and post-test evaluation of a group activity, students learned better and remembered more information about functional groups and drawings of complicated compounds after using AR technology. On the basis of our results, we can conclude that using AR has a positive impact on enthusiasm and learning in higher education chemistry courses for subdegree students, and this technology should be broadly used as a digital tool to promote active learning during the COVID-19 pandemic.
An Accelerated Inexact Proximal Point Method for Solving Nonconvex-Concave Min-Max Problems

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 16 January 2020Accepted: 09 July 2021Published online: 25 October 2021Keywordsquadratic penalty method, composite nonconvex problem, iteration complexity, inexact proximal point method, first-order accelerated gradient method, minimax problemAMS Subject Headings47J22, 90C26, 90C30, 90C47, 90C60, 65K10Publication DataISSN (print): 1052-6234ISSN (online): 1095-7189Publisher: Society for Industrial and Applied MathematicsCODEN: sjope8
Artificial Intelligence Methodologies for Data Management

This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the main methodological contributions of artificial intelligence through machine learning. The methodology used to study the documents was content analysis; the basic terminology of the study corresponds to machine learning, artificial intelligence, and big data between the years 2017 and 2021. For this study, we selected 181 references, of which 120 are part of the literature review. The conceptual framework includes 12 categories, four groups, and eight subgroups. The study of data management using AI methodologies presents symmetry in the four machine learning groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. Furthermore, the artificial intelligence methods with more symmetry in all groups are artificial neural networks, Support Vector Machines, K-means, and Bayesian Methods. Finally, five research avenues are presented to improve the prediction of machine learning.
Heterogeneous Temporal Graph Neural Network

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Heterogeneous Temporal Graph Neural NetworkYujie Fan, Mingxuan Ju, Chuxu Zhang, and Yanfang YeYujie Fan, Mingxuan Ju, Chuxu Zhang, and Yanfang Yepp.657 - 665Chapter DOI:https://doi.org/10.1137/1.9781611977172.74PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Graph neural networks (GNNs) have been broadly studied on dynamic graphs for their representation learning, majority of which focus on graphs with homogeneous structures in the spatial domain. However, many real-world graphs - i.e., heterogeneous temporal graphs (HTGs) - evolve dynamically in the context of heterogeneous graph structures. The dynamics associated with heterogeneity have posed new challenges for HTG representation learning. To solve this problem, in this paper, we propose heterogeneous temporal graph neural network (HTGNN) to integrate both spatial and temporal dependencies while preserving the heterogeneity to learn node representations over HTGs. Specifically, in each layer of HTGNN, we propose a hierarchical aggregation mechanism, including intra-relation, inter-relation, and across-time aggregations, to jointly model heterogeneous spatial dependencies and temporal dimensions. To retain the heterogeneity, intra-relation aggregation is first performed over each slice of HTG to attentively aggregate information of neighbors with the same type of relation, and then intra-relation aggregation is exploited to gather information over different types of relations; to handle temporal dependencies, across-time aggregation is conducted to exchange information across different graph slices over the HTG. The proposed HTGNN is a holistic framework tailored heterogeneity with evolution in time and space for HTG representation learning. Extensive experiments are conducted on the HTGs built from different real-world datasets and promising results demonstrate the outstanding performance of HTGNN by comparison with state-of-the-art baselines. Our built HTGs and code have been made publicly accessible at: https://github.com/YesLab-Code/HTGNN. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
CrowdXR - Pitfalls and Potentials of Experiments with Remote Participants

Although the COVID-19 pandemic has made the need for remote data collection more apparent than ever, progress has been slow in the virtual reality (VR) research community, and little is known about the quality of the data acquired from crowdsourced participants who own a head-mounted display (HMD), which we call crowdXR. To investigate this problem, we report on a VR spatial cognition experiment that was conducted both in-lab and out-of-lab. The in-lab study was administered as a traditional experiment with undergraduate students and dedicated VR equipment. The out-of-lab study was carried out remotely by recruiting HMD owners from VR-related research mailing lists, VR subreddits in Reddit, and crowdsourcing platforms. Demographic comparisons show that our out-of-lab sample was older, included more males, and had a higher sense of direction than our in-lab sample. The results of the involved spatial memory tasks indicate that the reliability of the data from out-of-lab participants was as good as or better than their in-lab counterparts. Additionally, the data for testing our research hypotheses were comparable between in- and out-of-lab studies. We conclude that crowdsourcing is a feasible and effective alternative to the use of university participant pools for collecting survey and performance data for VR research, despite potential design issues that may affect the generalizability of study results. We discuss the implications and future directions of running VR studies outside the laboratory and provide a set of practical recommendations.
Domain adaptive twin support vector machine learning using privileged information

In the fields of computer vision and machine learning, domain adaptation has been extensively studied and the main challenge in the case is how to transform the existing classifier(s) into an effective adaptive classifier to exploit the latent information in the new data source which typically has a different distribution compared with the original data source. Currently, the Adaptive Support Vector Machines (A-SVM) has been proposed to deal with the domain adaptation problem, which is an effective strategy. However, the resulting optimization task by minimizing a convex quadratic function in A-SVM can not effectively minimize the distance between a source and a target domain as much as possible and typically has high computational complexity. In order to handle these problems, in this paper, we extend the A-SVM by determining a pair of nonparallel up- and down-bound functions solved by two smaller sized quadratic programming problems (QPPs) to achieve a faster learning speed. Notably, our method yields two nonparallel separating hyperplanes to exploit the latent discriminant information based on SVM classification mechanism, which can naturally enhance the classification performance. This method is named as Adaptive Twin Support Vector Machine Learning (A-TSVM). Moreover, we consider a high-level learning paradigm with privilege information (LUPI) to learn a induced model that further constrains the solution in the target space. The learned model is named as domain Adaptive Twin Support Vector Machine Learning Using Privileged Information (A-TSVM+). Finally, a series of comparative experiments with many other methods are performed on three datasets. The experimental results effectively indicate that the proposed method can not only greatly improve the accuracy of classification, but also save computing time.
Human Action Recognition Based on a Sequential Deep Learning Model

Human Action Recognition (HAR) is an application-oriented field that utilizes numerous Machine Learning methods to identify diverse human actions or movements to provide an appropriate or suitable response. A HAR method's success largely depends on the performance of the algorithms for data processing and activity prediction working in the background. This article inscribes an easy action recognition method based on a sequential Deep Learning model. Instead of using intricated signal processing, feature extraction, and feature selection techniques, we used a special type of matrix formulated directly from raw data. The described method has been tested on three different HAR datasets — namely UCI HAR, WISDM, and KU-HAR — and the maximum classification accuracies achieved on them were 98.99%, 96.53%, and 96.67%, respectively. Other evaluation metrics yield similar outcomes, which asserts the method's reliability. Detailed descriptions and discussions of each step of the methodology, reported results, and limitations have been provided with necessary illustrations.
Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-Task Baseline

Moving object segmentation is a crucial task for autonomous vehicles as it can be used to segment objects in a class agnostic manner based on their motion cues. It enables the detection of unseen objects during training (e.g., moose or a construction truck) based on their motion and independent of their appearance. Although pixel-wise motion segmentation has been studied in autonomous driving literature, it has been rarely addressed at the instance level, which would help separate connected segments of moving objects leading to better trajectory planning. As the main issue is the lack of large public datasets, we create a new InstanceMotSeg dataset comprising of 12.9K samples improving upon our KITTIMoSeg dataset. In addition to providing instance level annotations, we have added 4 additional classes which is crucial for studying class agnostic motion segmentation. We adapt YOLACT and implement a motion-based class agnostic instance segmentation model which would act as a baseline for the dataset. We also extend it to an efficient multi-task model which additionally provides semantic instance segmentation sharing the encoder. The model then learns separate prototype coefficients within the class agnostic and semantic heads providing two independent paths of object detection for redundant safety. To obtain real-time performance, we study different efficient encoders and obtain 39 fps on a Titan Xp GPU using MobileNetV2 with an improvement of 10% mAP relative to the baseline. Our model improves the previous state of the art motion segmentation method by 3.3%. The dataset and qualitative results video are shared in our website at https://sites.google.com/view/instancemotseg.
Model-Based Representation and Deinterleaving of Mixed Radar Pulse Sequences With Neural Machine Translation Network

Deinterleaving mixtures of radar pulse sequences is the first and the most vital step for modern electronic reconnaissance systems to intercept and analyze the intentions of noncooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This article proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). First, the mathematical representations are established to describe the structural characteristics of each source. Then, the deinterleaving problem is formulated as a minimization of a maximum-likelihood cost function that can be solved efficiently through a supervised neural machine translation (NMT) network, i.e., to translate each received pulse in the interleaved pulse sequence to the corresponding source label. A sequence-to-sequence NMT model is proposed to capture the structural relationships among the nonadjacent pulses originated from the same source in the mixtures and assign the corresponding label to each pulse. The proposed method does not require the exact knowledge of each component pulse sequence. The experimental results based on the time of arrival sequence of mixed pulse sequences show that the proposed method outperforms the state-of-the-art deinterleaving methods and achieves satisfactory performance under highly nonideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multivariate input conditions.
Toward pre-standardization of reputation-based trust models beyond 5G

In the last years, the number of connections in mobile telecommunication networks has increased rampantly, and in consequence, the number and type of relationships among entities. Should such interactions are to be profitable, entities will need to rely on each other. Hence, mobile telecommunication networks demand trust and reputation models that allow developing feasible communications in 5G and beyond networks, through which a group of entities can establish chains of services between cross-operators/domains, with security and trustworthiness. One of the key obstacles to achieving generalized connectivity beyond 5G networks is the lack of automatized, efficient, and scalable models for establishing security and trust. In this vein, this article proposes a pre-standardization approach for reputation-based trust models beyond 5G. To this end, we have realized a thorough review of the literature to match trust standardization approaches. An abstract set of requirements and key performance indicators has been extracted, and some pre-standardization recommendations proposed to fulfill essential conditions of future networks and to cover the lack of common trust and reputation models beyond 5G.
SQLVis: Visual Query Representations for Supporting SQL Learners

SQL is a typical query language for performing data analytics. Although its usage is ubiquitous, learners experience that query formulation in SQL is error-prone and time-consuming. Prior research has shown that this is due to low expressive ease, extensive training requirements and high cognitive load, all of which present a significant burden for SQL learners. Visual representations can assist learners to significantly lower this burden. The current dominant paradigm aims to facilitate SQL querying by helping users to avoid the syntax of SQL. Such Visual Querying Systems (VQS), however, are not effective for SQL learners as they hide the syntax of the language during query formulation, rather than assisting learners to write correct queries in SQL. Furthermore, training with VQSs is system specific, which leads to system dependency for learners. We argue that novices need support from Visual Query Representation (VQR) solutions which, instead, help them in learning how to write correct and portable SQL queries. In this paper we present SQLVis, a VQR to support novice SQL users in query writing. Our system represents the query as written in SQL by the user, which can improve the SQL writing proficiency of its users. Results of an in depth empirical study demonstrate the significant value of SQLVis for learners.
Detection of AI-Synthesized Speech Using Cepstral &amp; Bispectral Statistics

Digital technology has made possible unimaginable applications come true. It seems exciting to have a handful of tools for easy editing and manipulation, but it raises alarming concerns that can propagate as speech clones, duplicates, or maybe deep fakes. Validating the authenticity of a speech is one of the primary problems of digital audio forensics. We propose an approach to distinguish human speech from AI synthesized speech exploiting the Bi-spectral and Cepstral analysis. Higher-order statistics have less correlation for human speech in comparison to a synthesized speech. Also, Cepstral analysis revealed a durable power component in human speech that is missing for a synthesized speech. We integrate both these analyses and propose a model to detect AI synthesized speech.