Reviewerly/exHarmony huggingface dataset:
Temporal Reasoning Guided QoE Evaluation for Mobile Live Video Broadcasting

Quality of experience (QoE) that serves as a direct evaluation of viewing experience from the end users is of vital importance for network optimization, and should be constantly monitored. Unlike existing video-on-demand streaming services, real-time interactivity is critical to the mobile live broadcasting experience for both broadcasters and their audiences. While existing QoE metrics that are validated on limited video contents and synthetic stall patterns have shown effectiveness in their trained QoE benchmarks, a common caveat is that they often encounter challenges in practical live broadcasting scenarios, where one needs to accurately understand the activity in the video with fluctuating QoE and figure out what is going to happen to support the real-time feedback to the broadcaster. In this paper, we propose a temporal relational reasoning guided QoE evaluation approach for mobile live video broadcasting, namely TRR-QoE, which explicitly attends to the temporal relationships between consecutive frames to achieve a more comprehensive understanding of the distortion-aware variation. In our design, video frames are first processed by deep neural network (DNN) to extract quality-indicative features. Afterwards, besides explicitly integrating features of individual frames to account for the spatial distortion information, multi-scale temporal relational information corresponding to diverse temporal resolutions are made full use of to capture temporal-distortion-aware variation. As a result, the overall QoE prediction could be derived by combining both aspects. The results of experiments conducted on a number of benchmark databases demonstrate the superiority of TRR-QoE over the representative state-of-the-art metrics.
Decoraction: a Catalogue for Interactive Home Decor of the Nearest-Future

Home decor defines how people experience and share spaces, with the decorative elements forming the ‘interface’ to the home. Despite the opportunities of embedding technology within these elements, research to date has not explored this fully. This paper brings home decor to interaction design utilizing decorative elements as a vehicle to incorporate tangible interaction in domestic spaces. In an IKEA-like format, we designed a product catalogue of our own prototypes that illustrate the possibilities of the nearest future. These design illustrations should offer inspiration to those who wish to work with interactive materials (e.g. appearance-changing and soft-sensing), particularly in the context of interactive spaces. Through making, situating, and speculating, we show how designing interactive decor can be a promising area in Research-though-Design.
Pricing and Profit Management Models for SaaS Providers and IaaS Providers

Cloud computing has rapidly penetrated enterprise and user computing markets with three prominent service models: software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). Cloud computing has also proven to be one of the most important environmentally sustainable technological innovations in the year of Industrial Revolution 4.0. While SaaS and IaaS are the two largest revenue generating services in the cloud service market, the pricing and profit generating mechanisms of the SaaS and IaaS providers have not yet been well understood. Unless the SaaS providers’ profit-maximizing decision is considered, any pricing decision by the IaaS providers is likely to be suboptimal. Hence, this paper proposes a Stackelberg game pricing decision model with the aim of maximizing the profit of the IaaS provider, given the best response of the SaaS provider. This paper develops an analytical closed-form solution to the pricing problem and presents sensitivity analyses to give valuable insights into the pricing dynamics and negotiation between the SaaS provider and IaaS provider. Finally, implications of these findings and future research directions are discussed.
Assessment of Augmented Reality Technology’s Impact on Speed of Learning and Task Performance in Aeronautical Engineering Technology Education

(2021). Assessment of Augmented Reality Technology’s Impact on Speed of Learning and Task Performance in Aeronautical Engineering Technology Education. The International Journal of Aerospace Psychology: Vol. 31, No. 3, pp. 219-229.
Sharding-Enabled Blockchain for Software-Defined Internet of Unmanned Vehicles in the Battlefield

The Internet of Unmanned Vehicles (IoUV) is regarded as an emerging technology for military applications not only to make surveillance systems and battlefield operations fully coordinated and automated, but also to provide significant strategic advantages. All the UVs form a coordinated network for exchanging information in IoUV, which enhances context awareness and risk analysis, and improves response time to make the mission effective. This article provides a perspective of fusion of blockchain technology with software defined IoUV, also known as extended 5G, for the battlefield scenario. Software defined IoUV dynamically configures the network parameters and provides network visibility for better security and manageability of the network in IoUV. Block-chain technology helps to provide trustworthy command and control operations in IoUV and stores those operations in tamper-resistant digital ledgers in the form of transactions. Unmanned vehicles (UVs) with high computing, storage, battery life and transmission power act as miners that validate every transaction and are responsible for creating blocks of the blockchain. However, current blockchain technology suffers from scalability; thus, we propose sharding-enabled blockchain to address the issue where shards of lightweight UVs maintain the required number of miners/ auditors to handle the issues when a miner gets destroyed or damaged in the battlefield. Some participating UVs are used as wireless stations to provide persistent wireless connectivity in IoUV. The proposed framework aims to increase the trust and accountability, and to reduce business friction among different units involved in the battlefield.
Word embedding and text classification based on deep learning methods

Traditional manual text classification method has been unable to cope with the current huge amount of data volume. The improvement of deep learning technology also accelerates the technology of text classification. Based on this background, we presented different word embedding methods such as word2vec, doc2vec, tfidf and embedding layer. After word embedding, we demonstrated 8 deep learning models to classify the news text automatically and compare the accuracy of all the models, the model ‘2 layer GRU model with pretrained word2vec embeddings’ model got the highest accuracy. Automatic text classification can help people summary the text accurately and quickly from the mass of text information. No matter in the academic or in the industry area, it is a topic worth discussing.
A Machine-Learning-Based Tool for Passive OS Fingerprinting With TCP Variant as a Novel Feature

With the emergence of Internet of Things (IoT), securing and managing large, complex enterprise network infrastructure requires capturing and analyzing network traffic traces in real time. An accurate passive operating system (OS) fingerprinting plays a critical role in effective network management and cybersecurity protection. Passive fingerprinting does not send probes that introduce extra load to the network and hence it has a clear advantage over active fingerprinting since it also reduces the risk of triggering false alarms. This article proposes and evaluates an advanced classification approach to passive OS fingerprinting by leveraging state-of-the-art classical machine learning and deep learning techniques. Our controlled experiments on benchmark data, emulated, and realistic traffic is performed using two approaches. Through an Oracle-based machine learning approach, we found that the underlying TCP variant is an important feature for predicting the remote OS. Based on this observation, we develop a sophisticated tool for OS fingerprinting that first predicts the TCP flavor using passive traffic traces and then uses this prediction as an input feature for another machine learning algorithm for predicting the remote OS from passive measurements. This article takes the passive fingerprinting problem one step further by introducing the underlying predicted TCP variant as a distinguishing feature. In terms of accuracy, we empirically demonstrate that accurately predicting the TCP variant has the potential to boost the evaluation performance from 84% to 94% on average across all our validation scenarios and across different types of traffic sources. We also demonstrate a practical example of this potential, by increasing the performance to 91.2% and 95.3% on average using a tool for loss-based and delay-based TCP variants prediction in an emulated setting. To the best of our knowledge, this is the first study that explores the potential for using the knowledge of the TCP variant to significantly boost the accuracy of passive OS fingerprinting.
Artificial intelligence grading of breast cancer: a promising method to refine prognostic classification for management precision

Artificial intelligence (AI)-based breast cancer grading may help to overcome perceived limitations of human assessment. Here, the potential value of AI grade was evaluated at the molecular level and in predicting patient outcome.A supervised convolutional neural network (CNN) model was trained on images of 612 breast cancers from The Cancer Genome Atlas (TCGA). The test set, obtained from the Cooperative Human Tissue Network (CHTN), comprised 1058 cancers with corresponding survival data. Upon reversal, a CNN was trained from images of 1537 CHTN cancers and tested on 397 TCGA cancers. In TCGA, mRNA models were trained using AI grade and Nottingham grade (NG) as labels. Performance of mRNA models in predicting patient outcome was evaluated using data from 1807 cancers from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. In selecting images for training, nucleolar prominence determined high- versus low-grade cancer cells. In CHTN, NG corresponded to significant survival stratification in stages 1, 2 and 3 cancers, while AI grade showed significance in stages 1 and 2 and borderline in stage 3 tumours. In METABRIC, the mRNA model trained from AI grade was not significantly different to the NG-based model. The gene which best described AI grade was TRIP13, a gene involved with mitotic spindle assembly.An AI grade trained from the morphologically distinctive feature of nucleolar prominence could transmit significant patient outcome information across three independent patient cohorts. AI grade shows promise in gene discovery and for second opinions.
Distance-Based Multivariate Anomaly Detection in Wire Arc Additive Manufacturing

Wire Arc Additive Manufacturing (WAAM) offers the possibility to build up large-scale metal parts.Data which is obtained from a multivariate sensor system in-situ must be analyzed automatically to ensure an early and reliable detection of defects to reduce the costs due to production scrap.For that reason, a modular anomaly detector for multivariate time series in WAAM was investigated in this paper.The approach adressed major topics in real-life data sets of industrial applications such as miscellaneous signal sample rates, lack of synchronization and concept drift.A reference data set based on an anomaly-dependently splitted time horizon was defined to reduce the sensitivity loss of the detector after an anomaly.To avoid the need for labeled data, an unsupervised anomaly detection method based on neural networks was used.Hence, no time and costs for artificial defect creation on the machine tool are required when implementing the approach in industrial applications.
Sparse Twin Support Vector Clustering Using Pinball Loss

Clustering is a widely used machine learning technique for unlabelled data. One of the recently proposed techniques is the twin support vector clustering (TWSVC) algorithm. The idea of TWSVC is to generate hyperplanes for each cluster. TWSVC utilizes the hinge loss function to penalize the misclassification. However, the hinge loss relies on shortest distance between different clusters, and is unstable for noise-corrupted datasets, and for re-sampling. In this paper, we propose a novel Sparse Pinball loss Twin Support Vector Clustering (SPTSVC). The proposed SPTSVC involves the ϵ-insensitive pinball loss function to formulate a sparse solution. Pinball loss function provides noise-insensitivity and re-sampling stability. The ϵ-insensitive zone provides sparsity to the model and improves testing time. Numerical experiments on synthetic as well as real world benchmark datasets are performed to show the efficacy of the proposed model. An analysis on the sparsity of various clustering algorithms is presented in this work. In order to show the feasibility and applicability of the proposed SPTSVC on biomedical data, experiments have been performed on epilepsy and breast cancer datasets.
Parallel-fusion LSTM with synchronous semantic and visual information for image captioning

For synchronously combining the dynamic semantic and visual information in the decoder part of image captioning, we propose a novel parallel-fusion LSTM (pLSTM) structure in this paper. Two parallel LSTMs with attributes and visual information of image are fused by the hidden states at every time step, which makes the attributes and visual information complementary or enhanced for generating more accurate captions. According to the different ways of integrating semantic information from attribute LSTM to visual LSTM, we propose two models pLSTM with attention (pLSTM-A) and pLSTM with guiding (pLSTM-G). pLSTM-A can automatically capture the crucial semantic and visual information to generate captions, and pLSTM-G directly adjusts the hidden state of visual LSTM by synchronous semantic information to the critical region. For verifying the effectiveness of our proposed pLSTM, we conduct a series of experiments on MSCOCO and Flickr30K datasets, and the experimental results outperform some state-of-the-art image captioning methods.
An Analysis of Blockchain Consistency in Asynchronous Networks: Deriving a Neat Bound

Formal analyses of blockchain protocols have received much attention recently. Consistency results of Nakamoto's blockchain protocol are often expressed in a quantity c, which denotes the expected number of network delays before some block is mined. With μ (resp., ν) denoting the fraction of computational power controlled by benign miners (resp., the adversary), where μ+ν =1, we prove for the first time that to ensure the consistency property of Nakamoto's blockchain protocol in an asynchronous network, it suffices to have c to be just slightly greater than 2μ/(ln(μ/ν)). Such a result is both neater and stronger than existing ones. In the proof, we formulate novel Markov chains which characterize the numbers of mined blocks in different rounds.
Field Performance of South-Facing and East-West Facing Bifacial Modules in the Arctic

This paper presents the first systematic comparison between south-facing monofacial and bifacial photovoltaic (PV) modules, as well as between south-facing bifacial and vertical east-west facing bifacial PV modules in Alaska. The state’s solar industry, driven by the high price of energy and dropping equipment costs, is quickly growing. The challenges posed by extreme sun angles in Alaska’s northern regions also present opportunities for unique system designs. Annual bifacial gains of 21% were observed between side by side south-facing monofacial and bifacial modules. Vertical east-west bifacial modules had virtually the same annual production as south-facing latitude tilt bifacial modules, but with different energy production profiles.
Development of Augmented Reality System for Productivity Enhancement in Offshore Plant Construction

As the scale of offshore plants has gradually increased, the amount of management points has significantly increased. Therefore, there are needs for innovative process control, quality management, and an installation support system to improve productivity and efficiency for timely construction. In this paper, we introduce a novel approach to deal with these issues using augmented reality (AR) technology. The core of successful AR implementation is up to scene matching through accurate pose (position and alignment) estimation using an AR camera. To achieve this, this paper first introduces an accurate marker registration technique that can be used in huge structures. In order to improve the precision of marker registration, we propose a method that utilizes the natural feature points and the marker corner points in the optimization step simultaneously. Subsequently, a method of precisely generating AR scenes by utilizing these registered markers is described. Finally, to validate the proposed method, the best practices and its effects are introduced. Based on the proposed AR system, construction workers are now able to quickly navigate to onboard destinations by themselves. In addition, they are able to intuitively install and inspect outfitting parts without paper drawings. Through field tests and surveys, we confirm that AR-based inspection has a significant time-saving effect compared to conventional drawing-based inspection.
Cost-Effective Authenticated Data Redaction With Privacy Protection in IoT

In a typical e-healthcare system, it is common for users' physiological data collected by Internet-of-Things (IoT) devices to be processed and shared in a third-party environment. To improve service quality, healthcare data sharing in third-party environments needs to ensure the integrity, source authentication, and privacy of the data. Redactable signature schemes (RSSs) are designed to address this concern over the past decades. More concretely, an RSS allows a signature holder to delete privacy-sensitive parts of the signed data and derive a valid signature for the retained data without any help from the original signer. This also provides a flexible data sharing mechanism in a bandwidth-saving manner. However, almost all of the existing RSSs are built on top of public-key infrastructure (PKI) systems, which involve heavyweight public-key management problems and are not suitable for resource-limited IoT applications. Besides, we argue that the only known PKI independent RSS for IoT has some security flaws and requires a large storage space. In this work, we eliminate some of the costs associated with PKI and certificates (such as key managements and certificate verifications) in traditional RSS and propose the first identity-based RSS satisfying the requirements of protecting the integrity and source authentication with selective disclosure control for healthcare data sharing in IoT. We prove the security of the scheme in the random oracle model under the k-SDH assumption. Theoretical comparison and experimental analysis show that our construction has a practical performance. As an extension, we also discuss how to extend our design to achieve fine-grained redaction control, which provides a feasible strategy for a signer to prevent additional redaction or arbitrary redaction from dishonest signature holders.
LOCO: Logistics Objects in Context

Machine perception is a key challenge towards autonomous systems. Especially in the field of computer vision, numerous novel approaches have been introduced in recent years. This trend is based on the availability of public datasets. Logistics is one domain that could benefit from such innovations. Yet, there are no public datasets available. Accordingly, we create the first public dataset for scene understanding in logistics. The Logistics Objects in COntext (LOCO) dataset contains 39,101 images. In its first release there are 5,593 bounding-box annotated images. In total 151,428 instances of pallets, small load carriers, stillages, forklifts and pallet trucks were annotated. We also present and discuss our data acquisition approach which features enhanced privacy protection for workers. Finally, we provide an in-depth analysis of LOCO, compare it to other datasets (i.e. OpenImages and MS COCO) and show that it has far more annotations per image and also a considerably smaller annotation size. The dataset and future extensions will be available on our website (https://github.com/tum-fml/loco).
Convolutional Neural Network Training with Distributed K-FAC

Training neural networks with many processors can reduce time-to-solution; however, it is challenging to maintain convergence and efficiency at large scales. The Kroneckerfactored Approximate Curvature (K-FAC) was recently proposed as an approximation of the Fisher Information Matrix that can be used in natural gradient optimizers. We investigate here a scalable K-FAC design and its applicability in convolutional neural network (CNN) training at scale. We study optimization techniques such as layer-wise distribution strategies, inverse-free second-order gradient evaluation, and dynamic K-FAC update decoupling to reduce training time while preserving convergence. We use residual neural networks (ResNet) applied to the CIFAR10 and ImageNet-1k datasets to evaluate the correctness and scalability of our K-FAC gradient preconditioner. With ResNet-50 on the ImageNet-1k dataset, our distributed K-FAC implementation converges to the 75.9% MLPerf baseline in 18-25% less time than does the classic stochastic gradient descent (SGD) optimizer across scales on a GPU cluster.
A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification

In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected, and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from the patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field. In addition, the AM module and transfer learning technique allow the network to generalize well to other types of image data except histopathology images, and we obtain 95.5% and 95.8% accuracies on IG02 and Oxford-IIIT Pet Datasets.