Reviewerly/exHarmony huggingface dataset:
Toward Cost Efficient Cloud Bursting

While private clouds are still widely adopted due primarily to privacy and security reasons, they are often less resilient with fluctuating workloads compared to public clouds. Workload surges in private clouds can be dealt with by offloading some workload/jobs to public clouds; this is referred to as cloud bursting. Although the dynamic use of public clouds is claimed to be cost efficient, the actual realization of such cost efficiency is highly dependent on judicious scheduling decisions. In this paper, we present Cost Efficient Cloud Bursting Scheduler (CECBS) as a new scheduling framework that optimizes cost efficiency while preserving privacy by taking advantage of benefits of each of two cloud types. In particular, CECBS schedules jobs taking into account (1) public cloud pricing policy (billing cycle), (2) privacy of data/job and (3) local electricity rates for private clouds. Based on simulation results obtained from real workload traces, CECBS achieves 20% cost savings on average compared with costs of Resource Management Service (RMS) [11].
What’s Wrong with My Dishwasher: Advanced Analytics Improve the Diagnostic Process for Miele Technicians

The authors developed a system that was based on statistics learned from historical data about technician visits. They combined both structured and unstructured data to create a probabilistic model and then used a novel process in which a semantic model informed the creation of the probabilistic model.
Search Results Viewing Behavior vis-à-vis Relevance Criteria

We conducted a lab-based experiment to investigate relationship between multiple criteria used in information relevance judgments and eye fixation behavior on search engine results. We collected eye-tracking data and conducted gaze-cued retrospective think-aloud (RTA). Data from RTA was coded with criteria used by participants in judging search results as relevant. The criteria were analyzed in relation to search engine result page (SERP) sequence and result rank on SERPs. The results of our study aligned with previous research, showing the effect of result rank on SERPs. Our results newly showed that specific source and topicality were the two most often used criteria for relevance judgments. Specific source was the most often used criteria initially but was then surpassed by topicality on subsequent SERPs and on lower result ranks. On first SERPs, fixation duration was significantly longer on results judged on topicality than on specific source. Pupils dilated significantly on the top ranked result on most SERP pages.
Reversible Data Hiding Based on Adaptive Block Selection Strategy

Recently, a reversible data hiding (RDH) method was proposed based on local histogram shifting. This method selects the peak bin of the local histogram as a reference and expands the two neighboring bins of the peak bin to carry the message bits. Since the peak bin keeps unchanged during the embedding process, the neighboring bins can be easily identified at the receiver end, and the original image can be restored completely while extracting the embedded data. In this article, as an extension of the algorithm, the authors propose an RDH scheme based on adaptive block selection strategy. Via a new block selection strategy, those blocks of the carrier image may carry more message bits whereas introducing less distortion will take precedence over data hiding. Experimental results demonstrate that higher visual quality can be obtained compared with the original method, especially when the embedding rate is low.
Novel image restoration method based on multi‐frame super‐resolution for atmospherically distorted images

In this study, the authors propose a novel multi-frame super-resolution method using frame selection and multiple fusions for atmospherically distorted, zoomed-in, image-quality enhancement. When a small part of the image captured by placing a target several kilometres away from the fixed camera is enlarged, the quality of the part becomes poor owing to low resolution, spatial deformations and noise that are mainly caused by long distance and atmospheric turbulence. Thus, the authors propose an adaptive frame selection method that selects only a few frames with small blur based on the corresponding images with relatively clear edges. Further, they propose multiple fusion schemes to reconstruct the selected frames, thereby suppressing the influence of deformation. By converting all the frames into high-resolution based on each frame and integrating them, deformation and noise are effectively removed without high computation cost using the multiple fusion scheme. The proposed method, which enhances the quality of atmospherically distorted zoomed-in images, exhibits superior performance than the state-of-the-art image super-resolution methods with regard to high accuracy, efficiency and ease of implementation, ensuring that the proposed method is suitable for enhancing the quality of an image captured using a general digital camera or a smartphone.
An Adaptive Monitoring Service Exploiting Data Correlations in Fog Computing

In smart environments, a big amount of information is generated by sensors and monitoring devices. Moving data from the edge where they are generated to the cloud might introduce delays with the growth of data volume. We propose an adaptive monitoring service, able to dynamically reduce the amount of data moved in a fog environment, exploiting the dependencies among the monitored variables dynamically assessed through correlation analysis. The adaptive monitoring service enables the identification of dependent variables that can be transmitted at a highly reduced rate and the training of prediction models that allow deriving the values of dependent variables from other correlated variables. The approach is demonstrated in a smart city scenario.
Standoff Annotation for the Ancient Greek and Latin Dependency Treebank

This contribution presents the work in progress to convert the Ancient Greek and Latin Dependency Treebank (AGLDT) into standoff annotation using PAULA XML. With an increasing number of annotations of any kind, it becomes more and more urgent that annotations related to the same texts be added standoff. Standoff annotation consists in adding any kind of annotation in separate documents, which are ultimately linked to a main text, the so-called "base text," which is meant to be unchangeable. References occur via a graph-based system of IDs, which allows an annotation layer (contained in a separate file) to be linked to another annotation layer (contained in another separate file). All the annotations/files create a labeled directed acyclic graph, whose root is represented by the base text. Standoff annotation enables easy interoperability and extension, in that single annotation layers can reference other layers of annotation independently, thus overcoming the problem of conflicting hierarchies. Moreover, standoff annotation also allows addition of different annotations of the same kind to the same text (e.g., two different interpretations of the POS tag for a given token). In the present contribution, I show how the annotations of the AGLDT can become standoff using PAULA XML, which is an open access format following the LAF principles. More precisely, I show the case study of Caesar's De Bello Civili. I detail the PAULA XML files created for its tokenization and sentence split, which are preliminary required to add morphosyntactic annotation.
Rule-Based Derivational Stemmer for Sindhi Devanagari Using Suffix Stripping Approach

Stemming is one of the important tasks of Natural Language Processing Applications, such as in Information retrieval and Machine Translation. In this research paper, we focused on Derivational Stemmer for resource-poor language Sindhi, in Devanagari Script by using suffix Stripping approach. A dictionary of frequent words is added to reduce over and under stemming error. This is our first attempt to develop a Rule-based Derivational Stemmer in Sindhi Devanagari Script. We compared the results of this derivational stemmer with inflectional stemmer of Sindhi Devanagari Script, previously developed by us.
Vieni anche te?

Abstract In this paper we return to the question of the use of te, the Italian personal pronoun, as subject instead of complement. According to scientific literature, the diffusion of te in substitution of tu as the subject of the clause is attributed to the spoken variety of language. We aim at verifying this hypothesis and to further document the use of te as subject using existing corpora of both written and spoken language. The research considers old and new issues (for example the diffusion of the coordinative phrase io e te instead of io e tu , on which see Blasco Ferrer, 1992 ) and intends to represent the phenomenon in quantitative and qualitative terms. In the conclusions it is stated that the phenomenon, even better represented in the spoken versus the written, seems diachronically stable, and sensitive to diatopic factors. The most significant results of the current research consist in having evaluated the presence of the phenomenon separately in the different constructs in which it occurs, and in having identified other contexts of attestation.
Comparison of Denoising Methods Applied to CTA Images of 3D Segmentation of Aortic Dissection

Noise is a disturbance of the image quality that characterized by the presence of pixels that are not desired in the image. Removing or reducing the appearance of the noise is still a challenging problem in the image processing field. We present several denoising methods in this work, such as anisotropic filter, bilateral filter, and Yaroslavsky filter. The objective of this work is to reduce the distortion in the medical images, particularly in Computed Tomography Angiography (CTA) images. It is essential to take care of valuable information about human organ (e.g., in the aorta) carried by medical images since they will be used to diagnose. This work is carried out in the aortic segmentation system which can detect and display the dissection part of the aorta. We embedded the denoising methods in the system in order to enhance the detection of the aortic contour, which subsequently leads to more accurate aortic segmentation results. We compare the performance of each method by using the aortic segmentation result represented in the form of the Dice Similarity Coefficient, and also the visual quality image. The results showed that the segmentation results of all denoising techniques presented in this work are encouraging, although not all the visualization of processed images are well maintained. Anisotropic diffusion is one noise reduction technique that obtained the best in both segmentation result and denoised image.
MODERN DIRECTIONS FOR PREVENTION OF DESTRUCTIVE INFLUENCE OF TEACHER PROFESSIONAL DEFORMATION

Purpose: The purpose of the article is to substantiate the modern directions to overcome the influence of the professional deformation of General education teachers.&#x0D; Methodology: The article describes the results of experimental work in the framework of the project aimed at the study of modern approaches to assessing the quality of education and its relationship with the overall educational results. Example of the training course module for teachers’ additional professional education, the purpose of which is the development of modern directions to prevent professional deformation.&#x0D; Result: The possibilities for supporting the positive psycho-emotional state of the teacher, teaching the practices to overcome subjectivity in making evaluation decisions - the interiorization of the teacher deontological code are revealed.&#x0D; Applications: This research can be used for universities, teachers, and students.&#x0D; Novelty/Originality: In this research, the model of the Modern Directions for Prevention of Destructive Influence of Teacher Professional Deformation is presented in a comprehensive and complete manner.
Conditions for Legitimate Memory Kernel Master Equation

We provide conditionsChruściński, Dariusz for the memory kernel governing time non-local quantum master equation which guarantee that the corresponding dynamical map is completely positive and trace-preserving. This approach gives rise to the new parametrization of dynamical maps in terms of two completely positive maps – so called legitimate pair. Actually, this new parameterizations is a natural generalization of Markovian semigroup. Interestingly our class contains recently studied models like semi-Markov evolution and collision models.
Building Extraction Using Mask Scoring R-CNN Network

Extracting buildings from high resolution remotely sensed images is very practical, which can be applied to urban modeling and so on. The development of computer vision has become better, and the accuracy of recognition of convolutional neural networks has exceeded the accuracy of recognition of human eyes. In this paper, we used a deep convolutional neural network in remote sensing to achieve building extraction. The method in this paper is not based on semantic segmentation, but instance segmentation, which considered each building as an independent individual to achieve building extraction. The results showed that the proposed method is able to extract buildings with high accuracy.
Complete Data Deletion Based on Hadoop Distributed File System

There is a disk residual problem for the deletion mechanism of the Hadoop file storage system. This paper proposes a "plug and play" deletion method to achieve the purpose of completely deleting data. After simulation and recovery experiments, this method can effectively destroy the original data. At the same time, this method can be applied to some Big data clusters which are based on Hadoop. It does not need to redeploy the cluster and can be directly deployed in the already running big data cluster.
A Novel Scheme for Training Two-Stream CNNs for Action Recognition

Human actions recognition from realistic video data constitutes a challenging and relevant research area. Leading the state-of-the-art we can find those methods based on Convolutional Neural Networks (CNNs) and specially two-stream CNNs (appearance and motion). In this paper we present a novel scheme for training two-stream CNNs that increases the accuracy of the fusion (when one of the channels does not perform as well as the other one) and reduces the total time used for training the entire architecture. In addition, we introduce a new descriptor for motion representation that improves the state-of-the-art. Based on this more efficient scheme, we developed an early recognition system. The proposed approach is evaluated on the UCF101 data set with competitive results.
GOES-R Series Data Access and Dissemination

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellites (GOES)-R Series systems, data centers, broadcasts, and data access portals serve a large and diverse user community. The GOES-R Series direct broadcast (DB) services are the GOES Rebroadcast (GRB) and High Rate Information Transmission (HRIT)/Emergency Managers Weather Information Network (EMWIN). The Product Distribution and Access (PDA) capability of the Environmental Satellite Processing Center (ESPC) distributes Level 1b products, Level 2 + products, and associated mission data to authorized operational customers. Non real-time users can access products through NOAA’s Comprehensive Large Array-data Stewardship System (CLASS). NOAA also disseminates GOES-R Series products through a commercial rebroadcast, called GEONETCast Americas (GNC-A). GNC-A users can install a receive station at a relatively low cost. Users can also download imagery from websites maintained by NOAA, National Aeronautics and Space Administration (NASA), NOAA Cooperative Institutes, universities, meteorological services, and other organizations.
A Cooking Support System by Extracting Difficult Scenes for Cooking Operations from Recipe Short Videos

Recently, short cooking videos such as Kurashiru and DELISH KITCHEN are attracting attention. These cooking videos can help people learn the essentials of cooking in a short time. However, it is difficult to understand cooking operations by watching a video only one time. Therefore, in this paper, we propose a novel cooking support system to extract cooking video's difficulty by extracting cooking ingredients and operations based on their appearance time from open captions of videos. We also show the effectiveness of our proposed cooking video's difficulty extraction method.
Temporary Vehicle ID With Deep Hashing

The growing development of sensing and deep learning technologies greatly extend vehicle perception capabilities, which are critical for intelligent vehicles. However, to achieve high quality perception within the vehicle's onboard electrical control unit (ECU), the high efficiency of edge computing and data encoding is essential. Unfortunately, this is a less studied upon topic from the deep learning perspective, as it is always associated with high computational overhead and data throughput, which limit the wide applications of deep learning in intelligent connected vehicles. To solve this problem, we propose a novel deep-learning architecture to simultaneously implement vehicle on-board sensing and detected vehicle identification (ID) creation. To this end, a single pass deep neural network with triplet structure and hashing encoder are delicately designed. The remarkable properties of the vehicle ID include discriminant representation, privacy preservation, geolocation awareness, and minimal communication requirements. All these potentials make the proposed vehicle ID be a viable representation that can meet the strict requirements of realtime large-scale sensing analytics in practical applications of intelligent vehicle systems.
A Novel Color Image Watermarking Algorithm Based on Digital Signature

A novel color watermarking algorithm based on digital signature is proposed. First, the color host image is split into red, green and blue components. Discrete Wavelet Transform level-3 is performed on blue component to obtain various sub-bands. Next, the cD2 sub-band is decomposed by Singular value decomposition, and the watermark information is embedded into the singular values. Then, the digital signature of the orthogonal matrices is generated, and the digital signature is embedded into subbands cH3 and cV3. Last, a set of common attacks are imposed on the watermarked image to test the performance of the new algorithm. The experimental results show that the new algorithm obtains outstanding imperceptibility and robustness compared to the previous algorithms. Meanwhile, the security of the new algorithm catches a higher level by combing digital signature.
A MODEL TO PROJECT A RATING PLAN FOR THE ACADEMIC DISCIPLINE

The article examines the problem of using a score-rating system at a higher education establishment. Requirements for a score-rating system are formulated and the basic shortcomings of the existing variants are identified. The authors propose a model to project a rating plan taking into account didactic tasks of a certain academic discipline and relative workload for the student’s educational activity. The paper provides a sample rating plan developed using the proposed model and evaluates the efficiency of introducing the model of the mentioned score-rating system into educational process.
Facilitating Data Exploration in Industry 4.0

Industrial Internet of Things (IIoT) devices operating in manufacturing plants allow capturing raw data generated by machines, regarding some indicators of interest. Multi-purpose dashboards facilitate a real-time visualization of all those raw data captured, and thus provide knowledge of each indicator. However, sometimes, domain experts interested in analyzing data belonging to specific domains find those dashboards too rigid. In this paper, we present a proposal that we have developed in a real Industry 4.0 scenario. Due to the customized visualizations that it provides, it enables domain experts to gain a greater value and insights out of the captured data. The core of the system is a new ontology that we have built, where, among others, the sensors used to capture indicators about the performance of a machine have been modeled. This semantic description allows to provide customized representations of the manufacturing machine, query formulation at a higher level of abstraction and customized graphical visualizations of the results.
Residual CNN Image Compression

We present a neural network architecture inspired by the end-to-end compression framework [1]. Our model consists of an image compression module, an arithmetic encoder, an arithmetic decoder, and an image reconstruction module. We evaluate the compression rate and the closeness of the reconstructed image to the original image for this model. Structural similarity metrics and peak signal to noise ratio are used to evaluate the image quality. We have also measured the net reduction in file size after compression and compared it with other lossy image compression techniques. Our architecture achieves better results in terms of these metrics compared to traditional and newly proposed image compression algorithms. In particular, an average PSNR of 28.48 and SSIM value of 0.86 are obtained as compared to 28.45 PSNR and 0.81 SSIM value in the previously mentioned network.
Architecture Design Data: Practice Competency in the Era of Computation

Phil Bernstein situates Architecture Design Data in a transformational moment, when the development of building information modeling (BIM) tools has become a catalyst for improving the efficiency o...
Conv-LSTM: Pedestrian Trajectory Prediction in Crowded Scenarios

Pedestrian trajectory prediction is a challenging problem in the crowded and chaotic scenarios. Currently, the prediction error is still high because the input of Long Short-Term Memory (LSTM) network is a 1D vector, which cannot represent the spatial information of pedestrians. To tackle this, we propose to use tensors to represent the complex environmental information. Meanwhile, LSTM internal full connection is converted into full convolution to predict the spatiotemporal pedestrian trajectory sequences. The results show that our method reduces the displacement offset error better than recent works including Social-LSTM, SS-LSTM, CNN, Social-GAN, Scene-LSTM, providing more realistic trajectory prediction for the chaotic crowd.