Reviewerly/exHarmony huggingface dataset:
Condition monitoring and fault diagnosis of rotating machinery based on feature extraction and expression of vibration signals

To further explore the state monitoring of rotating machinery, the state monitoring and fault diagnosis functions have been presented by using vibration signal feature extraction and expression technology. After describing the development status of vibration signal extraction and expression, the s tate monitoring and fault diagnosis model of rotating machinery are constructed by using this technology. Based on the algorithm flow and evaluation model, an optimization and update scheme is proposed, and an adequate evaluation model is established by evaluating the fault detection status effectively. A genetic algorithm is added to the model to assist optimization to achieve timely analysis and processing of data. In the testing of mechanical fault detection model, the efficiency of data processing and the number of fault detection are tested for vibration signal feature extraction. The test results show that the vibration signal feature extraction technology speeds up the state detection effect and the processing results are more precise.
A Formal Framework for the Ontology Evolution

Ontologies are a formal tool for expressing knowledge. They are based on providing a logic theory about some selected universe of discourse, which defines a decomposition of a topic of interest into its basic elements and formally describes certain restrictions and constraints that need to be met. In the modern days, users cannot expect that ontologies in their initial states will be resistant to changes and remain static throughout the lifespan of their particular application. In this paper we want to focus on expressing alterations, that can be applied to ontologies in time, due to changing business requirements, emerging new knowledge or modification of the scope that an ontology needs to cover.
Shortest Unique Palindromic Substring Queries on Run-Length Encoded Strings

For a string S, a palindromic substring S[i..j] is said to be a shortest unique palindromic substring ( $$ SUPS $$ ) for an interval [s, t] in S, if S[i..j] occurs exactly once in S, the interval [i, j] contains [s, t], and every palindromic substring containing [s, t] which is shorter than S[i..j] occurs at least twice in S. In this paper, we study the problem of answering $$ SUPS $$ queries on run-length encoded strings. We show how to preprocess a given run-length encoded string $$ RLE _{S}$$ of size m in O(m) space and $$O(m \log \sigma _{ RLE _{S}} + m \sqrt{\log m / \log \log m})$$ time so that all $$ SUPSs $$ for any subsequent query interval can be answered in $$O(\sqrt{\log m / \log \log m} + \alpha )$$ time, where $$\alpha $$ is the number of outputs, and $$\sigma _{ RLE _{S}}$$ is the number of distinct runs of $$ RLE _{S}$$ .
A Survey of Blockchain Frameworks and Applications

The applications of the blockchain technology are still being discovered. When a new potential disruptive technology emerges, there is a tendency to try to solve every problem with that technology. However, it is still necessary to determine what approach is the best for each type of application. To find how distributed ledgers solve existing problems, this study looks for blockchain frameworks in the academic world. Identifying the existing frameworks can demonstrate where the interest in the technology exists and where it can be missing. This study encountered several blockchain frameworks in development. However, there are few references to operational needs, testing, and deploy of the technology. With the widespread use of the technology, either integrating with pre-existing solutions, replacing legacy systems, or new implementations, the need for testing, deploying, exploration, and maintenance is expected to intensify.
Efficient Driving Scene Image Creation Using Deep Neural Network

The development of artificial intelligence technology shows the possibility of technologies such as autonomous vehicles and A.I robots. Correspondingly, there are many researches and developments for the machine learning. As the evolution of machine learning technology occurs, classification techniques such as image recognition are not as demanding as before. This recognition technique is also important in autonomous vehicle system. For that, we succeeded in training and classifying a few classes (man, car, truck, etc.) in the roadway driving situation using the darkflow which combined the YOLO framework and the tensorflow. In this way, collecting, learning, and recognizing simple objects is relatively easy, but the recognition system for a safe autonomous vehicle system must also be able to recognize certain situations. So we designed the system for training the specific driving conditions on the roadway such as `Car too close', `Under the construction', `People caution' etc. However, it is difficult to gather a lot of specific training data on machine learning. For example, simple forklifts or automobile images on the web can be easily obtained, but data on road construction situation or data on cars just before the crash will be hard to collect. In this paper, we propose a system that can generate a specific situation data using Generative Adversarial Network (GAN).
Geochemical, mineralogical and petrogenetic studies of the calc-alkaline younger gabbros, Sinai of Egypt with a special emphasis on the role of hydrous fluids

Neoproterozoic younger gabbroic intrusions cropping out in Wadi Watir, Gabal Minadir and Gabal Imleih, southern Sinai, Egypt were investigated. The Imleih gabbros are characterized by fresh mineral constituents while Watir and Minadir gabbros are slightly altered. They are layered and consist of pyroxenites, olivine gabbros, pyroxene gabbros, pyroxene-hornblende gabbros, hornblende gabbros and anorthosites. The clinopyroxenes are of subalkaline affinities and range in composition from augite to diopside, while the orthpyroxenes are clino-enstatite. The primary amphiboles are of mantle origin and represented mainly by pargasite and edenite whereas actinolite and magnesiohornblende represent the secondary one. The investigated gabbros were originated from high Al, hydrous basaltic magmas that were evolved by fractional crystallization at the base of magma chamber to produce calc-alkaline magmas that were emplaced in a transitional stage between volcanic arc and within plate settings. They were crystallized at temperature of ∼800–1200 °C; pressure ∼4.8–6 Kb (∼15–20 km) and high oxygen fugacity. The gabbroic magmas have undergone significant crustal contamination and differentiation which affect on the geochemical characteristics of them. The enrichment of LILE and LREE relative to HFSE and HREE, respectively reflects the effect of slab-derived fluids and metasomatism of their enriched mantle source.
Generating Stable Building Block Structures From Sketches

This paper presents a structure generation algorithm, which converts rough human drawings into stable structures comprising rectangular blocks, suitable for physics-based 2-D environments. Generating viable structures for a physics-based environment imposes many additional requirements above those of most traditional sketch-based domains. Our method is sophisticated enough to deal with these requirements, while still ensuring that the generated structure accurately represents the original sketch. We describe and implement a framework for this process, allowing inexperienced users to create complex structures with ease. Multiple structure possibilities are identified for a single drawing and are then compared based on their similarity to the original sketch using a heuristic value. We evaluate our approach by investigating its ability to replicate structures for the video game Angry Birds, based on human drawn sketches of the original levels.
Layout Cross-Platform and Cross-Browser Incompatibilities Detection using Classification of DOM Elements

Web applications can be accessed through a variety of user agent configurations, in which the browser, platform, and device capabilities are not under the control of developers. In order to grant the compatibility of a web application in each environment, developers must manually inspect their web application in a wide variety of devices, platforms, and browsers. Web applications can be rendered inconsistently depending on the browser, the platform, and the device capabilities which are used. Furthermore, the devices’ different viewport widths impact the way web applications are rendered in them, in which elements can be resized and change their absolute positions in the display. These adaptation strategies must also be considered in automatic incompatibility detection approaches in the state of the art. Hence, we propose a classification approach for detecting Layout Cross-platform and Cross-browser incompatibilities, which considers the adaptation strategies used in responsive web applications. Our approach is an extension of previous Cross-browser incompatibility detection approaches and has the goal of reducing the cost associated with manual inspections in different devices, platforms, and browsers, by automatically detecting Layout incompatibilities in this scenario. The proposed approach classifies each DOM element which composes a web application as an incompatibility or not, based on its attributes, position, alignment, screenshot, and the viewport width of the browser. We report the results of an experiment conducted with 42 Responsive Web Applications, rendered in three devices (Apple iPhone SE, Apple iPhone 8 Plus, and Motorola Moto G4) and browsers (Google Chrome and Apple Safari). The results (with F-measure of 0.70) showed evidence which quantify the effectiveness of our classification approach, and it could be further enhanced for detecting Cross-platform and Cross-browser incompatibilities. Furthermore, in the experiment, our approach also performed better when compared to a former state-of-the-art classification technique for Cross-browser incompatibilities detection.
Text detection in natural images with hybrid stroke feature transform and high performance deep Convnet computing

Summary Detecting Text in Images is an important step in Scene Text Recognition. It still remains a very difficult task because of the variation in size, fonts, orientation, illumination conditions, and complex backgrounds in image. In this paper, a new method to detect text in natural images with a hybrid technique using MSER and stroke feature transform and feature classification with Deep convolution neural network is proposed. The Candidate character region from the image is extracted with MSER and stroke feature transform. Next, a Deep convolution neural network is used to extract deep high level features and they are fused with fully connected layers to classify features. The proposed method achieves F‐measures of 0.73, 0.886, 0.889, and 0.885 on four benchmark Datasets SVT, ICDAR 2011, ICDAR 2013, and ICDAR 2015, respectively.
A simple, analytic solution of the semiclassical Rabi model in the red-detuned regime

We consider the semiclassical Rabi model in the large, red-detuned regime. Using the method of multiple-scales we obtain a simple, analytic, and approximate solution that describes the evolution of the system accurately for long times and for arbitrary values of the qubit-field coupling. It is used to characterize the probability to find the qubit in the excited state and the trajectory of the associated Bloch vector. Finally, we present physical situations where the results can be applied.
Object Detection from SAR Images based on Curvelet Despeckling

SAR images are increasingly gaining importance in the field of remote sensing applications. The receiving SAR data are affected by multiplicative speckle noise which form granular patterns. Hence despeckling is done prior to object detection. Here a comparison of several transform domain techniques are carried out for despeckling SAR images. Performance parameters such as ENL, PSNR, SSIM etc. are used to evaluate the result. From the result, Curvelet is chosen as the optimum technique for despeckling. In this paper Curvelet based despeckling of SAR images is performed and further object detection is done using CA-CFAR.
Implementation and Analysis of Contextual Neural Networks in H2O Framework

Contextual neural networks utilizing conditional multi-step aggregation functions have many useful properties. For example, their ability to decrease the activity between internal neuron connections may decrease computational costs, whereas their built-in automatic selection of attributes required for proper classification can simplify problem setup. The research of contextual neural networks was motivated by a limited number of satisfactory machine learning solutions providing these features. An implementation of the CxNN model in the H2O.ai machine learning framework was also developed to validate the method. In this article we explain relevant terms and the implementation of contextual neural networks as well as conditional multi-step aggregation functions. To validate the solution, experiments and their results are presented for selected UCI benchmarks and Cancer Gene Expression Microarray data.
Symmetric-Key Multi-Factor Biometric Authentication Scheme

Authentication is achieved by using different techniques, like using smart-card, identity password and biometric techniques. Some of the proposed schemes use a single factor for authentication while others combine multiple ways to provide multi-factor authentication for better security. lately, a new scheme for multi-factor authentication was presented by Cao and Ge and claimed that their scheme is highly secure and can withstand against all known attacks. In this paper, it is revealed that their scheme is still vulnerable and have some loopholes in term of reflection attack. Therefore, an improved scheme is proposed to overcome the security weaknesses of Cao and Ge's scheme. The proposed scheme resists security attacks and secure. Formal testing is carried out under a broadly-accepted simulated tool ProVerif which demonstrates that the proposed scheme is well secure.
Say No to Rack Boundaries

Data center networks are designed to interconnect large clusters of servers. However, their static, rack-based architecture poses many constraints. For instance, due to over-subscription, bandwidth tends to be highly unbalanced---while servers in the same rack enjoy full bisection bandwidth through a top-of-rack (ToR) switch, servers across racks have much more constrained bandwidth. This translates to a series of performance issues for modern cloud applications. In this paper, we propose a rackless data center (RDC) architecture that removes this fixed "rack boundary". We achieve this by inserting circuit switches at the edge layer, and dynamically reconfiguring the circuits to allow servers from different racks to form "locality groups". RDC optimizes the topology between servers and edge switches based on the changing workloads, and achieves lower flow completion times and improved load balance for realistic workloads.
A privacy-preserving spectrum auction scheme using paillier cryptosystem with public verification

In recent years, a large number of spectrum mechanisms have been proposed, but these mechanisms ignore the security issues that arise during the design of the mechanism. In this paper, two secure models for sealed-bid spectrum auction are given based on Wang’s generic spectrum auction mechanism. O ne is the basic model and another improved model based on the basic model is proposed, which maximizes Social welfare while it is a Privacy-preserving Spectrum auction mechanism with public Verification namely SPSV. The SPSV scheme achieves the properties of maximizing the social welfare but also, by using the double paillier cryptosystem, it is privacy-preserving for bidders’ bids without revealing any sensitive information to auctioneer or agent during the entire spectrum auction. Oblivious transfer is applied to ensure the anonymity of bidders. Furthermore, the use of inequality comparison proof also provides the public verification of winner group to verify the comparison relationship between winner groups and losing groups. At last, the performance analysis are given.
M-Channel Graph Filter Banks: Polyphase Analysis and Structures

Two channel critically sampled filter banks for signal over graph domains were first proposed for undirected bipartite graphs by Narang and Ortega. Extension to the M-channel critically sampled case for balanced M-block cyclic graphs was then proposed by Teke and Vaidynathan but the filter bank does not achieve strict perfect reconstruction (PR), only generalized PR. In this letter, we consider the more general case of filter banks on unbalanced M-block cyclic graphs where strict PR is achieved. A polyphase analysis to derive the implementation structures in the downsampled domain is presented here. The relevant system/filter matrices have interesting cyclic properties and projection operators are needed to map signals between subgraphs.
A multilevel cybersecurity and safety monitor for embedded cyber-physical systems

Cyber-physical systems (CPS) are composed of various embedded subsystems (often realized on system on chip technology) and require specialized software, firmware and hardware to coordinate with the rest of the system. These multiple levels of integration expose attack surfaces which can be susceptible to attack vectors that require novel architectural methods to effectively secure against. We present a multilevel monitor architecture cybersecurity approach applied to a flight control system (FCS). We develop formal framework for the architecture using Event Calculus to define the interactions among the monitors and the system under observation.
Driving Scene Understanding Using Hybrid Deep Neural Network

Currently, artificial intelligence is used in many fields, especially real-time object recognition in vision is very efficient and powerful than other vision processing methods, and it is used for robots and autonomous vehicles. However, a more meaningful judgment by artificial intelligence requires recognizing the position of the detected object or the batch among the objects beyond the object detection and recognizing the situation. In other words, we can interpret that a person would measure a situation after seeing an object. In this paper, we propose a system to combine real-time object detection and situation recognition. We represent object detection and situation recognition by networks (DNN) with different characteristics. We propose an innovative system that can efficiently combine two networks.
Inductive Transfer Learning for Detection of Well-Formed Natural Language Search Queries

Users have been trained to type keyword queries on search engines. However, recently there has been a significant rise in the number of verbose queries. Often times such queries are not well-formed. The lack of well-formedness in the query might adversely impact the downstream pipeline which processes these queries. A well-formed natural language question as a search query aids heavily in reducing errors in downstream tasks and further helps in improved query understanding. In this paper, we employ an inductive transfer learning technique by fine-tuning a pretrained language model to identify whether a search query is a well-formed natural language question or not. We show that our model trained on a recently released benchmark dataset spanning 25,100 queries gives an accuracy of 75.03% thereby improving by $$\sim $$ 5 absolute percentage points over the state-of-the-art.
Interval Type-2 Fuzzy Logic for Semisupervised Multimodal Hashing

Retrieving nearest neighbors across correlated data in multiple modalities, such as image-text pairs on Facebook and video-tag pairs on YouTube, has become a challenging task due to the huge amount of data. Multimodal hashing methods that embed data into binary codes can boost the retrieving speed and reduce storage requirements. Since unsupervised multimodal hashing methods are usually inferior to supervised ones and the supervised ones require too much manually labeled data, the proposed method in this paper utilizes partially available labels to design a semisupervised multimodal hashing method. The labels for unlabeled data are treated as an interval type-2 fuzzy set and estimated by the available labels. By defuzzifying the estimated labels using hard partitioning, a supervised multimodal hashing method is used to generate binary codes. Experiments show that the proposed semisupervised method with 50% labels can get a medium performance among the compared supervised ones and achieve approximate performance to the best supervised method with 90% labels. With only 10% labels, the proposed method can still compete with the worst compared supervised one. Furthermore, the proposed label estimation method has been experimentally proven to be more feasible for a multilabeled MIRFlickr data set in a hash lookup task.
Mobile Experience in Learning Chinese

In the age of experience economy, students are also attaching greater importance to enlightening and entertaining experience in learning. Earlier studies on mobile learning experience have generated increasing amount of knowledge on experience-oriented learning. Through reviewing earlier studies on mobile experience in learning Chinese as a second language and recommending dimensions and directions for further studies, this chapter aims at inviting further studies on how to enhance mobile learning experience in general by applying the six stages of mobile experience, assisted with the 3M (mapping, measuring, and modeling) approach with a focus on the gap between the normative and the empirical dimensions of mobile experience in learning Chinese in the context of Confucius Institutes around the world.
Codata in Action

Computer scientists are well-versed in dealing with data structures. The same cannot be said about their dual: codata. Even though codata is pervasive in category theory, universal algebra, and logic, the use of codata for programming has been mainly relegated to representing infinite objects and processes. Our goal is to demonstrate the benefits of codata as a general-purpose programming abstraction independent of any specific language: eager or lazy, statically or dynamically typed, and functional or object-oriented. While codata is not featured in many programming languages today, we show how codata can be easily adopted and implemented by offering simple inter-compilation techniques between data and codata. We believe codata is a common ground between the functional and object-oriented paradigms; ultimately, we hope to utilize the Curry-Howard isomorphism to further bridge the gap.
Image quality classification algorithm based on InceptionV3 and SVM

In this work we investigate the use of deep learning for image quality classification problem. We use a pre-trained Convolutional Neural Network (CNN) for image description, and the Support Vector Machine (SVM) model is trained as an image quality classifier whose inputs are normalized features extracted by the CNN model. We report on different design choices, ranging from the use of various CNN architectures to the use of features extracted from different layers of a CNN model. To cope with the problem of a lack of adequate amounts of distorted picture data, a novel training strategy of multi-scale training, which is selecting a new image size for training after several batches, combined with data augmentation is introduced. The experimental results tested on the actual monitoring video images shows that the proposed model can accurately classify distorted images.