Reviewerly/exHarmony huggingface dataset:
User-Side Service Synchronization in Multiple Devices Environment

Today, a single user owning multiple devices is a reality. Moreover, with the advent of the concept of Everything-as-a-Service (XaaS), a cloud-based term that allows for a wide variety of services and applications deployed by the user, the multiple devices scenario gain more relevance, mainly due to the lack of interoperability between operating systems and services of these devices. We focus on multiple device’s environments for synchronizing web services at the client-side without continuously depending on a cloud-based system. We discuss a model-based architecture that allows us to fluently migrate services/data and sessions from one device to another regardless of the operating system. The architecture, called The CUBE [12], is based on user-centric principles combined with REST and RESTful concepts. In this extension paper, we present two main contributions. First, with a description of technical and conceptual aspects of the CUBE, and its relation with the devices/applications and web services. Then, a feasibility test for a tight-coupling service such as YouTube streaming. Within a set of ten users, we presented preliminary results that had measured the wasted time to play a given video with and without the CUBE towards five different devices. The results demonstrate that when the users used the CUBE, they spend only 5.821 s to migrate the video, while without the CUBE, they spend 68.101 s to do the same procedure. That means the CUBE is up to $$\approx $$ 12 times faster than the traditional YouTube cloud-based synchronization procedure.
ITNO-K2PC: An improved K2 algorithm with information-theory-centered node ordering for structure learning

The Bayesian Network is considered as one of the most efficient theoretical models in the uncertain reasoning and knowledge representation fields. The Bayesian Network construction consists of two main phases: Structure learning and Parameter Learning. Actually, determining the correct structure represents a major challenge that had been widely studied and still needs to be resolved. In this paper, we introduce a novel algorithm combining an improvement of score based algorithm with an effective nodes ordering method. Based on the parents searching principle of K2 algorithm, we proposed an extension of the used search-space. Besides, using graph acyclic propriety and information theory, we introduce node ordering method. Experiment simulations on the well-known networks prove that the proposed algorithm can efficiently and accurately extract the nearest topology of the original. The real application of our method exhibits its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in Gafsa area (southwestern Tunisia).
An International Website Analysis of Companies' Reaction to the Corona Pandemic

The web addresses (URLs) of 872,773 companies obtained from Dataprovider.com, a private company specialized in business web data, serve as the data basis for our analysis. We consider only for-profit organizations and websites that are frequently updated. We also dropped governmental organizations and the health care sector (US SIC 2-digit codes larger than 80). The websites of the companies are queried and downloaded using a web indexing algorithm. The downloaded websites are then examined for variations of the term corona and its synonyms. These keywords have been translated in 10 languages. Our approach allows an initial estimate of the proportion of companies reporting on the Corona pandemic on their website. We obtained 63,872 companies which mention Corona on their websites (that is 7,32% of the total). We complement this information with basic company characteristics such as the company’s location and the industry in which it operates.
Research on the Accurate Identification Method of College Students with Financial Difficulties Based on Big Data

In order to realize the accurate identification of college students with financial difficulties, this paper puts forward a novel identification method of college students with financial difficulties based on big data. This method is based on big data technology and uses the factor evaluation method to mine the family data, students' personal data and the fluctuation data of students' family economy. The research results show that this method has higher recognition accuracy and can effectively reduce the work intensity of subsidized staff in colleges and universities.
Ranking concrete and abstract words using Google Books Ngram data

Creation of dictionaries of abstract and concrete words is a well-known task. Such dictionaries are important in several applications of text analysis and computational linguistics. Usually, the process of assembling of concreteness scores for words begins with a lot of manual work. However, the process can be automated significantly using information from large corpora. In this paper we combine two datasets: a dictionary with concreteness scores of 40,000 English words and the GoogleBooks Ngram dataset, in order to test the following hypothesis: in text concrete words tend to occur with more concrete words, than with abstract words (and inverse: abstract words tend to occur with more abstract words, than with concrete words). Using the hypothesis, we proposed a method for automatic evaluation concreteness scores of words using a small amount of initial markup.
Realization of Quantized Neural Network for Super-resolution on PYNQ

Vision tasks usually require vast amount of computation and memory resources, which create barriers to edge computing applications. Quantized neural network can provide memory saving, scalability and energy efficiency, while the accuracies of results may decrease. In this paper, we adjust the data-width of feature maps, weights and temporary variables in SRCNN to achieve a trade-off between precision and accuracy. Also, we design a dedicated convolutional acceleration for data stream under the heterogeneous CPU-FPGA platform: PYNQ, including the changed data streaming order and im2col for convolution. Results show when data-width was set to 12-bit, quantization had almost no effect on visual perception of superresolved images. The acceleration of quantized convolution on FPGA can achieve a speed up ratio of 120x at 250MHz, compared with ARM CPU.
Well-Matchedness in Euler and Linear Diagrams

A key feature of diagrams is well-matchedness, referred to as iconicity in philosophy. A well-matched diagram has a structural resemblance to its semantics and is believed to be an effective representation. In this paper, we view well-matchedness as a feature of diagrams’ meaning carriers – syntactic relationships that convey meaning. Each meaning carrier may or may not structurally resemble, i.e. be well-matched to, its semantics. This paper provides the first empirical study that evaluates the impact of well-matched meaning carriers on effectiveness in Euler diagrams and linear diagrams. There are two key take-away messages: using only well-matched meaning carriers led to the best task performance and using both well-matched and non-well-matched meaning carriers in a single diagram should be approached with caution.
Variational Learning of Bayesian Neural Networks via Bayesian Dark Knowledge

Bayesian neural networks (BNNs) have received more and more attention because they are capable of modeling epistemic uncertainty which is hard for conventional neural networks. Markov chain Monte Carlo (MCMC) methods and variational inference (VI) are two mainstream methods for Bayesian deep learning. The former is effective but its storage cost is prohibitive since it has to save many samples of neural network parameters. The latter method is more time and space efficient, however the approximate variational posterior limits its performance. In this paper, we aim to combine the advantages of above two methods by distilling MCMC samples into an approximate variational posterior. On the basis of an existing distillation technique we first propose variational Bayesian dark knowledge method. Moreover, we propose Bayesian dark prior knowledge, a novel distillation method which considers MCMC posterior as the prior of a variational BNN. Two proposed methods both not only can reduce the space overhead of the teacher model so that are scalable, but also maintain a distilled posterior distribution capable of modeling epistemic uncertainty. Experimental results manifest our methods outperform existing distillation method in terms of predictive accuracy and uncertainty modeling.
About possibilities of applying logical analysis of natural language in computer science

This paper deals with the comparison of the most popular methods of a logical analysis of natural language Montague intensional logic and Transparent intensional logic. At first, these logical apparatuses are compared in terms of their founding theoretical principles. Later, the selected sentence is examined through the logical analysis. The aim of the paper is to identify a more expressive logical method, which will be a suitable basis for the future design of an algorithm for the automated translation of the natural language into a formal representation of its meaning through a semantic machine.
Analysis of Different Methods to Close the Reality Gap for Instance Segmentation in a Flexible Assembly Cell

The training of deep learning models for perception requires large annotated datasets, which are expensive and tedious to generate for real world applications. Small job shops with very low production volumes can often not spend their resources on such data generation tasks. One possible solution is the use of simulated data. But there always exists a discrepancy between simulated and real data, which may severely decrease the real world performance of models trained only on simulated data. Therefore, in this publication, we investigate different methods to account for this: photo-realistic rendering, domain randomization and domain adaptation. We analyze the individual and combined effectiveness of these approaches for an instance segmentation model. The target setting is a flexible assembly cell for low volume production with limited resources for data generation and training. We critically discuss the results and show that even simple rendering techniques, when combined with domain randomization, can lead to good results.
CRITICIZING YOGYAKARTA ENVIRONMENT ON THE OFFICIAL WEBSITE OF INDONESIAN TOURISM: AN ECOLINGUISTIC STUDY

Tourism is one of the sectors which defines the identity of a country. In Indonesia, rich in culture and nature, the use website is benefited by the government to portray as well as gain profit from the tourism sites. This paper, thus, intends to discuss how the environment in Yogyakarta tourism is criticized on the website. The interest of linguistics to criticize how its usage affects the environment triggers the development of ecolinguistics. Saphir distinguishes three ecological territories: physical, economic and social. Ecological domains are exercised to present Yogyakarta on Wonderful Indonesia, the official website of Indonesian tourism. This paper observed the employment of clauses of Yogyakarta text found in the Destination Highlight on the front page of the website at www.indonesia.travel on March 31, 2019. The method applied is ecolinguistics by exercising the ideational metafunction of language proposed by Halliday's Systemic Functional Linguistics (SFL) to figure out how the website portrays the real world of Yogyakarta environment seen from the choice of process, participants and circumstances. The analysis reveals that social environment dominates the website, meaning that Yogyakarta is advertised as a carrier and attribute in terms of its culture and value. Yogyakarta, is attached to the attributes as a city with respect for values. This paper proposes criticizes to present Yogyakarta through material processes to demonstrate how local people, as well as tourists, value and engage in preserving the environment of Yogyakarta.Â
From Top-Down Control to Self-Organisation: The “Thaw” and Motor Action Theory

The period from the late 1950s to the mid-1960s in the Soviet Union was known as the “Thaw,” a political era that fostered hopes of restoring the rule of law and democracy to the country. In that period cybernetics came to symbolize both scientific progress and social change. The Soviet intelligentsia had survived the hardship of Stalinist repression and now regarded the new discipline, which brought together the natural sciences and the human sciences, as a pathway to building a freer and more equal society. After decades of domination by Pavlovian doctrine, a paradigm shift was under way in physiology and psychology. Cybernetics reinforced the new paradigm, which put forward ideas of purposive behavior and self-organization in living and non-living systems. The conditioned reflex and a simplistic one-to-one view of connections in the nervous system gave way to more sophisticated and complex models, which could be formalized mathematically. Previous models of control in living organisms were mostly hierarchical and included top-down control of peripheral movement by the motor centers. The new models supplemented this picture with feedback commands from the periphery to the center. By the time cybernetics had made its appearance in the Soviet Union, new models of control had already been formulated in physiology by Nikolay Bernstein (1896– 1966). He termed the feedback from afferent signals “sensorial corrections,” meaning that they play an important part in adapting central control to the changing situation at the periphery of movement. The new paradigm emphasized horizontal connections over vertical ones, and new models took hold based on less “totalitarian” and more “democratic” principles, such as the idea of automatic or autonomous functioning of intermediate centers, the mathematical concept of well-organized functions, the theory of “the collective behavior of automata,” etc. This line of research was carried out in the USSR as well as abroad by Bernstein’s students and followers who formed the Moscow School of Motor Control. The author argues that this preference for less hierarchical models was one expression of the Thaw’s trend toward liberalization of life within the USSR and greater involvement in international politics.
A Tags Mining Approach for Automatic Image Annotation Using Neighbor Images Tree

With the growth of the internet and the volume of images, both online (such as Flicker and Facebook) and offline (such as image datasets or personal/organizational collections), in recent years, annotation of the image has taken broad attention. Image annotation, is a method where labels or keywords for an image are created. This may be biased to popular labels in the automatic image annotation relying on the closet neighbors. Furthermore, when confronting images with less common and unique keywords, the efficiency of these methods will reduce. This article developed a new model to addressing these issues using a tree mechanism of photos related to the target image. Firstly, focused on the correlation of the feature vector between the target image and the image database, neighbor’s images are obtained in the form of a matrix. Afterwards, to indicate the different tags of interest, the non-redundant tags subspaces are mined. Next, based on the tags it has in common with the query image, a neighbor’s images tree structure is drawn. Finally, recommendation tags to the query image are obtained through using the tree structure. The Proposed method applied to well-known benchmarks of annotated datasets, Corel5k, IAPR TC12 and MIR Flickr. The results of the experiments indicate that the method suggested is the better performance and improves the results in comparison with the other methods.
LB952 Multi-scale network for melanoma histopathology image analysis

Histopathology images have distinct histomorphological features on different scales. In the pathological image analysis, pathologists can only make proper diagnosis by comprehensively analyzing histomorphological features of dif-ferent scales. However, the current convolutional neural network with single-scale image input does not have the powerful ability to merge features of different scales. Accordingly, we propose a multi-scale convolutional network architecture for merging features of different scale images, with refer-ence to the doctors’ diagnosis process. Specifically, we create the multi-scale network search (MSNS) algorithm to further optimize the architecture . The architecture can be used in pop convolutional neural networks(CNNs).To develop and evaluate the proposed methods, a melanoma dataset was constructed within 634 digital whole-slide images (185 melanomas, 188 intradermal nevus, 119 compound nevus, 142 junctional nevus). We combined the MSNS with stae-of-the-art CNNs, such as ResNet, EfficientNet and InceprionNet. The model with multi-scale convolutional network architecture is superior to the original network with relative accuracy gains of 2%∼3%. Experimental results on melanoma dataset imply that incorporating diagnosis can help neural network identify more adequate features, by which we can achieve higher diagnosis accuracy than the original neural network.
An Image Encryption Scheme Using Chaotic Baker Map

In this paper, we propose a new image encryption method based on Chaos Baker map and Lanczos algorithm. Two levels of security are achieved to enhance the level of image security. In the first level, the Chaotic Baker map is a randomization technique used to make the pixels more shuffled. A pseudo-random generator is used with the second-level Lanczos algorithm which is applied to generate eigenvalues and eigenvectors. The proposed method resists various attacks: plaintext attacks, maximum deviation, correlation analysis and key sensitivity. Experimental results show that this method has better time complexity when protecting images.
Improving radiograph analysis throughput through transfer learning and object detection

Background: SIGN Fracture Care International partners with surgeons in low-resource hospitals worldwide to provide access to effective orthopedic care by donating educational materials and innovatively designed surgical implants. Over two decades, SIGN’s Online Surgical Database (SOSD) has grown to contain over 500,000 medical images, with radiographs holding the majority share. Methods: One challenge in working with hospitals worldwide is that both the radiographs uploaded to the SOSD and the data entry accompanying the uploads vary in quality. To improve the accuracy of data in the SOSD, we trained a model to detect surgical implants in radiographs. We first developed a tool to automatically detect radiographs, then trained an object detection model to determine the number and placement of surgical implants visible in the radiograph. Active learning was used to generate a training set containing 2,510 radiographs with screws, nails, and plates labeled by bounding boxes. Results: Training a model to simultaneously recognize all three classes of implants gave a low average precision (AP) for the plate class, likely due to the low number of plate instances in our training set and the large variety of surgical plates used by SIGN-partnered surgeons. Applying standard image augmentation techniques to increase the plate count in our training set did not appreciably increase the AP of plate detection. To improve plate detection, we redrew the bounding boxes to account for correlations between the screw and plate classes. Training one model to detect nails and screws and a separate model to detect plates increased the AP of plate detection by 78.8 percentage points. The AP of each class was 80.7% for screws, 93.6% for nails, and 92.6% for plates; meanwhile, the sensitivity was 92% for screws, 86% for nails, and 81% for plates. Conclusions: We show that object detection methods can be used to detect surgical implants in radiographs of varying quality; however, the detection ability is dependent on the type of implant, and some implants, in our case plates, must be treated differently than others. Such tools can improve the throughput of radiograph analysis, assisting physicians and surgeons with the treatment of bone fractures.
Unsupervised and Semi-Supervised Learning with 2-D Graph Convolution

Recent methods based on graph convolutional neural networks have shown promising results in graph-based learning, due to the use of graph convolution for learning effective node representations. However, they commonly adopt 1-D graph convolution that operates on the object link graph while completely overlooking informative relational information on other data dimensions. This significantly limits their modeling capability and may lead to inferior performance on noisy and sparse real-world networks. To address this issue, we propose to explore relations among object attributes to complement object links for node representation learning. In particular, we use 2-D graph convolution to jointly model the relations on the two data dimensions and develop a computationally efficient dimensionwise separable 2-D graph convolutional filter (DSGC). Theoretically, we show that DSGC can reduce intra-class variance of node features on both the object dimension and the attribute dimension to facilitate learning. Empirically, we demonstrate that by modeling attribute relations, DSGC achieves significant performance gain over state-of-the-art methods for node classification and clustering on several real-world networks.
An Approach for Detection of Dust on Solar Panels Using CNN from RGB Dust Image to Predict Power Loss

Energy and Environment are important aspects of today’s world. The solar Photovoltaic (PV) has become one of the emerging renewable energy source which has revolutionized energy sector across the world. In recent era growing number of solar farm are not free from maintenance and operation challenges. Environment induced dust on solar panel hampers power generation at large. This paper focuses on CNN based approach to detect dust on solar panel and predicted the power loss due to dust accumulation. We have taken RGB image of solar panel from our experimental setup and predicted power loss due to dust accumulation on solar panel.
Quantum Computing II

This chapter deals with some of the most prominent successes of quantum computing. The most well-known quantum computing algorithm, Shor’s algorithm for factoring a number in its prime factors, is discussed in details. The key to Shor’s algorithm is the quantum Fourier transform that is explained with the help of simple examples. The role of quantum entanglement is also discussed. The next important quantum computing algorithm is Grover’s algorithm that helps in searching an item in an unsorted database. This algorithm is motivated by first discussing a quantum shell game in which a pea hidden under one of the four shells is found in one measurement with certainty each time. This amazing result is then generalized to an arbitrary number of objects and Grover’s algorithm.
Application of Data Mining Technology in Music Curriculum Resources

Abstract As an indispensable medium between teaching theory and teaching practice, teaching mode can not only effectively guide teaching activities in theory but also help develop effective operating procedures and countermeasures for specific teaching practices. The music curriculum model based on data mining technology is a novel music teaching mode that focuses closely on the technology guideline of “Learning with technology” based on the educational cloud architecture with the collection of multimedia music curriculum resources and properly applies data mining technology to incorporate information technology (IT) means into the music teaching. In this paper, information means such as database and education cloud are effectively used to make an in-depth exploration of the music curriculum model based on cloud computing and data mining technology to provide higher quality information services to schools, teachers and students.