Reviewerly/exHarmony huggingface dataset:
How Does Emerging Patent Case Law in the US and Europe Affect Precision Medicine? How Will Today’s Patent Law Affect Tomorrow’s Innovation in the Areas of Biomarkers and Nature-Based Products; Diagnostics; and Algorithms, Big Data and AI?

Precision medicine has stirred the excitement of patients, physicians, industry, entrepreneurs, and investors — it is already a market that is worth tens of billions of dollars with very rapid growth expected. But the future of precision medicine heavily depends on the intellectual property frameworks that apply to its various forms. In order to chart this future, we compare recent patent case law in the US and Europe in three areas of precision medicine: (1) biomarkers and nature-based products; (2) diagnostics; and (3) algorithms, big data, and AI. We explain how today’s law will affect tomorrow’s innovation.
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces

As a consequence of an ever-increasing number of service robots, there is a growing demand for highly accurate real-time 3D object recognition. Considering the expansion of robot applications in more complex and dynamic environments, it is evident that it is not possible to pre-program all object categories and anticipate all exceptions in advance. Therefore, robots should have the functionality to learn about new object categories in an open-ended fashion while working in the environment. Towards this goal, we propose a deep transfer learning approach to generate a scale- and pose-invariant object representation by considering shape and texture information in multiple color spaces. The obtained global object representation is then fed to an instance-based object category learning and recognition, where a non-expert human user exists in the learning loop and can interactively guide the process of experience acquisition by teaching new object categories, or by correcting insufficient or erroneous categories. In this work, shape information encodes the common patterns of all categories, while texture information is used to describes the appearance of each instance in detail. Multiple color space combinations and network architectures are evaluated to find the most descriptive system. Experimental results showed that the proposed network architecture outperformed the selected state-of-the-art in terms of object classification accuracy and scalability. Furthermore, we performed a real robot experiment in the context of serve_a_beer scenario to show the real-time performance of the proposed approach.
The estimation of the fractal dimension of a Gaussian field via Euler characteristic

Fractal dimension is a useful characteristic for measuring the irregularity of random fields. In this work, we employ the Euler characteristic (EC) of the excursion sets of a field to estimate the fractal dimension of the random field using the roughness of the underlying realization. The EC of a non‐differentiable Gaussian field is either zero or infinity with probability one and hence we employ a weighted smooth version of the field. Under some weak assumptions, the estimator is consistent when the weight function is constant. The results are examined through a simulation study and applied on real data.
Infrared Image Enhancement Using Differential Evolution Based on double plateau histogram equalization

In the era of smart cities, infrared image enhancement techniques serve a great purpose in smart city applications such as traffic monitoring systems, intelligent urban surveillance systems, etc. Different algorithms have been proposed but most of them focus only on overemphasizing the global contrast. In this paper, we propose a differential evolution-based histogram equalization which utilizes a local entropy weighted histogram to enhance both contrasts as well as the local details contained in the noisy raw image. Initially, we generate a histogram using the local entropy of the raw infrared image. After that, we use the differential evolution algorithm to estimate the double plateau thresholds. To refine the foreground and the background contrast separately we divide the histogram into two parts using a threshold using the OTSU’s method. Finally, according to the constrained sub-local entropy weighted histogram, for each sub-image, the histogram equalization will be done independently.
Predicting Decision-Making in the Future: Human Versus Machine

Deep neural networks (DNNs) have become remarkably successful in data prediction, and have even been used to predict future actions based on limited input. This raises the question: do these systems actually “understand” the event similar to humans? Here, we address this issue using videos taken from an accident situation in a driving simulation. In this situation, drivers had to choose between crashing into a suddenly-appeared obstacle or steering their car off a previously indicated cliff. We compared how well humans and a DNN predicted this decision as a function of time before the event. The DNN outperformed humans for early time-points, but had an equal performance for later time-points. Interestingly, spatio-temporal image manipulations and Grad-CAM visualizations uncovered some expected behavior, but also highlighted potential differences in temporal processing for the DNN.
Randomness Analysis With Runge Kutta Methods

Chaotic systems are widely used in encryption because of their sensitivity to initial conditions and parameters, high ergodicity, mixing properties, and highly complex structures.Various analyzes are available to understand whether a system is chaotic.The most used analyzes are time series analysis, phase portraits, Lyapunov exponents, and bifurcation diagrams.Modeling of chaotic systems is also possible with numerical analysis methods.These methods are; Houses, Heun, 4th and 5th degree Runge Kutta methods are the most common differential solution methods.
Algebraic Fault Analysis of Subterranean 2.0

Algebraic Fault Analysis (AFA) is based on the principles of algebraic cryptanalysis in conjunction with fault analysis. One of the main benefits of AFA is the ability to use off the shelf solving tools like SAT solvers to conduct fault analysis in an automated fashion. In this work we show how the principles of AFA can be applied to the authenticated encryption scheme Subterranean 2.0, a second round candidate of the ongoing NIST-LWC competition. In order to find the optimal parameters for a fault injection we investigated the fault model’s influence on the solving time. The optimal fault parameters turned out as a single bitflip fault in conjunction with a known but randomly chosen fault location, where the fault is applied just one cycle before the tag generation. We verify the efficiency of our attack by means of simulation. Conducting our proposed attack with optimal fault parameters requires only five fault injections to recover the secret key of Subterranean 2.0 in less than four seconds.
ASPECT-BASED SENTIMENT ANALYSIS OF POLITICAL DISCOURSE

The existing systems for accurate sentiment analysis are mainly based on statistical and mathematical principles. However, more promising are the works that are devoted to the study of the linguistic features of the evaluation expression. The results of this formalization can be applied both in the field of affective computing for further improvement of automatic systems and for linguistics and related sciences. The novelty of this study lies mainly in the development of an algorithm based on the identified linguistic rules. In addition, the research material is political discourse, which has not yet been studied enough by specialists of affective computing. The relevance of this work is justified by the growing need for categorization of information published on the Internet. The purpose of the study is to develop a system for machine sentiment analysis of English-language political texts, as well as to identify aspects and their distribution for subsequent use in enhancement. The article discusses the linguistic features of sentiment analysis and suggests a classification of linguistic units with sentiment potential in relation to levels of language structure. The results of an experiment on testing the operation of the sentiment analysis system, conducted on 300 news articles and user comments taken from reddit.com/r/politics, are also presented. The accuracy of the system is 92%. In addition, the selected 40 comments were manually marked up and tagged; during this process the expert identified 25 aspects. Furthermore, 3 formal patterns were identified in the distribution of aspect terms, which is necessary for creating an automatic system. The first peculiarity is that the aspect terms are repeated in two consecutive sentences. The second is that aspect terms are often the themes of sentences. Finally, the third — a high frequency of distribution of aspect terms at the beginning and end of the text (document) was revealed.
A Region Alignment and Matching Method for Fractured Object Reassembly

Reassembling fractured objects has many popular applications, such as relics restoration and solving puzzle. Mostly corrosions are encountered on the shared regions of the fractured object pieces and this data loss makes the reassembly process much more difficult. In this study a minimum bounding box (MBB) based alignment and key-point based matching method is proposed. Our method first divides every single fractured object piece into regions and generates MBB for each region. Then the key-points are determined based on the distances of the middle surface computed from two opposing surfaces of the MBB of the base region. Matching between the base and the candidate regions is carried out depending on similarities of the distances computed according to key-points. The alignment surface is one of the MBB surfaces of the base region and candidate regions are also regenerated on the alignment surface, so the base and the candidate regions are aligned in this manner. We have tested our alignment and matching method on sample fractured corroded objects. The similarity results show that the proposed method aligns and matches right candidate region from other candidates accurately.
Edge-Preserving Denoising of Image Sequences

To monitor the Earth's surface, the satellite of the NASA Landsat program provides us image sequences of any region on the Earth constantly over time. These image sequences give us a unique resource to study the Earth's surface, changes of the Earth resource over time, and their implications in agriculture, geology, forestry, and more. Besides natural sciences, image sequences are also commonly used in functional magnetic resonance imaging (fMRI) of medical studies for understanding the functioning of brains and other organs. In practice, observed images almost always contain noise and other contaminations. For a reliable subsequent image analysis, it is important to remove such contaminations in advance. This paper focuses on image sequence denoising, which has not been well-discussed in the literature yet. To this end, an edge-preserving image denoising procedure is suggested. The suggested method is based on a jump-preserving local smoothing procedure, in which the bandwidths are chosen such that the possible spatio-temporal correlations in the observed image intensities are accommodated properly. Both theoretical arguments and numerical studies show that this method works well in the various cases considered.
A Study on Text Classification for Applications in Special Education

Text classification, or text categorization, is the process that categorizes text into organized groups. Text classifiers can parse text and categorize it based on its content. The text analysis is done using Natural Language Processing (NLP). In recent years we have much more understanding to learning disabilities and how to diagnose and categorize their impact. In this paper we studied learning difficulties as a classification problem and use the NLP tool to solve it. In addition, the results of classifiers such as Naive Bayes, SVM and CNN are compared. The precision results show that the Naïve Bayes classifier had the lowest accuracy of 71.72%, compared to the other two, where the CNN classifier has a 82.79% accuracy and the SVM has the best accuracy in percentage of 93.03%.
Towards Using Live Photos to Mitigate Image Quality Issues In VQA Photography

Studies show Visual Question Answering (VQA) systems are valuable tools for people with visual impairments to quickly obtain information from an image. In this poster, we present our ongoing work towards developing uses of live photos to mitigate quality issues in photography from people with visual impairments for VQA. New contributions building on our prior research include an expanded live photos dataset, a more in-depth analysis of VQA results, and an analysis of features of our live photos compared with existing data collected from people with visual impairments. We show that live photos are a promising method for improving accuracy of VQA and that our sample live photos mimic the types of images taken in real-world settings by people with visual impairments for the task of VQA.
Design and Realization of Webpage Operation Computer System Based on Cloud Computing

In recent years, our country's computer industry has developed vigorously. With people's increasing demand for computer service data processing capabilities and computer operating system portability and cross-platform requirements, traditional computers are no longer competent, and there will always be calculations. Speed cannot take into account portability and cross-platform, the embarrassing situation that computing speed cannot meet the standard for portability. And now is the time for the rapid development of the Internet industry, and the development of network speed has promoted this new model of Cloud Computing(CC). CC is a network-based shared computing resource pool service model with on-demand access and capacity expansion. It is one of the solutions for massive data storage and processing, large-scale concurrent operations and other services. In order to solve the awkward situation between portability and performance in the computer field, this paper designs a web-based computer system based on the CC model to provide private cloud and public cloud services to individual users and corporate users, encapsulate the underlying services, and implement the system. The unique system application framework provides interfaces to third-party developers while implementing the background system. Webpage operating systems try to gradually transform CC from concept to materialization. CC's on-demand services, on-demand access, independent resource pool division, fast flexibility, and measurable access are integrated. After system performance testing and a period of online public testing, the implementation of web-based cloud operating system and system performance have been guaranteed.
Development of real size IT systems with language competence as a challenge for a Less-Resourced Language: a methodological proposal for Indo-Aryan languages

In this paper, based on the example of our early works for Polish, we want to share our experience in the challenging task of developing NLP-based technologies in the situation of initial scarcity of digital language resources that ranked Polish among the Less-Resourced Languages. We present some of our projects aiming at language resources and tools we had to create in order to be able to process texts in Polish and develop real-scale systems with language understanding competence. The case study we present here is the rule-based system POLINT-112-SMS for improving information management in emergency situations. We argue in favour of the lexicon-grammar approach to the formal description of inflecting languages and present our current work on this grammatical paradigm. Our current work is on the implementation of the ideas presented in the first part of the paper on three prominent Indian languages, that is, Hindi, Odia, and Bengali.
Structure of finite dimensional exact estimation algebra on state dimension 3 and linear rank 2*

The estimation algebra plays an important role in classification of finite dimensional filters. When finite dimensional estimation algebra has maximal rank, Yau et al. [Yau (2003 Yau, S. S.-T. (2003). Complete classification of finite-dimensional estimation algebras of maximal rank. International Journal of Control, 76(7), 657–677. https://doi.org/10.1080/0020717031000098435[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). Complete classification of finite-dimensional estimation algebras of maximal rank. International Journal of Control, 76(7), 657–677; Yau & Hu (2005 Yau, S. S.-T., & Hu, G. Q. (2005). Classification of finite-dimensional estimation algebras of maximal rank with arbitrary state-space dimension and Mitter conjecture. International Journal of Control, 78(10), 689–705. https://doi.org/10.1080/00207170500107596[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). Classification of finite-dimensional estimation algebras of maximal rank with arbitrary state-space dimension and Mitter conjecture. International Journal of Control, 78(10), 689–705.] have proved that η must be a degree 2 polynomial. In this paper, we study the structure of finite dimensional exact estimation algebra with state dimension 3 and rank 2. We establish a sufficient and necessary condition for estimation algebra with nonmaximal rank to be finite dimensional. Importantly, in the new filtering system, η needs not to be a degree 2 polynomial and can be of any degree 4n1+2,n1∈Z+. It is the first time to systematically analyse nonmaximal rank exact estimation algebra in which η is a polynomial of any degree 4n1+2,n1∈Z+. For Riccati-type equation, estimates have been done from the viewpoints of both classical solution and weak solution respectively. Finally, finite dimensional filters of Benés type are constructed successfully.
Voice Assistants in Voice Commerce: The Impact of Social Cues on Trust and Satisfaction

Voice assistants (VAs) such as Google Assistant and Amazon Alexa are spreading rapidly. They offer users the opportunity to order products online in a spoken dialogue (voice commerce). However, the widespread use of voice commerce is hindered by a lack of satisfaction and trust among VA users. This study investigates whether social cues and the accompanying perception of the VA’s humanness and social presence can overcome existing obstacles in voice commerce. The empirical comparison (N = 323) of two VAs (low vs. high level of social cues) shows that providing VAs with more cues increases user satisfaction. Nevertheless, the analysis does not reveal entirely positive effects on perceived trust and its dimensions of benevolence, competence, and integrity. Surprisingly, users had less trust in the integrity of a VA with more social cues. For a differentiated view, a more in-depth analysis of the individual cues and their interactions is required.
Res-SVDNet: A Metric Learning Method for Few-shot Image Classification

Metric learning based few-shot image classification has recently received much attention for simplicity and efficiency. Its performance depends highly on the feature extractor and classifier. In this paper, we propose an alternative metric learning method for few-shot image classification––Res-SVDNet. Taking the advantages of good representation and anti-overfitting, the proposed Res-SVDNet utilizes ResNet-18 architecture as the backbone to extract image features. As for the classifier, Euclidean distance is employed to meet the few-shot classification preference for simple inductive bias. Considering the requirement of Euclidean distance for orthogonality, singular value decomposition (SVD) is further involved between feature extraction and classification to improve the classification performance of Res-SVDNet. Experimental results on Mini-ImageNet and Tired-ImageNet demonstrate that the proposed Res-SVDNet outperforms the state-of-the-art methods such as prototypical networks by 2%~6%.
Triplet Loss based Metric Learning for Cervical Cell Classification

Cervical cancer screening mainly relies on manual reading, which is time-consuming and laborious. To classify cervical cells more accurately and robustly, the triplet loss based metric learning has been proposed for cervical cell classification. First, Herlev data set is used to train the metric learning model with the bench hard triplet loss. The cervical cell images been mapped to the feature space by trained model and the corresponding feature vectors can be obtained. Second, the feature vectors and the cell labels are used to train the SVM, the trained SVM is used as a classifier for cervical cell diagnosis. The metric learning is utilized to extract features of cervical cells, and the SVM has been applied to distinguish cells. Feature visualization shows the feature expression of cells, and cells in same class have similar feature expression. Dimensionality reduction visualization illustrates that different cells have unique cluster centers and robust boundaries, which proves the reliability of feature extraction. Finally, the experimental results shown the reliability of feature extraction and the classification accuracy has also reached the state of the art. It is of great significance to the screening of cervical cells and can be applied to various cell diagnosis in the future.
Comparison of Sugeno-type and Mamdani-type fuzzy inference systems for end milling process

This paper is to study the Fuzzy inference processes and develop Sugeno-type and Mamdani-type fuzzy models for the end milling process. Comparison is made between the outcomes of the two Fuzzy inference schemes (FIS). The fundamental distinction between the Sugeno-type FIS and the Mamdani-type FIS is defined in this article. It also reveals that one of the two FISs is an improved alternative for the end milling process. The Sugeno-type FIS’s findings demonstrate that it can be combined with neural networks, genetic algorithms, or other optimization methods to tailor the controller to specific users.
DETECTION ZONES OF DISJUNCTIVE DISLOCATIONS FOR THE TERRITORY OF NORTHERN SAKHALIN BY DATA OF SPACE SURVEYS

The article presents results of the research of disjunctive dislocations of the earth's crust in northern part of the Sakhalin Island by analyzing the fields of lineaments, which identified on basis of satellite surveys using the LEFA software package. The object of research was chosen not by chance: eastern part of territory of the north of Sakhalin Island is quite well studied by geological and geophysical methods in order to identify oil and gas-bearing structures, as well as in connection with the close attention to it after catastrophic Neftegorsk earthquake on May 28, 1995. The result of work of the predecessors made it possible for verify the reliability of results obtained and assess of prospects to using automated decryption technologies to identify seismically active zones. The purpose of work was to create maps of active fault zones on studied territory. In process of the work, the SRTM data and Landsat-8 satellite images for 2015-2020, obtained in eight spectral channels, were analyzed using algorithms for detecting the location of lineaments based on methods of mathematical processing of images using different algorithms. It was revealed that images obtained in the red, infrared zones of the electromagnetic spectrum (4, 5, 6, 7), and the panchromatic channel (8) are most informative for identifying disjunctive disorders. Based on the data obtained, maps of disjunctive disorders of Sakhalin Island on scale of 1: 500,000 compiled. The article has certain novelty due to fact that the LEFA technological techniques were first used for research the geologically complex structures of Northern Sakhalin. The main results of the work will be useful in choosing places for construction of economic and industrial facilities, as well as in the exploration, search and operation of mineral deposits.