Reviewerly/exHarmony huggingface dataset:
Services Based on the Enriched Profile of a Person Within a Smart University

This paper aims to review the literature on context-aware services inside a smart university by using a person’s profile enrichment. An exploratory investigation was conducted through a systematic state of the art review, to find relationships among a selection of context-aware services with profile-based services such as location, likes, preferences, behavior, and applicability. Also, conceptual pillars of smart university, such as wellbeing, knowledge, environment, governance, and society, are used to find relationships with context-aware services. Subsequently, different features of a profile were identified in order to enrich the design of the services through the non-intrusive acquisition of data from emerging technologies. After analysis, research opportunities in context-aware services through profile enrichment are presented to guide further work as well as experimental validation protocols.
Procedure for the Contextual, Textual and Ontological Construction of Specialized Knowledge Bases

Information Retrieval (IR) from data sources such as the web, databases, sensors, etc., and structuring it through ontologies is an ambitious research field that leads to the reduction of information search time, improvement of the quality of found information and the efficient decision making. In this work, we have proposed a system for extracting information from several data sources to enrich an ontology in the field of plant pathology. The objective of this work is to develop a knowledge base in the field of plant pathology, by structuring the data present in various data sources accessible mainly through the web. To do this, we first propose a modular development approach of a domain ontology. The construction of the ontology at first uses the text-based construction technique and then, we use the generic architecture of a web data extraction system technique from literature to propose a data extraction system for the enrichment of the different ontology modules of the domain. For each subsystem of our extraction system, we have developed the algorithms to be implemented.
Chapter 34. Stochastic Boolean Satisfiability

Stochastic satisfiability (SSAT) is an extension of satisfiability (SAT) that merges two important areas of artificial intelligence: logic and probabilistic reasoning. Initially suggested by Papadimitriou, who called it a “game against nature”, SSAT is interesting both from a theoretical perspective–it is complete for PSPACE, an important complexity class–and from a practical perspective–a broad class of probabilistic planning problems can be encoded and solved as SSAT instances. This chapter describes SSAT and its variants, their computational complexity, applications of SSAT, analytical results, algorithms and empirical results, related work, and directions for future work.
DLC Re-Builder: Sketch Based Recognition and 2-D Conversion of Digital Logic Circuit

Sketch owing to its ability to convey ideas and ease of creation has found application in almost all spheres. From client presentations to documentations, sketching is ideal for conveying thoughts. Sketching of initial ideas or drafts for brainstorming sessions is a common practice. However, these rough sketches require remodelling to a formal format, which adds an overhead to the process. Thus, arises a need to efficiently create, store and update hand-drawn sketches. Computer interfaces for sketching, that can augment the process by maintaining fair drafts, recognizing objects in the sketch, analyzing and summarizing the sketch and more, have been developed. A system that does similar processing for logic circuits is aimed. The complete application is designed optimally to give an accuracy of 89.41% using R-CNN and 82.44% using YOLO. DLC Re-builder(Digital Logic Circuit Re-builder) is a system that would accept sketched logic circuits as input, recognize various components of the circuit and convert the sketch-based circuit into formal graphical format. It would also generate a boolean expression for the final reconstructed 2-D output along with the truth table.
MS-DIAL: Multi-Source Domain Alignment Layers for Unsupervised Domain Adaptation

In general, deep neural networks trained on a given labeled dataset are expected to produce equivalent results when tested on a new unlabeled dataset. However, data are generally collected by different devices or under varying conditions and thus they often are not part of a same domain, yielding poor results. This is due to the domain shift between data distributions and has been the goal of a research area known as unsupervised domain adaptation. Many prior works have been designed to transfer knowledge between two domains: one source to one target. Since data may be taken from different sources and with different distributions, multi-source domain adaptation has received increasing attention. This paper presents the Multi-Source DomaIn Alignment Layers (MS-DIAL), which reduce the domain shift between multiple sources and a given target by embedding domain alignment layers in any given network. Except for the embedded layers, all the other network parameters are shared among all domains, saving processing time and memory usage. Experiments were performed on digit and object recognition tasks with five public datasets widely used to evaluate domain adaptation methods. Results show that the proposed method is promising and outperforms state-of-the-art approaches.
Chaos of Learning Beyond Zero-sum and Coordination via Game Decompositions

It is of primary interest for ML to understand how agents learn and interact dynamically in competitive environments and games (e.g. GANs). But this has been a difficult task, as irregular behaviors are commonly observed in such systems. This can be explained theoretically, for instance, by the works of Cheung and Piliouras (COLT 2019; NeurIPS 2020), which showed that in two-person zero-sum games, if agents employ one of the most well-known learning algorithms, Multiplicative Weights Update (MWU), then Lyapunov chaos occurs everywhere in the payoff space. In this paper, we study how persistent chaos can occur in the more general normal game settings, where the agents might have the motivation to coordinate (which is not true for zero-sum games) and the number of agents can be arbitrary. We characterize bimatrix games where MWU, its optimistic variant (OMWU) or Follow-the-Regularized-Leader (FTRL) algorithms are Lyapunov chaotic almost everywhere in the payoff space. Technically, our characterization is derived by extending the volume-expansion argument of Cheung and Piliouras via the canonical game decomposition into zero-sum and coordination components. Interestingly, the two components induce opposite volume-changing behaviors, so the overall behavior can be analyzed by comparing the strengths of the components against each other. The comparison is done via our new notion of matrix domination or via a linear program. For multi-player games, we present a local equivalence of volume change between general games and graphical games, which is used to perform volume and chaos analyses of MWU and OMWU in potential games.
Exploring the essence and peculiarities of world-class research and education centers

This article is devoted to the consideration the features of world-class scientific and educational centers. The first five world-class SECs in 2019 as part of the national project “Science”. Until 2024, it is planned to create at least 15 world-class scientific and educational centers. The need to create and develop a world-class SEC is explained by the possibility of generating more qualitatively new scientific ideas that will be created within the framework of centers and universities and bringing innovative ideas to the market with the help of interested enterprises. In the future, it is assumed that a number of ideas being developed will correspond to the world level. Ideas related to the socio-economic development of the region will also be of great importance. Cooperation between universities, scientific organizations, enterprises and other stakeholders within the framework of the center will benefit all participants in cooperation. Currently, SECs have been created in five regions – in the Belgorod region, Kemerovo region, Nizhny Novgorod region, Perm Territory and Tyumen region. The specialization of the centers was chosen in accordance with the already formed priority areas of the regions. The activities of the world-class SEC are focused on improving the quality of education, developing networking between representatives of science, education and business and commercializing innovation. The article defines a number of problems that impede the development of worldclass scientific and educational centers.
Detection of Adversarial Examples Based on Sensitivities to Noise Removal Filter

An injection of malicious noise causes a serious problem in machine learning system. Due to the uncertainty of the system, the noise may misleads the system to the wrong output determined by a malicious party. The created images, videos, speeches are called adversarial examples. The study of fooling an image classifier have been reported as a potential threat for the CNN-based systems. The noise is well-designed so that the existence in an image is kept hidden from human eyes as well as computer-based classifiers. In this paper, we propose a novel method for detecting adversarial images by using the sensitivities of image classifiers. As adversarial images are created by adding noise, we focus on the behavior of outputs of image classifier for differently filtered images. Our idea is to observe the outputs by changing the strength of a noise removal filtering operation, which is called operation-oriented characteristics. With the increase of the strength, the output from a softmax function in an image classifier is drastically changed in case of adversarial images, while it is rather stable in case of normal images. We investigate the operation-oriented characteristics for some noise removal operations and the propose a simple detector of adversarial images. The performance is quantitatively evaluated by experiments for some typical attacks.
Introduction to the Microsoft Conversational AI Platform

Conversational AI (short for artificial intelligence) is a term which can be described as a subfield or discipline of artificial intelligence in general. This area deals with the question on how to expose software or services through a conversational interface, enhanced by AI algorithms. Those conversational interfaces usually range from simple text-based chat interfaces to rather complex voice and speech interfaces. The primary goal is to let people interact with software services through those conversational interfaces to make the interaction between humans and machines more natural. An example of that would be the following:Imagine you sit in your car and want to regulate the heating. In the present you would likely need to push a button or turn a switch within your car to turn the heating either up or down. In a more modern scenario, it would be far more natural to just say something like “Car, please turn the heating to 22 degrees Celsius” to achieve the same. In that second case, you would not need to push a button which distracts you from driving; you would only need to speak and tell the device what it should do. Therefore, you would not only have the possibility to stay focused on the main thing you do which is driving, but this could also be beneficial as it may be faster to say something rather than to control a system via buttons or switches.
Improving VAEs' Robustness to Adversarial Attack

Variational autoencoders (VAEs) have recently been shown to be vulnerable to adversarial attacks, wherein they are fooled into reconstructing a chosen target image. However, how to defend against such attacks remains an open problem. We make significant advances in addressing this issue by introducing methods for producing adversarially robust VAEs. Namely, we first demonstrate that methods proposed to obtain disentangled latent representations produce VAEs that are more robust to these attacks. However, this robustness comes at the cost of reducing the quality of the reconstructions. We ameliorate this by applying disentangling methods to hierarchical VAEs. The resulting models produce high--fidelity autoencoders that are also adversarially robust. We confirm their capabilities on several different datasets and with current state-of-the-art VAE adversarial attacks, and also show that they increase the robustness of downstream tasks to attack.
Prediction of Drinking Water Facility Conditions Using the Naive Bayes Algorithm

This study aims to optimize the selection of raw water source options at the location of Pamsimas III program. This analysis will affect the condition of the facilities that will function properly. The data mining method used in this study is a classification model with the Naive Bayes algorithm using the Rapidminer application tool. The data processed is SIM Pamsimas III data with the object of research on the new village drinking water facilities for the 2017-2019 Pamsimas program. This research analyzes the prediction of the condition of drinking water facilities based on the option of raw water sources. So this research can helps to determine the level of potential facilities that will function properly based on the specified raw water source options. Based on the research conducted, predictive analysis using Naive Bayes has an accuracy rate of 85.05%. So that the selection of the raw water source option can predict the condition of the drinking water facilities being built will function properly.
A low-complexity learning-based algorithm for joint application of demosaicking and up-scaling

This paper proposes a low-complexity algorithm based on machine learning to deal with demosaicking and upscaling processes simultaneously. First, the color difference planes are calculated to extract the edge orientation and reconstruct the missing channel of G. Then the correlation of neighboring pixels is adopted to reconstruct the missing channels of RB and refine reconstructed the color planes. Finally, a novel machine learning structure is employ to up-scale image by optical interpolation kernels on the basis of designate feature descriptor. The refined operation and machine learning structure are two crucial contribution in this paper, which bring about decrease of the combination error and improvement of reconstructed quality. In experimental result, it is confirmed that the proposed method gets better reconstructed quality with lower cost of calculation than previous related methods.
Gland segmentation in pancreas histopathology images based on selective multi-scale attention

Pathology is an important subject in the treatment of pancreatic cancer. The tumor presented in the pathological images includes not only the tumor cells, but also the surrounding background structures. Automatic and accurate gland segmentation in histopathology images plays a significant role for cancer diagnosis and clinical application, which assist pathologists to diagnose the malignancy degree of pancreas caner. Due to the large variability of size and shape in glandular appearance and the heterogeneity between different cells, it is a challenging task to accurately segment glands in histopathology images. In this paper, a selective multi-scale attention (SMA) block is proposed for gland segmentation. First, a selection unit is used between the encoder and decoder to select features by amplifying effective information and suppressing redundant information according to a factor obtained during training. Second, we propose a multi-scale attention module to fuse feature maps at different scales. Our method is validated on a dataset of 200 images of size 512×512 from 24 H&amp;E stained pancreas histological images. Experimental results show that our method achieves more accurate segmentation results than that of state-of-the-art approaches.
Service Life Prediction Model of Reinforced Concrete Structure based on Evolutionary Neural Network

When the traditional model predicts the service life of a concrete structure, the number of iterations of its stress and strain parameter points is less, which leads to errors in the calculation value of the concrete structure's bearing capacity. To this end, a prediction model for the service life of reinforced concrete structures based on evolutionary neural networks is constructed. Use different material properties of reinforced concrete structure to numerically simulate multiple mechanical properties, use evolutionary neural network, evolve mechanical parameter points, use concrete buckling strength and buckling bearing capacity as the main parameters of the model, obtain the parameter limit load value, and use the full probability Distribution method, the decay time of the concrete structure is obtained, which is the remaining service life. Comparing this model with two traditional models, the results show that this model reduces the calculation error of the structural bearing capacity, the model input parameters are more accurate, and the reliability of the life prediction value of the reinforced concrete structure is improved.
Neuromorphic Photonics: Current Status and Challenges

Artificial intelligence and neuromorphic computing driven by neural networks has enabled many applications. Software implementations of neural networks on electronic platforms are limited in speed and energy efficiency. Neuromorphic photonics aims to build processors in which optical hardware mimic neural networks in the brain.
Optical tomography dynamic for time-dependent coherent states generated by an open qubit-cavity system

Optical tomography is investigated for time-dependent quantum states, which are generated from coherent even and odd coherent cavity fields interacting with a two-level system (qubit) in the presence of phase damping. The effects of the qubit-cavity coupling, detuning, and cavity phase damping on the optical tomography distribution are studied. The dynamics of the optical tomography is explored for an open cavity field. We show an aspect of the alteration of the optical tomography distribution.
Expediting population diversification in evolutionary computation with quantum algorithm

Quantum computing's uniqueness in commencing parallel computation renders unprecedented efficient optimisation as possible. This paper introduces the adaptation of quantum processing to crowding, one of the genetic algorithmic procedures to secure undeveloped individual chromosomes in pursuit of diversifying the target population. We argue that the nature of genetic algorithm to find the best solution in the process of optimisation can be greatly enhanced by the capability of quantum computing to perform multiple computations in parallel. By introducing the relevant quantum mathematics based on Grover's selection algorithm and constructing its mechanism in a quantum simulator, we come to conclusion that our proposed approach is valid in such a way that it can precisely reduce the amount of computation query to finish the crowding process without any impairment in the middle of genetic operations.
Learning knowledge from longitudinal data of mammograms to improving breast cancer risk prediction

Breast cancer risk prediction is becoming increasingly important especially after recent advances in deep learning models. In breast cancer screening, it is common that patients have multiple longitudinal mammogram examinations, where the longitudinal imaging data may provide additional information to boost the learning of a risk prediction model. In this study, we aim to leverage quantitative imaging features extracted from prior mammograms to augment the training of a risk prediction model, through two technical approaches: 1) prior data-enabled transfer learning, and 2) multi-task learning. We evaluated the two approaches on a study cohort of 306 patients in a case-control setting, where each patient has 3 longitudinal screening mammogram examinations. Our results show that both two approaches improved the 1-, 2-, and 3-year risk prediction, indicating that additional knowledge can be learned by our approaches from longitudinal imaging data to improve near-term risk prediction.
Deformation Monitoring Method of Large Section Shallow Buried Tunnel Based on GNSS Technology

In the deformation monitoring of large-section shallow buried tunnels, the traditional monitoring methods are affected by the temporal and spatial effects of tunnel construction, resulting in insufficient safety of the monitoring methods. Therefore, a deformation monitoring method based on GNSS technology for large cross-section shallow excavation tunnel is proposed. The GNSS automation equipment and its technology are combined to realize the real-time acquisition of monitoring data, and the displacement trend diagram of monitoring points is displayed. According to the known monitoring parameters, the basis for judging the deformation stability is determined. Combined with the calculation results of tunnel surrounding rock and support structure parameters, the optimal fitting monitoring results are obtained. The experimental results show that the total power factor of the designed large-section shallow buried tunnel deformation monitoring method based on GNSS technology is stable, the monitoring results are basically consistent with the actual tunnel deformation, and its safety has been improved.
Accurate and fast mitotic detection using an anchor-free method based on full-scale connection with recurrent deep layer aggregation in 4D microscopy images

Abstract Background To effectively detect and investigate various cell-related diseases, it is essential to understand cell behaviour. The ability to detection mitotic cells is a fundamental step in diagnosing cell-related diseases. Convolutional neural networks (CNNs) have been successfully applied to object detection tasks, however, when applied to mitotic cell detection, most existing methods generate high false-positive rates due to the complex characteristics that differentiate normal cells from mitotic cells. Cell size and orientation variations in each stage make detecting mitotic cells difficult in 2D approaches. Therefore, effective extraction of the spatial and temporal features from mitotic data is an important and challenging task. The computational time required for detection is another major concern for mitotic detection in 4D microscopic images. Results In this paper, we propose a backbone feature extraction network named full scale connected recurrent deep layer aggregation (RDLA++) for anchor-free mitotic detection. We utilize a 2.5D method that includes 3D spatial information extracted from several 2D images from neighbouring slices that form a multi-stream input. Conclusions Our proposed technique addresses the scale variation problem and can efficiently extract spatial and temporal features from 4D microscopic images, resulting in improved detection accuracy and reduced computation time compared with those of other state-of-the-art methods.