Reviewerly/exHarmony huggingface dataset:
An improved numerical method for variational image registration model with intensity correction

In this paper, we propose an efficient numerical method for registration and intensity correction of images. We first introduced a variational image registration model in our previous work. The variational minimization of the functional in the model is solved by using an alternating minimization method. In this work, we improve the numerical solution of the minimization problem by applying the Split Bregman method together with the alternating minimization method. As expected, our numerical experiments show that the proposed method converges rapidly and is significantly more accurate than the method used in our previous work.
When People Vanish: A Study to Investigate How Human Presence Changes the Scenario Speculation

Designers have used everyday photos as stimuli to speculate alternative scenarios about the future. Many studies have developed diverse ways of re-designing roles of an actor to stimulate scenario speculation; however, a lack of understandings of how the presence of an actor influences the scenario speculation process. Therefore, this work conducted a study that investigates how everyday photos with and without human actors affect people to speculate possible scenarios. We recruited 29 crowd workers from Amazon Mechanical Turk to generate 80 scenarios for two groups of photos (i.e., with-actor and non-actor photos). We analysed these scenarios and found that with-actor photos led people to speculate consistent scenarios around human actors and their activities. By contrast, non-actor photos allowed people to speculate the scenarios flexibly and diversely. These insights suggest a new design opportunity of using non-actor photos as potential materials to encourage people to generate diverse scenarios.
Quantum Digital–Analogue Computing

Nature is the relations between processes and phenomena. Nothing exists in a Universe without relations. Computing is the transaction of relations between data using control and execution mechanisms. Quantum relations are a superposition of particles and their states. Quantum computing is unconditional transactions of relations between qubit data. Entanglement is a nonlocal superposition of deterministic states. A quantum computer is an analog device for solving combinatorial problems in parallel. Practically focused definitions of the concepts of quantum computing represent a path to the development of scalable quantum parallel algorithms for solving combinatorial problems. Any algorithm can be reduced to a sequence of operations without conditions, because any truth table is a collection of a complete system of conditions-states. Any sequence of actions can always be reduced to one parallel operation. Conditions and sequences arise only when the developer uses previously created primitives-constructs to build an always non-optimal calculator. The deterministic paradigm of creating quantum computing by using photon transactions on the electrons of an atom can exclude the use of quantum logic. The evolutionary path of quantum computing from the classical one: Memory-Address-Transaction (MAT) $\rightarrow$ Electron-Address-Transaction $\rightarrow$Electron-Address-Quantaction (EAQ)$\rightarrow$State-Superposition- Logic. What is calculated on a quantum computer can be calculated in parallel on a classical computer due to the redundancy of memory. MAT-computing implements any algorithms through transactions (read-write) in memory. Qubit-vector models for describing functionalities differ from the known truth tables in terms of compactness of description and technological implementation of parallel algorithms for the synthesis and analysis of digital devices and SoC-components. An example is given – a memory-driven algorithm for simulating digital devices based on qubit-vector forms of describing functionalities for a significant increase in the productivity of computing processes by parallel execution of logical operations. Comparative evaluations of the use of qubit models and methods show an increase in the efficiency of algorithms for simulating digital devices in comparison with tabular ones. The superposition of classical, quantum and analog computing is integrally presented, which makes it possible to search the best solutions for recognition and decision making.
Multiple Semantic Embedding with Graph Convolutional Networks for Multi-Label Image Classification

Multi-label image classification focuses on predicting a set of object labels presented in an image, which is a practical yet challenging task since labels are normally co-occurred in an image while mutual interactions among labels and the correspondence between a given image and the corresponding labels are rarely considered in the existing methods. To address above challenges, we propose a Multiple Semantic Embedding model with Graph Convolutional Networks (MSEGCN) for multi-label image classification by capturing important dependencies related labels. Specifically, the proposed MSEGCN leverages graph structure to guide label co-occurrence propagation among different categories to obtain appropriate label representations. Then, by formulating multi-label classification problem as a label ranking problem with the aid of end-to-end convolutional neural network framework, we focus on learning a transform matrix to seek the image-label relevance relations in embedding space. Furthermore, an adaptive weighting strategy is introduced to effectively improve the classification performance. Experimental studies across a wide range of benchmark datasets show that our method achieves highly competitive performances against other state-of-the-art approaches.
Automatic Classification of Plutonic Rocks with Machine Learning Applied to Extracted Shades and Colors on iOS Devices

Light/dark shades and color are some properties used for the classification of plutonic rocks but are difficult to measure because they depend on the experience of the observer. Moreover, the classification of plutonic rocks using various instrumental techniques tends to be expensive and time-consuming. To address this situation, we extracted dominant shades and colors from 283 plutonic rock images in RGB and CIELAB formats to train several machine learning models. The best model was deployed on an iOS application that identifies four classes of plutonic rocks from darkest to lightest: gabbro, diorite, granodiorite, and granite. The best results were for the K-Nearest Neighbors model using CIELAB dominant colors data with accuracy, precision, recall, and F-score of 93%.
Definition of Professional Vector in Educational Systems of Training Future It Specialists

Nowadays, the quality of vocational education directly depends on such characteristics as readiness for independent work, individualization of programs, self-improvement and self-development, the ability to acquire knowledge and use them productively in professional activities. Thus, the individualization of education plays an important role. This article describes and analyzes the method of determining career guidance, known as the John Holland test, presents a model of its work and justifies the results. Also, the previously implemented recommendation system is mentioned and considered, which allows to form an individual educational trajectory for students of IT specialties. This system uses a sub-module for determining career guidance, which is implemented on the basis of the Dutch test. Accordingly, the use of the Dutch method in the work of the recommendation system for the formation of an individual educational trajectory will provide a more accurate and desirable result. Holland’s test was also used to survey the graduating class to choose an IT profession and showed unexpected results.
Multi-stage Knowledge Propagation Network for Recommendation

As the knowledge graph can provide items with rich attributes, it has become an important way to alleviate cold start and sparsity in recommender systems. Recently, some knowledge graph based collaborative filtering methods use graph convolution networks to aggregate information from each item's neighbors to capture the semantic relatedness, and significantly outperform the state-of-the-art methods. However, in the process of knowledge graph convolution, only the item nodes can make use of knowledge, while the user nodes only contain the original ID information. This gap in information modeling makes it difficult for prediction function to capture the user preference for high-order attribute nodes in knowledge graph, which leads to the introduction of noise data. In order to give full play to the ability of knowledge graph convolution in mining high-order knowledge, we propose Multi-Stage Knowledge Propagation Networks (MSKPN), an end-to-end recommender framework which combines the graph convolution on both knowledge graph and user-item graph. It uses the collaborative signal latent in user-item interactions to build an information propagation channel between the user nodes and item nodes, so as to complement user representations. We conduct extensive experiments on two public datasets, demonstrating that our MSKPN model significantly outperforms other state-of-the-art models. Further analyses are provided to verify the rationality of our model.
ANALYSIS APPROACH TOWARDS PIXE TECHNIQUE BASED ON MAJOR AREAS

Pixe is the analysis of elements using a beam of high energy particles such as materialistic probes(wire) and have X-rays characteristics. The paper focuses on analyzing the gap between the two researchers’ methodology and the thinking processes. The paper focuses on all the accepts based on pixe Technique used to analyses the problems. Authors have collaborated with least knowledge authors for the research. The original topic that was pixe was researched the least.
Entangled photons from composite cascade emitters

One of the most versatile sources for entangled photons are emitters that interact via more than one tunable mechanism. Here, we demonstrate how hybridization and dipole-dipole interactions-potentially simultaneously available in colloidal quantum dots and molecular aggregates-leveraged in conjunction can couple simple, well understood emitters into composite emitters with flexible control over the level structure. We show that cascade decay through carefully designed level structures can result in emission of frequency-entangled photons with Bell states and three-photon GHZ states as example cases. These results pave the way toward rational design of quantum optical emitters of entangled photons.
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning

Existing self-supervised learning methods learn representation by means of pretext tasks which are either (1) dis-criminating that explicitly specify which features should be separated or (2) aligning that precisely indicate which features should be closed together, but ignore the fact how to jointly and principally define which features to be repelled and which ones to be attracted. In this work, we combine the positive aspects of the discriminating and aligning methods, and design a hybrid method that addresses the above issue. Our method explicitly specifies the repulsion and attraction mechanism respectively by discriminative predictive task and concurrently maximizing mutual information between paired views sharing redundant information. We qualitatively and quantitatively show that our proposed model learns better features that are more effective for the diverse downstream tasks ranging from classification to semantic segmentation. Our experiments on nine established benchmarks show that the proposed model consistently outperforms the existing state-of-the-art results of self-supervised and transfer learning protocol. Code can be found at https://github.com/AnjanDutta/codial.
Artificial Intelligence in Metrology Data Collection

Many companies have tried to automate data collection for handheld Digital Multimeters (DMM) using Optical Character Recognition (OCR). Only recently have companies tried to perform this task using Artificial Intelligence (AI) technology, Cal Lab Solutions being one of them in 2020. But when we developed our first prototype application, we discovered the difficulties of getting a good value with every measurement and test point.A year later, lessons learned and equipped with better software, this paper is a continuation of that AI project. In Beta-,1 we learned the difficulties of AI reading segmented displays. There are no pre-trained models for this type of display, so we needed to train a model. This required the testing of thousands of images, so we changed the scope of the project to a continual learning AI project. This paper will cover how we built our continuous learning AI model to show how any lab with a webcam can start automating those handheld DMMS with software that gets smarter over time.
Improved few shot learning classification methods fused with attention mechanism

In view of the problem that deep learning training samples are less likely to be over-fitted, and the embedding module is easy to ignore important feature information, this paper proposes an improved few-shot learning classification method that integrates the attention mechanism. The channel attention mechanism and the fusion attention mechanism is embedded in different stages of the network to extract semantic and texture features of different scales. In addition, the swish function is introduced into the embedding module, which effectively reduces the dependence between parameters and alleviates the occurrence of overfitting, which better shows the nonlinear modeling ability of the few-shot learning network. Test results on public data sets such as Omniglot and miniImagenet have improved. The experimental results show that the proposed method can effectively extract complex important feature information in the data set and alleviate the occurrence of overfitting with a small number of training samples, and is useful in image classification tasks. A good performance improvement has been achieved in the.
ABAE: Utilize Attention to Boost Graph Auto-Encoder

Graph Auto-Encoder(GAE) emerged as a powerful node embedding method, has attracted extensive interests lately. GAE and most of its extensions rely on a series of encoding layers to learn effective node embeddings, while corresponding decoding layers trying to recover the original features. Promising performances on challenging tasks have demonstrated GAE’s powerful ability of representation. On the other hand, Subgraph Convolutional Networks(SCNs), as an extension of Graph Convolutional Networks(GCNs), can aggregate both tagged and local structural features in an artful way. In this paper, we show that SCNs can be improved (AttSCNs) by an attention mechanism to acquire better representational capability, which is competent for the duty of encoder. Then we develop inversed AttSCNs and propose a novel auto-encoder, i.e., Attention-Based Auto-Encoder(ABAE). This architecture utilizes attention mechanism to get insight of the data. We perform experiments on some challenging tasks to show the effectiveness of our models. Moreover, we construct AttSCNs for Node Classification. The results demonstrate that AttSCNs can produce considerable embeddings. Furthermore, we launch Link Prediction task for the proposed ABAE. Experimental results show that our ABAE has its fantastic power and achieves state-of-the-art in Link Prediction.
Crawler Detection in Location-Based Services Using Attributed Action Net

Malicious Web crawlers threaten information system due to heavily taking up bandwidth resources and stealing private user data. Ele.me, a prevalent on-demand food delivery platform in China, suffers from the negative impact of crawlers. The crawler detection systems face two major challenges: spatial patterns of the crawler behaviors and limited labeled data for training. In this paper, we present efficient solutions to tackle these challenges. Specifically, we propose a new Attributed Action Net (AANet for short) model to detect Location-Based Services~(LBS) crawlers and a three-stage learning framework to train the model. AANet consists of three different embedding modules, including the action token sequence, temporal-spatial attributes of users, and the context information of the raw data. We have deployed the model at Ele.me, and both offline experiments and online A/B tests show that the proposed method is superior to the state-of-the-art models for sequence data classification on the food delivery platform.
ՌԵՖԼԵՔՍԻՎ ՄԻՋԱՎԱՅՐԻ ՁԵՎԱՎՈՐՄԱՆ ՄԱՆԿԱՎԱՐԺԱԿԱՆ ՊԱՅՄԱՆՆԵՐԸ / PEDAGOGICAL CONDITIONS FOR THE FORMATION OF A REFLEXIVE ENVIRONMENT

The analysis of the relevant literature shows that the reflexive environment is an important condition for the implementation of effective educational work, but it is also a kind of indicator of the level of the educational process. The reflexive environment performs different functions: informational, evaluating, diagnostic and implies cooperation of the participants in the learning process, as well as transparency of actions, sincerity, empathy in the process of overcoming difficulties. Informationsemantic, social, material and technological components are distinguished in the structure of the reflexive environment. It is fundamentally important that personality-oriented, interactive methods be successfully applied only in the presence of a reflexive environment. On the one hand, interactive technologies reflect the content, the dynamics of the development of the reflexive environment. On the other hand, the reflexive environment promotes the introduction of new, effective teaching and educational methods.
Class-Based Order-Independent Models of Natural Language for Bayesian Auto-Complete Inference

We introduce a model for auto-complete of general queries via Bayesian inference. To that end, we address three issues: First, the problem of predicting a word given previous words in a text. Usually, the context words are treated as a directional sequence. In our approach, we introduce a set-based class language model with order-independence, modeling the context words as a set of classes. Second, towards the task of predicting the next word’s class based on the classes of previous words plus an incomplete word prefix, we present a Bayesian framework that incorporates the set-based class language model in conjunction with an ontology. Third, regarding the auto-complete problem, we provide complete query suggestions via abstract class-space search which determines similar historical queries that contain the classes of previous words plus the next word’s predicted class. Subsequently, we apply the model to auto-complete inference in a system setting, in which users can access data via natural language queries.
Practical Phase Noise Model for Continuous Variable Quantum Key Distribution with a Real Local Oscillator

In continuous-variable quantum key distribution with a local local oscillator (LLO CV-QKD), the phase noise after phase compensation is mainly composed of three parts: the relative phase drift noise, the relative phase accumulated noise, and the phase-reference measurement noise. Here, we develop a practical phase noise model for LLO CV-QKD protocols, in which part of the phase-reference measurement noise that can be locally calibrated by Bob at the receiver side is considered to be trusted noise. It is reasonable to adopt this model because Bob”s apparatus is inaccessible to the eavesdropper. We show that the secret key rate and the transmission distance of the LLO CV-QKD system under the practical phase noise model is significantly improved in comparison to that under the conventional phase noise model. We show that the secret key rate is improved more than 65% and the transmission distance is increased more than 70% with some typical simulation parameters.
Exploring Non-Functional Requirements for Blockchain-Oriented Systems

Blockchain is a type of distributed ledger technology that is characterized by the five characteristics: Consensus, Decentralization, Immutability, Finality, and Provenance. Blockchain characteristics have been rapidly recognized as a turning point in many use case scenarios beyond the financial sector. Conversely, there is still a significant incongruity regarding how the blockchain’s unique characteristics can be mapped to the elicited non-functional requirements (NFRs) of blockchain-based systems. Motivated by exploring the potential categories of NFRs that dominate the landscape of blockchain-based systems, we conducted an inductive content analysis on a sample of extracted requirements from 7 different blockchain-based projects developed by two startup firms. This paper presents the initial view of the constructed mapping between the blockchain characteristics on one side and the dominating NFRs for the blockchain-based system.
Regulating Algorithmic Learning in Digital Platform Ecosystems through Data Sharing and Data Siloing: Consequences for Innovation and Welfare

Algorithmic learning gives rise to a data-driven network effects, which allow a dominant platform to reinforce its dominant market position. Data-driven network effects can also spill over to related markets and thereby allow to leverage a dominant position. This has led policymakers to propose data siloing and mandated data sharing remedies for dominant data-driven platforms in order to keep digital markets open and contestable. While data siloing seeks to prevent the spillover of data-driven network effects generated by algorithmic learning to other markets, data sharing seeks to share this externality with rival firms. Using a game-theoretic model, we investigate the impacts of both types of regulation. Our results bear important policy implications, as we demonstrate that data siloing and data sharing are potentially harmful remedies, which can reduce the innovation incentives of the regulated platform, and can lead to overall lower consumer surplus and total welfare.
A Novel Variant-Optimized Search Algorithm for Nuclei Detection in Histopathogy Breast Cancer Images

Histology is the study of tissues examining under a microscope to identify the severity of disease. It became very critical in biomedical research and clinical practice. Processing tissues from histopathological images has become now fully computerized, and pathologist observes digital pictures on a computer rather than on microscope in order to predict the seriousness of cancer. Routine analysis of tissues selection will be very difficult, and manual task can be done only by trained pathologists at a huge cost. Hence, we need some supervised algorithms to identify stage of cancer and the seriousness of cancer, so that it helps in diagnosis of disease. It is a very difficult task, because of cell nuclei that are very small, irregular, and contains significant noise. Therefore, an optimized segmentation method was suggested for nuclei recognition in histopathology breast cancer images. For experimental analysis, the dataset is collected from Break His database. And next, preprocessing is done by applying image normalization (contrast stretching) which improves image quality. After enhancing the quality of image, an iterative clustering algorithm is employed to select the super pixels and later a novel variant of particle swarm optimizer (PSO)-gray wolf optimizer (GWO) search algorithm is employed to select the optimal cluster centroids (Saturi and Prem Chand in 2020 International conference on inventive computation technologies (ICICT), ). The purpose of this proposed system is to perform segmentation and find optimized cluster centroids for automatic nuclei cell recognition. Hence, the proposed work achieved good results compared to the previous work. Further we can separate the overlapping cells from segmented image and apply supervised learning to predict the stage of cancer.