Reviewerly/exHarmony huggingface dataset:
SgWalk: Location Recommendation by User Subgraph-Based Graph Embedding

Popularity of Location-based Social Networks (LBSNs) provide opportunity to collect massive multi modal datasets that contain geographical information, as well as time and social interactions. Such data is a useful resource for generating personalized location recommendations. Such heterogeneous data can be further extended with notions of trust between users, popularity of locations and expertise of users. Recently the use Heterogeneous Information Network (HIN) models and graph neural architectures have proven successful for recommendation problem. One limitation of such solution is on capturing the contextual relationships between the nodes in the heterogeneous network. In location recommendation, spatial context is a frequently used consideration such that users prefer to get recommendations within their spatial vicinity. To solve this challenging problem, we propose a novel Heterogeneous Information Network (HIN) embedding technique, SgWalk, which explores the proximity between users and locations and generates location recommendations via subgraph-based node embedding. SgWalk follows four steps: building users subgraphs according to location context, generating random walk sequences over user subgraphs, learning embeddings of nodes in LBSN graph, and generating location recommendations using vector representation of the nodes. SgWalk is differentiated from existing techniques relying on meta-path or bi-partite graphs by means of utilizing the contextual user subgraph. By this way, it is aimed to capture contextual relationships among heterogeneous nodes more effectively. The recommendation accuracy of SgWalk is analyzed through extensive experiments conducted on benchmark datasets in terms of top-n location recommendations. The accuracy evaluation results indicate minimum 23% (@5 recommendation) average improvement in accuracy compared to baseline techniques and the state-of-the-art heterogeneous graph embedding techniques in the literature.
Comparison of quantum state protection against decoherence via weak measurement, a survey

One of the crucial tasks in quantum systems is to reduce the effects of decoherence due to the unavoidable interactions between a system and its environment. Many protection schemes have been proposed recently, among them the weak measurement quantum measurement reversal (WMQMR), weak measurement-based quantum feedback control (QFBC) and quantum feedforward control (QFFC) are reviewed in this paper. By considering weak measurement, the aim is to find a balance between information gain and disturbance of the system caused by the measurement. We classify different types of measurement and give the definition of noise sources and their effects on the state of the system. Finally, we compare and analyze the performance of the discussed protection schemes for different noise sources by numerical simulations.
Using MS Power BI Tools in the University Management System to Deepen the Value Proposition

The article explores the possibility of using MS Power BI tools at the level of university management to justify and ensure the correctness of management decisions on the formation of value proposition. The definition of the concept of the university’s value proposition is offered. The factors of influence on correctness of decisions on forming the value proposition: external and internal, behavioral and effective, the factors of direct and indirect influence are identified and systematized according to a set of features. MS Power BI toolkit is tested by means of BigData, the algorithms of quantitative influence of factors with defining the data fields are described. The algorithms for applying records to data models based on statistical and analytical indicators have been developed, which is the basis for building DashBoard as a tool for visualizing management decisions to justify valuable proposals.
Multi-Class Cell Detection Using Spatial Context Representation

In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks. Classifying cells into subtypes, such as tumor cells, lymphocytes or stromal cells is particularly challenging. Existing methods focus on morphological appearance of individual cells, whereas in practice pathologists often infer cell classes through their spatial context. In this paper, we propose a novel method for both detection and classification that explicitly incorporates spatial contextual in-formation. We use the spatial statistical function to describe local density in both a multi-class and a multi-scale manner. Through representation learning and deep clustering techniques, we learn advanced cell representation with both appearance and spatial context. On various benchmarks, our method achieves better performance than state-of-the-arts, especially on the classification task.
Offending Concentration on the Internet: An Exploratory Analysis of Bitcoin-related Cybercrime

Crime research has repeatedly shown that small proportions of offenders are responsible for large proportions of crimes. While there is a substantial body of evidence for this ‘offending concentration’ in connection to traditional offline crime, there is limited research assessing the concentration of offending for cybercrime. This research analyzes victim reports of Bitcoin-related cybercrimes (blackmail, ransomware, sextortion, darknet market fraud, Bitcoin tumbler fraud) to illuminate the extent of cybercrime offending concentration and to identify groups of offenders involved in online crime. Our results indicate that a large proportion of cybercrimes are associated with a small number of very active Bitcoin addresses. However, Bitcoin addresses associated to high numbers of reports are not necessarily those that generate the largest financial benefits.
Improving the Accuracy of Predicting Bank Depositor’s Behavior Using a Decision Tree

Telemarketing is a widely adopted direct marketing technique in banks. Since customers hardly respond positively, data prediction models can help in selecting the most likely prospective customers. We aim to develop a classifier accuracy to predict which customer will subscribe to a long-term deposit proposed by a bank. Accordingly, this paper focuses on a combination of resampling, in order to reduce the imbalanced data, using feature selection, to reduce the complexity of data computing and dimension reduction of inefficiency data modeling. The performed operation has shown an improvement in the performance of the classification algorithm in terms of accuracy. The experimental results were run on a real bank dataset and the J48 decision tree achieved 94.39% accuracy prediction, with 0.975 sensitivity and 0.709 specificity, showing better results when compared to other approaches reported in the existing literature, such as logistic regression (91.79 accuracy; 0.975 sensitivity; 0.495 specificity) and Naive Bayes classifier (90.82% accuracy; 0.961 sensitivity; 0.507 specificity). Furthermore, our resampling and feature selection approach resulted in improved accuracy (94.39%) when compared to a state-of-the-art approach based on a fuzzy algorithm (92.89%).
USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments

We present a freely available speech corpus for the Uzbek language and report preliminary automatic speech recognition (ASR) results using both the deep neural network hidden Markov model (DNN-HMM) and end-to-end (E2E) architectures. The Uzbek speech corpus (USC) comprises 958 different speakers with a total of 105 h of transcribed audio recordings. To the best of our knowledge, this is the first open-source Uzbek speech corpus dedicated to the ASR task. To ensure high quality, the USC has been manually checked by native speakers. We first describe the design and development procedures of the USC, and then explain the conducted ASR experiments in detail. The experimental results demonstrate promising results for the applicability of the USC for ASR. Specifically, 18.1% and 17.4% word error rates were achieved on the validation and test sets, respectively. To enable experiment reproducibility, we share the USC dataset, pre-trained models, and training recipes in our GitHub repository (https://github.com/IS2AI/Uzbek_ASR).
An extensive critique of sliding mode control and adaptive neuro‐fuzzy inference system for nonlinear system

Abstract Nonlinear control hypothesis is the region of control hypothesis which manages frameworks that are nonlinear, time‐variant, or both. Nonlinear control structures have extended high prevalence fundamentally because of the wide use of hypothetical intends to deal with credible issues in different zones of engineering and medical aspects. Sliding mode control (SMC) is a nonlinear control strategy applied when the deterministic model of the nonlinear system is discrepant with actual plant in terms of plant parameters and undetermined external disturbances. SMC is a sort of nonlinear control that accentuates zero error convergence modeling error, demonstrating nonlinearities and outside aggravations. Adaptive neuro‐fuzzy inference system (ANFIS) captures neural associations and soft reason guidelines in a single framework. ANFIS causes fewer errors and is more obvious to the customer isolated from the ANN. The paper centers that neural organizations accept a critical capacity in controlling nonlinear frameworks. This review aims an overview of existing methods for nonlinear control and application of SMC for nonlinear system based on ANFIS.
Method of Structural Identification the Interval Models of Static Objects

The task of structural identification the interval models of static objects is considered. It is shown that this task at each iteration is a task the forming and solving interval systems of nonlinear algebraic equations. The method of structural identification the interval models of static objects based on the analysis of interval data is proposed and substantiated. It, unlike the existing ones, on procedures of self-organization and self-adaptation of computational procedures by analogy with foraging models of honey bee colony is based. Possibility of obtaining interval models of characteristic with a simpler structure compared to the known ones, by example of building the mathematical model of characteristics of an environmentally friendly power generation system are showed.
Extending the Hong-Ou-Mandel effect: The power of nonclassicality

We show that the parity (evenness or oddness) of a nonclassical state of light has a dominant influence on the interference effects at a balanced beam splitter, irrespective of the state initially occupying the other input mode. Specifically, the parity of the nonclassical state gives rise to destructive interference effects that result in deep valleys in the output joint number distribution of which the Hong-Ou-Mandel (HOM) effect is a limiting case. The counter-intuitive influence of even a single photon to control the output of a beam splitter illuminated by any field, be it a coherent or even a noisy thermal field, demonstrates the extraordinary power of non-classicality. The canonical example of total destructive interference of quantum amplitudes leading to the absence of coincidence counts from a 50/50 beam splitter is the celebrated HOM effect, characterized by the vanishing of the joint probability of detecting singe photons in each of the output beams. We show that this is a limiting case of more general input states upon which a 50/50 BS can create total, or near total, destructive interference of quantum amplitudes. For odd photon number input Fock states of arbitrary value n>0 we show that the joint photon number probabilities vanish when detecting identical photon numbers in each output beams. We examine the mixing of photon number states of n = 1, 2, and 3 with a CV state, such as a coherent state of arbitrary amplitude, and a thermal state. These vanishing joint probabilities form what we call a central nodal line -- a contiguous set of zeros representing complete destructive interference of quantum amplitudes. For odd or even photon number Fock states with n>1 there will be additional off-diagonal pseudo-nodal curves along which the joint photon number probabilities are either zero, or near zero, which constitute a near, but not complete, destructive interference.
Industrial time series determinative anomaly detection based on constraint hypergraph

The explosive growth of time series captured by sensors in industrial pipelines gives rise to the flourish of intelligent industry. Exploiting the value of these time series is conductive to workload balancing and production optimization. Unfortunately, knowledge obtained from the mining process turns out to be insufficient for use due to widespread anomalies, indicating machine breakdown, sensor failure or working status shifts. To tackle this problem, we propose a constraint hypergraph-based method, combining multiple constraints for anomaly detection. We develop strategies for adaptive determinative anomaly detection and anomaly pattern mining. We also investigate the problem of Anomaly Pattern Matching, prove its NP-completeness, and propose algorithms to obtain its global and local optimum. Finally, we demonstrate our approach with three real world datasets from a real powerplant, a chemical production pipeline and a hydraulic system. The experimental results show that our approach can effectively and efficiently work under different circumstances.
Predictive Feature Learning for Future Segmentation Prediction

Future segmentation prediction aims to predict the segmentation masks for unobserved future frames. Most existing works addressed it by directly predicting the intermediate features extracted by existing segmentation models. However, these segmentation features are learned to be local discriminative (with rich details) and are always of high resolution/dimension. Hence, the complicated spatiotemporal variations of these features are difficult to predict, which motivates us to learn a more predictive representation. In this work, we develop a novel framework called Predictive Feature Autoencoder. In the proposed framework, we construct an autoencoder which serves as a bridge between the segmentation features and the predictor. In the latent feature learned by the autoencoder, global structures are enhanced and local details are suppressed so that it is more predictive. In order to reduce the risk of vanishing the suppressed details during recurrent feature prediction, we further introduce a reconstruction constraint in the prediction module. Extensive experiments show the effectiveness of the proposed approach and our method outperforms state-of-the-arts by a considerable margin.
Culprit-Prune-Net: Efficient Continual Sequential Multi-domain Learning with Application to Skin Lesion Classification

Despite recent advances in deep learning based medical image computing, clinical implementations in patient-care settings have been limited with lack of sufficiently diverse data during training remaining a pivotal impediment to robust real-life model performance. Continual learning (CL) offers a desirable property of deep neural network models (DNNs), namely the ability to continually learn from new data to accumulate knowledge whilst retaining what has been previously learned. In this work we present a simple and effective CL approach for sequential multi-domain learning (MDL) and showcase its utility in the skin lesion image classification task. Specifically, we propose a new pruning criterion that allows for a fixed network to learn new data domains sequentially over time. Our MDL approach incrementally builds on knowledge gained from previously learned domains, without requiring access to their training data, while simultaneously avoiding catastrophic forgetting and maintaining accurate performance on all domain data learned. Our new pruning criterion detects culprit units associated with wrong classification in each domain and releases these units so they are dedicated for subsequent learning on new domains. To reduce the computational cost associated with retraining the network post pruning, we implement MergePrune, which efficiently merges the pruning and training stages into one step. Furthermore, at inference time, instead of using a test-time oracle, we design a smart gate using Siamese networks to assign a test image to the most appropriate domain and its corresponding learned model. We present extensive experiments on 6 skin lesion image databases, representing different domains with varying levels of data bias and class imbalance, including quantitative comparisons against multiple baselines and state-of-the-art methods, which demonstrate superior performance and efficient computations of our proposed method.
Chebyshev Pooling: An Alternative Layer for the Pooling of CNNs-Based Classifier

The CNNs-based model has been proven to achieve impressive performance on a wide range of classification tasks. However, the convoluted results will only retain the local features and discard the global information when using max-pooling is performed with decreasing resolutions. some features of similar data are always diluted during several convolutions, so the decision will be more difficult after max pooling. In this work, we propose a novel pooling layer called Chebyshev Pooling. It makes use of Chebyshev's inequality to produce results about the probability distributions within the kernel which contains the functions of maximum and average pooling. In addition, the proposed layer can ensure that its output is in the range of (0. 0, 1. 0), which is more stable for subsequent processing. Experiments illustrate that our proposed pooling layer can improve the classification performance of various data sets. Moreover, the design and implementation can be easily deployed in some type of CNNs-based classification systems.
Cervical cancer diagnosis using convolution neural network with conditional random field

Cervical cancer is the second most common disease in women worldwide, and the Pap smear is one of the most used methods for detecting cervical cancer early on. Developing nations, such as India, must confront hurdles in order to manage an increasing number of patients on a daily basis. Various online and offline machine learning techniques were used on benchmarked data sets to diagnose cervical cancer in this paper. the importance of machine learning can be seen in the various fields as it provides various benefits in the completion of the task. Medical image analysis is done for diagnostic purposes in the medical form but creating pictures of the structures and activities inside the body. The use of machine learning for medical image analysis provides various benefits during the diagnosis of a person's diseases. CNN-CRF provides various applications for analyzing the structure and capturing the picture of the inside body structure of the human. Different applications of machine learning help in analyzing the different types of the medical image such as neural networks and CT scans. Medical image analysis is the area that has been largely benefited by machine learning.
A note on the applications of artificial intelligence in the hospitality industry: preliminary results of a survey

Intelligent technologies are widely implemented in different areas of modern society but specific approaches should be applied in services. Basic relationships refer to supporting customers and people responsible for services offering for these customers. The aim of the paper is to analyze and evaluate the state-of-the art of artificial intelligence (AI) applications in the hospitality industry. Our findings show that the major deployments concern in-person customer services, chatbots and messaging tools, business intelligence tools powered by machine learning, and virtual reality & augmented reality. Moreover, we performed a survey (n = 178), asking respondents about their perceptions and attitudes toward AI, including its implementation within a hotel space. The paper attempts to discuss how the hotel industry can be motivated by potential customers to apply selected AI solutions. In our opinion, these results provide useful insights for understanding the phenomenon under investigation. Nevertheless, since the results are not conclusive, more research is still needed on this topic. Future studies may concern both qualitative and quantitative methods, devoted to developing models that: a) quantify the potential benefits and risks of AI implementations, b) determine and evaluate the factors affecting the AI adoption by the customers, and c) measure the user (guest) experience of the hotel services, fueled by AI-based technologies.
Quantum tomography benchmarking

Recent advances in quantum computers and simulators are steadily leading us toward full-scale quantum computing devices. Due to the fact that debugging is necessary to create any computing device, quantum tomography (QT) is a critical milestone on this path. In practice, the choice between different QT methods faces the lack of comparison methodology. Modern research provides a wide range of QT methods, which differ in their application areas, as well as experimental and computational complexity. Testing such methods is also being made under different conditions, and various efficiency measures are being applied. Moreover, many methods have complex programming implementations; thus, comparison becomes extremely difficult. In this study, we have developed a general methodology for comparing quantum-state tomography methods. The methodology is based on an estimate of the resources needed to achieve the required accuracy. We have developed a software library (in MATLAB and Python) that makes it easy to analyze any QT method implementation through a series of numerical experiments. The conditions for such a simulation are set by the number of tests corresponding to real physical experiments. As a validation of the proposed methodology and software, we analyzed and compared a set of QT methods. The analysis revealed some method-specific features and provided estimates of the relative efficiency of the methods.
Code Review is just reviewing code? A qualitative study with practitioners in industry

Code review is essentially the process of assessing code made by a developer from the perspective of others. Considering the Modern Code Review (MCR) activities within the industry, many opportunities can be evaluated with the practitioners that adopt MCR within everyday software development. Based on that, we evaluated the perspective of software practitioners on code review by applying a survey to understand more about the aspects of MCR in their software development routine. Our qualitative results show that MCR activities are not only about reviewing code but also with knowledge sharing applied at team/project level or between different levels of seniority. We also found other important aspects such as identification of Cognitive Empathy and Career Development related to the MCR process. This research contributes to increase perspective that can be obtained with MCR process applied in the software development industry.