Reviewerly/exHarmony huggingface dataset:
The New Normal?

The article discusses the preliminary results of the forced transition to entire online learning in the higher education system in the context of the general growth trend of the corresponding form of education in various universities around the world. The ideology defining this trend is considered. Special attention is paid to the analysis of the reality of a sharp increase in the level of control of both teachers and students by the creators of training platforms, as well as the possible consequences of this situation. It is shown that universities in different countries are seeking suitable forms of supplementing regular education with remote ones. The overall trend is a steady growth in online learning with significant variations across countries (examples include the United States, Australia, Germany, and China). It is obvious that national education systems differ significantly from each other, and with certain general trend towards online learning, each educational system is looking for its own, most suitable for the national culture, forms of education. It is also necessary to understand how online learning can be integrated into existing system without harm. At the moment, this is not clear either on the content level or methodological one. The article analyzes the temporary and long-term problems associated with the transition to distant education. The problem of technical support is probably the easiest to be solved. More serious and requiring new technologies is the problem of changing the nature of communication, which requires quite different efforts of both teachers and students if compare with the usual ones. Working on the platforms that are intended to radically change the educational environment under the slogans of ensuring an individual educational trajectory, in fact leads to the opposite. The author dwells on the problem of possible widespread replacement of conventional courses with recorded ones and, especially, the ideology of transition to online learning in the format of virtual reality, which allows the creators to exercise full control over the individual. The problem of monitoring of educational activities is discussed, which already in the current conditions makes it possible to record any actions and states of all participants in the process. The article is a reaction to the beginning of the process of widespread introduction of online technologies, and this approach, according to the author, allows to observe vividly the most painful aspects of the new situation (like the first impression of a meeting with the unknown).
A Research on Breast Cancer Prediction using Data Mining Techniques

Early detection and diagnosis of breast cancer plays a significant role in the welfare of women. The mortality rate due to breast cancer is on an all-time high. Factors such as food habits, environmental pollution, hectic lifestyle and genetics are commonly attributed to breast cancer. In order to detect and diagnose such types of cancer, intelligent systems are implemented. Automated diagnosis gets impacted by prediction accuracy when compared with surgical biopsy. Bioinformatics mining has emerged as the area of research that involves analyzing both data mining and Bioinformatics. In order to statistically find significant associations on a breast cancer data set, the result is conceivable. Using a larger data set results in discovering the correlations between a bigger set of gene. The algorithm has to be improved to perceive the interactions with low marginal. This research field affords most intelligent and reliable data mining models in breast cancer prediction and decision making. This survey reviews various data mining algorithms on large breast cancer biological datasets. The merits and demerits of various procedures and comparison of their corresponding results are presented in this work.
Dynamic PSO for Task Scheduling Optimization in Cloud Computing

Task scheduling is still a challenge in cloud computing as no existing scheduling algorithms are not effectively provisioning and scheduling the resources in the cloud. Existing authors considered only metrics like makespan, execution time and turnaround time etc. and the previous authors concentrated only to optimize the above mentioned metrics. But no existing authors were considered about the effective provisioning of the resources in the cloud i.e, compute, storage and network capacities and still many resources in the cloud were underutilized. In this paper, we want to propose an algorithm which can effectively utilize the resources in the cloud by extending Particle Swarm Optimization by addressing the metrics Bandwidth utilization and Memory utilization particularly. We have simulated this algorithm by using cloudsim and compared the modified Dynamic PSO with the PSO algorithm and it outperforms in terms of Bandwidth and Memory utilization and the makespan is also optimized.
Artificial intelligence in automatic classification of invasive ductal carcinoma breast cancer in digital pathology images

Artificial intelligence in automatic classification of invasive ductal carcinoma breast cancer in digital pathology images
2020 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium

First posted September 3, 2020 Revised October 5, 2020 For additional information, contact: Director, Earth Resources Observation and Science Center (EROS)U.S. Geological Survey47914 252nd Street Sioux Falls, SD 57198 The Joint Agency Commercial Imagery Evaluation (JACIE) is a collaboration between five Federal agencies that are major users and producers of satellite land remote sensing data. In recent years, the JACIE group has observed ever-increasing numbers of remote sensing satellites being launched. This rapidly growing wave of new systems creates a need for a single reference for land remote sensing satellites that provides basic system specifications and linkage to any JACIE assessment that may have been completed on existing systems. This volume has been assembled by the Requirements, Capabilities, and Analysis for Earth Observation Project under the U.S. Geological Survey National Land Imaging Program as a contribution to the JACIE community. This is the second edition of the JACIE compendium, which is planned to be updated and released annually.
Lightweight Authenticated Encryption Mode of Operation for Tweakable Block Ciphers

The use of a small block length is a common strategy when designing lightweight (tweakable) block ciphers (TBCs), and several 64-bit primitives have been proposed. However, when such a 64-bit primitive is used for an authenticated encryption with birthday-bound security, it has only 32-bit data complexity, which is subject to practical attacks. To employ a short block length without compromising security, we propose PFB, a lightweight TBC-based authenticated encryption with associated data mode, which achieves beyond-birthday-bound security. For this purpose, we extend iCOFB, which is originally defined with a tweakable random function. Unlike iCOFB, the proposed method can be instantiated with a TBC using a fixed tweak length and can handle variable-length data. Moreover, its security bound is improved and independent of the data length; this improves the key lifetime, particularly in lightweight blocks with a small size. The proposed method also covers a broader class of feedback functions because of the generalization presented in our proof. We evaluate the concrete hardware performances of PFB, which benefits from the small block length and shows particularly good performances in threshold implementation.
Termination Analysis of Probabilistic Programs with Martingales

For non-probabilistic programs, a key question in static analysis is termination, which asks whether a given program terminates under a given initial condition. In the presence of probabilistic behaviour, there are two fundamental extensions of the termination question: (a) the almost-sure termination question, which asks whether the termination probability is 1; and (b) the bounded-time termination question, which asks whether the expected termination time is bounded. There are many active research directions to address these two questions; one important such direction is the use of martingale theory for termination analysis. In this chapter, we survey the main techniques of the martingale-based approach to the termination analysis of probabilistic programs.
Developing Methods and Algorithms for Forming of Informative Features’ Space on the Base K-Types Uniform Criteria

A new method for extracting the set of informative features on the base k-types uniform criteria is proposed in this paper.
Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking

In dialogue state tracking, dialogue history is a crucial material, and its utilization varies between different models.However, no matter how the dialogue history is used, each existing model uses its own consistent dialogue history during the entire state tracking process, regardless of which slot is updated.Apparently, it requires different dialogue history to update different slots in different turns.Therefore, using consistent dialogue contents may lead to insufficient or redundant information for different slots, which affects the overall performance.To address this problem, we devise DiCoS-DST to dynamically select the relevant dialogue contents corresponding to each slot for state updating.Specifically, it first retrieves turn-level utterances of dialogue history and evaluates their relevance to the slot from a combination of three perspectives: (1) its explicit connection to the slot name; (2) its relevance to the current turn dialogue; (3) Implicit Mention Oriented Reasoning.Then these perspectives are combined to yield a decision, and only the selected dialogue contents are fed into State Generator, which explicitly minimizes the distracting information passed to the downstream state prediction.Experimental results show that our approach achieves new state-of-the-art performance on MultiWOZ 2.1 and MultiWOZ 2.2, and achieves superior performance on multiple mainstream benchmark datasets (including Sim-M, Sim-R, and DSTC2).
Demonstration of Entanglement-Enhanced Covert Sensing

The laws of quantum physics endow superior performance and security for information processing: quantum sensing harnesses nonclassical resources to enable measurement precision unmatched by classical sensing, whereas quantum cryptography aims to unconditionally protect the secrecy of the processed information. Here, we present the theory and experiment for entanglement-enhanced covert sensing, a paradigm that simultaneously offers high measurement precision and data integrity by concealing the probe signal in an ambient noise background so that the execution of the protocol is undetectable with a high probability. We show that entanglement offers a performance boost in estimating the imparted phase by a probed object, as compared to a classical protocol at the same covertness level. The implemented entanglement-enhanced covert sensing protocol operates close to the fundamental quantum limit by virtue of its near-optimum entanglement source and quantum receiver. Our work is expected to create ample opportunities for quantum information processing at unprecedented security and performance levels.
Multivariate neural network interpolation operators

In this paper, we introduce and study fractional neural network interpolation operators activated by a sigmoidal function belonging to the extended class of multivariate sigmoidal functions. We examine the rates of approximation by the operators in Lp-spaces using the modulus of continuity. Moreover, we give some special examples with graphics for the extended class of multivariate sigmoidal functions, and present some illustrative examples to demonstrate the interpolation quality of the operators based on various activation functions. Finally, as an application, we present an efficient image processing algorithm by the proposed neural network interpolation operators for both general and medical gray-level images.
Simulated bifurcation assisted by thermal fluctuation

Abstract Various kinds of Ising machines based on unconventional computing have recently been developed for practically important combinatorial optimization. Among them, the machines implementing a heuristic algorithm called simulated bifurcation have achieved high performance, where Hamiltonian dynamics are simulated by massively parallel processing. To further improve the performance of simulated bifurcation, here we introduce thermal fluctuation to its dynamics relying on the Nosé–Hoover method, which has been used to simulate Hamiltonian dynamics at finite temperatures. We find that a heating process in the Nosé–Hoover method can assist simulated bifurcation to escape from local minima of the Ising problem, and hence lead to improved performance. We thus propose heated simulated bifurcation and demonstrate its performance improvement by numerically solving instances of the Ising problem with up to 2000 spin variables and all-to-all connectivity. Proposed heated simulated bifurcation is expected to be accelerated by parallel processing.
Dense Convolutional Neural Network for Detection of Cancer from CT Images

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods.
An overview of quantum error mitigation formulas

Minimizing the effect of noise is essential for quantum computers. The conventional method to protect qubits against noise is through quantum error correction. However, for current quantum hardware in the so-called noisy intermediate-scale quantum (NISQ) era, noise presents in these systems and is too high for error correction to be beneficial. Quantum error mitigation is a set of alternative methods for minimizing errors, including error extrapolation, probabilistic error cancellation, measurement error mitigation, subspace expansion, symmetry verification, virtual distillation, etc. The requirement for these methods is usually less demanding than error correction. Quantum error mitigation is a promising way of reducing errors on NISQ quantum computers. This paper gives a comprehensive introduction to quantum error mitigation. The state-of-art error mitigation methods are covered and formulated in a general form, which provides a basis for comparing, combining and optimizing different methods in future work.
To Incentivize or Not: Impact of Blockchain-Based Cryptoeconomic Tokens on Human Information Sharing Behavior

Cryptoeconomic incentives in the form of blockchain-based tokens are seen as an enabler of the sharing economy that could shift society towards greater sustainability. Nevertheless, knowledge of the impact of these tokens on human sharing behavior is still limited and this poses a challenge to the design of effective cryptoeconomic incentives. This study applies the theory of self-determination to investigate the impact of such tokens on human behavior in an information-sharing scenario. By utilizing an experimental methodology in the form of a randomized control trial with a 2x2 factorial design involving 132 participants, the effects of two token incentives on human information-sharing behavior are analyzed. Individuals obtain these tokens in exchange for their shared information. Based on the collected tokens, individuals receive a monetary payment and build reputation. Besides investigating the effect of these incentives on the quantity of shared information, the study includes quality characteristics of the information, such as <i>accuracy</i> and <i>contextualization</i>. The focus on quantity while excluding quality has been identified as a limitation in previous work. In addition to confirming previously known effects such as a crowding-out of intrinsic motivation by incentives, which also exists for blockchain-based tokens, the findings of this paper point to a hitherto unreported <i>interaction effect</i> between multiple tokens when applied simultaneously. The findings are critically discussed and put into the context of recent work and ethical considerations. The theory-based-empirical study is of interest to those investigating the effect of cryptoeconomic tokens or digital currencies on human behavior and supports the community in the design of effective personalized incentives for sharing economies.
Recursive Decomposition Network for Deformable Image Registration

Deformation decomposition serves as a good solution for deformable image registration when the deformation is large. Current deformation decomposition methods can be categorized into cascade-based methods and pyramid-based methods. However, cascade-based methods suffer from heavy computational burdens and long inference time due to their structures of repeated subnetworks, while the effectiveness of pyramid-based methods is constrained by their limited numbers of resolution levels. In this paper, to address both the insufficient and inefficient decomposition problems in current deformation decomposition methods, we propose a recursive decomposition network (RDN) to offer a novel solution for deformable image registration. Stage-wise recursion can efficiently decompose a large deformation into different pyramid estimation stages without using repeated subnetworks like in cascade-based methods. Level-wise recursion can sufficiently decompose the deformation inside each resolution level instead of only one-time estimation like in pyramid-based methods. Extensive experiments and ablation studies on two representative datasets validate the effectiveness and efficiency of our proposed RDN.
Blind Image Quality Assessment for Authentic Distortions by Intermediary Enhancement and Iterative Training

With the boom of deep neural networks, blind image quality assessment (BIQA) has achieved great processes. However, the current BIQA metrics are limited when evaluating low-quality images as compared to medium-quality and high-quality images, which restricts their applications in real world problems. In this paper, we first identify that two challenges caused by distribution shift and long-tailed distribution lead to the compromised performance on low-quality images. Then, we propose an intermediary enhancement-based bilateral network with iterative training strategy for solving these two challenges. Drawing on the experience of transitive transfer learning, the proposed metric adaptively introduces enhanced intermediary images to transfer more information to low-quality images for mitigating the distribution shift. Our metric also adopts an iterative training strategy to deal with the long-tailed distribution. This strategy decouples feature extraction and score regression for better representation learning and regressor training. It not only transfers the knowledge learned from the earlier stage to the latter stage, but also makes the model pay more attention to long-tailed low-quality images. We conduct extensive experiments on five authentically distorted image quality datasets. The results show that our metric significantly improves the evaluating performance on low-quality images and delivers state-of-the-art intra-dataset results. During generalization tests, our metric also achieves the best cross-dataset performance.
On the Ethical Considerations of Text Simplification

This paper outlines the ethical implications of text simplification within the framework of assistive systems.We argue that a distinction should be made between the technologies that perform text simplification and the realisation of these in assistive technologies.When using the latter as a motivation for research, it is important that the subsequent ethical implications be carefully considered.We provide guidelines for the framing of text simplification independently of assistive systems, as well as suggesting directions for future research and discussion based on the concerns raised.
A Global Secure Ranked Multikeyword Search Based on the Multiowner Model for Cloud-Based Systems

Ensuring the privacy of data outsourced to the cloud, while also ensuring data usability, remains challenging. In this article, we propose a global secure ranked multikeyword search scheme based on multiple data owners in cloud-based systems. Compared to prior arts, our scheme improves search precision and efficiency and eliminates keyword redundancies. To improve search efficiency, we use a tree-based index structure, where we remove the need to search all related files. To improve search precision, we design a global computing scheme in which we compute the relevance score in a global manner with the global document frequency. To reduce the redundancies of keywords, we design a keyword conversion scheme, where we re-encrypt keywords with a third entity. As a result, the same keyword extracted by different data owners will be encrypted into the same form. Also, we present the security analysis and demonstrate the performance of the scheme with experiments.
Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences

Existing studies on next point-of-interest (POI) recommendation mainly attempt to learn user preference from the past and current sequential behaviors. They, however, completely ignore the impact of future behaviors on the decision-making, thus hindering the quality of user preference learning. Intuitively, users' next POI visits may also be affected by their multi-step future behaviors, as users may often have activity planning in mind. To fill this gap, we propose a novel Context-aware Future Preference inference Recommender (CFPRec) to help infer user future preference in a self-ensembling manner. In particular, it delicately derives multi-step future preferences from the learned past preference thanks to the periodic property of users' daily check-ins, so as to implicitly mimic user’s activity planning before her next visit. The inferred future preferences are then seamlessly integrated with the current preference for more expressive user preference learning. Extensive experiments on three datasets demonstrate the superiority of CFPRec against state-of-the-arts.