Reviewerly/exHarmony huggingface dataset:
Search strategies improve with practice, but not with time pressure or financial incentives.

When searching for an object, do we minimize the number of eye movements we need to make?Under most circumstances, the cost of saccadic parsimony likely outweighs the benefit, given the cost is extensive computation and the benefit is a few hundred milliseconds of time saved.Previous research has measured the proportion of eye movements directed to locations where the target would have been visible in the periphery as a way of quantifying the proportion of superfluous fixations.A surprisingly large range of individual differences has emerged from these studies, suggesting some people are highly efficient and others much less so.Our question in the current study is whether these individual differences can be explained by differences in motivation.In two experiments, we demonstrate that neither time pressure nor financial incentive led to improvements of visual search strategies; the majority of participants continued to make many superfluous fixations in both experiments.The wide range of individual differences in efficiency observed previously was replicated here.We observed small but consistent improvements in strategy over the course of the experiment (regardless of reward or time pressure) suggesting practice, not motivation, makes participants more efficient. Public Significance StatementIn this study, neither time pressure nor financial incentive led to improvements in visual search strategies, ruling out motivation as the explanation for the large individual differences in search efficiency seen in previous studies.Small but consistent improvements in strategy over time suggest experience, rather than differences in motivation, could help explain why some participants are more efficient than others.
Understanding counterfactuals in transparent and nontransparent context: An event-related potential investigation.

Counterfactuals describe imagined alternatives to reality that people know to be false. Successful counterfactual comprehension therefore requires people to keep in mind both an imagined hypothetical world and the presupposed real world. Counterfactual transparency, that is, the degree to which a context makes it easy to determine counterfactuality, might affect semantic processing. This might especially be the case for languages like Chinese which lack dedicated counterfactual markers and therefore are more context-dependent. Using event-related potentials, this study investigates the role of counterfactual transparency on the comprehension of Chinese counterfactuals. For transparent contexts (e.g., "If everything in the world could go back in time . . ."), in which the information needed to identify counterfactuality is highly accessible, discourse incongruent words elicited P600 effects. In contrast, for nontransparent contexts (e.g., "If better preparations were made at that time . . .") in which readers must attend to specific discourse context and engage pragmatic information to arrive at the counterfactual interpretation, discourse incongruencies gave rise to N400 effects. These findings suggest that (a) provided a constraining context, semantic processing is not disrupted by the dual nature of counterfactuality (i.e., readers can rapidly make contextually appropriate inferences to interpret subsequent narratives) and (b) the degree of transparency of the counterfactual can affect the nature of subsequent semantic processing. Our findings support the usage-based view that Chinese counterfactual comprehension is highly context-dependent and pragmatics-driven. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
A Resampling Univariate Analysis Approach to Ovarian Cancer From Clinical and Genetic Data

Ovarian cancer (OC) is the second most common gynecological malignancy and the gynecological tumor with the worst prognosis. To try to improve this situation, Data Science technologies could be a useful tool to help clinicians to know more about the disease. In our case, we are interested in exploring OC data to discover relationships between clinical and genetic factors and the disease progression. For it, we propose an analysis framework for simple and univariate statistical descriptions of features of different types, based on bootstrap resampling. Foremost, we define the framework for metric, categorical, and dates variables and determine what are the advantages and disadvantages of using different bootstrap resampling strategies, based on their statistical basis. Then, we use it to perform a univariate analysis over an OC dataset that allows to explore how is the disease progression, having platinum-free interval as indicator, in relation to clinical and genetic features of different types. Also, it provides a first set of variables possibly relevant for survival prediction. Results obtained show that some features have led to individual differences between both platinum resistant (<; 6 months) and platinum sensitive(>6 months) groups. It can be concluded that this could be an indicator that the database could be discriminatory for the hypotheses studied, though it is convenient to make multivariate analyses to check how relationships among features are influenced.
Load balancing in cloud environs: Optimal task scheduling via hybrid algorithm

In cloud computing, a lot of challenges like the server failures, loss of confidentiality, improper workloads, etc. are still bounding the efficiency of cloud systems in real-world scenarios. For this reason, many research works are being performed to overcome the shortcoming of existing systems. Among them, load balancing seems to be the most critical issue that worsen the performance of the cloud sector, and hence there necessitates the optimal load balancing with optimal task scheduling. With the intention of accomplishing optimal load balancing by effectual task deployment, this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic algorithms. Usually, handling of tasks in cloud system is an NP-hard problem and moreover, nonpreemptive independent tasks are crucial in cloud computing. This paper goes with the introduction of a new optimal load balancing model by considering three major objectives: minimum makespan, priority, and load balancing, respectively. Moreover, a new single-objective function is also defined that incorporates all the three objectives mentioned above. Furthermore, the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insisted WOA (FM-WOA) is introduced. This FM-WOA is the conceptual amalgamation of standard Whale Optimization Algorithm (WOA) and Firefly (FF) algorithm. Finally, the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan, task completion (priority), and degree of imbalance as well.
Searching for Alignment in Face Recognition

A standard pipeline of current face recognition frameworks consists of four individual steps: locating a face with a rough bounding box and several fiducial landmarks, aligning the face image using a pre-defined template, extracting representations and comparing. Among them, face detection, landmark detection and representation learning have long been studied and a lot of works have been proposed. As an important step with a big impact on recognition performance, the alignment step has attracted little attention. In this paper, we first explore and highlight the effects of different alignment templates on face recognition. Then, for the first time, we try to automatically search for the optimal template. We construct a well-defined searching space by decomposing the template searching into the crop size and vertical shift, and propose an efficient method Face Alignment Policy Search (FAPS). Besides, a well-designed benchmark is proposed to evaluate the searched policy. Experiments on our proposed benchmark validate the effectiveness of our method to improve the face recognition performance.
Secure Electronic Voting System using Blockchain Technology

The Secure Electronic Voting System using Blockchain Technology is ensured to make the current voting process to take place in an honest, accurate and highly secure way.This system stores the details of the voters and votes in two separate blockchains, which provides transparency into election results by allowing voters to independently audit the ballot box while protecting each voter's right to privacy.All the details of the voters get stored into one Blockchain, and this guarantees greater security by providing a PIN confirmed before the vote is taken into consideration.By casting votes as transactions, we can create another blockchain which keeps track of the tallies of the votes.This way, everyone can count the votes themselves, they can verify that no votes were changed or removed, and no illegitimate votes were added and as a result, is made public everyone can agree upon the final count.This system is only taking the current process of voting in an election and bringing that process entirely online, in an attempt to make it highly secure and also more accessible by allowing the voter to vote at his/her location and also reducing the effort put by staff members.
Perspectives on the 2 × 2 Matrix: Solving Semantically Distinct Problems Based on a Shared Structure of Binary Contingencies

Cognition is both empowered and limited by representations. The matrix lens model explicates tasks that are based on frequency counts, conditional probabilities, and binary contingencies in a general fashion. Based on a structural analysis of such tasks, the model links several problems and semantic domains and provides a new perspective on representational accounts of cognition that recognizes representational isomorphs as opportunities, rather than as problems. The shared structural construct of a 2 × 2 matrix supports a set of generic tasks and semantic mappings that provide a unifying framework for understanding problems and defining scientific measures. Our model's key explanatory mechanism is the adoption of particular perspectives on a 2 × 2 matrix that categorizes the frequency counts of cases by some condition, treatment, risk, or outcome factor. By the selective steps of filtering, framing, and focusing on specific aspects, the measures used in various semantic domains negotiate distinct trade-offs between abstraction and specialization. As a consequence, the transparent communication of such measures must explicate the perspectives encapsulated in their derivation. To demonstrate the explanatory scope of our model, we use it to clarify theoretical debates on biases and facilitation effects in Bayesian reasoning and to integrate the scientific measures from various semantic domains within a unifying framework. A better understanding of problem structures, representational transparency, and the role of perspectives in the scientific process yields both theoretical insights and practical applications.
From Low Probability to High Confidence in Stochastic Convex Optimization

Standard results in stochastic convex optimization bound the number of samples that an algorithm needs to generate a point with small function value in expectation. More nuanced high probability guarantees are rare, and typically either rely on “light-tail” noise assumptions or exhibit worse sample complexity. In this work, we show that a wide class of stochastic optimization algorithms for strongly convex problems can be augmented with high confidence bounds at an overhead cost that is only logarithmic in the confidence level and polylogarithmic in the condition number. The procedure we propose, called proxBoost, is elementary and builds on two well-known ingredients: robust distance estimation and the proximal point method. We discuss consequences for both streaming (online) algorithms and offline algorithms based on empirical risk minimization.
Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study

Background COVID-19 has challenged global public health because it is highly contagious and can be lethal. Numerous ongoing and recently published studies about the disease have emerged. However, the research regarding COVID-19 is largely ongoing and inconclusive. Objective A potential way to accelerate COVID-19 research is to use existing information gleaned from research into other viruses that belong to the coronavirus family. Our objective is to develop a natural language processing method for answering factoid questions related to COVID-19 using published articles as knowledge sources. Methods Given a question, first, a BM25-based context retriever model is implemented to select the most relevant passages from previously published articles. Second, for each selected context passage, an answer is obtained using a pretrained bidirectional encoder representations from transformers (BERT) question-answering model. Third, an opinion aggregator, which is a combination of a biterm topic model and k-means clustering, is applied to the task of aggregating all answers into several opinions. Results We applied the proposed pipeline to extract answers, opinions, and the most frequent words related to six questions from the COVID-19 Open Research Dataset Challenge. By showing the longitudinal distributions of the opinions, we uncovered the trends of opinions and popular words in the articles published in the five time periods assessed: before 1990, 1990-1999, 2000-2009, 2010-2018, and since 2019. The changes in opinions and popular words agree with several distinct characteristics and challenges of COVID-19, including a higher risk for senior people and people with pre-existing medical conditions; high contagion and rapid transmission; and a more urgent need for screening and testing. The opinions and popular words also provide additional insights for the COVID-19–related questions. Conclusions Compared with other methods of literature retrieval and answer generation, opinion aggregation using our method leads to more interpretable, robust, and comprehensive question-specific literature reviews. The results demonstrate the usefulness of the proposed method in answering COVID-19–related questions with main opinions and capturing the trends of research about COVID-19 and other relevant strains of coronavirus in recent years.
Communication-Based Book Recommendation in Computational Social Systems

This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users’ neighbors more accurately. Second, computational social system strategies can be adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm.
Entity-Centric Fully Connected GCN for Relation Classification

Relation classification is an important task in the field of natural language processing, and it is one of the important steps in constructing a knowledge graph, which can greatly reduce the cost of constructing a knowledge graph. The Graph Convolutional Network (GCN) is an effective model for accurate relation classification, which models the dependency tree of textual instances to extract the semantic features of relation mentions. Previous GCN based methods treat each node equally. However, the contribution of different words to express a certain relation is different, especially the entity mentions in the sentence. In this paper, a novel GCN based relation classifier is propose, which treats the entity nodes as two global nodes in the dependency tree. These two global nodes directly connect with other nodes, which can aggregate information from the whole tree with only one convolutional layer. In this way, the method can not only simplify the complexity of the model, but also generate expressive relation representation. Experimental results on two widely used data sets, SemEval-2010 Task 8 and TACRED, show that our model outperforms all the compared baselines in this paper, which illustrates that the model can effectively utilize the dependencies between nodes and improve the performance of relation classification.
Contextual Bandit Learning for Activity-Aware Things-of-Interest Recommendation in an Assisted Living Environment

Recommendation systems are crucial for providing services to the elderly with Alzheimer’s disease in IoT-based smart home environments. Therefore, we present a Reminder Care System to help Alzheimer patients live safely and independently in their homes. The proposed recommendation system is formulated based on a contextual bandit approach to tackle dynamicity in human activity patterns for accurate recommendations meeting user needs without their feedback. Our experiment results demonstrate the feasibility and effectiveness of the proposed Reminder Care System in real-world IoT-based smart home applications.
AFOD: Adaptive Focused Discriminative Segmentation Tracker

Visual object tracking is a fundamental task in computer vision which could be integrated into numerous real-world applications. Traditional object tracking methods focus on providing the bounding box as object position, while some recent trackers start to consider the combination of segmentation module to generate the binary segmentation mask, pursuing more accurate localization. However, how to effectively integrate different information for accurate and robust tracking is an open question. In this paper, we propose a novel Adaptive FOcused Discriminative (AFOD) segmentation tracker with the following advanced components. For localization, a more discriminative light weight online target appearance model is employed to provide robust position estimation. For segmentation, leveraging the backbone semantic feature, the coarse segmentation feature, and the localization feature, an offline trained fine segmentation model with IoU optimization is utilized to generate the accurate high resolution masks. The boundary detection further enhances the segmentation quality. For combination, an adaptive prediction strategy is proposed to better integrate the information from two types of predictions, i.e. box and mask. AFOD achieves leading performance on two tracking benchmarks including the bounding box annotated VOT2018 and the segmentation mask annotated VOT2020, while running close to real-time.
A Hybrid Blockchain-Based Authentication Scheme for Smart Home

With the development of the Internet of Things technology, the use of smart devices is ubiquitous, thereby making the smart home become an innovative IoT application to change our lives. However, the application of smart home should serve thousands of families and the number of edge servers and terminal devices s huge. Thus, it is difficult for a single central server or cluster to manage such large-scale devices with high efficiency. In addition, the structure of IoT is flexible and the edge or terminal devices can join a certain IoT system without strict restriction. The system may suffer from unauthorized access from malicious devices. In this paper, a physically secure authentication scheme for smart home based on a hybrid blockchain is proposed. A novel structure of the blockchain is leveraged to relieve the stress of the central server and storage device’s information permanently. Physical unclonable functions are used to provide identities for authorized devices. The security and evaluation analysis shows that our scheme has reliable security features and is the potential to improve the efficiency of the smart home system.
Pykg2vec: A Python Library for Knowledge Graph Embedding

Pykg2vec is an open-source Python library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec's flexible and modular software architecture currently implements 16 state-of-the-art knowledge graph embedding algorithms, and is designed to easily incorporate new algorithms. The goal of pykg2vec is to provide a practical and educational platform to accelerate research in knowledge graph representation learning. Pykg2vec is built on top of TensorFlow and Python's multiprocessing framework and provides modules for batch generation, Bayesian hyperparameter optimization, mean rank evaluation, embedding, and result visualization. Pykg2vec is released under the MIT License and is also available in the Python Package Index (PyPI). The source code of pykg2vec is available at this https URL.
Pictographs, Ideograms, and Emojis (PIE): A Framework for Empirical Research Using Non-verbal Cues

We propose an empirical framework to understand the impact of non-verbal cues across various research contexts. A large percentage of communication on the Internet uses text-driven non-verbal communication cues often referred to as emojis. Our framework proposes two types of factors to understand the impact of emojis. The first type consists of pictographs, ideograms, and emojis (PIE) factors such as usage, valence, position, and skin tone, and the second type consists of contextual factors depending on the research context, such as fake news, which has high social impact. We discuss how the effect of PIE factors and contextual factors can be used to measure belief, trust, reputation, and intentions across these contexts.
BREAST CANCER DETECTION USING MARK RCNN SEGMENTATION AND ENSEMBLE CLASSIFICATION WITH FEATURE EXTRACTION

Breast cancer continues to be the common cancer which causes more decease among women and about more than two million cases is identified every year and according to the record 523,000 deaths are caused per year due to breast cancer.Mammographic mass identification and segmentation are accomplished usually as sequential and distinct tasks, where in previous studies segmentation was often manually performed only on true positive cases.Machine learning (ML) approaches have been grown from manually provided inputs to systematic initializations.The developments in ML techniques have produced independent and more intelligent computeraided diagnosis (CAD) systems.Moreover, due to the learning ability, ML techniques have been upgraded constantly.Recently, ML techniques are progressive with deeper and varied representation methods, generally termed as deep learning (DL) techniques, and have produced significant impacts on increasing the ability of diagnosing using CAD systems.So this paper proposes the novel architecture in detecting the breast cancer.Here the input database is mammogram image dataset.Initially this image has been resized by pre-processing process, then this pre-processed image has been segmented using Mask RCNN (Region-based convolution neural networks).Then this segmented image has been sent for extracting the feature using inception V3 and ResNet 152 networks.The ensemble classifier of decision tree and random forest has been used for classification and this classified output is detected based on the color variation and tumor size gives the enhanced accuracy in detecting the cancer.