Reviewerly/exHarmony huggingface dataset:
Prioritizing positive feature values: a new hierarchical feature selection method

In this work we address the problem of feature selection for the classification task in hierarchical and sparse feature spaces, which characterize many real-world applications nowadays. A binary feature space is deemed hierarchical when its binary features are related via generalization-specialization relationships, and is considered sparse when in general the instances contain much fewer “positive” than “negative” feature values. In any given instance, a feature value is deemed positive (negative) when the property associated with the feature has been (has not been) observed for that instance. Although there are many methods for the traditional feature selection problem in the literature, the proper treatment to hierarchical feature structures is still a challenge. Hence, we introduce a novel hierarchical feature selection method that follows the lazy learning paradigm—selecting a feature subset tailored for each instance in the test set. Our strategy prioritizes the selection of features with positive values, since they tend to be more informative—the presence of a relatively rare property is usually a piece of more relevant information than the absence of that property. Experiments on different application domains have shown that the proposed method outperforms previous hierarchical feature selection methods and also traditional methods in terms of predictive accuracy, selecting smaller feature subsets in general.
An Advanced Automated Image Analysis Model for Scoring of ER, PR, HER-2 and Ki-67 in Breast Carcinoma

Immunohistochemistry (IHC) plays an important role in evaluating the status of ER, PR, Ki-67 and human epidermal growth factor receptor 2 (HER-2) during diagnosis of breast cancer. Although some existing automated approaches can solve the high time-consumption and inter-/intra-observer variability drawbacks to a certain extent, most of them are can't analyze both nuclear staining and cell membrane staining using the same method. This is attributed to the difference in localization of the positive signal of immunohistochemical staining in different biological markers. The present study proposes a novel automated image analysis model for scoring and grading of ER, PR, Ki-67 and HER-2 immunohistochemical images based on whole tissue sections in breast cancer. The scoring results of the trained model and manual interpretation of ER, PR, Ki-67 and HER-2 were then finally analyzed and compared. Experimental results show that the F1-measure was 0.8450, 0.8533 and 0.7962 for nuclear recognition of Ki-67, ER/PR and HER-2 respectively. For stain grading of Ki-67, ER/PR and HER-2, the F1-measure was 0.9776, 0.8306 and 0.9573 respectively. The scoring consistency of ER/PR, Ki-67 and HER-2 between our model and expert interpretation was 0.9279, 0.9712 and 0.8046 respectively. Our results demonstrate that artificial intelligence technology is a feasible and accurate method for accurate quantitative immunohistochemical analysis that can solve the drawbacks of low repeatability and time consumption brought by manual counting. The main contribution of our proposed model is that it can recognize both nuclear staining and cell membrane staining and grade the staining intensity as a sequential learning task.
High-fidelity heralded quantum squeezing gate based on entanglement

Squeezing operation is critical in Gaussian quantum information. A high-fidelity heralded squeezing gate was recently realized using a noiseless linear amplifier with moderate ancillary squeezing. Here we analyze the heralded scheme based on squeezing [J. Zhao, Nat. Photonics, 14 , 306 (2020)] and find that its fidelity depends heavily on the purity of auxiliary squeezing, and even the fidelity with a 6 dB pure squeezed state is better than with a 15 dB thermal squeezed state. On this basis, we construct a new heralded squeezing gate based on teleportation, which can overcome the shortcomings of the heralded scheme based on squeezing and is immune to the purity of input squeezing. It can better use the current best available squeezing (15 dB) to realize a perfect squeezing gate for fault-tolerant continuous-variable quantum computation. This scheme is promising to realize other single-mode Gaussian operations and non-classical state squeezing operations.
On the role of effective and referring questions in GuessWhat?!

Task success is the standard metric used to evaluate referential visual dialogue systems.In this paper we propose two new metrics that evaluate how each question contributes to the goal.First, we measure how effective each question is by evaluating whether the question discards objects that are not the referent.Second, we define referring questions as those that univocally identify one object in the image.We report the new metrics for human dialogues and for state of the art publicly available models on GuessWhat?!.Regarding our first metric, we find that successful dialogues do not have a higher percentage of effective questions for most models.With respect to the second metric, humans make questions at the end of the dialogue that are referring, confirming their guess before guessing.Human dialogues that use this strategy have a higher task success but models do not seem to learn it.
College English Online Examination System Design Based on Cloud Computing Platform

With the deep integration of information technology and online teaching, online examination will gradually become the dominant form of college examination. College English, as a public basic course in colleges and universities, has the characteristics of large number of examinees, large scale and large load. Focusing on this issue, this paper starts with an overview of the feasibility and inevitability of college English online exam. It proposes a plan to build a systematic, integrated, intelligent and extensible college English online exam on the cloud computing platform by using the open-source Hadoop framework.
A Communication-efficient Linearly Convergent Algorithm with Variance Reduction for Distributed Stochastic Optimization

This paper considers a distributed stochastic strongly convex optimization, where agents over a network aim to cooperatively minimize the average of all agents' local cost functions. Due to the stochasticity of gradient estimation and distributedness of local objective, fast linearly convergent distributed algorithms have not been achieved yet. This work proposes a novel distributed stochastic gradient tracking algorithm with variance reduction, where the local gradients are estimated by an increasing batch-size of sampled gradients. With an undirected connected communication graph and a geometrically increasing batch-size, the iterates are shown to converge in mean to the optimal solution at a geometric rate (achieving linear convergence). The iteration, communication, and oracle complexity for obtaining an c-optimal solution are established as well. Particulary, the communication complexity is O(ln(1/ε)) while the oracle complexity (number of sampled gradients) is O(1/ε <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), which is of the same order as that of centralized approaches. Hence, the proposed scheme is communication-efficient without requiring extra sampled gradients. Numerical simulations are given to demonstrate the theoretic results.
BYE-TAL

It is not easy for people to consistently take care of their health without motivation. Existing smartphone apps have assisted with people managing their health, but there still remain questions about whether target users' needs were addressed to support the users. We report on each stage of a design thinking process for redesigning experiences of people who are in their 20s to 40s for managing their health. Especially, we were able to empathize with our target users by making the empathy map. This helped us to identify real users' needs. Outcomes from each stage of the design thinking process show that the process is applicable to other domains when people create a smartphone app to address the needs of their target users effectively.
Factors influencing youths fashion leadership behavior of fashion retailing stores in Jordan

Since consumer's needs, wants, and preferences establish a cornerstone for developing new products, this research aims to determine the effect of hedonic shopping motivations of Jordanian youth's fashion leadership behavior through a social psychology approach.Based on the diffusion and human motivations theories, six hedonic shopping motivations have been examined: (1) value shopping motivations; (2) gratification shopping motivations; (3) idea shopping motivations; (4) adventure shopping motivations; (5) social shopping motivations; and (6) role shopping motivations.A self-administered questionnaire has been developed to collect data from the research sample comprising 500 students from four private universities in Amman.Results showed that fashion leadership behavior is significantly related only to four hedonic shopping motivations: value shopping motivations, idea shopping motivations, social shopping motivations; and role shopping motivations, whereas gratification and adventure shopping motivations were found to not have a significant impact on leadership behavior.Research recommendations and future research were also discussed.
Semantic Web and Linked Data for Information Exchange between the Building and Product Manufacturing Industries: A Literature Review

Easy and solid information exchange constitutes an important part in BIM-based projects.To conduct reliable simulations and optimizations, information about products, such as luminaries, windows, doors, etc., need to be provided by the manufacturing industries to the building designers at the early design stage.Several studies and ongoing researches presented the Semantic Web (SW) and Linked Data (LD) technologies as an interoperable and flexible approach for providing product information from the manufacturers during the design and construction stage as well as gathering consumers data to the manufacturers during the operation stage.A systematic literature review has been conducted to investigate benefits and limitations of using the SW and LD technologies for developing product data templates for information exchanges between the building and product manufacturing industries in comparison with the conventional approach of information ex-changes using the openBIM standards.Results show that SW and LD could apport considerable benefits to the information exchange in the building sector.Moreover, SW and LD technologies enable the sharing of products' information from manufacturers to designers who can search product information, compare different products and take informed design decisions during the very beginning of the design process.
Verifying genuine multipartite entanglement of the whole from its separable parts

We prove experimentally the predicted existence of a three-qubit quantum state with genuine multipartite entanglement which can be certified solely from its separable two-qubit reduced density matrices.The qubits are encoded into different degrees of freedom of a pair of correlated photons and the state is prepared by letting the photons to propagate through a linear optical circuit.The presence of genuine multipartite entanglement is verified by finding numerically a fully decomposable entanglement witness acting nontrivially only on the reductions of the global state.Our result confirms viability of detection of emerging global properties of composite quantum systems from their parts which lack the properties.
Multi-station and multi-robot welding path planning based on greedy interception algorithm

Aiming at the multi-station and multi-robot coordinated welding task, the weld joints assignment, and welding path planning algorithm were studied. In this paper, the multi-constraint optimization problem is divided into four parts to reduce the complexity of each part, namely welding joint grouping, welding joint distribution, welding path planning, and optimize groups. To achieve the balance of welding tasks among multi-robot, a grouping algorithm based on distance ratio and a heuristic interception distribution algorithm are proposed. Based on the virtual centroid distance among the allocated weld joint groups, these groups were reassembled to reduce the possibility of interference. The simulation results show that the algorithm can realize the uniform distribution of weld joints and improve welding efficiency.
An Off-Chip Attack on Hardware Enclaves via the Memory Bus

This paper shows how an attacker can break the confidentiality of a hardware enclave with Membuster, an off-chip attack based on snooping the memory bus. An attacker with physical access can observe an unencrypted address bus and extract fine-grained memory access patterns of the victim. Membuster is qualitatively different from prior on-chip attacks to enclaves and is more difficult to thwart. We highlight several challenges for Membuster. First, DRAM requests are only visible on the memory bus at last-level cache misses. Second, the attack needs to incur minimal interference or overhead to the victim to prevent the detection of the attack. Lastly, the attacker needs to reverse-engineer the translation between virtual, physical, and DRAM addresses to perform a robust attack. We introduce three techniques, critical page whitelisting, cache squeezing, and oracle-based fuzzy matching algorithm to increase cache misses for memory accesses that are useful for the attack, with no detectable interference to the victim, and to convert memory accesses to sensitive data. We demonstrate Membuster on an Intel SGX CPU to leak confidential data from two applications: Hunspell and Memcached. We show that a single uninterrupted run of the victim can leak most of the sensitive data with high accuracy.
Advances in dynamic modeling and simulation of soft machines

Soft machines are a sort of mechanical systems mainly composed of soft materials and soft structures. Its soft compliance enables motion of large deformation, which results in a series of functionalities that traditional machines are not capable to achieve and the possibility to interact closely with humans. Therefore, soft machines are not only important complement to artificial machines but also a fascinating branch in future development of advanced machines. Mechanical modeling and analysis offer essential ways to designing and manufacturing soft machines. Dynamic modeling and simulation become increasingly effective and necessary for systematic design and motion control. This paper reviews and analyzes advances in dynamic modeling and simulation of soft machines. First, recent developments are overviewed from five aspects, namely kinematics, modeling of soft material, contact dynamics of flexible bodies, efficient computation of transient dynamics, and dynamic control. Second, problems and challenges are discussed. Several balances in the research of dynamics of soft machines are proposed. Third, potential directions in future research are predicted according to the current developments and challenges. Summary and prospects on dynamics and control of soft machines are given.
Detecting tampered regions in JPEG images via CNN

Often, digital pictures are used as evidence in criminal investigations. Therefore, it is essential to check whether they have been tampered with or not. In this study, we propose a method for detecting the tampered region in a JPEG image by using a convolutional neural network (CNN). In the proposed method, DCT coefficients are input to the CNN. The output is a binary segmented image in which the tampered and non-tampered regions are represented using white and black pixels, respectively. In our experiment, 45 types of CNN models were created and compared with one another. The detection accuracy of the best model was 0.63 in terms of the F-measure, which is approximately 2.3 times that achieved using our preliminary method, which was based on a support vector machine.
Quantum speed limit time, non-Markovianity, and quantum phase transition of the Dicke model

We study the behaviors of quantum speed limit time (QSLT) of a two-level atom and non-Markovianity of an atomic ensemble described by the Dicke model. Our results show that both quantities can accurately locate the critical point of quantum phase transition between normal and super-radiant phases of the model. The numerical simulations demonstrate a rapid increase in the non-Markovianity in the super-radiant phase with increasing size of the ensemble. The growing non-Markovianity, in turn, results in speeding up quantum evolution of the two-level atom.
A survey of distance measures for mixed variables

Distance measures are base for many statistical and data science methods with their applicability in various fields of science.Mixed variables data which is combination of continuous and categorical variables occurs frequently in fields such as medical, agriculture, remote sensing, biology, marketing, ecology etc., but a little work has been done for evaluating distance for such type of data.As there is not much literature available on distance measures for mixed data, therefore the fundamental sources that provide a comprehensive detail of a particular measure for mixed variables data were studied and reviewed in this paper.
UDDSketch: Accurate Tracking of Quantiles in Data Streams

We present UDDSketch (Uniform DDSketch), a novel sketch for fast and accurate tracking of quantiles in data streams. This sketch is heavily inspired by the recently introduced DDSketch, and is based on a novel bucket collapsing procedure that allows overcoming the intrinsic limits of the corresponding DDSketch procedures. Indeed, the DDSketch bucket collapsing procedure does not allow the derivation of formal guarantees on the accuracy of quantile estimation for data which does not follow a sub-exponential distribution. On the contrary, UDDSketch is designed so that accuracy guarantees can be given over the full range of quantiles and for arbitrary distribution in input. Moreover, our algorithm fully exploits the budgeted memory adaptively in order to guarantee the best possible accuracy over the full range of quantiles. Extensive experimental results on both synthetic and real datasets confirm the validity of our approach.
Empirical Negation, Co-negation and Contraposition Rule I: Semantical Investigations

We investigate the relationship between M. De's empirical negation in Kripke and Beth Semantics. It turns out empirical negation, as well as co-negation, corresponds to different logics under different semantics. We then establish the relationship between logics related to these negations under unified syntax and semantics based on R. Sylvan's CCω.
Why is getting credit for your data so hard?

Institutions, funding bodies, and national research organizations are pushing for more data sharing and FAIR data. Institutions typically implement data policies, frequently supported by an institutional data repository. Funders typically mandate data sharing. So where does this leave the researcher? How can researchers benefit from doing the additional work to share their data? In order to make sure that researchers and institutions get credit for sharing their data, the data needs to be tracked and attributed first. In this paper we investigated where the research data ended up for 11 research institutions, and how this data is currently tracked and attributed. Furthermore, we also analysed the gap between the research data that is currently in institutional repositories, and where their researchers truly share their data. We found that 10 out of 11 institutions have most of their public research data hosted outside of their own institution. Combined, they have 12% of their institutional research data published in the institutional data repositories. According to our data, the typical institution had 5% of their research data (median) published in the institutional repository, but there were 4 universities for which it was 10% or higher. By combining existing data-to-article graphs with existing article-to- researcher and article-to-institution graphs it becomes possible to increase tracking of public research data and therefore the visibility of researchers sharing their data typically by 17x. The tracking algorithm that was used to perform analysis and report on potential improvements has subsequently been implemented as a standard method in the Mendeley Data Monitor product. The improvement is most likely an under-estimate because, while the recall for datasets in institutional repositories is 100%, that is not the case for datasets published outside the institutions, so there are even more datasets still to be discovered.
Evaluation of group index in spectrally resolved white light interferometry

Different methods to determine the group index by measuring the material dispersion in spectrally resolved white-light interferometry were analysed. Four methods were investigated: computing the phase derivative directly, using the group index definition to calculate it from the refractive index derivative, locating the equalisation wavelength, and locally evaluating the fringe periodicity. For each method, different paths or variants were investigated to evaluate their efficacy and determine their advantages and drawbacks. We considered the measurement range of a typical CCD camera, from 400 to 1000 nm, and tested every method with two different samples: a 1-mm thick BK7 plate and an optical cell filled with deionised water; obtaining similar results in both cases. Only methods requiring the acquisition of a few frames were considered.
The Development of Artificial Intelligence Education Resources under the Background of the Internet of Things

Under the guidance of China's "New Generation of Artificial Intelligence Development Plan", the popularization of artificial intelligence technology has become a task that cannot be ignored in the field of education. Under the background of the lack of resources for the popularization of artificial intelligence education, how to effectively complete education popularization needs to be exploration. This article introduces a domestic artificial intelligence popularization education resource and the exploratory work on the popularization of artificial intelligence technology on this platform.
Performance Prediction for Convolutional Neural Network on Spark Cluster

Applications with large-scale data are processed on a distributed system, such as Spark, as they are data- and computation-intensive. Predicting the performance of such applications is difficult, because they are influenced by various aspects of configurations from the distributed framework level to the application level. In this paper, we propose a completion time prediction model based on machine learning for the representative deep learning model convolutional neural network (CNN) by analyzing the effects of data, task, and resource characteristics on performance when executing the model in Spark cluster. To reduce the time utilized in collecting the data for training the model, we consider the causal relationship between the model features and the completion time based on Spark CNN’s distributed data-parallel model. The model features include the configurations of the Data Center OS Mesos environment, configurations of Apache Spark, and configurations of the CNN model. By applying the proposed model to famous CNN implementations, we achieved 99.98% prediction accuracy about estimating the job completion time. In addition to the downscale search area for the model features, we leverage extrapolation, which significantly reduces the model build time at most to 89% with even better prediction accuracy in comparison to the actual work.