Reviewerly/exHarmony huggingface dataset:
Demonstration of a Nonstoquastic Hamiltonian in Coupled Superconducting Flux Qubits

Quantum annealing (QA) is a heuristic algorithm for finding low-energy configurations of a system, with applications in optimization, machine learning, and quantum simulation. Up to now, all implementations of QA have been limited to qubits coupled via a single degree of freedom. This gives rise to a stoquastic Hamiltonian that has no sign problem in quantum Monte Carlo (QMC) simulations. In this paper, we report implementation and measurements of two superconducting flux qubits coupled via two canonically conjugate degrees of freedom (charge and flux) to achieve a nonstoquastic Hamiltonian. Such coupling can enhance performance of QA processors, extend the range of quantum simulations. We perform microwave spectroscopy to extract circuit parameters and show that the charge coupling manifests itself as a YY interaction in the computational basis. We observe destructive interference in quantum coherent oscillations between the computational basis states of the two-qubit system. Finally, we show that the extracted Hamiltonian is nonstoquastic over a wide range of parameters.
A survey of semantic web technology for agriculture

Semantic web technologies have become a popular technique to apply meaning to unstructured data. They have been infrequently applied to problems within the agricultural domain when compared to complementary domains. Despite this lack of application, agriculture has a large number of semantic resources that have been developed by large NGOs such as the Food and Agriculture Organization (FAO). This survey is intended to motivate further research in the application of semantic web technologies for agricultural problems, by making available a self contained reference that provides: a comprehensive review of preexisting semantic resources and their construction methods, data interchange standards, as well as a survey of the current applications of semantic web technologies.
Adapting Software with Affective Computing: A Systematic Review

Strategies aimed at keeping the user's interest in using computer applications are being studied to provide greater user engagement, and can influence how people interact with computers. One of the approaches that can promote user engagement is Affective Computing (AC), based on the premise of recognizing the user's emotional state and adjusting the computer application to respond to such state in real-time. Although it is a relatively new area, over the past few years many research works have investigated the use of AC in various activities and objectives. To provide an overview on the use of AC in computer applications, this article presents a systematic literature review based on available articles on the main scientific databases of the Computer Science area. The main contribution of this review is the analysis of different types of applications. Based on the 58 articles analyzed, the main emotion recognition techniques and approaches to the adaptation of computer applications, as well as the limitations and challenges to be overcome were compiled. Our conclusions present the limitations and challenges still to be overcome in the area of automatic adaptation of computer applications by means of AC.
Application of artificial intelligence algorithms in image processing

As the main media of human communication and understanding of the world, image is one of the important information sources of human intelligence activities. With the development of the times, the demand for image processing technology is increasing day by day. The rapid development of computer technology also provides a platform for the application of image processing. In order to achieve better image processing effect, this paper focuses on the application of artificial intelligence algorithm in image processing. Image segmentation is a technology that decomposes images into regions with different characteristics and extracts useful targets. It can be regarded as a combinatorial optimization problem. It is completely feasible to apply artificial intelligence algorithm to optimization problems. Firstly, this paper introduces the ant colony algorithm in artificial intelligence algorithm, elaborates the basic principle and mathematical model of the ant colony algorithm. Secondly, in order to improve the ability of global search of ant colony algorithm, this paper introduces the crowding degree function of fish into the ant colony algorithm. Finally, the improved ant colony algorithm is used in image segmentation to improve the effect of image segmentation. The simulation results show that it is feasible to use ant colony algorithm in image segmentation. And the optimization improvement of ant colony algorithm is effective. The improved ant colony algorithm applied in image segmentation can significantly improve the segmentation performance.
NNPIM: A Processing In-Memory Architecture for Neural Network Acceleration

Neural networks (NNs) have shown great ability to process emerging applications such as speech recognition, language recognition, image classification, video segmentation, and gaming. It is therefore important to make NNs efficient. Although attempts have been made to improve NNs' computation cost, the data movement between memory and processing cores is the main bottleneck for NNs' energy consumption and execution time. This makes the implementation of NNs significantly slower on traditional CPU/GPU cores. In this paper, we propose a novel processing in-memory architecture, called NNPIM, that significantly accelerates neural network's inference phase inside the memory. First, we design a crossbar memory architecture that supports fast addition, multiplication, and search operations inside the memory. Second, we introduce simple optimization techniques which significantly improves NNs' performance and reduces the overall energy consumption. We also map all NN functionalities using parallel in-memory components. To further improve the efficiency, our design supports weight sharing to reduce the number of computations in memory and consecutively speedup NNPIM computation. We compare the efficiency of our proposed NNPIM with GPU and the state-of-the-art PIM architectures. Our evaluation shows that our design can achieve 131.5× higher energy efficiency and is 48.2× faster as compared to NVIDIA GTX 1,080 GPU architecture. Compared to state-of-the-art neural network accelerators, NNPIM can achieve on an average 3.6× higher energy efficiency and is 4.6× faster, while providing the same classification accuracy.
Bringing Citations and Usage Metrics Together to Make Data Count

Over the last years, many organizations have been working on infrastructure to facilitate sharing and reuse of research data. This means that researchers now have ways of making their data available, but not necessarily incentives to do so. Several Research Data Alliance (RDA) working groups have been working on ways to start measuring activities around research data to provide input for new Data Level Metrics (DLMs). These DLMs are a critical step towards providing researchers with credit for their work. In this paper, we describe the outcomes of the work of the Scholarly Link Exchange (Scholix) working group and the Data Usage Metrics working group. The Scholix working group developed a framework that allows organizations to expose and discover links between articles and datasets, thereby providing an indication of data citations. The Data Usage Metrics group works on a standard for the measurement and display of Data Usage Metrics. Here we explain how publishers and data repositories can contribute to and benefit from these initiatives. Together, these contributions feed into several hubs that enable data repositories to start displaying DLMs. Once these DLMs are available, researchers are in a better position to make their data count and be rewarded for their work.
Pixel-Wise Attentional Gating for Scene Parsing

To achieve dynamic inference in pixel labeling tasks, we propose Pixel-wise Attentional Gating (PAG), which learns to selectively process a subset of spatial locations at each layer of a deep convolutional network. PAG is a generic, architecture-independent, problem-agnostic mechanism that can be readily “plugged in” to an existing model with fine-tuning. We utilize PAG in two ways: 1) learning spatially varying pooling fields that improve model performance without the extra computation cost associated with multi-scale pooling, and 2) learning a dynamic computation policy for each pixel to decrease total computation (FLOPs) while maintaining accuracy. We extensively evaluate PAG on a variety of per-pixel labeling tasks, including semantic segmentation, boundary detection, monocular depth and surface normal estimation. We demonstrate that PAG allows competitive or state-of-the-art performance on these tasks. Our experiments show that PAG learns dynamic spatial allocation of computation over the input image which provides better performance trade-offs compared to related approaches (e.g., truncating deep models or dynamically skipping whole layers). Generally, we observe PAG can reduce computation by 10% without noticeable loss in accuracy and performance degrades gracefully when imposing stronger computational constraints.
Deep rolling: A novel emotion prediction model for a multi-participant communication context

Nowadays, the amount of user-generated contents (UGCs) or texts has surged exponentially. Therefore, recognizing emotions from these texts can bring about lots of advantages. In this paper, we have proposed a novel model named Deep Rolling to predict emotion for target participant in a multi-participant communication context. First, the proposed method converts a text collection into a set of n-dimension vectors for emotion representation and re-organizes texts into a sequence in time order. Then, Deep Rolling can predict the emotion of target participant corresponding to a future time point. Second, apart from simply taking in texts posted by target participant via LSTM, the proposed method has also incorporated texts posted by other participants at every time step by CNN. In this way, Deep Rolling can predict target participant’s emotion by processing emotions from both the target and all the other participants in an ensemble way. Finally, data factorization has also been introduced into Deep Rolling to enhance the overall prediction efficiency. According to experimental results, compared with the state-of-art methods, our proposed model has achieved the best prediction precision on different target participants. At the same time, Deep Rolling has also maintained the prediction efficiency at an acceptable level.
Evolutionary Algorithms-Based Multi-Objective Optimal Mobile Robot Trajectory Planning

Summary In this research study, trajectory planning of mobile robot is accomplished using two techniques, namely, a new variant of multi-objective differential evolution (heterogeneous multi-objective differential evolution) and popular elitist non-dominated sorting genetic algorithm (NSGA-II). For this research problem, a wheeled mobile robot with differential drive is considered. A practical, feasible and optimal trajectory between two locations in the presence of obstacles is determined through the proposed algorithms. A safer path is obtained by optimizing certain objectives (travel time and actuators effort) taking into account the limitations of mobile robot’s geometric, kinematic and dynamic parameters. Robot motion is represented by a cubic NURBS trajectory curve. The capability of the proposed optimization techniques is analyzed through numerical simulations. Results ensure that the proposed techniques are more desirable for this problem.
Cybersecurity education in a developing nation: the Ecuadorian environment

The ability to prevent successful cyber attacks against a nation’s critical infrastructure depends on the availability of a skilled cyber-literate workforce, and therefore, on an educational system that can build such capabilities. While it is possible to hire foreign nationals or to outsource many operations, this is not a sustainable solution and raises other concerns. The current literature provides strategic guidelines on developing a national cybersecurity workforce; however, there has been relatively little research on identifying the factors that are responsible for impeding the development of cybersecurity education in developing economies. Based on qualitative analysis of data from 28 semi-structured interviews with educational leaders from thirteen Ecuadorian institutions of higher education, this article explores challenges faced by the higher educational system of Ecuador in advancing cybersecurity education. On the basis of the insights gained, opportunities for enhancing the system are then identified and discussed. Today cybersecurity education is mostly elementary in Ecuador. Nationwide, interviewees at only four of the thirteen universities studied expressed some confidence in their institution’s ability to provide students with reasonable preparation. The challenges that domestic cybersecurity education faces include: cybersecurity skills, structural capabilities, social integration, economic resources, and governance capacity. To enhance current preparation, there is an urgent need for a national cybersecurity education strategy that bolsters multiple initiatives as well as a multi-stakeholder space in which government, industry, and academia can actively work together to address national cybersecurity educational requirements. Further initiatives should include strengthening educators’ training and cybersecurity academic programs, as well as advocating research (and development) capabilities and cybersecurity awareness. Recent revisions in Ecuador’s higher education system offer a timely opportunity to advocate for advancing academic cybersecurity competencies.
A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation

The popularity of location-based social networks (LBSNs) has led to a tremendous amount of user check-in data. Recommending points of interest (POIs) plays a key role in satisfying users needs in LBSNs. While recent work has explored the idea of adopting collaborative ranking (CR) for recommendation, there have been few attempts to incorporate temporal information for POI recommendation using CR. In this article, we propose a two-phase CR algorithm that incorporates the geographical influence of POIs and is regularized based on the variance of POIs popularity and users activities over time. The time-sensitive regularizer penalizes user and POIs that have been more time-sensitive in the past, helping the model to account for their long-term behavioral patterns while learning from user-POI interactions. Moreover, in the first phase, it attempts to rank visited POIs higher than the unvisited ones, and at the same time, apply the geographical influence. In the second phase, our algorithm tries to rank users favorite POIs higher on the recommendation list. Both phases employ a collaborative learning strategy that enables the model to capture complex latent associations from two different perspectives. Experiments on real-world datasets show that our proposed time-sensitive collaborative ranking model beats state-of-the-art POI recommendation methods.
A Spam Filtering Method Based on Multi-Modal Fusion

In recent years, the single-modal spam filtering systems have had a high detection rate for image spamming or text spamming. To avoid detection based on the single-modal spam filtering systems, spammers inject junk information into the multi-modality part of an email and combine them to reduce the recognition rate of the single-modal spam filtering systems, thereby implementing the purpose of evading detection. In view of this situation, a new model called multi-modal architecture based on model fusion (MMA-MF) is proposed, which use a multi-modal fusion method to ensure it could effectively filter spam whether it is hidden in the text or in the image. The model fuses a Convolutional Neural Network (CNN) model and a Long Short-Term Memory (LSTM) model to filter spam. Using the LSTM model and the CNN model to process the text and image parts of an email separately to obtain two classification probability values, then the two classification probability values are incorporated into a fusion model to identify whether the email is spam or not. For the hyperparameters of the MMA-MF model, we use a grid search optimization method to get the most suitable hyperparameters for it, and employ a k-fold cross-validation method to evaluate the performance of this model. Our experimental results show that this model is superior to the traditional spam filtering systems and can achieve accuracies in the range of 92.64–98.48%.
Exploiting user interactivity in quality assessment of point cloud imaging

Point clouds are a new modality for representation of plenoptic content and a popular alternative to create immersive media. Despite recent progress in capture, display, storage, delivery and processing, the problem of a reliable approach to subjectively and objectively assess the quality of point clouds is still largely open. In this study, we extend the state of the art in projection-based objective quality assessment of point cloud imaging by investigating the impact of the number of viewpoints employed to assess the visual quality of a content, while discarding information that does not belong to the object under assessment, such as background color. Additionally, we propose assigning weights to the projected views based on interactivity information, obtained during subjective evaluation experiments. In the experiment that was conducted, human observers assessed a carefully selected collection of typical contents, subject to geometry and color degradations due to compression. The point cloud models were rendered using cubes as primitive elements with adaptive sizes based on local neighborhoods. Our results show that employing a larger number of projected views does not necessarily lead to better predictions of visual quality, while user interactivity information can improve the performance.
The Use of a Mobile Learning Tool by Medical Students in Undergraduate Anatomy and its Effects on Assessment Outcomes

Hand‐held devices have revolutionized communication and education in the last decade. Consequently, mobile learning (m‐learning) has become popular among medical students. Nevertheless, there are relatively few studies assessing students' learning outcomes using m‐learning devices. This observational study presents an anatomy m‐learning tool ( eMed ‐App), an application developed to accompany an anatomy seminar and support medical students' self‐directed learning of the skeletal system. Questionnaire data describe where, how frequently, and why students used the app. Multiple choice examination results were analyzed to evaluate whether usage of the app had an effect on test scores. The eMed ‐App application was used by 77.5% of the students, mainly accessed by Android smartphones, and at students' homes (62.2%) in order to prepare themselves for seminar sessions (60.8%), or to review learning content (67%). Most commonly, students logged on for less than 15 minutes each time (67.8%). Frequent app users showed better test results on items covering eMed ‐App learning content. In addition, users also achieved better results on items that were not related to the content of the app and, thus, gained better overall test results and lower failure rates. The top quartile of test performers used the eMed ‐App more frequently compared to students in lower quartiles. This study demonstrated that many students, especially the high‐performing ones, made use of the eMed ‐App. However, the app itself did not result in better outcomes, suggesting that top students might have been more motivated to use the app than students who were generally weak in anatomy.
Toward Visibility Guaranteed Visual Servoing Control of Quadrotor UAVs

This paper studies the visibility problem in the visual servoing control of quadrotor unmanned aerial vehicles (UAVs). Visual servoing is a useful technique for the inspection, positioning, landing, and tracking of quadrotor UAVs. However, one prerequisite in previous research works is that the visual target stays inside the field of view of the camera for all time. While lose sight of the visual target will result in fatal failure of the task, how to guarantee this prerequisite is often overlooked. In this paper, we aim to achieve reliable visual servoing by deriving a provable solution to guarantee visibility in quadrotor visual serving control. The main idea is to ensure the forward invariance of a set using control barrier functions. The set is constructed as the visible set, which specifies the upper bound of the distance between the center of the image plane and the coordinates of visual features in the image plane. Then, the visibility constraint is developed based on control barrier function. The original control inputs are minimally modified to satisfy the visibility constraint, thus preserving visibility. Finally, some simulations and experimental results are presented to validate this method.
Why is Getting Rid of <i>P</i>-Values So Hard? Musings on Science and Statistics

The current concerns about reproducibility have focused attention on proper use of statistics across the sciences. This gives statisticians an extraordinary opportunity to change what are widely regarded as statistical practices detrimental to the cause of good science. However, how that should be done is enormously complex, made more difficult by the balkanization of research methods and statistical traditions across scientific subdisciplines. Working within those sciences while also allying with science reform movements—operating simultaneously on the micro and macro levels—are the key to making lasting change in applied science.
Flexible unsupervised feature extraction for image classification

Dimensionality reduction is one of the fundamental and important topics in the fields of pattern recognition and machine learning. However, most existing dimensionality reduction methods aim to seek a projection matrix W such that the projection WTx is exactly equal to the true low-dimensional representation. In practice, this constraint is too rigid to well capture the geometric structure of data. To tackle this problem, we relax this constraint but use an elastic one on the projection with the aim to reveal the geometric structure of data. Based on this context, we propose an unsupervised dimensionality reduction model named flexible unsupervised feature extraction (FUFE) for image classification. Moreover, we theoretically prove that PCA and LPP, which are two of the most representative unsupervised dimensionality reduction models, are special cases of FUFE, and propose a non-iterative algorithm to solve it. Experiments on five real-world image databases show the effectiveness of the proposed model.
An automated methodology for grain segmentation and grain size measurement from optical micrographs

Grain size of a material and its distribution has tremendous impact on strength and toughness of a material. An image processing based automated methodology has been developed in this work and applied on the optical micrographs of different types of interstitial-free steels. Polished and etched surfaces of the specimens were observed in an optical microscope and number of images was captured by digital camera. The methodology developed in this work has been applied over these images to find the grain size of the steel and their distributions. In this methodology, a sequence of steps has been applied on the images to obtain closed contours of the grains which enable measurement of grain size. The outcome of the proposed methodology has been found comparable with other commonly used methods.