Reviewerly/exHarmony huggingface dataset:
Model selection for accurate daily global solar radiation prediction in China

Solar energy can share with a big percentage in solving in the environmental pollution and energy crisis in China. For the most application of solar energy, an accurate information of solar radiation amount received by a horizontal surface is the first step. In this study, nine-day of the year based models are calibrated and evaluated to estimate the daily global solar radiation (DGSR) on a horizontal surface using long-term data of 84 stations all over China. After dividing China into five solar climatic zones, the highest performance model for each zone is chosen to be a general day of the year based models and then calibrate and evaluated at each solar zone. Based on statistical indicator; the root mean square error (RMSE), the mean absolute bias error (MABE), coefficient of determination (R2) and the correlation coefficient (r) and the uncertainty based on a 95% confidence level (U95) and Taylor diagram, results show that the models perform best in the solar zones that receive a high amount of solar radiation and the performance decreases with decreasing of the daily solar radiation incidence. The hybrid sine and cosine wave day of the year based models exhibit the best performance, model D7 is the best for zone I and model D8 for zones II and IV. For zones II and V, the Gaussian form model (D3 model) and the 4th order polynomial model (D4 model) yield high performance, respectively. Whereas, the two Gaussian form model is the worth performance for all zones. The developed general day of the year based models for each zone in this study can be used with high accuracy to estimate the (DGSR) on a horizontal surface in areas where there is no data or the data is missing all over China.
An FPGA-Based CNN Accelerator Integrating Depthwise Separable Convolution

The Convolutional Neural Network (CNN) has been used in many fields and has achieved remarkable results, such as image classification, face detection, and speech recognition. Compared to GPU (graphics processing unit) and ASIC, a FPGA (field programmable gate array)-based CNN accelerator has great advantages due to its low power consumption and reconfigurable property. However, FPGA’s extremely limited resources and CNN’s huge amount of parameters and computational complexity pose great challenges to the design. Based on the ZYNQ heterogeneous platform and the coordination of resource and bandwidth issues with the roofline model, the CNN accelerator we designed can accelerate both standard convolution and depthwise separable convolution with a high hardware resource rate. The accelerator can handle network layers of different scales through parameter configuration and maximizes bandwidth and achieves full pipelined by using a data stream interface and ping-pong on-chip cache. The experimental results show that the accelerator designed in this paper can achieve 17.11GOPS for 32bit floating point when it can also accelerate depthwise separable convolution, which has obvious advantages compared with other designs.
IoT + AR: pervasive and augmented environments for “Digi-log” shopping experience

Abstract The current bare Internet of Things (IoT) infrastructure has recently been extended to include smarter and more effective user interactions. Individual or meaningful sets and groups of IoT objects can be imbued with data and/or content in a distributed manner and efficiently utilized by the client. This distribution makes it possible to scale and customize interaction techniques such as augmented reality (AR). This paper proposes an architecture for combining the AR interface with IoT for an improved shopping experience. The proposed architecture is distributed and therefore scalable such that any IoT product can be accessed on the spot locally without any server restriction and provide intuitive AR-based visualization and interaction for a flexible product trial in the showroom. We identify three key architectural components required to support such a seamless and scalable AR service and experience for IoT-ready products: (1) object-centric data management and visualization, (2) mechanism for accessing, controlling, and interacting with the object, and (3) content exchange interoperability. We illustrate the possible scenarios of shopping in the future with the interactive and smart digital information combined with the analog, that is, the real world. A proof-of-concept implementation is presented as applied to such a “digital–analog” style of shopping. In addition, its usability is experimentally assessed as compared to using the conventional control interface. Our experimental study shows that the subjects clearly experience higher usability and greater satisfaction with AR-interactive shopping, thereby demonstrating the potential of the proposed approach.
Multi-Person Pose Estimation Using Bounding Box Constraint and LSTM

This paper presents a new method for single-image pose estimation of multiple people combining the traditional bottom-up and the top-down methods. Specifically, we extract features from the input image by a residual network and use a multistage CNN to learn both the confidence maps of joints and the connection relationships, between joints. During testing, we perform the network feedforwarding in a bottom-up manner, and then use the predicted confidence maps, the connection relationships, and the corresponding bounding boxes to parse the poses of all people in a top-down manner. In contrast to the previous top-down methods, our method is robust to bounding box shift and tightness, works well for largely overlapped people, and achieves faster running speed. In contrast to the bottom-up method, our method avoids mistake propagation across different people, and addresses disconnected joints effectively. To estimate human pose from videos, we impose a weight-sharing scheme to the multi-stage CNN, and rewrite it as a recurrent neural network. Thus, we can reuse the prediction results from the previous frames so as to reduce the total stage number, yielding significantly faster speed in invoking the network on videos. And we adopt LSTM units between frames to capture the temporal correlation among video frames. We found that LSTM handles input-quality degradation in videos well and successfully stabilizes the sequential outputs.
Not all bugs are the same: Understanding, characterizing, and classifying bug types

Modern version control systems, e.g., GitHub, include bug tracking mechanisms that developers can use to highlight the presence of bugs. This is done by means of bug reports, i.e., textual descriptions reporting the problem and the steps that led to a failure. In past and recent years, the research community deeply investigated methods for easing bug triage, that is, the process of assigning the fixing of a reported bug to the most qualified developer. Nevertheless, only a few studies have reported on how to support developers in the process of understanding the type of a reported bug, which is the first and most time-consuming step to perform before assigning a bug-fix operation. In this paper, we target this problem in two ways: first, we analyze 1280 bug reports of 119 popular projects belonging to three ecosystems such as Mozilla, Apache, and Eclipse, with the aim of building a taxonomy of the types of reported bugs; then, we devise and evaluate an automated classification model able to classify reported bugs according to the defined taxonomy. As a result, we found nine main common bug types over the considered systems. Moreover, our model achieves high F-Measure and AUC-ROC (64% and 74% on overall, respectively).
Optimal Utility-Privacy Trade-Off With Total Variation Distance as a Privacy Measure

The total variation distance is proposed as a privacy measure in an information disclosure scenario when the goal is to reveal some information about available data in order to receive utility, while preserving the privacy of sensitive data from the legitimate receiver. The total variation distance is motivated as a measure of privacy-leakage by showing that: i) it satisfies the post-processing and linkage inequalities, which makes it consistent with an intuitive notion of a privacy measure; ii) the optimal utility-privacy trade-off can be solved through a standard linear program when total variation distance is employed as the privacy measure; iii) it provides a bound on the privacy-leakage measured by mutual information, maximal leakage, or the improvement in an inference attack with an arbitrary bounded cost function1.1This work has been carried out when the first author was with the Information Processing and Communications Laboratory at Imperial College London.
Does the use of educational technology in personalized learning environments correlate with self-reported digital skills and beliefs of secondary-school students?

In order to develop 21st-century skills, especially with respect to digital technologies, students are supposed to know how to use digital technologies for learning and problem solving. In recent years, approaches to personalized learning have been considered to be particularly promising in this regard. A clearly defined concept of personalized learning is still lacking, however. Rather, it serves an umbrella term for educational approaches that try to do justice to the individual abilities, knowledge and learning needs of each student. Our study deals with theoretical dimensions of this multilayered concept and investigates the in-school use of digital technologies for personalized learning as reported by students in connection with their self-assessed digital skills and beliefs about the usefulness of ICT in learning. For this purpose, we have analyzed data from a survey of N = 860 students (8th grade) from 31 Swiss schools that have introduced personalized learning concepts. A structural equation model shows that increased reported use of digital technologies in learning environments that are characterized by open teaching methods is an important aspect of personalized learning because it has a positive effect on both self-reported digital skills and self-perceived ICT-related beliefs in learning. At the same time, the experienced freedom of choice in learning activities that include digital technologies, which is often considered a crucial dimension of personalized learning, has no significant effect.
Multilayer one-class extreme learning machine

One-class classification has been found attractive in many applications for its effectiveness in anomaly or outlier detection. Representative one-class classification algorithms include the one-class support vector machine (SVM), Naive Parzen density estimation, autoencoder (AE), etc. Recently, the one-class extreme learning machine (OC-ELM) has been developed for learning acceleration and performance enhancement. But existing one-class algorithms are generally less effective in complex and multi-class classifications. To alleviate the deficiency, a multilayer neural network based one-class classification with ELM (in short, as ML-OCELM) is developed in this paper. The stacked AEs are employed in ML-OCELM to exploit an effective feature representation for complex data. The effective kernel based learning framework is also investigated in the stacked AEs of ML-OCELM, leading to a multilayer kernel based OC-ELM (in short, as MK-OCELM). The MK-OCELM has advantages of less human-intervention parameters and good generalization performance. Experiments on 13 benchmark UCI classification datasets and a real application on urban acoustic classification (UAC) are carried out to show the superiority of the proposed ML-OCELM/MK-OCELM over the OC-ELM and several state-of-the-art algorithms.
Classification for plastic bottles recycling based on image recognition

Recycling of used plastic bottles is an important measure to protect the environment and save energy. Usually, bottles in different colors have different value for recycling. Classification of plastic bottles recycling based on image recognition during recycling is an effective way, where the position and color recognition are the key technologies. To classify the plastic bottles on the conveyor belt, their position relationships are firstly defined as three categories, i.e. disjoint, adjacent and overlapping. The disjoint ones can be easily identified by the ratio of concave and convex area based on their image. For the adjacent and overlapping bottles, a combination method called distance transformation and threshold segmentation is proposed to distinguish their position relationships. Once the adjacent bottles are identified, the method of concave point search based on convex hull will be used to separate the adjacent recycled bottles further. Then, the color of both the disjoint and adjacent bottles is identified because it is too complex and difficult to recognize color of and separate the overlapping bottles. In the aspect of color recognition, the colors of recycled bottles are divided into seven categories in the sorting process. Color features of the bottom section are used to represent the one of the recycled bottle because there may be a bottle cap and a label on the top and in the middle of the bottle, respectively, resulting in the wrong recognition. ReliefF algorithm is applied to select color features of recycled bottles and the color is identified by support vector machine (SVM) algorithm. The influence of training sample size on classification model is studied and the experimental results show that the accuracy of color recognition of recycled bottles reach 94.7%.
Salp Swarm Algorithm: Theory, Literature Review, and Application in Extreme Learning Machines

Salp Swarm Algorithm (SSA) is a recent metaheuristic inspired by the swarming behavior of salps in oceans. SSA has demonstrated its efficiency in various applications since its proposal. In this chapter, the algorithm, its operators, and some of the remarkable works that utilized this algorithm are presented. Moreover, the application of SSA in optimizing the Extreme Learning Machine (ELM) is investigated to improve its accuracy and overcome the shortcomings of its conventional training method. For verification, the algorithm is tested on 10 benchmark datasets and compared to two other well-known training methods. Comparison results show that SSA based training methods outperforms other methods in terms of accuracy and is very competitive in terms of prediction stability.
Copy-Move Forgery Detection: A State-of-the-Art Technical Review and Analysis

This paper presents the state-of-the-art technical reviews and analysis of recent copy-move forgery detection (CMFD) techniques.A new CMFD process pipeline was introduced.In addition, the techniques used in each stage of the CMFD pipeline are summarized and classified into small categories.Furthermore, the tables of comparison are provided as a quick reference.This technical review paper is expected to help provide useful insights and updated information regarding recent advancements in CMFD to researchers in the field.
Effects of applying a VR‐based two‐tier test strategy to promote elementary students’ learning performance in a Geology class

Abstract Science and technology are driving people’s life changes, including education and the environment. Many scholars have attempted to import technology into the classroom to help students learn in different subjects. However, students often need assistance with unfamiliar learning approaches and learning environments. This study proposed a non‐immersive virtual reality (VR) guidance system combined with a two‐tier strategy to help students learn geology knowledge. Two groups of students used different learning approaches: the experimental group students were guided by the two‐tier test VR guidance system, and the control group students learned with the conventional VR guidance system. According to the experimental result, the two‐tier test VR guidance system not only improved the students’ learning achievement in natural science, but also enhanced their learning motivation. In addition, according to the sequential results, we found that VR learning materials or environmental resources can help students answer questions and solve problems more effectively.
A survey on 3D hand pose estimation: Cameras, methods, and datasets

3D Hand pose estimation has received an increasing amount of attention, especially since consumer depth cameras came onto the market in 2010. Although substantial progress has occurred recently, no overview has kept up with the latest developments. To bridge the gap, we provide a comprehensive survey, including depth cameras, hand pose estimation methods, and public benchmark datasets. First, a markerless approach is proposed to evaluate the tracking accuracy of depth cameras with the aid of a numerical control linear motion guide. Traditional approaches focus only on static characteristics. The evaluation of dynamic tracking capability has been long neglected. Second, we summarize the state-of-the-art methods and analyze the lines of research. Third, existing benchmark datasets and evaluation criteria are identified to provide further insight into the field of hand pose estimation. In addition, realistic challenges, recent trends, dataset creation and annotation, and open problems for future research directions are also discussed.
What Can We Learn from Augmented Reality (AR)?

Emerging technologies such as Augmented Reality (AR), have the potential to radically transform education by making challenging concepts visible and accessible to novices. In this project, we have designed a Hololens-based system in which collaborators are exposed to an unstructured learning activity in which they learned about the invisible physics involved in audio speakers. They learned topics ranging from spatial knowledge, such as shape of magnetic fields, to abstract conceptual knowledge, such as relationships between electricity and magnetism. We compared participants' learning, attitudes and collaboration with a tangible interface through multiple experimental conditions containing varying layers of AR information. We found that educational AR representations were beneficial for learning specific knowledge and increasing participants' self-efficacy (i.e., their ability to learn concepts in physics). However, we also found that participants in conditions that did not contain AR educational content, learned some concepts better than other groups and became more curious about physics. We discuss learning and collaboration differences, as well as benefits and detriments of implementing augmented reality for unstructured learning activities.
RealityCheck

Today's virtual reality (VR) systems offer chaperone rendering techniques that prevent the user from colliding with physical objects. Without a detailed geometric model of the physical world, these techniques offer limited possibility for more advanced compositing between the real world and the virtual. We explore this using a realtime 3D reconstruction of the real world that can be combined with a virtual environment. RealityCheck allows users to freely move, manipulate, observe, and communicate with people and objects situated in their physical space without losing the sense of immersion or presence inside their virtual world. We demonstrate RealityCheck with seven existing VR titles, and describe compositing approaches that address the potential conflicts when rendering the real world and a virtual environment together. A study with frequent VR users demonstrate the affordances provided by our system and how it can be used to enhance current VR experiences.
Swire

Sketches and real-world user interface examples are frequently used in multiple stages of the user interface design process. Unfortunately, finding relevant user interface examples, especially in large-scale datasets, is a highly challenging task because user interfaces have aesthetic and functional properties that are only indirectly reflected by their corresponding pixel data and meta-data. This paper introduces Swire, a sketch-based neural-network-driven technique for retrieving user interfaces. We collect the first large-scale user interface sketch dataset from the development of Swire that researchers can use to develop new sketch-based data-driven design interfaces and applications. Swire achieves high performance for querying user interfaces: for a known validation task it retrieves the most relevant example as within the top-10 results for over 60% of queries. With this technique, for the first time designers can accurately retrieve relevant user interface examples with free-form sketches natural to their design workflows. We demonstrate several novel applications driven by Swire that could greatly augment the user interface design process.
Service Candidate Identification from Monolithic Systems Based on Execution Traces

Monolithic systems increasingly suffer from maintainability and scalability issues as they grow in functionality, size, and complexity. It is widely believed that (micro)service-based architectures can alleviate these problems as each service is supposed to have the following characteristics: clearly defined functionality, sufficient modularity, and the ability to evolve independently. Industrial practices show that service extraction from a legacy monolithic system is labor-intensive and complex. Existing work on service candidate identification aims to group entities of a monolithic system into potential service candidates, but this process has two major challenges: first, it is difficult to extract service candidates with consistent quality; second, it is hard to evaluate the identified service candidates regarding the above three characteristics. To address these challenges, this paper proposes the Functionality-oriented Service Candidate Identification (FoSCI) framework to identify service candidates from a monolithic system. Our approach is to record the monolith's execution traces, and extract services candidates using a search-based functional atom grouping algorithm. We also contribute a comprehensive service candidate evaluation suite that uses interface information, structural/conceptual dependency, and commit history. This evaluation system consists of 8 metrics, measuring functionality, modularity, and evolvability respectively of identified service candidates. We compare FoSCI with three existing methods, using 6 widely-used open-source projects as our evaluation subjects. Our results show that FoSCI outperforms existing methods in most measures.
Feature selection based on brain storm optimization for data classification

Brain storm optimization (BSO) is a new and effective swarm intelligence method inspired by the human brainstorming process. This paper presents a novel BSO-based feature selection technique for data classification. Specifically, the Fuzzy ARTMAP (FAM) model, which is employed as an incremental learning neural network, is combined with BSO, which acts as a feature selection method, to produce the hybrid FAM-BSO model for feature selection and optimization. Firstly, FAM is used to create a number of prototype nodes incrementally. Then, BSO is used to search and select an optimal sub-set of features that is able to produce high accuracy with the minimum number of features. Ten benchmark problems and a real-world case study are employed to evaluate the performance of FAM-BSO. The results are quantified statistically using the bootstrap method with the 95% confidence intervals. The outcome indicates that FAM-BSO is able to produce promising results as compared with those from original FAM and other feature selection methods including particle swarm optimization, genetic algorithm, genetic programming, and ant colony optimization.