Reviewerly/exHarmony huggingface dataset:
Fusion of Multi-RSMOTE With Fuzzy Integral to Classify Bug Reports With an Imbalanced Distribution

With the help of automated classification, severe bugs can be rapidly identified so that the latent damage to software projects can be minimized. However, bug report datasets commonly suffer from disproportionate number of category samples. When presented with the situation of class imbalance, most standard classification learning approaches fail to properly learn the distributive characteristics of the samples and tend to result in unfavorable performance to predict class label. In this case, imbalanced learning becomes critical to advance classification algorithms. In this paper, we propose an improved synthetic minority oversampling technique to avoid the degraded performance caused by class imbalance in bug report datasets. Moreover, to lessen the chance of occasionalities in random sampling process, we propose a repeated sampling technique to train different, but related classifiers. Finally, an ensemble algorithm based on Choquet fuzzy integral is employed to combine the wisdom of crowds and make better decisions. We conduct comprehensive experiments on several bug report datasets from real-world bug repositories. The results demonstrate that the proposed method boosts the classification performance across the classes of the data. Specifically, compared with various ensemble learning techniques, the Choquet fuzzy integral achieves outstanding results on integrating multiple random oversampling techniques.
Spatial-Temporal Attention Res-TCN for Skeleton-Based Dynamic Hand Gesture Recognition

Dynamic hand gesture recognition is a crucial yet challenging task in computer vision. The key of this task lies in an effective extraction of discriminative spatial and temporal features to model the evolutions of different gestures. In this paper, we propose an end-to-end Spatial-Temporal Attention Residual Temporal Convolutional Network (STA-Res-TCN) for skeleton-based dynamic hand gesture recognition, which learns different levels of attention and assigns them to each spatial-temporal feature extracted by the convolution filters at each time step. The proposed attention branch assists the networks to adaptively focus on the informative time frames and features while exclude the irrelevant ones that often bring in unnecessary noise. Moreover, our proposed STA-Res-TCN is a lightweight model that can be trained and tested in an extremely short time. Experiments on DHG-14/28 Dataset and SHREC’17 Track Dataset show that STA-Res-TCN outperforms state-of-the-art methods on both the 14 gestures setting and the more complicated 28 gestures setting.
Explainability scenarios

Integral to the adoption and uptake of AI systems in real-world settings is the ability for people to make sense of and evaluate such systems, a growing area of development and design efforts known as XAI (Explainable AI). Recent work has advanced the state of the art, yet a key challenge remains in understanding unique requirements that might arise when XAI systems are deployed into complex settings of use. In helping envision such requirements, this paper turns to scenario-based design, a method that anticipates and leverages scenarios of possible use early on in system development. To demonstrate the value of the scenario-based design method to XAI design, this paper presents a case study of aging-in-place monitoring. Introducing the concept of "explainability scenarios" as resources in XAI design, this paper sets out a forward-facing agenda for further attention to the emergent requirements of explainability-in-use.
Multi-scale Optimal Fusion model for single image dehazing

Image acquisition is usually vulnerable to bad weathers, like haze, fog and smoke. Haze removal, namely dehazing has always been a great challenge in many fields. This paper proposes an efficient and fast dehazing algorithm for addressing transmission map misestimation and oversaturation commonly happening in dehazing. We discover that the transmission map is commonly misestimated around the edges where grayscale change abruptly. These Transmission MisEstimated (TME) edges further result in halo artifacts in patch-wise dehazing. Although pixel-wise method is free from halo artifacts, it has trouble with oversaturation. Therefore, we firstly propose a TME recognition method to distinguish TME and non-TME regions. Secondly, we propose a Multi-scale Optimal Fusion (MOF) model to fuse pixel-wise and patch-wise transmission maps optimally to avoid misestimated transmission region. This MOF is then embedded into patch-wise dehazing to suppress halo artifacts. Furthermore, we provide two post-processing methods to improve robustness and reduce computational complexity of the MOF. Extensive experimental results demonstrate that, the MOF can achieve additional improvement beyond the prototypes of the benchmarks; in addition, the MOF embedded dehazing algorithm outperforms most of the state-of-the-arts in single image dehazing. For implementation details, source code can be accessed via https://github.com/phoenixtreesky7/mof_dehazing.
The Potential of Blockchain in Building Construction

Construction customers want more complex facilities delivered faster and at a lower cost. Transaction costs account for a significant proportion of each new or refurbished facility (a 2017 report from the Infrastructure Client Group in the UK suggests as high as 50%), yet they contribute no value to the customer. Blockchain is being suggested as a way to reduce transaction costs by eliminating the need for intermediaries to build trust as a prerequisite for successfully executed agreements. This study first describes the thinking that underpins blockchain technology, outlining how it works, and the potential limitations of the technology. Second, using a case study, reviews the potential cost savings from the use of blockchain for a real estate company. The results reveal a potential cost savings from blockchain deployment at 8.3% of the total cost of residential construction, with a standard deviation of 1.26%. Third, we explore the implications, risks and applications of blockchain technology for improving flow in the end-to-end design and construction process and we identify opportunities for future research on blockchain applications in construction.
Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters

The soil, Soul of Infinite Life, is the entity responsible for sustaining life on earth. In spite of significant advances in the service sector, agriculture remains the major provider of employment and source of revenue in India. Soil testing is a valuable tool for evaluating the available nutrient status of soil and helps to determine the proper amount of nutrients to be added to a given soil based on its fertility and crop needs. In the current study, the soil test report values are used to classify several significant soil features like village wise soil fertility indices of Available Phosphorus (P), Available Potassium (K), Organic Carbon (OC) and Boron (B), as well as the parameter Soil Reaction (pH). The classification and prediction of the village wise soil parameters aids in reducing wasteful expenditure on fertilizer inputs, increase profitability, save the time of chemical soil analysis experts, improves soil health and environmental quality. These five classification problems are solved using the fast learning classification technique known as Extreme Learning Machine (ELM) with different activation functions like gaussian radial basis, sine-squared, hyperbolic tangent, triangular basis, and hard limit. After the performance analysis of ELMs with diverse activation functions for these soil parameter classifications, the gaussian radial basis function attains the maximum performance for four out of five problems, which goes above 80% in most of the accuracy rate calculations in every problem, followed by hyperbolic tangent, hard limit, triangular basis, and sine-squared. However, the performance of the final classification problem, i.e. the pH classification, gives moderate values with the gaussian radial basis and best performance (near 90%), with the hyperbolic tangent.
HomeSnitch

The widespread adoption of smart home IoT devices has led to a broad and heterogeneous market with flawed security designs and privacy concerns. While the quality of IoT device software is unlikely to be fixed soon, there is great potential for a network-based solution that helps protect and inform consumers. Unfortunately, the encrypted and proprietary protocols used by devices limit the value of traditional network-based monitoring techniques. In this paper, we present HomeSnitch, a building block for enhancing smart home transparency and control by classifying IoT device communication by semantic behavior (e.g., heartbeat, firmware check, motion detection). HomeSnitch ignores payload content (which is often encrypted) and instead identifies behaviors using features of connection-oriented application data unit exchanges, which represent application-layer dialog between clients and servers. We evaluate HomeSnitch against an independent labeled corpus of IoT device network flows and correctly detect over 99% of behaviors. We further deployed HomeSnitch in a home environment and empirically evaluated its ability to correctly classify known behaviors as well as discover new behaviors. Through these efforts, we demonstrate the utility of network-level services to classify behaviors of and enforce control on smart home devices.
Social mimic optimization algorithm and engineering applications

Increase in complexity of real world problems has provided an area to explore efficient methods to solve computer science problems. Meta-heuristic methods based on evolutionary computations and swarm intelligence are instances of techniques inspired by nature. This paper presents a novel social mimic optimization (SMO) algorithm inspired by mimicking behavior to solve optimization problems. The proposed algorithm is evaluated using 23 test functions. Obtained results are compared with 14 known optimization algorithms including Whale optimization algorithm (WOA), Grasshopper optimization algorithm (GOA), Particle Swarm Optimization (PSO), Stochastic fractal search (SFS), Grey Wolf Optimizer (GWO), Optics Inspired Optimization (OIO), League Championship Algorithm (LCA), Wind Driven Optimization (WDO), Harmony search (HS), Firefly Algorithm (FA), Artificial Bee Colony (ABC), Biogeography Based Optimization (BBO), Bat Algorithm (BA), and Teaching Learning Based Optimization (TLBO). Obtained results indicate higher capability of the SMO algorithm in solving high-dimensional decision variables. Furthermore, SMO is used to solve two classic engineering design problems. Three important features of SMO are simple implementation, solving optimization problems with minimum population size and not requiring control parameters. Results of various evaluations show superiority of the proposed method in finding the optimal solution with minimum function evaluations. This superiority is achieved based on reducing number of initial population. The proposed method can be applied to applications like automatic evolution of robotics, automatic control of machines and innovation of machines in finding better solutions with less cost.
Single Image Defogging Based on Multi-Channel Convolutional MSRCR

In order to solve the problem of image degradation in foggy weather, a single image defogging method based on a multi-scale retinex with color restoration (MSRCR) of multi-channel convolution (MC) is proposed. The whole defogging process mainly consists of four key parts: estimation of illumination components, guided filter operation, reconstruction of fog-free images, and white balance operation. First, the multi-scale Gaussian kernels are employed to extract precise features to estimate the illumination component. After that, the MSRCR method is applied to enhance the global contrast, detail information, and color restoration of the image. Second, the smoothing constraints of both illumination component and reflected component are considered together by using the guided filter twice, thus the enhanced image satisfies the smoothing constraint and the noise in the enhanced image is reduced. Third, the enhanced image by the MSRCR and the image processed by the secondary guided filter are fused by linear weighting to reconstruct the final fog-free image. Finally, in order to eliminate the influence of illumination on the color of the defogged image, the final defogged image is processed by white balance. The experimental results demonstrated that the proposed method can outperform state-of-the-art methods in both qualitative and quantitative comparisons.
Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope

In this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance.We developed algorithms to pre-process pathology-labeled cervigrams and extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugol's iodine, and a combination of the two contrasts.The proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively, when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, and 63% accuracy).The results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugol's iodine images may provide unbiased automation of cervigrams.This would enable automated, expert-level diagnosis of cervical pre-cancer at the point of care.
An automatic literature knowledge graph and reasoning network modeling framework based on ontology and natural language processing

With the advancement of scientific and engineering research, a huge number of academic literature are accumulated. Manually reviewing the existing literature is the main way to explore embedded knowledge, and the process is quite time-consuming and labor intensive. As the quantity of literature is increasing exponentially, it would be more difficult to cover all aspects of the literature using the traditional manual review approach. To overcome this drawback, bibliometric analysis is used to analyze the current situation and trend of a specific research field. In the bibliometric analysis, only a few key phrases (e.g., authors, publishers, journals, and citations) are usually used as the inputs for analysis. Information other than those phrases is not extracted for analysis, while that neglected information (e.g., abstract) might provide more detailed knowledge in the article. To tackle with this problem, this study proposed an automatic literature knowledge graph and reasoning network modeling framework based on ontology and Natural Language Processing (NLP), to facilitate the efficient knowledge exploration from literature abstract. In this framework, a representation ontology is proposed to characterize the literature abstract data into four knowledge elements (background, objectives, solutions, and findings), and NLP technology is used to extract the ontology instances from the abstract automatically. Based on the representation ontology, a four-space integrated knowledge graph is built using NLP technology. Then, reasoning network is generated according to the reasoning mechanism defined in the proposed ontology model. To validate the proposed framework, a case study is conducted to analyze the literature in the field of construction management. The case study proves that the proposed ontology model can be used to represent the knowledge embedded in the literatures’ abstracts, and the ontology elements can be automatically extracted by NLP models. The proposed framework can be an enhancement for the bibliometric analysis to explore more knowledge from the literature.
A multi-centrality index for graph-based keyword extraction

Keyword extraction aims to capture the main topics of a document and is an important step in natural language processing (NLP) applications. The use of different graph centrality measures has been proposed to extract automatic keywords. However, there is no consensus yet on how these measures compare in this task. Here, we present the multi-centrality index (MCI) approach, which aims to find the optimal combination of word rankings according to the selection of centrality measures. We analyze nine centrality measures (Betweenness, Clustering Coefficient, Closeness, Degree, Eccentricity, Eigenvector, K-Core, PageRank, Structural Holes) for identifying keywords in co-occurrence word-graphs representation of documents. We perform experiments on three datasets of documents and demonstrate that all individual centrality methods achieve similar statistical results, while the proposed MCI approach significantly outperforms the individual centralities, three clustering algorithms, and previously reported results in the literature.
Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network

Recently, the application of satellite remote sensing images is becoming increasingly popular, but the observed images from satellite sensors are frequently in low-resolution (LR). Thus, they cannot fully meet the requirements of object identification and analysis. To utilize the multi-scale characteristics of objects fully in remote sensing images, this paper presents a multi-scale residual neural network (MRNN). MRNN adopts the multi-scale nature of satellite images to reconstruct high-frequency information accurately for super-resolution (SR) satellite imagery. Different sizes of patches from LR satellite images are initially extracted to fit different scale of objects. Large-, middle-, and small-scale deep residual neural networks are designed to simulate differently sized receptive fields for acquiring relative global, contextual, and local information for prior representation. Then, a fusion network is used to refine different scales of information. MRNN fuses the complementary high-frequency information from differently scaled networks to reconstruct the desired high-resolution satellite object image, which is in line with human visual experience (“look in multi-scale to see better”). Experimental results on the SpaceNet satellite image and NWPU-RESISC45 databases show that the proposed approach outperformed several state-of-the-art SR algorithms in terms of objective and subjective image qualities.
A Machine Learning Framework for Drill-Core Mineral Mapping Using Hyperspectral and High-Resolution Mineralogical Data Fusion

Mining companies heavily rely on drill-core samples during exploration campaigns as they provide valuable geological information to target important ore accumulations. Traditional core logging techniques are time-consuming and subjective. Hyperspectral (HS) imaging, an emerging technique in the mining industry, is used to complement the analysis by rapidly characterizing large amounts of drill-cores in a nondestructive and noninvasive manner. As the accurate analysis of drill-core HS data is becoming more and more important, we explore the use of machine learning techniques to improve speed and accuracy, and help to discover underlying relations within large datasets. The use of supervised techniques for drill-core HS data represents a challenge since quantitative reference data is frequently not available. Hence, we propose an innovative procedure to fuse high-resolution mineralogical analysis and HS data. We use an automatic high-resolution mineralogical imaging system (i.e., scanning electron microscopy-mineral liberation analysis) for generating training labels. We then resample the MLA image to the resolution of the HS data and adopt a soft labeling strategy for mineral mapping. We define the labels for the classes as mixtures of geological interest and use the classifiers (random forest and support vector machines) to map the entire drill-core. We validate our framework qualitatively and quantitatively. Thus, we demonstrate the ability of the proposed technique to fuse and up-scale high-resolution mineralogical analysis with drill-core HS data.
Learning Support Correlation Filters for Visual Tracking

For visual tracking methods based on kernel support vector machines (SVMs), data sampling is usually adopted to reduce the computational cost in training. In addition, budgeting of support vectors is required for computational efficiency. Instead of sampling and budgeting, recently the circulant matrix formed by dense sampling of translated image patches has been utilized in kernel correlation filters for fast tracking. In this paper, we derive an equivalent formulation of a SVM model with the circulant matrix expression and present an efficient alternating optimization method for visual tracking. We incorporate the discrete Fourier transform with the proposed alternating optimization process, and pose the tracking problem as an iterative learning of support correlation filters (SCFs). In the fully-supervision setting, our SCF can find the globally optimal solution with real-time performance. For a given circulant data matrix with n2 samples of n ×n pixels, the computational complexity of the proposed algorithm is O(n2 logn) whereas that of the standard SVM-based approaches is at least O(n4). In addition, we extend the SCF-based tracking algorithm with multi-channel features, kernel functions, and scale-adaptive approaches to further improve the tracking performance. Experimental results on a large benchmark dataset show that the proposed SCF-based algorithms perform favorably against the state-of-the-art tracking methods in terms of accuracy and speed.
Continuous-variable quantum computing in the quantum optical frequency comb

Abstract This topical review introduces the theoretical and experimental advances in continuous-variable (CV)—i.e. qumode-based in lieu of qubit-based—large-scale, fault-tolerant quantum computing and quantum simulation. An introduction to the physics and mathematics of multipartite entangled CV cluster states is given, and their connection to experimental concepts is delineated. Paths toward fault tolerance are also presented. It is the hope of the author that this review attract more contributors to the field and promote its extension to the promising technology of integrated quantum photonics.
Efficient Keyword Spotting Using Dilated Convolutions and Gating

We explore the application of end-to-end stateless temporal modeling to small-footprint keyword spotting as opposed to recurrent networks that model long-term temporal dependencies using internal states. We propose a model inspired by the recent success of dilated convolutions in sequence modeling applications, allowing to train deeper architectures in resource-constrained configurations. Gated activations and residual connections are also added, following a similar configuration to WaveNet. In addition, we apply a custom target labeling that back-propagates loss from specific frames of interest, therefore yielding higher accuracy and only requiring to detect the end of the keyword. Our experimental results show that our model outperforms a max-pooling loss trained recurrent neural network using LSTM cells, with a significant decrease in false rejection rate. The underlying dataset - "Hey Snips" utterances recorded by over 2.2K different speakers - has been made publicly available to establish an open reference for wake-word detection.