Reviewerly/exHarmony huggingface dataset:
A socio-technical approach to the assessment of sustainable tourism: Adding value with a comprehensive process-oriented framework

Despite the increasing interest in sustainable tourism (ST), the actual implementation of sustainability practices within the tourism sector remains sporadic. Performance evaluation has emerged as a viable solution for this problem, but no universal ST assessment model has yet been developed for this purpose. Various authors have proposed different approaches, but these still exhibit limitations, especially regarding criteria selection and weighting. This study sought to create an assessment system for ST by combining cognitive mapping and the Choquet integral (CI), providing a rational, transparent foundation for the selection and weighting of evaluation criteria. The development of the proposed assessment system involved group meetings with a panel of ST experts, as well as a final validation session with a senior representative of the Portuguese Tourism Confederation. The results of a practical application of the system developed show that its process-oriented nature facilitates the ranking of tourism regions according to their degree of sustainability (i.e., Lisbon and Tagus Valley is the Portuguese tourism region with the most ST, whereas Alentejo comes last compared to the other alternatives). No prior research was found that has applied cognitive mapping and the CI in this study context, and new insights into ST can be obtained through an analysis of the cause-and-effect relationships between evaluation criteria. The contributions and implications of the proposed system are also discussed.
Optimization of a BIPV system to mitigate greenhouse gas and indoor environment

The solar architecture is defined as a kind of building integrated photovoltaic (BIPV) which the Photovoltaic (PV) modules are deployed to passive solar concepts, to minimize the heating & cooling load, to upgrade the indoor environmental quality and to be adjustable for regional weather and to continuously succeed architectural culture and history. The most ideal form of a BIPV is a multi-functional and ecological convergence through a passive concept application of a photovoltaic module for a building. The solar architecture needs to consider the architectural culture and history of the region through an ecological convergence which is applicable to a passive concept if the environment, energy and comfort problems will be effectively mitigated. The evaluation standard is needed to fulfill these requirements of solar architecture. The renewable energy sources are getting very hot issues, due to the environmental pollution, global warming, and energy shortage, etc. It is reasonable to disseminate the representative energy systems which could be ecologically convergent with the regional architecture. Most renewable energy systems including PV could not be activate or not be environmentally friendly if the systems are planned simply without multi-functional and ecological convergence. This paper describes the ecological criteria to optimize solar architecture through an ecological convergence of a passive solar architecture and photovoltaic system.
Adding concurrency to smart contracts

Modern cryptocurrency systems, such as the Ethereum project, permit complex financial transactions through scripts called smart contracts. These smart contracts are executed many, many times, always without real concurrency. First, all smart contracts are serially executed by miners before appending them to the blockchain. Later, those contracts are serially re-executed by validators to verify that the smart contracts were executed correctly by miners. Serial execution limits system throughput and fails to exploit today's concurrent multicore and cluster architectures. Nevertheless, serial execution appears to be required: contracts share state, and contract programming languages have a serial semantics. This paper presents a novel way to permit miners and validators to execute smart contracts in parallel, based on techniques adapted from software transactional memory. Miners execute smart contracts speculatively in parallel, allowing non-conflicting contracts to proceed concurrently, and "discovering" a serializable concurrent schedule for a block's transactions, This schedule is captured and encoded as a deterministic fork-join program used by validators to re-execute the miner's parallel schedule deterministically but concurrently. We have proved that the validator's execution is equivalent to miner's execution. Smart contract benchmarks run on a JVM with ScalaSTM show that a speedup of 1.39$$\times $$ can be obtained for miners and 1.59$$\times $$ for validators with just three concurrent threads.
Online User Representation Learning Across Heterogeneous Social Networks

Accurate user representation learning has been proven fundamental for many social media applications, including community detection, recommendation, etc. A major challenge lies in that, the available data in a single social network are usually very limited and sparse. In real life, many people are members of several social networks in the same time. Constrained by the features and design of each, any single social platform offers only a partial view of a user from a particular perspective. In this paper, we propose MV-URL, a multi-view user representation learning model to enhance user modeling by integrating the knowledge from various networks. Different from the traditional network embedding frameworks where either the whole framework is single-network based or each network involved is a homogeneous network, we focus on multiple social networks and each network in our task is a heterogeneous network. It's very challenging to effectively fuse knowledge in this setting as the fusion depends upon not only the varying relatedness of information sources, but also the target application tasks. MV-URL focuses on two tasks: user account linkage (i.e., to predict the missing true user account linkage across social media) and user attribute prediction. Extensive evaluations have been conducted on two real-world collections of linked social networks, and the experimental results show the superiority of MV-URL compared with existing state-of-art embedding methods. It can be learned online, and is trivially parallelizable. These qualities make it suitable for real world applications.
Discriminative Neural Embedding Learning for Short-Duration Text-Independent Speaker Verification

Short duration text-independent speaker verification remains a hot research topic in recent years, and deep neural network based embeddings have shown impressive results in such conditions. Good speaker embeddings require the property of both small intra-class variation and large inter-class difference, which is critical for the ability of discrimination and generalization. Current embedding learning strategies can be grouped into two frameworks: “Cascade embedding learning” with multiple stages and “direct embedding learning” from spectral feature directly. We propose new approaches to achieve more discriminant speaker embeddings. Within the cascade framework, a neural network based deep discriminant analysis (DDA) is proposed to project i-vector to more discriminative embeddings. Within the direct embedding framework, a deep model with more advanced center loss and A-softmax loss is used, the focal loss is also investigated in this framework. Moreover, the traditional i-vector and neural embeddings are finally combined with neural network based DDA to achieve further gain. Main experiments are carried out on a short-duration text-independent speaker verification dataset generated from the SRE corpus. The results show that the newly proposed method is promising for short-duration text-independent speaker verification, and it is consistently better than traditional i-vector and neural embedding baselines. The best embeddings achieve roughly 30% relative EER reduction compared to the i-vector baseline, which could be further enhanced when combined with the i-vector system.
In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID Tags

In-air gesture interaction enables a natural communication between a man and a machine with its clear semantics and humane mode of operation. In this paper, we propose a real-time recognition system on multiple gestures in the air. It uses the commodity off-the-shelf (COTS) reader with three antennas to detect the radio frequency (RF) signals of the passive radio frequency identification (RFID) Tags attached to the fingers. The idea derives from the crucial insight that the sequential phase profile of the backscatter RF signals is a reliable and well-regulated indicator insinuating space-time situation of the tagged object, which presents a close interdependency with tag's movements and positions. The KL divergence is utilized to extract the dynamic gesture segment by confirming the endpoints of the data flow. To achieve the template matching and classification, we bring in the dynamic time warping (DTW) and k-nearest neighbors (KNN) for similarity scores calculation and appropriate gesture recognition. The experiment results show that the recognition rates for static and dynamic gestures can reach 85% and 90%, respectively. Moreover, it can maintain satisfying performance under different situations, such as diverse antenna-to-user distances and being hidden from view by nonconducting obstacles.
Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function

Although data preprocessing is a universal technique that can be widely used in neural networks (NNs), most research in this area is focused on designing new NN architectures. This paper, we propose a preprocessing technique that enriches the original image data using local intensity information; this technique is motivated by human perception. To encode this information into an image, we introduce a new image structure named image represented by a fuzzy function. When using this structure, a crisp intensity value of each pixel is replaced by a fuzzy set given by a membership function constructed with the usage of extremal values from the particular neighborhood of that pixel. We describe this structure and its properties and propose a way in which it can be used as an input into existing NNs without any modifications. Based on our benchmark consisting of three well-known datasets and five NN architectures, we show that the proposed preprocessing can, in most cases, decrease classification error compared with a baseline and two other preprocessing methods. To support our claim, we have also selected several publicly available projects and tested the impact of the preprocessing with a positive result.
An efficient framework for visible–infrared cross modality person re-identification

Visible-infrared cross-modality person re-identification (VI-ReId) is an essential task for video surveillance in poorly illuminated or dark environments. Despite many recent studies on person re-identification in the visible domain (ReId), there are few studies dealing specifically with VI-ReId. Besides challenges that are common for both ReId and VI-ReId such as pose/illumination variations, background clutter and occlusion, VI-ReId has additional challenges as color information is not available in infrared images. As a result, the performance of VI-ReId systems is typically lower than that of ReId systems. In this work, we propose a four-stream framework to improve VI-ReId performance. We train a separate deep convolutional neural network in each stream using different representations of input images. We expect that different and complementary features can be learned from each stream. In our framework, grayscale and infrared input images are used to train the ResNet in the first stream. In the second stream, RGB and three-channel infrared images (created by repeating the infrared channel) are used. In the remaining two streams, we use local pattern maps as input images. These maps are generated utilizing local Zernike moments transformation. Local pattern maps are obtained from grayscale and infrared images in the third stream and from RGB and three-channel infrared images in the last stream. We improve the performance of the proposed framework by employing a re-ranking algorithm for post-processing. Our results indicate that the proposed framework outperforms current state-of-the-art with a large margin by improving Rank-1/mAP by 29.79%/30.91% on SYSU-MM01 dataset, and by 9.73%/16.36% on RegDB dataset.
HiFinger: One-Handed Text Entry Technique for Virtual Environments Based on Touches between Fingers

We present a text entry technique called HiFinger, which is an eyes-free, one-handed wearable text entry technique for immersive virtual environments by thumb-to-fingers touch. This technique enables users to input text quickly, accurately, and comfortably with the sense of touch and a two-step input mode. It is especially suitable for mobile scenarios where users need to move (such as walking) in virtual environments. Various input signals can be triggered by moving the thumb towards ultra-thin pressure sensors placed on other fingers. After acquiring the comfort range of the touch between the thumb and other fingers, six placement modes for text entry are designed and tested, resulting in an optimal placement mode that leverages six pressure sensors for the text entry and two for the control function. A three-day study is conducted to evaluate the proposed technique, and experimental results show that novices can achieve an average text entry efficiency of 9.82 words per minute (WPM) in virtual environments based on head-mounted displays after a training period of 25 min.
WhoDo: automating reviewer suggestions at scale

Today's software development is distributed and involves continuous changes for new features and yet, their development cycle has to be fast and agile. An important component of enabling this agility is selecting the right reviewers for every code-change - the smallest unit of the development cycle. Modern tool-based code review is proven to be an effective way to achieve appropriate code review of software changes. However, the selection of reviewers in these code review systems is at best manual. As software and teams scale, this poses the challenge of selecting the right reviewers, which in turn determines software quality over time. While previous work has suggested automatic approaches to code reviewer recommendations, it has been limited to retrospective analysis. We not only deploy a reviewer suggestions algorithm - WhoDo - and evaluate its effect but also incorporate load balancing as part of it to address one of its major shortcomings: of recommending experienced developers very frequently. We evaluate the effect of this hybrid recommendation + load balancing system on five repositories within Microsoft. Our results are based around various aspects of a commit and how code review affects that. We attempt to quantitatively answer questions which are supposed to play a vital role in effective code review through our data and substantiate it through qualitative feedback of partner repositories.
Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines

Classifying data patterns is one of the most recurrent applications in machine learning. The number of input features influences the predictive performance of many classification models. Most classifiers work with high-dimensional spaces. Therefore, there is a great interest in facing the task of reducing the input space. Manifold learning has been shown to perform better than classical dimensionality reduction approaches, such as Principal Component Analysis and Linear Discriminant Analysis. In this sense, Autoencoders (AEs) provide an automated way of performing feature fusion, finding the best manifold to reconstruct the data. There are several models and architectures of AEs. For this reason, in this study an exhaustive analysis of the predictive performance of different AEs models with a large number of datasets is proposed, aiming to provide a set of useful guidelines. These will allow users to choose the appropriate AE model for each case, depending on data traits and the classifier to be used. A thorough empirical analysis is conducted including four AE models, four classification paradigms and a group of datasets with a variety of traits. A convenient set of rules to follow is obtained as a result.
Overview of Image Matching Based on ORB Algorithm

Abstract ORB image matching is of great significance in the field of image processing, which is mainly used in navigation, target recognition and classification, image stitching and remote sensing registration. Based on the existing literature on ORB algorithm research, this paper first introduces the classic ORB algorithm. Then, the various improved algorithms of ORB are expounded, and the performance index of image matching algorithm based on feature points is introduced. Finally, the shortcomings of the ORB algorithm are pointed out and the development direction of the algorithm is prospected.
Automatic Multi-class Non-Functional Software Requirements Classification Using Neural Networks

Advances in machine learning (ML) algorithms, graphics processing units, and readily available ML libraries have enabled the application of ML to open software engineering challenges. Yet, the use of ML to enable decision-making during the software engineering lifecycle is not well understood as there are various ML models requiring parameter tuning. In this paper, we leverage ML techniques to develop an effective approach to classify software requirements. Specifically, we investigate the design and application of two types of neural network models, an artificial neural network (ANN) and a convolutional neural network (CNN), to classify non-functional requirements (NFRs) into the following five categories: maintainability, operability, performance, security and usability. We illustrate and experimentally evaluate this work through two widely used datasets consisting of nearly 1,000 NFRs. Our results indicate that our CNN model can effectively classify NFRs by achieving precision ranging between 82% and 94%, recall ranging between 76% and 97% with an F-score ranging between 82% and 92%.
Increasing the views and reducing the depth in random forest

Random forest (RF) is one of the most powerful ensemble methods with high performance when dealing with high dimensional data. In this paper, we present a novel approach, inspired by multi-view theory and by human multi-view approach in object recognition, to increase the performance of RF by increasing the number of trees and limiting the number of levels for each tree in RF. The standard RF algorithm constructs the trees to a maximum possible depth until no further classification is needed, or no further feature is available. In the proposed method, trees of RF are bounded to a certain depth to allow increasing the views. That is why this approach is called “Increasing the Views and Reducing the Depth (IVRD)” approach in RF. Each bounded tree is considered as a local view of the problem and the more local view means the better classification/recognition capability. IVRD approach in RF has been tested on a group of synthetic datasets, a standard dataset, and two datasets of Down Syndrome subjects. Several evaluations have been conducted to determine the effects of the tree depth and the number of trees in a forest on the accuracy of IVRD approach. The results showed by bounding the number of levels in each tree and increasing the number of trees in the forest, the accuracy can be improved, especially in very high dimensional problems.
Making the most of mental models: Advancing the methodology for mental model elicitation and documentation with expert stakeholders

Eliciting stakeholders' mental models is an important participatory modeling (PM) tool for building systems knowledge, a frequent challenge in natural resource management. Therefore, mental models constitute a valuable source of information, making it imperative to document them in detail, while preserving the integrity of stakeholders' beliefs. We propose a methodology, the Rich Elicitation Approach (REA), which combines direct and indirect elicitation techniques to meet these goals. We describe the approach in the context of the effects of climate change on Baltic salmon. The REA produced holistic depictions of mental models, with more variables and causal relationships per diagram than direct elicitation alone, thus providing a solid knowledge base on which to begin PM studies. The REA was well received by stakeholders and fulfilled the substantive, normative, instrumental, and educational functions of PM. However, motivating stakeholders to confirm the accuracy of their models during the verification stage of the REA was challenging.
Joint Segmentation and Fine-Grained Classification of Nuclei in Histopathology Images

Nuclei segmentation and classification are two important tasks in the histopathology image analysis, because the morphological features of nuclei and spatial distributions of different types of nuclei are highly related to cancer diagnosis and prognosis. Existing methods handle the two problems independently, which are not able to obtain the features and spatial heterogeneity of different types of nuclei at the same time. In this paper, we propose a novel deep learning based method which solves both tasks in a unified framework. It can segment individual nuclei and classify them into tumor, lymphocyte and stroma nuclei. Perceptual loss is utilized to enhance the segmentation of details. We also take advantages of transfer learning to promote the training of deep neural networks on a relatively small lung cancer dataset. Experimental results prove the effectiveness of the proposed method. The code is publicly available.
Facial Landmark Machines: A Backbone-Branches Architecture With Progressive Representation Learning

Facial landmark localization plays a critical role in face recognition and analysis. In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network (BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings. Our proposed BB-FCN generates facial landmark response maps directly from raw images without any preprocessing. BB-FCN follows a coarse-to-fine cascaded pipeline, which consists of a backbone network to roughly detect the locations of all facial landmarks and one branch network for each type of detected landmark to further refine its location. Furthermore, to facilitate the facial landmark localization under unconstrained settings, we propose a large-scale benchmark named SYSU16K, which contains 16 000 faces with large variations in pose, expression, illumination, and resolution. Extensive experimental evaluations demonstrate that our proposed BB-FCN can significantly outperform the state of the art under both constrained (i.e., within detected facial regions only) and unconstrained settings. We further confirm that high-quality facial landmarks localized with our proposed network can also improve the precision and recall of face detection.