Reviewerly/exHarmony huggingface dataset:
GIS-Based Multi-Criteria Decision Analysis of Site Selection for Photovoltaic Power Plants in Çanakkale Province

Energy demand is gradually increasing due to rapid population growth. Using fossil fuels for energy production destroys the environment and fossil energy-based reserves are expected to be depleted in the future. Renewable energy sources are needed to minimize environmental impacts. Solar energy is among the most researched and invested renewable energy sources. There is diversity in renewable energy sources in Çanakkale province but it is not sufficient when the potential is considered. Other renewable power plants except the wind should also be increased among the newly planned plants. Carrying out a comprehensive solar power plant analysis is the first step to provide a cost-effective and well-performing project, and so choosing a suitable location is a critical point towards developing solar energy projects. Knowing potential sites is a fundamental point for annual power plant production forecasting and financial feasibility. With the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA); a more reliable analysis is carried out, the most suitable places can be determined by considering various criteria. Within this study, using spatial analysis, it is aimed to determine areas where photovoltaic (PV) solar power plant can be established in Çanakkale province by GIS-MCDA and how much of energy demand can be supplied in case of installation. Criteria and restriction factors to be used were determined by scanning the literature in detail as part of the relevant legal regulations. According to the restriction map generated, 96% of Çanakkale province is restricted site since the restriction factors was considered refinedly. Analytical hierarchy process (AHP) will be used as a method of analysis and after acquiring the optimal sites, the annual electric power generation potential in those sites will be calculated. The outputs of this study will contribute development strategy and national energy planning.
Relationship between costs for quantum error mitigation and non-Markovian measures

Quantum error mitigation (QEM) has been proposed as an alternative method of quantum error correction to compensate errors in quantum systems without qubit overhead. While Markovian gate errors on digital quantum computers have been mainly considered previously, it is indispensable to discuss a relationship between QEM and non-Markovian errors because non-Markovian noise effects inevitably exist in most of the solid-state systems. In this work, we investigate the QEM for non-Markovian noise, and show that there is a clear relationship between costs for QEM and non-Markovian measures. As examples, we show several non-Markovian noise models to bridge a gap between our theoretical framework and concrete physical systems. This discovery may help in designing better QEM strategies for realistic quantum devices with non-Markovian environments.
LCSegNet: An Efficient Semantic Segmentation Network for Large-Scale Complex Chinese Character Recognition

Complex scene character recognition is a challenging yet important task in machine learning, especially for languages with large character sets, such as Chinese, which is composed of hieroglyphics with large-scale categories and similar glyphs. Recently, state-of-the-art methods based on semantic segmentation have achieved great success in scene parsing and have been applied in scene text recognition. However, because of limitations in terms of memory and computation, they are only applied in the small category recognition tasks, such as tasks involving English alphabets and digits. In this paper, we propose an efficient semantic segmentation model based on label coding (LC), called LCSegNet, to recognize large-scale Chinese characters. First, to reduce the number of labels, we design a new label coding method based on the Wubi Chinese characters code, called Wubi-CRF. In this method, glyphs and structure information of Chinese characters are encoded into 140-bit labels. Second, we employ an efficient semantic segmentation model for pixel-wise prediction and utilize a conditional random field (CRF) module to learn the constraint rules of Wubi-like coding. Finally, experiments are conducted on three benchmarks: a large Chinese text dataset in the wild (CTW), ICDAR2019-ReCTS, and HIT-OR3C dataset. Results show that the proposed method achieves state-of-the-art performances in both complex scene and handwritten character recognition tasks.
Fostering Customer Bargaining and E-Procurement Through a Decentralised Marketplace on the Blockchain

Firms have available many forms of collaboration, including cooperatives or joint ventures, in this way leveraging their market power. Customers, however, are atomic agents with few mechanisms for collaborating, leading to an unbalanced buyer-supplier relationship and economic surpluses that shift to producers. Some group buying websites helped alleviate the problem by offering bulk discounts, but more advancements can be made with the emergence of technologies, such as the blockchain. In this article, we propose a customer-push e-marketplace built on top of Ethereum, where customers can aggregate their proposals, and suppliers try to outcompete each other in reverse auction bids to fulfil the order. Furthermore, smart contracts make it possible to automate many operational activities, such as payment escrows/release upon delivery confirmation, increasing the efficiency along the supply chain. The implementation of this network is expected to improve market efficiency by reducing transaction costs, time delays, and information asymmetry. Furthermore, concepts such as increased bargaining power and economies of scale, and their effects in buyer-supplier relationships, are also explored.
Regularized Regression With Fuzzy Membership Embedding for Unsupervised Feature Selection

Although fuzziness universally diffuses in the real-world data, the fuzzy information is tricky to harness for feature selection such that it is rarely utilized. Therefore, how to efficiently exploit fuzzy information has become the major focus for feature selection recently. In this article, a novel unsupervised feature selection method is proposed via exploiting the sparse fuzzy membership efficiently. In general, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2,1}$</tex-math></inline-formula> norm is utilized to induce sparsity, while Frobenius norm is used to prevent overfitting. To obtain sparsity and avoid overfitting simultaneously, adaptive loss regularization is introduced to the least-squares regression, such that a sparse and nontrivial projection matrix can be achieved via continuous interpolation between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2,1}$</tex-math></inline-formula> and Frobenius regularization. Additionally, the fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means problem is further embedded with the adaptive loss regression model to avoid the trivial solution caused by the linearity of fuzzy membership. Therefore, the fuzzy cluster structure of fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means is exploited for the efficient feature selection. By performing fuzzy clustering and subspace regression simultaneously, the embedded problem is then reformulated into a general quadratic problem with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> ball constraint. Equipped with an auxiliary variable and standard augmented Lagrangian method, the quadratic problem, i.e., the corresponding dual problem can be solved with the closed form solutions regarding the fuzzy membership and the projection matrix. Consequently, empirical results are provided to demonstrate the effectiveness of the proposed feature selection approach.
Attention-Aware Encoder–Decoder Neural Networks for Heterogeneous Graphs of Things

Recent trend focuses on using heterogeneous graph of things (HGoT) to represent things and their relations in the Internet of Things, thereby facilitating the applying of advanced learning frameworks, i.e., deep learning (DL). Nevertheless, this is a challenging task since the existing DL models are hard to accurately express the complex semantics and attributes for those heterogeneous nodes and links in HGoT. To address this issue, we develop attention-aware encoder-decoder graph neural networks for HGoT, termed as HGAED. Specifically, we utilize the attention-based separate-and-merge method to improve the accuracy, and leverage the encoder-decoder architecture for implementation. In the heart of HGAED, the separate-and-merge processes can be encapsulated into encoding and decoding blocks. Then, blocks are stacked for constructing an encoder-decoder architecture to jointly and hierarchically fuse heterogeneous structures and contents of nodes. Extensive experiments on three real-world datasets demonstrate the superior performance of HGAED over state-of-the-art baselines.
Independent Moving Object Detection Based on a Vehicle Mounted Binocular Camera

Accurate detection of independent moving objects is the key to ensure the safety of driverless vehicles during travelling. In this study, a moving object detection method based on a vehicle-mounted binocular camera is proposed. Firstly, a feature matching points selection strategy is designed for high accuracy camera ego-motion estimation. Then, the residual optical flow filed generated only by moving objects is estimated by taking into account the camera ego-motion and global mixed optical flow. A dynamic threshold segmentation method based on disparity is proposed to separate the moving regions from the residual optical flow field. Finally, 3D and 2D information are combined for extracting each single moving object from moving regions. The innovation of the proposed method is that it can detect any type and any size of moving object theoretically by using the dense motion information of the images. By taking the data in KITTI database as the test samples, the detection precision of the moving objects using the proposed algorithm can reach up to 91% without considering tracking strategy.
School Librarians as Literacy Educators Within a Complex Role

Librarians in schools are expected to play an important role as literacy educators, and have a positive impact on young people’s literacy learning. However in the context of their diverse workload, relatively little is known about how this aspect of their role sits within its competing demands, and the exact scope of the literacy educator requirements. Using a hybrid approach to content analysis, this article analyses 40 recent job description documents to identify the nature and prevalence of different aspects of the role, and to explore the literacy educator aspect of this profession. Findings suggest that while the literacy educator aspect is one of the most common role requirements, it sits within a complex workload, and the literacy educator aspect is itself multi-faceted and demanding.
Twisted Reed–Solomon Codes With One-Dimensional Hull

The hull of a linear code is defined to be the intersection of the code and its dual. When the size of the hull is small, it has been proved that some algorithms for checking permutation equivalence of two linear codes and computing the automorphism group of a linear code are very effective in general. Maximum distance separable (MDS) codes are codes meeting the Singleton bound. Twisted Reed-Solomon codes is a generalization of Reed-Solomon codes, which is also a nice construction for MDS codes. In this short letter, we obtain some twisted Reed-Solomon MDS codes with one-dimensional hull. Moreover, these codes are not monomially equivalent to Reed-Solomon codes.
Adaptive Packet Padding Approach for Smart Home Networks: A Tradeoff Between Privacy and Performance

The presence of connected devices in homes introduces numerous threats to privacy via the analysis of the encrypted traffic these devices generate. Prior works have shown that traffic attributes such as packet size combined with machine learning techniques enable the inference of private information from Internet of Things users. One of the commonly used techniques to mitigate those privacy threats is traffic obfuscation, such as packet padding. Most padding mechanisms that were previously proposed statically select the number of bytes inserted in the packets, which incurs high overhead and ineffective privacy improvement. These static mechanisms are particularly unsuitable for networks whose traffic patterns are significantly dynamic, such as smart homes. This article proposes an adaptive packet padding approach based on software-defined networking (SDN) that adjusts the number of bytes inserted into packets in response to variations in the home network utilization. The proposed technique monitors the network to instruct a padding mechanism through a representational state transfer (REST) interface proposed in this article. This mechanism ensures that the length of packets generated by connected devices is modified. The evaluation includes four supervised learning mechanisms, random forest (RF), support vector machine (SVM), decision tree, and k-nearest neighbors (KNNs), to measure privacy improvement through the metrics accuracy, recall, and F1-score. Goodput, jitter, and packet loss induced by the proposal are also evaluated. Our proposal is shown to overcome the state-of-the-art solutions in privacy preservation with a significantly lower overhead. For instance, the accuracy of RF on identifying devices decreases from 96% to 4.96%.
The Embeddings That Came in From the Cold: Improving Vectors for New and Rare Products with Content-Based Inference

Training product embeddings in a multi-tenant scenario involves solving the challenges of ever changing catalogs across dozens of deployments, without supervision. In this work, we detail how we deal with new and rare products when building neural representations at scale: we show how to inject product knowledge into behavior-based embeddings to provide the best accuracy with minimal engineering changes in existing infrastructure and without additional manual effort.
What is learned from exposure: an error-driven approach to productivity in language

How language users become able to process forms they have never encountered in input is central to our understanding of language cognition.A range of models, including rule-based models, stochastic models, and analogy-based models have been proposed to account for this ability.Despite the fact that all three models are reasonably successful, we argue that productivity in language is more insightfully captured through learnability than by rules or probabilities.Using a combination of computational modelling and behavioural experimentation we show that the basic principle of errordriven learning allows language users to detect relevant patterns of any degree of systematicity.In case of allomorphy, these patterns are found at a level that cuts across phonology and morphology and is not considered by mainstream approaches to language.Our findings thus highlight how a learning-based approach applies to phenomena on the continuum from rule-based over probabilistic to "unruly" and constrains our inferences about the types of structures that should be targeted on a cognitively realistic account of allomorphic representation.
Reinforcement Online Active Learning Ensemble for Drifting Imbalanced Data Streams

Applications challenged by the joint problem of concept drift and class imbalance are attracting increasing research interest. This paper proposes a novel Reinforcement Online Active Learning Ensemble for Drifting Imbalanced data stream (ROALE-DI). The ensemble classifier has a long-term stable classifier and a dynamic classifier group which applies a reinforcement mechanism to increase the weight of the dynamic classifiers, which perform better on the minority class, and decreases the weight of the opposite. When the data stream is class imbalanced, the classifiers will lack the training samples of the minority class. To supply training samples, when creating a new classifier, the labeled instances buffer is used to provide instances of the minority class. Then, a hybrid labeling strategy that combines the uncertainty strategy and imbalance strategy is proposed to define whether to obtain the real label of an instance. An experimental evaluation compares the classification performance of the proposed method with semi-supervised and supervised algorithms on both real-world and synthetic data streams. The results show that the ROALE-DI achieves higher Area Under the ROC Curve (AUC) and accuracy values with even fewer real labels, and the labeling cost dynamically adjusts according to the concept drift and class imbalance ratio.
Deep autoencoder for false positive reduction in handgun detection

Abstract In an object detection system, the main objective during training is to maintain the detection and false positive rates under acceptable levels when the model is run over the test set. However, this typically translates into an unacceptable rate of false alarms when the system is deployed in a real surveillance scenario. To deal with this situation, which often leads to system shutdown, we propose to add a filter step to discard part of the new false positive detections that are typical of the new scenario. This step consists of a deep autoencoder trained with the false alarm detections generated after running the detector over a period of time in the new scenario. Therefore, this step will be in charge of determining whether the detection is a typical false alarm of that scenario or whether it is something anomalous for the autoencoder and, therefore, a true detection. In order to decide whether a detection must be filtered, three different approaches have been tested. The first one uses the autoencoder reconstruction error measured with the mean squared error to make the decision. The other two use the k -NN ( k -nearest neighbors) and one-class SVMs (support vector machines) classifiers trained with the autoencoder vector representation. In addition, a synthetic scenario has been generated with Unreal Engine 4 to test the proposed methods in addition to a dataset with real images. The results obtained show a reduction in the number of false positives between 22.5% and 87.2% and an increase in the system’s precision of 1.2% $$-47$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>47</mml:mn> </mml:mrow> </mml:math> % when the autoencoder is applied.
Tutorial on Conversational Recommendation Systems

Recent years have witnessed the emerging of conversational systems, including both physical devices and mobile-based applications. Both the research community and industry believe that conversational systems will have a major impact on human-computer interaction, and specifically, the RecSys community has begun to explore Conversational Recommendation Systems. Conversational recommendation aims at finding or recommending the most relevant information (e.g., web pages, answers, movies, products) for users based on textual- or spoken-dialogs, through which users can communicate with the system more efficiently using natural language conversations. Due to users' constant need to look for information to support both work and daily life, conversational recommendation system will be one of the key techniques towards an intelligent web. The tutorial focuses on the foundations and algorithms for conversational recommendation, as well as their applications in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating conversational recommendation methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
Background-Oriented Schlieren Imaging of Supersonic Aircraft in Flight

This paper describes the development and use of background-oriented schlieren imaging of a full-scale supersonic jet in flight. A series of flight tests was performed in April 2011, October 2014, February 2015, and December 2018 using the flora of the desert floor in the supersonic flight corridor near the Edwards Air Force Base as a background. Flight planning was designed based on the camera resolution, the mean size and color of the predominant plants, and coordination of several aircraft. Image processing was done with both cross correlation and optical flow algorithms. The planning proved to be effective, and the vast majority of the passes of the target aircraft were successfully recorded. Results were obtained that are the most detailed schlieren imagery of an aircraft in flight to date.
Hardware Root-of-Trust-based integrity for shared library function pointers in embedded systems

Many security measures designed for large-scale compute hardware (e.g., workstations and servers) are not optimized for embedded systems. One such measure, Relocation Read-Only (RelRO), protects binary relocation sections from tampering after dynamic linking; however, RelRO depends on the presence of memory management hardware that not all embedded systems include. More generally, the latest hardware modifications to processor architectures may not always be available to provide security in small-scale embedded systems. In this paper, we propose another solution for relocation protection for use in embedded systems that have a field-programmable gate array (FPGA) on chip. Our solution prevents relocation section overwrites from diverting control flow as they would in an unprotected binary by implementing a hardware Root of Trust into which to store and out of which to retrieve shared library function pointers. We offer two system variations which provide designers with flexibility to choose a lighter or a more robust protection. We also demonstrate a proof of concept implemented on a popular FPGA development board and provide comparison with RelRO. Our work provides embedded system developers with a security measure like RelRO without requiring a customized memory management unit. Our work is useful on computer systems that include embedded reconfigurable logic on chip. The authors believe this is the first paper in which reconfigurable logic is used to provide security functionality previously implemented in a custom instruction set or other computer architecture modifications. Our work points toward a future where FPGA logic embedded on chip can be adapted to improve the security of software.
ECG steganography based on tunable <scp>Q‐factor</scp> wavelet transform and singular value decomposition

Abstract The article presents a novel ECG steganography scheme based on the tunable Q‐factor wavelet transformation (TQWT) and also singular value decomposition (SVD) techniques that ensure better safety and confidentiality of patient information. Initial parameters such as Q , r , and J are used to decompose the cover signal into individual frequency sub‐bands with the tunable Q‐factor wavelet transform (TQWT). The singular value decomposition (SVD) technique is used to further decompose high‐frequency sub‐band coefficients into singular values. The watermark information is then embedded with high‐frequency sub‐band coefficients by involving the quantization process. The performance of this proposed system is successfully evaluated by considering various metrics, such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), percentage residual difference (PRD), and bit error rate (BER). The simulation results of the proposed scheme are observed to be better than other traditional algorithms.