Reviewerly/exHarmony huggingface dataset:
Uncertainty Evaluation of Object Detection Algorithms for Autonomous Vehicles

Abstract The safety of the intended functionality (SOTIF) has become one of the hottest topics in the field of autonomous driving. However, no testing and evaluating system for SOTIF performance has been proposed yet. Therefore, this paper proposes a framework based on the advanced You Only Look Once (YOLO) algorithm and the mean Average Precision (mAP) method to evaluate the object detection performance of the camera under SOTIF-related scenarios. First, a dataset is established, which contains road images with extreme weather and adverse lighting conditions. Second, the Monte Carlo dropout (MCD) method is used to analyze the uncertainty of the algorithm and draw the uncertainty region of the predicted bounding box. Then, the confidence of the algorithm is calibrated based on uncertainty results so that the average confidence after calibration can better reflect the real accuracy. The uncertainty results and the calibrated confidence are proposed to be used for online risk identification. Finally, the confusion matrix is extended according to the several possible mistakes that the object detection algorithm may make, and then the mAP is calculated as an index for offline evaluation and comparison. This paper offers suggestions to apply the MCD method to complex object detection algorithms and to find the relationship between the uncertainty and the confidence of the algorithm. The experimental results verified by specific SOTIF scenarios proof the feasibility and effectiveness of the proposed uncertainty acquisition approach for object detection algorithm, which provides potential practical implementation chance to address perceptual related SOTIF risk for autonomous vehicles.
Multi-strategy co-evolutionary differential evolution for mixed-variable optimization

In real world, engineering design problems have been regarded as common and complex problems which always include non-linear standard functions, many complex constraints and decision variables containing both continuous and discrete variables. Problems with these features also can be named as mixed-variable optimization problems (MVOPs). However, because the decision variables of MVOPs present different spatial distribution features, how to design an effective algorithm to solve MVOPs is still a challenge. In this paper, a new variation of differential evolution (DE), named multi-strategy co-evolutionary differential evolution (MCDEmv) is proposed to solve MVOPs. A mixed-variable co-evolutionary scheme simultaneously considering continuous and discrete variables in MVOPs is utilized. Based on this scheme, a multi-strategy co-evolutionary approach considering a dynamic adaptive selection mechanism and the combination of different characteristic mutation strategies and crossover operators is proposed for adapting to the all-inclusive situations in MVOPs. Furthermore, a statistics-based local search (SBA) specially designed for the optimization of discrete variables in MVOPs is proposed for enhancing the efficiency and flexibility of the MCDEmv. 28 artificial benchmark functions are adopted to verify the effectiveness and efficiency of the proposed algorithm. The experimental results show that our proposed algorithm is more competitive and efficient than other compared similar algorithms, i.e., AEDA, ACOMV, DEMV and PSOmv. Comparing with other similar algorithms in coping with two mixed-variable engineering design problems, MCDEmv also shows higher efficiency and performance.
Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving

Multi-object tracking (MOT) with camera-LiDAR fusion demands accurate results of object detection, affinity computation and data association in real time. This paper presents an efficient multi-modal MOT framework with online joint detection and tracking schemes and robust data association for autonomous driving applications. The novelty of this work includes: (1) development of an end-to-end deep neural network for joint object detection and correlation using 2D and 3D measurements; (2) development of a robust affinity computation module to compute occlusion-aware appearance and motion affinities in 3D space; (3) development of a comprehensive data association module for joint optimization among detection confidences, affinities and start-end probabilities. The experiment results on the KITTI tracking benchmark demonstrate the superior performance of the proposed method in terms of both tracking accuracy and processing speed.
An automated slice sorting technique for multi-slice computed tomography liver cancer images using convolutional network

An early detection and diagnosis of liver cancer can help the radiation therapist in choosing the target area and the amount of radiation dose to be delivered to the patients. The radiologists usually spend a lot of time in selecting the most relevant slices from thousands of scans, which are usually obtained from multi-slice CT scanners. The purpose of this paper multi-organ classification of 3D CT images of liver cancer suspected patients by convolution network. A dataset consisting of 63503 CT images of liver cancer patients taken from The Cancer Imaging Archive (TCIA) has been used to validate the proposed method. The method is a CNN for classification of CT liver cancer images. The classification results in terms of accuracy, precision, sensitivity, specificity, true positive rate, false negative rate, and F1 score have been computed. The results manifest a high validation accuracy of 99.1%, when convolution network is trained with the data augmented volume slices as compared to accuracy of 98.7% with that obtained original volume slices. The overall test accuracy for data augmented volume slice dataset is 93.1% superior to other volume slices. The main contribution of this work is that it will help the radiation therapist to focus on a small subset of CT image data. This is achieved by segregating the whole set of 63503 CT images into three categories based on the likelihood of the spread of cancer to other organs in liver cancer suspected patients. Consequently, only 19453 CT images had liver visible in them, making rest of 44050 CT images less relevant for liver cancer detection. The proposed method will help in the rapid diagnosis and treatment of liver cancer patients.
Is Bitcoin rooted in confidence? – Unraveling the determinants of globalized digital currencies

Although previous studies have shown the impact of sentiment on Bitcoin price, they have not produced clear and consistent empirical results regarding two main questions: how is sentiment measured and what are its effects on Bitcoin price over time? To address this gap, we develop an innovative empirical approach based on Bitcoin sentiment, drawing on trade data rather than judgments of individuals who are not actual Bitcoin users, as well as on the autoregressive distributed lag model and the nonlinear autoregressive distributed lag model, both of which capture the asymmetric effects of explanatory variables. In addition to revealing a significant impact of the Bitcoin Misery Index (BMI) on short- and long-term Bitcoin returns, this paper highlights the nonlinearity and asymmetry of this relationship in the short and long run, as well as the relevance of estimating Bitcoin sentiment using trade data.
Comprehensive feasibility assessment of building integrated photovoltaics (BIPV) on building surfaces in high-density urban environments

• A new comprehensive systematic for the feasibility of BIPV is proposed. • BIPV potential is combined with visual impact assessment from different viewpoints. • Influence of road-side vegetation on irradiation and visibility was also analysed. • A case study in a commercial district with high-rise buildings was carried out. • Strategies for BIPV application are suggested for different levels of visual impact. Deployment of building integrated photovoltaics (BIPV) requires smart planning to optimise the production of renewable energies, while preserving the aesthetic quality of the urban landscape, especially in densely built-up urban environments. A comprehensive mapping tool is proposed here to investigate the feasibility of BIPV applications, by introducing a quantitative visual impact assessment in addition to the traditional energy yield projections. The concept is tested in a case study of buildings along Orchard Road, the main commercial and tourist boulevard in the South-East Asian city state of Singapore. First, a visibility analysis of each building surface is conducted from the perspective of simulated pedestrians at street-level, based on line-of-sight. This is followed by an assessment of the solar energy harvesting potential based on simulated annual solar irradiation values on the various building surfaces. Lastly, the visibility assessment is overlaid on the 3D solar irradiation map to identify and visualise the most suitable building surfaces for BIPV applications. In addition, the influence of road-side trees on the feasibility of BIPV has been studied. Depending on the degree of visual impact, different strategies are proposed for the optimum deployment of BIPV, ranging from high-efficiency technologies to aesthetic BIPV and media walls. The proposed methodology could evolve as a standard tool for the decision-making process in high-density urban environments and thus further assist in promoting the large-scale adoption of BIPV.
Flexible high-resolution object detection on edge devices with tunable latency

Object detection is a fundamental building block of video analytics applications. While Neural Networks (NNs)-based object detection models have shown excellent accuracy on benchmark datasets, they are not well positioned for high-resolution images inference on resource-constrained edge devices. Common approaches, including down-sampling inputs and scaling up neural networks, fall short of adapting to video content changes and various latency requirements. This paper presents Remix, a flexible framework for high-resolution object detection on edge devices. Remix takes as input a latency budget, and come up with an image partition and model execution plan which runs off-the-shelf neural networks on non-uniformly partitioned image blocks. As a result, it maximizes the overall detection accuracy by allocating various amount of compute power onto different areas of an image. We evaluate Remix on public dataset as well as real-world videos collected by ourselves. Experimental results show that Remix can either improve the detection accuracy by 18%-120% for a given latency budget, or achieve up to 8.1× inference speedup with accuracy on par with the state-of-the-art NNs.
Ensemble of Classifiers and Term Weighting Schemes for Sentiment Analysis in Turkish

With the advancement of information and communication technology, social networking and microblogging sites have become a vital source of information. Individuals can express their opinions, grievances, feelings, and attitudes about a variety of topics. Through microblogging platforms, they can express their opinions on current events and products. Sentiment analysis is a significant area of research in natural language processing because it aims to define the orientation of the sentiment contained in source materials. Twitter is one of the most popular microblogging sites on the internet, with millions of users daily publishing over one hundred million text messages (referred to as tweets). Choosing an appropriate term representation scheme for short text messages is critical. Term weighting schemes are critical representation schemes for text documents in the vector space model. We present a comprehensive analysis of Turkish sentiment analysis using nine supervised and unsupervised term weighting schemes in this paper. The predictive efficiency of term weighting schemes is investigated using four supervised learning algorithms (Naive Bayes, support vector machines, the k-nearest neighbor algorithm, and logistic regression) and three ensemble learning methods (AdaBoost, Bagging, and Random Subspace). The empirical evidence suggests that supervised term weighting models can outperform unsupervised term weighting models.
An improved matrix factorization based model for many-objective optimization recommendation

As the application scenarios of recommendation algorithms are becoming increasingly complex, the efficiency of traditional recommendation algorithm based on accuracy is no longer satisfied. To solve this problem, an improved matrix factorization based model for many-objective optimization recommendation is proposed to simultaneously optimize the four recommendation objectives of novelty, diversity, accuracy, and recall. As a novel double-layer recommendation model, two improved algorithms are composed: 1) For the bottom layer, an improved matrix factorization algorithm with additional regularization constraints is used to predict unknown item ratings; 2) For the top layer, the recommendation list is optimized by a many-objective evolutionary algorithm. Comprehensive experiments demonstrate that the proposed model can effectively improve the four recommended evaluation metrics. And a recommended list with novel and diverse items is provided for users in a more efficient way while maintaining accuracy.
Continuous-Variable Quantum Secure Direct Communication Based on Gaussian Mapping

Quantum secure direct communication (QSDC) realizes the transmission of secret messages directly in a quantum channel. A continuous-variable- (CV) based quantum-communication system allows high-speed, large-capacity information transmission in optical telecommunication systems. In this work, we propose a QSDC protocol based on Gaussian mapping. As for Gaussian modulation, the designed Gaussian-mapping scheme effectively solves the problem, which results from the nonuniformity of the secret-message bitstream, and is applicable to different modulation variances. The system performance analysis shows that the proposed scheme can realize inerrant Gaussian modulation of secret messages, and secure transmission of messages under information theory. Moreover, our work hopes to stimulate discussion on experimental realization and practical advance of CV-QSDC protocol.
Trust, Incomplete Contracting, and Corporate Innovation

Innovation is a contract-intensive economic activity in a world of incomplete contracts. We show that trust mitigates incomplete contracting and enhances innovation by acting as an informal contracting mechanism. Trust plays an especially important role when formal laws and regulations are lacking, and it promotes innovation by encouraging collaboration and fostering tolerance for failure. Further analyses show that trust also facilitates cross-border technological spillover and innovation collaboration. Overall, our evidence highlights innovation as a key conduit through which trust affects economic growth. This paper was accepted by Gustavo Manso, finance.
PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection

The YOLO network has been extensively employed in the field of ship detection in optical images. However, the YOLO model rarely considers the global and local relationships in the input image, which limits the final target prediction performance to a certain extent, especially for small ship targets. To address this problem, we propose a novel small ship detection method, which improves the detection accuracy compared with the YOLO-based network architecture and does not increase the amount of computation significantly. Specifically, attention mechanisms in spatial and channel dimensions are proposed to adaptively assign the importance of features in different scales. Moreover, in order to improve the training efficiency and detection accuracy, a new loss function is employed to constrain the detection step, which enables the detector to learn the shape of the ship target more efficiently. The experimental results on a public and high-quality ship dataset indicate that our method realizes state-of-the-art performance in comparison with several widely used advanced approaches.
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models

Abstract An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly. We highlight many general pitfalls of ML model interpretation, such as using interpretation techniques in the wrong context, interpreting models that do not generalize well, ignoring feature dependencies, interactions, uncertainty estimates and issues in high-dimensional settings, or making unjustified causal interpretations, and illustrate them with examples. We focus on pitfalls for global methods that describe the average model behavior, but many pitfalls also apply to local methods that explain individual predictions. Our paper addresses ML practitioners by raising awareness of pitfalls and identifying solutions for correct model interpretation, but also addresses ML researchers by discussing open issues for further research.
Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery

Abstract. In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Accurate localized PV information, including location and size, is the basis for PV regulation and potential assessment of the energy sector. Automatic information extraction based on deep learning requires high-quality labeled samples that should be collected at multiple spatial resolutions and under different backgrounds due to the diversity and variable scale of PVs. We established a PV dataset using satellite and aerial images with spatial resolutions of 0.8, 0.3, and 0.1 m, which focus on concentrated PVs, distributed ground PVs, and fine-grained rooftop PVs, respectively. The dataset contains 3716 samples of PVs installed on shrub land, grassland, cropland, saline–alkali land, and water surfaces, as well as flat concrete, steel tile, and brick roofs. The dataset is used to examine the model performance of different deep networks on PV segmentation. On average, an intersection over union (IoU) greater than 85 % is achieved. In addition, our experiments show that direct cross application between samples with different resolutions is not feasible and that fine-tuning of the pre-trained deep networks using target samples is necessary. The dataset can support more work on PV technology for greater value, such as developing a PV detection algorithm, simulating PV conversion efficiency, and estimating regional PV potential. The dataset is available from Zenodo on the following website: https://doi.org/10.5281/zenodo.5171712 (Jiang et al., 2021).
Towards Interoperable Blockchains: A Survey on the Role of Smart Contracts in Blockchain Interoperability

The slower than expected adoption rate of blockchain technology has highlighted that there are barriers due to the diversity of its applications and its users. To overcome this limitation and take full advantage of the novel technology, researchers from academia as well as industry are dedicated to find different solutions, where two or more blockchains can interact with each other. As a result, several interoperability solutions have presented themselves. To investigate the functionalities and underlying mechanisms of interoperable blockchain solutions, researchers have conducted several surveys by discussing the features and innovations of these methods. However, the existing surveys tend to focus on the architectural description of the interoperability solutions and completely overlook the most promising aspect of blockchain adaptability, namely the smart contract. This paper fills the gap by exploring the role of smart contracts in blockchain interoperability solutions. Our research has classified the existing interoperability solutions into three main categories: heterogeneous blockchains and homogeneous smart contracts, homogeneous blockchains and homogeneous smart contracts, heterogeneous blockchains, and heterogeneous smart contracts. To provide a systematic overview of the smart contracts used in blockchain interoperability, each category is further divided into subcategories by identifying the functionalities of the smart contract used. Based on our survey, a taxonomy is proposed to help classify the blockchain interoperability solutions. The interoperability solutions in each category are analyzed in-depth, and the results are presented in tabular format to illustrate the characteristics of the interoperability solutions in a meaningful way. Finally, a number of open issues and research directions are discussed to overcome the limitations and improve the performance of blockchain interoperability.
Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

This study deals with the controlling the speed of a direct current (DC) motor via a fractional order proportional–integral–derivative (FOPID) controller and maintaining the terminal voltage level of an automatic voltage regulator (AVR) via a proportional–integral–derivative plus second order derivative (PIDD 2 ) controller. To adjust the parameters of those controllers, a novel improved slime mould algorithm (ISMA) is proposed. The latter is a novel metaheuristic algorithm developed in this work. The proposed algorithm aims to improve the original SMA in terms of exploration with the aid of a modified opposition-based learning scheme and in terms of exploitation with the aid of the Nelder–Mead simplex search method. A time domain objective function, which includes time response specifications of steady state error and maximum overshoot along with rise and settling times, is used as a performance index to design the FOPID controller-based DC motor system and PIDD 2 controller-based AVR system. The performance of the proposed novel approaches for both systems are assessed through time and frequency domain simulations along with statistical tests which show the greater performance of the improved algorithm. Further to this, the efficacy of the proposed approaches for both systems is compared with other available and effective approaches in the literature. The extensive comparative results demonstrate the proposed method to be superior to those state-of-the-art approaches for both DC motor speed and AVR control systems.
Comparative study of various approaches, applications and classifiers for sentiment analysis

In today's day and age, there are billions of volumes of textual content being generated everywhere. In-apps messages like WhatsApp, Telegram, social media sites like Facebook, Instagram, news publishing sites, google searches and many other sources. All these sources are constantly generating huge volumes of text data every second. And because of these huge volumes of text data NLP becomes a vital resource in understanding the textual content. In this paper, the main focus is on the popular NLP task of Sentiment analysis. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in textual data. sentiment analysis proves to be an incredible asset for users to extract essential information and assists organizations with understanding the social sentiment of their brand, product or service while monitoring online conversations. This paper investigates the different approaches and classification models used in the task of sentiment analysis.