Reviewerly/exHarmony huggingface dataset:
Systematic Error Source Analysis of a Real-World Multi-Camera Traffic Surveillance System

In this paper, we assess the performance of our real-world multi-camera traffic surveillance system along a segment of the A9 Autobahn north of Munich. Its principal component is a Labeled Multi-Bernoulli based tracking module that sequentially fuses the detection data from parallel camera processing pipelines. We present a systematic investigation of the system's characteristic failure modes that lead to a degradation of its performance. To this end, we assess state of the art metrics and performance measures in regard to their suitability for flagging unwanted behavior or failures in real-world multi-object tracking systems. Our analysis is structured into three levels of abstraction: target-level, time-step-level, and track-level. These abstraction levels allow us to systematically approach the analysis from different perspectives and to direct the focus on recurring errors and systemic deficiencies. In particular, the track-level analysis proved to be the most expedient approach since it drew our attention to system challenges like occlusions and other time-correlated detection errors. It further identified the system bias introduced by the adoption of class-dependent object extents. Our analysis is intended to guide the future development effort of our system and to serve as a basis for investigations and improvements of similar systems.
A Learning Distributed Gaussian Process Approach for Target Tracking over Sensor Networks

Tracking manoeuvring targets often relies on complex models with non-stationary parameters. Gaussian process (GP) based model-free methods can achieve accurate performance in a data-driven manner but face scalability challenges. Aiming to address such challenges, this paper proposes a distributed GP-based tracking approach able to learn the kernel hyperparameters in an online manner, to improve the tracking performance and scalability. It caters to the inherent distributed feature of sensor networks and does not need measurements to be transmitted among sensors for target states predictions. Theoretical upper confidence bounds about the tracking error are derived within the regret bound setting. Through this theoretical analysis, the tracking error per time step is upper bounded as a function of predictive variances from local sensors. The theoretical results are supported by simulation based ones over a case study for tracking over wireless sensor networks. With evaluation on challenging target trajectories, a comparison on state-of-the-art centralised and distributed GP approaches, numerical results demonstrate that the proposed approach achieves competitively high and robust tracking performance.
Efficient Bang-Bang Model Predictive Control for Quadcopters

Time-optimal model predictive control is important for achieving fast racing drones but is computationally intensive and thereby rarely used onboard small quadcopters with limited computational resources. In this work, we simplify the optimal control problem (OCP) of the position loop for several maneuvers by exploiting the fact that the solution resembles a so-called ‘bang-bang’ in the critical direction, where only the switching time needs to be found. The noncritical direction uses a ‘minimum effort’ approach. The control parameters are obtained through bisection search schemes on an analytical path prediction model. The approach is compared with a classical PID controller and theoretical time-optimal trajectories in simulations. We explain the effects of the OCP simplifications and introduce a method of mitigating one of these effects. Finally, we have implemented the ‘bang-bang’ controller as a model predictive controller (MPC) onboard a Parrot Bebop and performed indoor flights to compare the controller’s performance to a PID controller. We show that the light novel controller outperforms the PID controller in waypoint-to-waypoint flight while requiring only minimal knowledge of the quadcopter’s dynamics.
A collaborative filtering recommendation algorithm based on fuzzy C-means clustering

With the rapid growth of data scale, the problems of collaborative filtering recommendation algorithm are more and more obvious, such as data sparsity, cold start, scalability, and the change of user interest over time. About the existing problems, we introduce the fuzzy clustering and propose a collaborative filtering algorithm based on fuzzy C-means clustering. The algorithm performs fuzzy clustering on the item attribute information to make items belonging to different categories in different membership degree, increases the data density, effectively reduces the data sparsity, and solves the issue that the inaccuracy of similarity leads to the low recommendation accuracy. Meanwhile, the algorithm introduces the time weight function. Different evaluation times give different time weight values, and recently evaluated items are more representative of the user current interest, so we give a higher weight value, and early evaluated items have less effect on the user current interest, thus the weight value are relatively lower. The experimental results show that our algorithm can effectively alleviate the data sparsity problem and time migration of users preferences, thus achieve better performance.
Data enhancement based on M2-Unet for liver segmentation in Computed Tomography

Liver cancer is one of the diseases with the highest mortality in the world. Accurate segmentation of liver regions from liver CT data is a necessary preprocessing stage of computer-aided detection and diagnosis algorithms. In view of the small number of existing medical image data sets and difficulty in acquisition, this paper expanded the effective data set based on an improved conditional generation adversarial network pix2pix. We added random noise to the input of the generator, and the last layer of the output of the discriminator. Moreover, the original fully connected layer was replaced with a probability matrix with a size of 16 × 16. This is to make the discriminator more delicate. We merged the block module in MobileNet-v2 and U-Net (M2-Unet) to achieve liver segmentation. In addition to the training and validation using the public competition data set LiTs, we also constructed a new liver data set. The Dice similarity coefficient of the algorithm in this paper is 88.7%. The proposed method has improved to the ordinary U-Net algorithm. In the test phase, each image only needs 0.072 s to complete the segmentation on average, which exceeds the segmentation speed of specialists. Experimental results show that algorithm in this paper can accurately segment the liver and meet the requirements of realtime segmentation.
Estimation of global and diffuse horizontal irradiance by machine learning techniques based on variables from the Heliosat model

The increased interest in the sources of renewable electricity has drawn attention to the rapidly developing solar energy sector owing to its high cost-benefit ratio. To accurately calculate the potential electricity output of photovoltaic (PV) panels, the global horizontal irradiance (GHI) and diffuse horizontal irradiance (DHI) must be known. The worldwide presence of satellite imagery provides an efficient way to estimate current and historical GHI instead of using the high time- and cost-consuming in-situ measurement. The most of past studies utilized more satellite bands as the inputs for machine learning algorithms to estimate GHI. The further estimation of DHI was usually based on weather-related parameters. Thus, there is a research gap in using only one satellite band for both GHI and DHI estimations. Therefore, this study used machine learning algorithms to estimate GHI and DHI, with inputs delivered from the Heliosat model based on band 3 of Himawari-8 satellite imageries. The results were compared with the original and site-adapted Heliosat models and seven DHI separation models. The results indicated that the machine learning models were capable of performing with the same accuracy as the Heliosat models. However, their performance was better while estimating DHI, in which case they outperformed even the best separation model. Higher accuracy and precision were observed in those models where the additional solar zenith at time t+1h was used together with other input features. This highlighted the possibility of using only one satellite band together with the calculated solar position variables as the input. Overall, this research has established a new method for estimating GHI and DHI with high confidence based on satellite imageries and the Heliosat model through the application of machine learning techniques.
JGCL: Joint Self-Supervised and Supervised Graph Contrastive Learning

Semi-supervised and self-supervised learning on graphs are two popular avenues for graph representation learning. We demonstrate that no single method from semi-supervised and self-supervised learning works uniformly well for all settings in the node classification task. Self-supervised methods generally work well with very limited training data, but their performance could be further improved using the limited label information. We propose a joint self-supervised and supervised graph contrastive learning (JGCL) to capture the mutual benefits of both learning strategies. JGCL utilizes both supervised and self-supervised data augmentation and a joint contrastive loss function. Our experiments demonstrate that JGCL and its variants are one of the best performers across various proportions of labeled data when compared with state-of-the-art self-supervised, unsupervised, and semi-supervised methods on various benchmark graphs.
Exploiting Geodata to Improve Image Recognition with Deep Learning

Due to the widespread availability of smartphones and digital cameras with GPS functionality, the number of photos associated with geographic coordinates or geoinformation on the internet is continuously increasing. Besides the obvious benefits of geotagged images for the users, geodata can enable a better understanding of the image content and thus facilitate their classification. This work shows the added value of integrating auxiliary geodata during a multi class single label image classification task. Various ways of encoding and extracting auxiliary features from raw coordinates are compared, followed by an investigation of approaches to integrate these features into a convolutional neural network (CNN) by fusion models. We show the classification improvements of adding the raw coordinates and derived auxiliary features such as satellite photos and location-related texts (address information and tags).
Making Adversarially-Trained Language Models Forget with Model Retraining: A Case Study on Hate Speech Detection

Adversarial training has become almost the de facto standard for robustifying Natural Language Processing models against adversarial attacks. Although adversarial training has proven to achieve accuracy gains and boost the performance of algorithms, research has not shown how adversarial training will stand “the test of times” when models are deployed and updated with new non-adversarial data samples. In this study, we aim to quantify the temporal impact of adversarial training on naturally-evolving language models using the hate speech task. We conduct extensive experiments on the Tweet Eval benchmark dataset using multiple hate speech classification models. In particular, our findings indicate that adversarial training is highly task-dependent as well as dataset dependent as models trained on the same dataset achieve high prediction accuracy but fare poorly when tested with new dataset even after retraining models with adversarial examples. We attribute this temporal and limited effect of adversarial training to distribution shift of the training data which implies that models’ quality will degrade over-time as models are deployed in the real world and start serving new data.
Spot Virtual Machine Eviction Prediction in Microsoft Cloud

Azure Spot Virtual Machines (Spot VMs) utilize unused compute capacity at significant cost savings. They can be evicted when Azure needs the capacity back, therefore suitable for workloads that can tolerate interruptions. A good prediction of Spot VM evictions is beneficial for Azure to optimize capacity utilization and offers users information to better plan Spot VM deployments by selecting clusters to reduce potential evictions. The current in-service cluster-level prediction method ignores the node heterogeneity by aggregating node information. In this paper, we propose a spatial-temporal node-level Spot VM eviction prediction model to capture the inter-node relations and time dependency. The experiments with Azure data show that our node-level eviction prediction model performs better than the node-level and cluster-level baselines.
Fuzzy Cognitive Maps Based on D-Number Theory

In real life, there will be a lot of uncertainty problems, one of which is due to the vagueness of the concept of things, that is, it is difficult to determine whether an object conforms to this concept. This situation widely exists in some states, phenomena, parameters and interrelationships between things. For such uncertain events with heavy subjective influencing factors and incomplete data, it is suitable to use fuzzy methods to deal with them. In present our work, we firstly introduce the notion of D-number cognitive maps (DCMs), which are intelligent framework models based on D-number theory and cognitive maps. Compared with Evidential Cognitive Maps (ECMs) and Fuzzy Cognitive Maps (FCMs), DCMs can fuse multiple sources of information with uncertainty and construct a cognitive map model with incomplete and conflicting information. To better solve the problem of knowledge combination, D-number fuzzy cognitive maps (DFCMs) are also constructed based on D-number theory and fuzzy cognitive maps. In many practical applications, the establishment of fuzzy cognitive maps is usually completed by using a simple arithmetic average method for multiple experts to obtain comprehensive fuzzy cognitive maps. This simple processing method may lead to the loss of some important information so that the synthesized results do not reflect reality. To overcome this challenge, we synthesize the knowledge of multiple experts by using D-number theory and the characteristics of the representation of expert knowledge in FCMs. It is based on expert knowledge, and the synthesized reliability distribution function is used as the basis of final weight synthesis.
A Log-Logistic Predictor for Power Generation in Photovoltaic Systems

Photovoltaic (PV) systems are dependent on solar irradiation and environmental temperature to achieve their best performance. One of the challenges in the photovoltaic industry is performing maintenance as soon as a system is not working at its full generation capacity. The lack of a proper maintenance schedule affects power generation performance and can also decrease the lifetime of photovoltaic modules. Regarding the impact of environmental variables on the performance of PV systems, research has shown that soiling is the third most common reason for power loss in photovoltaic power plants, after solar irradiance and environmental temperature. This paper proposes a new statistical predictor for forecasting PV power generation by measuring environmental variables and the estimated mass particles (soiling) on the PV system. Our proposal was based on the fit of a nonlinear mixed-effects model, according to a log-logistic function. Two advantages of this approach are that it assumes a nonlinear relationship between the generated power and the environmental conditions (solar irradiance and the presence of suspended particulates) and that random errors may be correlated since the power generation measurements are recorded longitudinally. We evaluated the model using a dataset comprising environmental variables and power samples that were collected from October 2019 to April 2020 in a PV power plant in mid-west Brazil. The fitted model presented a maximum mean squared error (MSE) of 0.0032 and a linear coefficient correlation between the predicted and observed values of 0.9997. The estimated average daily loss due to soiling was 1.4%.
Image recommender system based on compact convolutional transformer image style recognition

Social network users face a large number of images; therefore, it is very important that social media finds and recommends images that satisfy the user's preferences properly. This article investigates the effect of using image style on the performance of a social image recommender system. The effect of distinctive visual styles is evident in art, photography, film, and advertising; however, to the best of our knowledge, it has not been used in social image recommendation systems. We propose image style recognition as a component of user preferences. To do this, we first propose a method based on deep features and compact convolutional transformer to recognize the style of images; then, an image recommender system based on an image style recognition is presented. We consider 15 distinct styles from the Flickr Style dataset, consisting of five categories: atmosphere, mood, genre, composition styles, and color. The experimental results show that style has a positive effect and can significantly improve Recall@k and Precision@k by ∼5 % − 10 % (for the 100 top images recommended) for personalized image recommendation compared with not using the.
A quantum scheme of state overlap based on quantum mean estimation and support vector machine

Given two unknown quantum states | ϕ 〉 and | ψ 〉 , the overlap between them is defined as | 〈 ϕ | ψ 〉 | 2 , i.e. the inner product between their state vectors. Estimating the overlap between two states is an important task with several applications. In most applications, overlap estimation is carried out through the swap test. A new scheme for estimating the overlap between two states based on Quantum Mean Estimation (QME) algorithm and Support Vector Machine (SVM) is presented. Compared with other schemes, this new scheme offers a quadratic speedup over the swap test while it obtains a better output with relatively high accuracy. Using QME, the uncertainty in the estimated value falls as O ( 1 q ) in the number of queries q to unitaries, which are used to the create the states 〈 ϕ | ψ 〉 . Using SVM, a more accurate estimate of the estimated value is obtained. The accuracy of new scheme has a significant improvement than using QME algorithm alone. The correctness and efficiency of the proposed scheme is verified by an experimental simulation on the IBM quantum cloud platform.
An adaptive framework to select the coordinate systems for evolutionary algorithms

Many evolutionary algorithms usually utilize the fixed original coordinate system to search and cannot effectively match different function landscapes. To solve this issue, this paper proposes an adaptive framework, named STCS, to select the coordinate systems for evolutionary algorithms. In STCS, the eigen coordinate system is constructed by an archive-based covariance matrix, which can capture the feature of the function landscape. What is more, the selection process of the original coordinate system and the eigen coordinate system is defined as a Markov decision process and is controlled by reinforcement learning algorithm. STCS is applied to three popular evolutionary algorithms, i.e., differential evolution, particle swarm optimization, and teaching–learning-based optimization. The experimental results on IEEE CEC2013, IEEE CEC2014, and IEEE CEC2017 test suites demonstrate that STCS is efficient and competitive. • An adaptive framework is proposed to select the coordinate systems for evolutionary algorithms. • An archive-based covariance matrix is presented for constructing the eigen coordinate system. • The proposed framework is applied to three popular evolutionary algorithms.
GT-SimNet: Improving code automatic summarization via multi-modal similarity networks

Code summarization aims to generate high-quality functional summaries of code snippets to improve the efficiency of program development and maintenance. It is a pressing challenge for code summarization models to capture more comprehensive code knowledge by integrating the feature correlations between the semantics and syntax of the code. In this paper, we propose a multi-modal similarity network based code summarization method: GT-SimNet. It proposes a novel code semantic modelling method based on a local application programming interface (API) dependency graph (Local-ADG), which exhibits an excellent ability to mask irrelevant semantics outside the current code snippet. For code feature fusion, GT-SimNet uses the SimNet network to calculate the correlation coefficients between Local-ADG and abstract syntax tree (AST) nodes and performs fusion under the influence of the correlation coefficients. Finally, it completes the prediction of the target summary by the generator. We conduct extensive experiments to evaluate the performance of GT-SimNet on two language datasets (Java and Solidity). The results show that GT-SimNet achieved BLEU scores of 38.73% and 41.36% on the two datasets, 1.47%∼2.68% higher than the best existing baseline. Importantly, GT-SimNet reduces the BLEU scores by 7.28% after removing Local-ADG. This indicates that Local-ADG is effective for the semantic representation of the code.
Experience in training specialists with mathematical computer modeling skills, taking into account the needs of the modern labor market

Today in most countries there is a lack of qualifications in areas, which require specialists with mathematical competencies, despite the high unemployment rate in many countries. At the same time, it is generally recognized that most likely those sciences are developing, the fundamental results of which can be formulated mathematically. Using mathematical methods, researchers draw important conclusions that could hardly be obtained otherwise. Digital transformation of all industries requires specialists with a sufficient level of mathematical competence and skills in ICT tools, including computer modeling using the approach called Inquiry-Based Mathematics Education (IBME).
Adaptive and Personalized Web Blog Searching Technique Using S-ANFIS

Day by day, the number of blog users and microblog users is increasing worldwide. It is easy to say that blogs have captured a significant portion of other web services. In the past few years, the number of users has exponentially increased. User count of Facebook, Twitter, and Instagram applications is not hidden from anyone. Users on such platforms share ideas, experiences, stories, opinions, and views and want to interact with people with the same set of interests. As per the user’s expectation, there is a requirement of two things: content curation and recommendations. The content curation algorithm will find the people and their posts on personalized search results. In addition, the recommendation system will help to find the most appropriate match to interact with. In this paper, both approaches are combined to show the user’s curated and recommended results. The article focuses on the hybrid model named S-ANFIS, and the results are compared with the well-known approaches like ANN, Deep Neural Network (DNN), and Recurrent Neural Network (RNN).