Reviewerly/exHarmony huggingface dataset:
Nonlinear Model Predictive Control for Pumped Storage Plants Based on Online Sequential Extreme Learning Machine with Forgetting Factor

With renewable energy (RE) being increasingly connected to power grids, pumped storage plants (PSPs) play a very important role in restraining the fluctuation of power grids. However, conventional control strategy could not adapt well to the different control tasks. This paper proposes an intelligent nonlinear model predictive control (NMPC) strategy, in which hydraulic-mechanical and electrical subsystems are combined in a synchronous control framework. A newly proposed online sequential extreme learning machine algorithm with forgetting factor (named WOS-ELM) is introduced to learn the dynamic behaviors of the coupling system. Specifically, the initial learning parameters are optimized by prior-knowledge learning and a new self-adaptive adjustment strategy is also put forward. Subsequently, the stair-like control strategy and artificial sheep algorithm (ASA) are used in rolling the optimization mechanism to replace the existing complex differential geometric solutions. Comparative experiments are carried out under different working conditions based on a PSP in China. The results show that the influence from coupling factors can be considerable and the proposed MPC strategy indicates superiority in voltage and load adjustment as well as the frequency oscillation suppression.
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs

This article unveils a new relation between the Nishimori temperature parametrizing a distribution P and the Bethe free energy on random Erdos-Renyi graphs with edge weights distributed according to P. Estimating the Nishimori temperature being a task of major importance in Bayesian inference problems, as a practical corollary of this new relation, a numerical method is proposed to accurately estimate the Nishimori temperature from the eigenvalues of the Bethe Hessian matrix of the weighted graph. The algorithm, in turn, is used to propose a new spectral method for node classification in weighted (possibly sparse) graphs. The superiority of the method over competing state-of-the-art approaches is demonstrated both through theoretical arguments and real-world data experiments.
Vehicle detection based on improved multitask cascaded convolutional neural network and mixed image enhancement

In order to increase the detection effect of fuzzy vehicles and small size vehicles, an improved multitask cascaded convolutional neural network (IMC-CNN) based on mixed image enhancement is proposed. Firstly, contrast limited adaptive histogram equalisation and multi-scale Retinex are used to enhance images. Mixed image enhancement can effectively solve the problems of image blurring, low contrast and uneven illumination when the imaging environment is not ideal. IMC-CNN includes two stages: object location and object classification. The object location network based on multi-layer feature fusion can locate and extract the object from complex background, and output regions contain only a single vehicle object. The object classification network is a lightweight convolutional neural network with only two convolutional layers, which can effectively reduce information loss and improve the classification accuracy of small objects and fuzzy objects. In addition, online hard example mining algorithm and focal loss function are adopted in network training. These strategies can solve the problem of unbalance between positive and negative samples. To verify the validity of the proposed algorithm, the experiments are performed on SYIT-Fuzzy dataset and COCO-Vehicle dataset. Compared with Faster R-CNN, YOLO v4 and other recent models, the average classification accuracy of the proposed method is significantly increased.
Implementing WordNet Measures of Lexical Semantic Similarity in a Fuzzy Logic Programming System

Abstract This paper introduces techniques to integrate WordNet into a Fuzzy Logic Programming system. Since WordNet relates words but does not give graded information on the relation between them, we have implemented standard similarity measures and new directives allowing the proximity equations linking two words to be generated with an approximation degree. Proximity equations are the key syntactic structures which, in addition to a weak unification algorithm, make a flexible query-answering process possible in this kind of programming language. This addition widens the scope of Fuzzy Logic Programming, allowing certain forms of lexical reasoning, and reinforcing Natural Language Processing (NLP) applications.
Source Model Selection for Transfer Learning of Image Classification using Supervised Contrastive Loss

Transfer learning is a framework that improves performance of target task by transferring knowledge from training source task. As deep learning research accumulate, more source models can be easily obtained. In time series domain, the Mean Silhouette Coefficient of the set of feature vectors which forward propagated through source models is used to select the best source model performs target task. But for image classification, the model which is better at generalization could have the lower coefficient. To adjust this, we propose to use another measure, Supervised Contrastive Loss. In this work, we evaluate which measure is better to select the best model. We present the superiority of using the supervised contrastive loss through the comparative experiment.
Is it Pointless? Modeling and Evaluation of Category Transitions of Spatial Gestures

To enable robots to select between different types of nonverbal behavior when accompanying spatial language, we must first understand the factors that guide human selection between such behaviors. In this work, we argue that to enable appropriate spatial gesture selection, HRI researchers must answer four questions: (1) What are the factors that determine the form of gesture used to accompany spatial language? (2) What parameters of these factors cause speakers to switch between these categories? (3) How do the parameterizations of these factors inform the performance of gestures within these categories? and (4) How does human generation of gestures differ from human expectations of how robots should generate such gestures? In this work, we consider the first three questions and make two key contributions: (1) a human-human interaction experiment investigating how human gestures transition between deictic and non-deictic under changes in contextual factors, and (2) a model of gesture category transition informed by the results of this experiment.
Time-Aware PolarisX: Auto-Growing Knowledge Graph

A knowledge graph is a structured graph in which data obtained from multiple sources are standardized to acquire and integrate human knowledge.Research is being actively conducted to cover a wide variety of knowledge, as it can be applied to applications that help humans.However, existing researches are constructing knowledge graphs without the time information that knowledge implies.Knowledge stored without time information becomes outdated over time, and in the future, the possibility of knowledge being false or meaningful changes is excluded.As a result, they can't re ect information that changes dynamically, and they can't accept information that has newly emerged.To solve this problem, this paper proposes Time-Aware PolarisX, an automatically extended knowledge graph including time information.Time-Aware PolarisX constructed a BERT model with a relation extractor and an ensemble NER model including a time tag with an entity extractor to extract knowledge consisting of subject, relation, and object from unstructured text.Through two application experiments, it shows that the proposed system overcomes the limitations of existing systems that do not consider time information when applied to an application such as a chatbot.Also, we verify that the accuracy of the extraction model is improved through a comparative experiment with the existing model.
A bad turn deserves another: linkages between terrorism, capital flight and industrialisation

This study examines how the association between terrorism and capital flight affects the process of industrialisation in 36 African countries. The empirical evidence is based on Generalised Method of Moments (GMM) and Quantile Regressions (QR). GMM-oriented findings show that capital flight interacts with terrorism to negatively affect industrialisation in 'domestic terrorism'- and 'total terrorism'-oriented regressions. With QR approach, the GMM results are confirmed exclusively in the 25th and 50th quantiles, in regressions pertaining to domestic terrorism, unclear terrorism and total terrorism. It follows that the negative effect from the investigated interaction is driven by bottom quantiles of the industrialisation distribution. This confirms existing literature that developed countries are more likely to limit the negative externalities from terrorism compared to their developing counterparts. Hence, the negative consequence of the association between terrorism and capital flight on industrialisation is a decreasing function of industrialisation.
Hybrid User Clustering-Based Travel Planning System for Personalized Point of Interest Recommendation

In the recent times, the massive amount of user-generated data acquired from Internet has become the main source for recommendation generation process in various real-time personalization problems. Among various types of recommender systems, collaborative filtering-based approaches are found to be more effective in generating better recommendations. The recommendation models that are based on this collaborative filtering approach are used to predict items highly similar to the interest of an active target user. Thus, a new hybrid user clustering-based travel recommender system (HUCTRS) is proposed by integrating multiple swarm intelligence algorithms for better clustering. The proposed HUCTRS is experimentally assessed on the large-scale datasets to demonstrate its performance efficiency. The results obtained also proved the potential of proposed HUCTRS over traditional approaches by means of improved user satisfaction.
A Survey on Near Duplicate Video Retrieval Using Deep Learning Techniques and Framework

The field of machine learning is going through its golden era. Deep Learning, the subfield of Machine Learning has seen amazing applications in various areas. The perception of information is extracted by using different layers of Deep Learning. Numerous deep learning algorithms like Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN) have completely changed the viewpoint of researchers of data science and big data. However, still there is huge scope of learning in this extremely quick-paced domain. The use of deep learning for Near Duplicate Video Retrieval (NDVR) shows the popularity of various algorithms of deep learning amongst researchers. This survey provides an overview of Near Duplicate Video Retrieval (NDVR) using deep learning and trends in development and usage of revolutionary Deep Learning frameworks, tools and their applications in recent years.
Inferring ground truth from crowdsourced data under local attribute differential privacy

Nowadays, crowdsourcing gains an increasing popularity as it can be adopted to solve many challenging question answering tasks that are easy for humans but difficult for computers. Due to the variety in the quality of users, it is important to infer not only the underlying ground truth of these tasks but also the users ability from the answers given by users. This problem is called Ground Truth Inference and has been studied for many years. However, since the answers collected from the users may contain sensitive information, ground truth inference raises serious privacy concern. Due to this reason, the problem of ground truth inference under local differential privacy (LDP) model has been recently studied. However, this problem is still not well understood and even some basic questions have not been solved yet. First, it is still unknown what is the average error of the private estimators to the underlying ground truth. Secondly, we do not know whether we can infer the ability of each user under LDP model and what is the estimation error w.r.t. the underlying users ability. Finally, previous work only shows that their methods have better performance than the private major voting algorithm through experiments. However, there is still no theoretically result which shows this priority formally or mathematically. In this paper, we partially solve these problems by studying the ground truth inference problem under local attribute differential privacy (LADP) model, which is a relaxation of LDP model, and propose a new algorithm called private Dawid-Skene method, which is motivated by the classical Dawid-Skene method. Specifically, we first provide the estimation errors for both ability of users and the ground truth under some assumptions of the problem if the algorithm start with some appropriate initial vector. Moreover, we propose an explicit instance and show that the estimation error of the ground truth achieved by the private major voting algorithm is always greater than the error achieved by our method.
Round-Preserving Parallel Composition of Probabilistic-Termination Cryptographic Protocols

An important benchmark for multi-party computation protocols (MPC) is their round complexity. For several important MPC tasks, such as broadcast, (tight) lower bounds on the round complexity are known. However, some of these lower bounds can be circumvented when the termination round of every party is not a priori known, and simultaneous termination is not guaranteed. Protocols with this property are called probabilistic-termination (PT) protocols. Running PT protocols in parallel affects the round complexity of the resulting protocol in somewhat unexpected ways. For instance, an execution of m protocols with constant expected round complexity might take $$O(\log m)$$ rounds to complete. In a seminal work, Ben-Or and El-Yaniv (Distributed Computing ‘03) developed a technique for a parallel execution of arbitrarily many broadcast protocols, while preserving expected round complexity. More recently, Cohen et al. (CRYPTO ‘16) devised a framework for universal composition of PT protocols, and provided the first composable parallel-broadcast protocol with a simulation-based proof. These constructions crucially rely on the fact that broadcast is “privacy-free,” and do not generalize to arbitrary protocols in a straightforward way. This raises the question of whether it is possible to execute arbitrary PT protocols in parallel, without increasing the round complexity. In this paper we tackle this question and provide both feasibility and infeasibility results. We construct a round-preserving protocol compiler, tolerating any dishonest minority of actively corrupted parties, that compiles arbitrary protocols into a protocol realizing their parallel composition, while having a black-box access to the underlying protocols. Furthermore, we prove that the same cannot be achieved, using known techniques, given only black-box access to the functionalities realized by the protocols, unless merely security against semi-honest corruptions is required, for which case we provide a protocol. To prove our results, we utilize the language and results by Cohen et al., which we extend to capture parallel composition and reactive functionalities, and to handle the case of an honest majority.
Phish Me, Phish Me Not

While phishing has evolved over the years, it still exploits one of the weakest links in any information system — humans. The present study aims at describing who the potential phishing victims are. We constructed two types of phishing messages that represented two basic categories of phishing e-mails: regular and spear-phishing. In cooperation with the IT management of a municipality in the southwestern region of the United States, we sent these messages to the municipality's employees and collected demographic data about individuals employed by the organization. We then applied eight supervised learning methods to classify the municipality's employees into two groups: phished and not-phished. Our results indicate that spear-phishing yields a significantly higher response rate than regular phishing and that some machine learning methods yield high classification accuracy in predicting phishing victims. We finally provide discussion of the results as well as the future implications.
An Architecture Combining Convolutional Neural Network (CNN) with Batch Normalization for Apparel Image Classification

In recent past, Convolutional Neural Networks (CNN) have been utilized in kind of areas, including style order. Web-based media, web-based business, and legitimate code are widely appropriate during this field. CNNs are proficient to prepare and situated to offer the preeminent exact prompts tackling world issues. In this paper, CNN based profound learning a cutting-edge (state-of-the-Art) model proposed for classification of Apparel images of Fashion MNIST dataset. Different CNN models proposed utilizing Dropout and Batch Normalization (BN) with Early Stopping to quicken learning measure and forestall overfitting. In view of correlations it is seen that proposed models improved accuracy and precision over the common best in class frameworks given in literature.
User behavior-ensemble learning based improving QoE fairness in HTTP adaptive streaming over SDN approach

Quality of Experience (QoE) for multimedia services is formed by a wealthy interaction between system, context and human factors. Whereas system and context variables are broadly inquired about, few research studies consider human variables as sources of orderly change. In other hand, Dynamic adaptive streaming over HTTP (DASH) has developed as an efficient innovation for video streaming but for a DASH framework, a most common case is that a limited server bandwidth is competed by multiusers. So, in order to enhance perceived QoE and guarantee fairness in wireless network, we propose in this paper to model Ensemble Learning approach based on user behavior factors to allocate the bandwidth collaboratively for multiusers and we use the MPEG-DASH technique to ameliorate the visual quality of each user. The performance evaluation is conducted by using network simulator and the obtained results show that our approach can give significant gain in terms of perceived QoE fairness.
Efficient SSD: A Real-Time Intrusion Object Detection Algorithm for Railway Surveillance

With the rapid development of the Chinese Railway, the research of lightweight and real-time railway perimeter intrusion detection algorithm is crucial for railway security. A single shot multibox detector (SSD) can detect intruding objects but cannot be deployed to the non-Graphics Processing Unit (GPU) equipment of the railway system to achieve real-time detection. In this paper, EfficientSSD, a real-time lightweight object detector is proposed for railway surveillance detection. Based on the conventional SSD model, EfficientSSD replaces VGG16 with EfficientNet-B3 as the backbone network. The detector was trained on the Pascal VOC dataset and then on a Railway dataset with the mAP of 76.6%. EfficientSSD runs at about 11 FPS on a CPU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> with an overall accuracy of 96.95%. Compared with the SSD, our method obtains a precision improvement of 4.8% with only 1/2x model size and 2x speed.
Authenticity of money using the method KNN (K-Nearest Neighbor) and CNN (Convolutional Neural Network)

Abstract Nowadays, the circulation of counterfeit money is significantly increased, encouraging to conduct research related to genuine real money detection or counterfeit money based on digital imagery. Technological sophistication one of the quality of printers whose ink is very good and can print money like the original makes the layman should be more wary of money ownership. In this research conducted the authenticity of money using the method KNN (K-Nearest Neighbor) and CNN (Convolutional Neural Network). Accuracy KNN method is 87,75%. While the detection accuracy used by CNN is 96,67%. The results obtained from these 2 methods can still be improved with advanced research namely with pre production on the set and the image used. The data set used has the same exposure level, image capture angle and image size.
A brain-inspired information processing algorithm and its application in text classification

Cognitive scientists believe that the human brain is constantly predicting the information it is going to receive, and this prediction ability is acquired based on the experiences gained from previous information it received over its lifetime. Inspired by this brain-behavior, we propose a new information processing algorithm with the building blocks named as boxes and routes. The function of boxes is to store the learned information and the function of routes is to represent the relationship between information. The novel algorithm features generality and objectivity. It imitates the mechanism of the human brain and has functions such as information learning, comparison, prediction, and forgetting. It also has advantages in dealing with continuous time series data by using routes and high-order boxes. The algorithm is self-adaptive and unsupervised. It does not need manual intervention information to train the model. It can learn useful information from undefined data and subsequently construct a hierarchical network corresponding to the characteristics of the input information, which can be used for classification or prediction. To prove the validity of this new algorithm, a classifier is constructed based on the hierarchical network to do text classification. We select a collection of Chinese literature from 30 litterateurs as samples to train the classifier. For 10 classes situation, the optimal average accuracy of classification reaches 79.5%, which outperforms other approaches commonly used in literatures, verifying the effectiveness of the proposed algorithm.
Investigation on the Eye-tracking Technology in Hazard Identification of Building Construction Engineering

The application of eye-tracking technology in construction safety management hazards is studied to accurately understand the personnel's cognition of construction hazards. Based on OSHA's construction safety training materials, this study aims to apply the eye-tracking technology to construction safety into virtual reality (VR) scene. The eye-tracking technology is combined with focus time, trajectory analysis, and other indicators. The tester validated the technology on the construction hazard. Finally, the research results are compared to OSHA construction hazard identification training materials, and feasible development suggestions and directions in the future are suggested.