Reviewerly/exHarmony huggingface dataset:
Fuzzy Rule-Based Models: A Design with Prototype Relocation and Granular Generalization

Fuzzy rule-based models and the extension of classical fuzzy models have been widely used in many domains. From a holistic perspective, regardless of the design methods and rules adopted in a fuzzy model, the determination of fuzzy sets is a pivotal issue. In the proposed methods, instead of traditional data clustering with no directional tendency, we introduce an optimization algorithm that can adjust the position of the prototypes of zero- and first-order fuzzy models to learn internal structure information from the data in the process of parameter identification. Furthermore, to build a granular fuzzy model, the prototypes are then scaled to more robust intervals by generating information granularity with specific semantics such that they split the whole output space. Particle swarm optimization algorithm is applied to adjust both the locations of the prototypes and the allocation of information granularity to improve the performance of the data-driven models. Experimental studies on synthetic and real-world datasets are provided to demonstrate the effectiveness of these methods.
Bitcoin Speculation or Value Creation? Corporate Blockchain Investments and Stock Market Reactions

Many corporate executives believe blockchain technology is broadly scalable and will achieve mainstream adoption, yet there is little evidence of significant shareholder value creation associated with corporate adoption of blockchain technology. We collect a broad sample of firms that invest in blockchain technology and examine the stock price reaction to the first public revelation of this news. Initial reactions average close to +13% and are followed by reversals over the next three months. However, we report a striking difference based on the credibility of the investment. Blockchain investments that are at an advanced stage or are confirmed in subsequent financial statements are associated with higher initial reactions and little or no reversal. The results suggest that credible corporate strategies involving blockchain technology are viewed favorably by investors.
A Low cost Augmented Reality system for Wide Area Indoor Navigation

In today's world, there are a lot of outdoor navigation apps for visually challenged people, but there are none that can precisely tell a user's location inside a large structure. Indoor navigation is a complex task for the visually challenged as well as for the general public, especially in large structures like malls, airports, museums, factories, etc. Present solutions and technologies are not cost-effective as well as complex. Hence, we are proposing a low-cost model that uses Augmented Reality to place virtual anchors across a structure, so a person can navigate from one location to another with the help of these anchors. The model doesn't use technologies like GPS. Machine Learning, and Artificial Intelligence but here, the anchors placed are pervasive and persistent across the indoor environment for smooth navigation. Once placed, these virtual anchors remain at their location and can be used at any time by any person registered on our app. This model can be extended for the general public in any indoor space and also can be enhanced by gamification for better user interaction and retention. This model can also be extended to collaborate with the Aarogya Setu app, which can help us identify routes that go through spaces through which covid positive patients have passed which in turn helps us avoid those routes in real-time navigation.
Blockchain at the Shop Floor for Maintenance

Blockchain and Industry 4.0 have the potential to revolutionize the way entities work together in the industrial environment. Former paper-based and manual tasks, such as maintenance, will be replaced by smart contracts in combination with automatic data collection. We consider different data sources and possibilities integrating them into a blockchain ecosystem in consideration of integration and security aspects. We identify potential security threats around the blockchain and propose potential countermeasures. Finally, we provide three solutions to protect blockchain users against three threats targeting data corruption, data protection and input falsification.
Implementation of The High-Speed SM4 Cryptographic Algorithm Based On Random Pseudo Rounds

The SM4 cryptographic algorithm is one of the standards of commercial cryptographic algorithm in China, which is used widely in the data encryption and other application scenarios for the advantages of less resources and fast speed. The attackers attack the first and last round of SM4 algorithm generally in the side channel attack. The number of the invalid random rounds before and after the SM4 cryptographic algorithm can make the side channel information collected by the attacker invalid in order to resist the side channel attack; At the same time, the multistage pipelining method is adopted to realize the SM4 cryptographic algorithm at the high speed, that is, four rounds of operations are realized every cycle, and one SM4 grouping operation is completed in eight cycles. The implementation results of the FPGA show that the clock frequency of SM4 cipher operation is up to 100MHz and the throughput rate under ECB mode is up to 1.6gbps, which has the advantages of high performance and anti-side channel attack.
Evolutionary fusion of classifiers trained on linear prediction based features for replay attack detection

Abstract Recently, linear prediction analysis (LP) related features have been successfully used for replay attack detection due to the imperfection in the LP‐based signal produced by recording and playback devices. In this paper, we propose a weighted linear combination of classifier scores for replay attack detection where our classifiers, including Gaussian mixture models (GMMs) and support vector machines (SVMs), are trained on a variety of LP and LP residual‐based features. In this way, we can benefits from all of the LP‐related features when we combine classifiers trained on these features. We determine classifier weights using two evolutionary algorithms: genetic algorithm and particle swarm optimization. Furthermore, we propose a new feature based on performing LP residuals analysis of Mel sub‐band energies. We also propose a deep structure for extracting deep features from LP‐based coefficients to consider the class labels (genuine or spoofed speaker) in the feature extraction process. Results of our classifier system on the ASVspoof 2017 version 2 dataset show equal error rates of 0.3% and 4.8% for its development and evaluation subset, respectively. We also applied our proposed replay attack detection method to an ASV system that has acceptable results.
Attendance Management System using Facial Recognition

In recent years, technologies related to Facial Recognition have undergone a remarkable upgradation in the domain of commerce as well as security. This paper presents an automated real-time attendance management system (AMS) using face recognition technique to reduce the human dependency and thereby saving the time. A modified local binary pattern histogram (MLBPH) algorithm based on calculation of based on pixel neighborhood gray median for extracting the significant features of the human face. More specifically, the facial landmarks are extracted to provide a completely unique result using MLBPH histogram. Further, the histogram of the input image is compared with database histogram using the classifier in the classification step. The human face matched with the database is used to mark attendance in the laboratory. The experiments reported a precision and recall of 97% and 95% respectively. This kind of biometric system is a real-time attendance system processing the human faces using simple and fast algorithms having higher accuracy can be deployed in schools.
A Methodological Approach to Compare Ontologies

Representation of the knowledge related to any domain with flexible and well-defined models, such as ontologies, provides the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. It is necessary to define how to compare such ontologies to decide which one is the most suitable for specific needs of users/developers. Since the emerging developing of ontologies, several studies have proposed criteria to evaluate them. Nevertheless, there is still a lack of practical and reproducible guidelines to drive a comparative evaluation of ontologies as a systematic process. In this paper, we propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural, and Domain Knowledge levels, considering Correctness and Quality perspectives. Since the evaluation methods of our proposal are based in a golden-standard, it can be customized to compare ontologies in any domain. To show the suitability of our proposal, we apply our methodological approach to conduct a comparative study of ontologies in the robotic domain, in particularly for the Simultaneous Localization and Mapping (SLAM) problem. With this study case, we demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domain (SLAM for our study case).
The symptom, cause and repair of workaround

In software development, issue tracker systems are widely used to manage bug reports. In such a system, a bug report can be filed, diagnosed, assigned, and fixed. In the standard process, a bug can be resolved as fixed, invalid, duplicated or won't fix. Although the above resolutions are well-defined and easy to understand, a bug report can end with a less known resolution, i.e., workaround. Compared with other resolutions, the definition of workarounds is more ambiguous. Besides the problem that is reported in a bug report, the resolution of a workaround raises more questions. Some questions are important for users, especially those programmers who build their projects upon others (e.g., libraries). Although some early studies have been conducted to analyze API workarounds, many research questions on workarounds are still open. For example, which bugs are resolved as workarounds? Why is a bug report resolved as workarounds? What are the repairs of workarounds? In this experience paper, we conduct the first empirical study to explore the above research questions. In particular, we analyzed 221 real workarounds that were collected from Apache projects. Our results lead to some interesting and useful answers to all the above questions. For example, we find that most bug reports are resolved as workarounds, because their problems reside in libraries (24.43%), settings (18.55%), and clients (10.41%). Among them, many bugs are difficult to be fixed fully and perfectly. As a late breaking result, we can only briefly introduce our study, but we present a detailed plan to extend it to a full paper.
Hand Frame Extraction in Surgical Video Images Using Convolutional Neural Network

Knee arthroplasty (KA) has led to substantial clinical results through the advancement of surgical procedures. Hand frame extraction is one of the important and crucial tasks to analyze video images of Orthopaedic surgery (OS). Although a number of vision-based techniques have been used to interpret hand movements in the field of computer vision, proper hand frame extraction in OS video is still a challenging task in the surgical work atmosphere. Therefore, this paper proposes a hand frame extraction method in KA surgical video images. For each video frame, the proposed method automatically recognizes three classes, hand, no-hand, or non-surgical area, by using Resnet-50. The method was validated by using five videos of UKA (Unicompartmental knee arthroplasty), a type of KA. The experimental results demonstrated that the model achieved a recognition accuracy of 96%. Moreover, the model has shown significantly good performance in extracting hand class frames in the test dataset (Precision = 0.95, Recall = 0.98, and F1-score = 0.97).
Spherical Evolution Enhanced with Salp Swarm Algorithm

Recently, a new population-based optimization algorithm called spherical evolution (SE), which is inspired by the traditional hyper-cube search style, has been proposed. It has significant ability of exploration and can avoid local optimum. On the other hand, salp swarm algorithm (SSA) has great advantage in local optimization of current random solutions. In this study, we are devoted to incorporating SSA into SE for solving optimization problems. In this hybrid algorithm, SE contributes to enhance the exploration during its iteration, and SSA is supposed to accelerate the convergence to optimal solutions. The experiment results on IEEE CEC 2017 benchmark functions indicate the effectiveness of this hybridization and show that spherical search style and salp swarm search mechanism are complementary. This study gives not only more insights into both original algorithms, but also a novel construction method of merging different algorithms.
Structural Hierarchy-Enhanced Network Representation Learning

Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP). However, existing NRL methods neither properly identify neighbor nodes that should be pushed together and away in the embedding space, nor model coarse-grained community knowledge hidden behind the network topology. In this paper, we propose a novel NRL framework, Structural Hierarchy Enhancement (SHE), to deal with such two issues. The main idea is to construct a structural hierarchy from the network based on community detection, and to utilize such a hierarchy to perform level-wise NRL. In addition, lower-level node embeddings are passed to higher-level ones so that community knowledge can be aware of in NRL. Experiments conducted on benchmark network datasets show that SHE can significantly boost the performance of NRL in both tasks of NC and LP, compared to other hierarchical NRL methods.
Mammograms Classification Using ELM Based on Improved Sunflower Optimization Algorithm

Abstract To assist specialists in detecting breast cancer on mammograms with better accuracy and less time consuming, this paper proposes an approach based on improved sunflower optimization algorithm (ISFO) and extreme learning machine (ELM). Firstly, features were extracted by using lifting scheme and gray-level co-occurrence matrix (GLCM). Then, the parameters of ELM were optimized by (ISFO) to obtain the final classification results. Finally, in order to avoid overfitting, the proposed model’s performance was evaluated with k-fold random stratified cross validation, and the experiments compared the model with other models on MIAS datasets. The experimental results show that the proposed model has higher classification accuracy, shorter learning time and stronger robustness on mammograms classification task. Thus, this method could be a promising application in bio-medical and provide a basis for the early diagnosis of breast cancer.
Configuration-Space Flipper Planning on 3D Terrain

Flippers are essential components of tracked robot locomotion systems for unstructured terrain, especially within a rescue scenario. Achieving full and semi-autonomy for such rescue robots is the goal of many research efforts. In this work, we propose an algorithm to plan the morphologies of a small rescue robot with four flippers to get over 3D ground without any extra sensors, such as pressure sensors. To achieve the goal, we simplify the rescue robot as a skeleton on inflated terrain. Its morphology can be represented by configurations of several parameters. Then we plan the mobile robot's movement on 3D terrain with four individually manipulated flippers. We perform real robot experiments on three different obstacles. The results show that we move the flippers very effectively and are thus able to tackle those terrains very well.
Using Blockchain Technology to Redesign Know-Your-Customer Processes Within the Banking Industry

Blockchain has emerged as a technology with the potential to innovate business processes. The potential benefits of this technology have attracted the attention of banks. In particular, banks are interested in redesigning compliance processes to reduce costs and risks. One of the most crucial compliance processes is that of the Know-Your-Customer (KYC) process. The KYC process serves to ensure that banks assess the risk of doing business with a client and adopt appropriate mitigation strategies. The KYC process is mandatory, expensive, reduces customer satisfaction, and does not add value to the banks. Therefore, banks seek to use technologies to redesign the KYC process. In recent years, blockchain technology has been considered for the redesign of the KYC process. However, history has taught us that merely substituting existing technology has limited value. Rather, it is necessary to use the capabilities of new technologies, such as blockchain, to enable redesign of the KYC process. In this paper, we examine how blockchain technology can enable the redesign of the Know-Your-Customer (KYC) process. To this end, we conduct a case study and discuss how the capabilities of blockchain technology can enable banks to redesign their KYC processes.
Application-oriented Model Management Method on Dispatching and Control Cloud

With the construction of the dispatching and control cloud platform, more and more dispatching applications need to rely on the model data of the cloud for function realization. The dispatching and control cloud uses the methods of unified modeling and integrated model maintenance to construct cloud models with unique coding, unique properties and unique parameters. In view of the differences between the storage form of the dispatching and control cloud model and the model form required for the dispatching applications, this paper proposes an application-oriented model management method on dispatching and control cloud, which can help the applications to define the model structure on demand, subscribe the cloud model data, save the historical model versions required for the application, and make cloud model support the dispatching applications.
Thermodynamics Reloaded: Experiencing Heating, Ventilation and Air Conditioning in AR

Augmented Reality (AR) has great potential for new didactic concepts in teaching. Environments, information and objects can be comprehensively and dynamically represented, supporting self-paced and holistic learning. This paper presents an implementation of a multimodal AR-application for the purpose of teaching complex features and mechanics of a ”Heating, Ventilation and Air Conditioning System” in a situated and engaging way. The application was designed and implemented by an interdisciplinary team and evaluated in a mixed-methods approach. Results show a high usability and acceptance of the application. Students recognized the benefit of the application regarding their motivation and learning gains and made suggestions for further improvements.
Covering of minimal solutions to fuzzy relational equations

A minimal solution to a fuzzy relational equation is, loosely speaking, a solution such that the values of all its elements cannot be decreased. To find some minimal solution is simple – one can just identify the greatest solution to the equation and then lower the values of all its elements as much as possible. However, finding all minimal solutions is significantly more difficult since when decreasing the values one needs to go through all combinations of the elements. The paper shows that when considering an n-element scale of truth values equipped with Łukasiewicz or Goguen operations, the set of all minimal solutions can be represented by constrained solutions which allows to skip a considerable amount of these combinations.
A Comparative Study on Automated Detection of Malaria by Using Blood Smear Images

Malaria is a parasitic disease or mosquito-borne blood disease. When the mosquito bites a human being, that particular parasite is freed into the human being bloodstream and infects the red blood cells which cause the malaria. We need to understand if the blood-related illness is malaria or not before we provide the right therapy. For this purpose, we must diagnose red blood cells by recognizing or counting red blood cells (erythrocytes). It is very difficult to manually count and recognize infected red blood cells while testing under a microscope by pathologists because maybe it leads to different variations. The current paper gives an overview of the comparison of three different papers with three different techniques used to identify that the red blood cells are infected or not with great accuracy and also to identify which methods are giving best result while performing the diagnosis automatically. With different techniques and methods like Otsu threshold method, global threshold method and classifiers like artificial neural network and support vector machines. All these techniques and methods are related to the diagnosis of the malaria automatically which will reduce the time taken for performing the diagnosis and also it improves the consistency and gives the accurate, rapid result in diagnosis. From the above three methods used, an attempt has been made to finalize the best method from the above three methods.
An Intelligent Traffic Monitoring System for Non-Helmet Wearing Motorcyclists Detection

Riding without a helmet is the most common road offense in India. Road safety rules and regulations have to be more strictly followed and enforced. Currently, police officers identify offenders with human surveillance. This existing system is very inefficient and labor-intensive. At present, all major cities are already under a large video surveillance network to keep a watch on a wide variety of threats. However, these networks are not intelligent enough to do tasks such as non-helmet wearing motorcyclist detection. Hence, we propose an intelligent traffic monitoring system, which can detect non-helmet wearing motorcyclists from video streams using a convolutional object detector in real-time. The system will detect a violation, then attempt to extract the registration number of the vehicle and then persist the violation with evidence. An office of the law can review and take appropriate actions on the violations. This system is designed to integrate into existing video surveillance networks. The convolutional object detector was able to detect non-helmet wearing motorcyclists with an accuracy of 84% on the validation set.
Accuracy Analysis of Pvsyst Software for Estimating the Generation of a Photovoltaic System at the Polo de Inovação Campos dos Goytacazes

The use of photovoltaic solar energy has been widespread in recent decades, mainly because it is considered a pollutant-free technology. Due to the increased demand for this type of energy source, various software has appeared on the market promising reliable estimates of electricity generation. Therefore, the article proposes to make a comparison between the generation obtained at the Instituto Federal Fluminense Campus Polo de Inovação Campos dos Goytacazes - Brazil and the simulations presented by the PVsyst software (test version), using four different meteorological databases. The results reveal that the software provides coherent generation estimates, with percentage differences of less than 5% when compared to the real system for the performed simulation. In addition, under those circumstances, when using the Atlas database, the annual estimates were the closest to reality and the monthly analysis showed that the INMET database presented the lowest absolute mean errors (MAPE) and standard deviations (DP).