Reviewerly/exHarmony huggingface dataset:
A Survey on Type-2 Fuzzy Logic Systems in Healthcare

Fuzzy logic plays a key role in various areas, particularly medicine. One of the major problems faced by developed and developing countries is medical treatment. There has been a great deal of concern in finding an alternative to traditional methods of diagnosis due to imprecision and inaccuracy. The fuzzy logic has made identification and diagnosis process rapid, thus minimizing the intervention time and treatment methods with greater accuracy. In fuzzy logic, type 2 logic systems and type 2 Fuzzy-based methods are preferred since they offer the major advantage of demonstrating a variety of outputs and provide a powerful framework for reasoning. This chapter includes the study of type 2 fuzzy logic systems used for treating particular diseases and their role in the development of the medical diagnostic system. The first aim broadly discusses how fuzzy logic has evolved over the years and its need in different sectors. The second aim is to analyze the applications of type 2 fuzzy logic in various branches and discusses recent developments that have occurred mainly in the medical sector. The third aim is to include type 2 fuzzy systems that are used to treat particular diseases paving the way to find further scope for further developments.
Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection

The traditional grey wolf algorithm is widely used for feature selection.However, within complex feature multi-dimensional problems, the grey wolf algorithm is prone to reach locally optimal solutions and premature convergence.In this paper, a threshold binary grey wolf optimizer based on multi-elite interaction for feature selection (MTBGWO) is proposed.Firstly, the multi-population topology is adopted to enhance the population's diversity for improving search space utilization.Secondly, an information interaction learning strategy is adopted for the update of sub-population elite wolf position (optimal position) via learning better position from other elite wolves; in order to improve the local exploitation ability of the sub-population.At the same time, the command of β and δ wolves (second and third best positions) for population position updates is removed.Finally, a threshold approach is employed to convert the continuous position of grey wolf individuals into binary one to apply in the feature selection problem.Further, The MTBGWO algorithm proposed in this paper is compared with the traditional binary grey wolf algorithm (BGWO), binary whale algorithm (BWOA), as well as some recently developed novel algorithms to exhibit its superiority and robustness.Totally 16 classification datasets, from the UCI Machine Learning Repository, are chosen for comparison.The Wilcoxon's rank-sum non-parametric statistical test is carried out at 5% significance level to evaluate whether the results of the proposed algorithms significantly differs from those of the other algorithms.In the experimental results for all datasets, the overall average accuracy of the MTBGWO algorithm is 94.7%, while the highest of the other algorithms is 92.8% and the selected feature subset is 25% of the total dataset, while the GWO and WOA algorithms are twice as high as MTBGWO.In terms of computational efficiency, the overall processing time of MTBGWO is 24.2 seconds, whereas HSGW is 44.1 seconds.The results reveal that the MTBGWO has shown its superiority in solving the feature selection problem.
Everything Has a Role to Play: Reconstruction of Vessel Function From Early Copper Age Graves in the Upper Tisza Region (Eastern Hungary)

Abstract Investigation into the utilitarian function of mortuary vessels is often a neglected aspect of ceramic examination. Since, in most cases, a direct link can be assumed between the vessels’ size, form, and most optimal utilitarian function, morphometry-based ceramic categories – along with ethnoarchaeological examples – can help to understand the role of these vessels in funerary contexts. This article focuses on the relationship between the utilitarian function of ceramics and their roles in graves through the analysis of eight Early Copper Age (4400/4300–4000/3900 BC) burial sites, associated with Tiszapolgár and Bodrogkeresztúr ceramic styles, from the Upper Tisza Region (Hungary). The deposition of ceramic assemblages in graves became common in this period; however, a systematic analysis of their function has never been carried out before. In this study, a morphometry-based methodology was developed to establish the vessels’ utilitarian function. These functions were considered in the analysis of the composition of mortuary assemblages, and how they may reflect social status or gender roles of the deceased individual. Results indicate that the Tiszapolgár and Bodrogkeresztúr assemblages cannot be unequivocally distinguished from each other based on morphometric and functional criteria, suggesting similar functional roles in the past funerary contexts.
QBERT: Generalist Model for Processing Questions

Using a single model across various tasks is beneficial for training and applying deep neural sequence models. We address the problem of developing generalist representations of text that can be used to perform a range of different tasks rather than being specialised to a single application. We focus on processing short questions and developing an embedding for these questions that is useful on a diverse set of problems, such as question topic classification, equivalent question recognition, and question answering. This paper introduces QBERT, a generalist model for processing questions. With QBERT, we demonstrate how we can train a multi-task network that performs all question-related tasks and has achieved similar performance compared to its corresponding single-task models.
Protection-Enhanced Watermarking Scheme Combined With Non-Linear Systems

Communication of the images over the network calls for an approach to ensure security and ownership protection while exchanging images over the network.This paper proposes an improved blind watermarking scheme capable of offering image authentication, protection, and detachment avoidance between the watermark and its corresponding image.The proposed technique is composed of three modules; generator, protector, and resizer.The generator deals with embedding and extracting the watermark from the host image.Protector deals with encryption and decryption using a high dimensional chaotic system.And resizer deals with compressing and decompressing the watermarked image.The novelty related scientific literature is the encryption of Singular Value Decomposition (SVD) components, and watermark in a criss-cross manner using logistic map along with hyperchaotic system to achieve the confidentiality and avoid false positive problem (FPP).Whereas, embedding into RGB (red, green, blue components) is centered on singular value decomposition and level-2 Discrete Wavelet Transform.The joint operation is performed at sender's side and receiver's side, i.e., encryption, embedding, and compression is performed at sender's side whereas decompression and decryption procedures are performed at the receiver's side.Additionally, compressing the data reduces the higher bandwidth requirements.The performance of the proposed scheme is based on subjective and objective evaluation.Experimental results such as PSNR (peak signal-to-noise ratio), NC (normalized correlation), BER (bit error rate), CR (compression ratio), NPCR (number of pixel change rate), and SSIM (structural similarity index) indicate that the proposed technique approaches the standard main level.
Intuitionistic Type-II Fuzzy Logic-Based Inference System and Its Realistic Applications to the Medical Field

In this chapter, we merge type-II fuzzy logic, and intuitionistic fuzzy logic in a broader way, to develop an intuitionistic type-II fuzzy logic. In intuitionistic type-II fuzzy logic, we deal the uncertainty, concerning with the truth values as well as false values. Intuitionistic type-II fuzzy logic is a stimulus of traditional intuitionistic type-I fuzzy logic in such a way that ambiguity is presented into linguistic variables that can be handled by using the truth grades and false grades with some hesitation margin. On the other hand, the proposed intuitionistic type-II fuzzy logic-based inference system accommodate intuitionistic fuzzy IF–THEN rules, which holds intuitionistic type-II fuzzy sets ( $${\mathrm{IFT}}_{\mathrm{y}}(\mathrm{II}))$$ . We also discussed the applications of proposed system in the various fields including; engineering, medical and agriculture etc. We applied over methodology over a data of lung cancer patients, which consists sixteen medical entities of infected patients. We also gave an example to illustrate our proposed technique.
Blockchain-Based Market Procurement of Reactive Power

A flexible and adaptable market procurement of reactive power has the potential to improve network efficiency, voltage stability, and operational costs.In Europe, such a dynamic procurement must be in accordance with the European Union directive EU 2019/944 on non-frequency ancillary services.In accordance with this situation, a blockchain-based framework of market procurement of reactive power is proposed and presented in this paper.The devised framework is in alignment with the European Union's directive to establish a non-discriminatory, transparent, and free market.Moreover, the blockchain-based framework is applicable at all network levels and allows for the variety of different distributed resources to participate.A two-layer blockchain topology is proposed to overcome scalability and transaction time issues.The first main-layer blockchain acts as the agent for trust and guarantees the immutability of data as well as the remuneration of participating market players.The second-layer blockchain facilitates direct access to tendering processes or auctions, fast transactions, and low transaction fees for involved stakeholders.Additionally, a decentralized oracle network is proposed to integrate external data into the second-layer blockchain.Data required for verifying the physical reactive power transactions are delivered by smart meters.The entire procurement process is automated by deploying smart contracts at the various blockchain layers.Thus, in principle, the involved stakeholders are the system operators and market players.For the purpose of validation, the holistic market procurement process is demonstrated and analyzed in a hardware-in-the-loop environment involving reactive power procurement at the distribution network level.
Design and Development of a Machine Learning-Based Task Orchestrator for Intelligent Systems on Edge Networks

This paper proposes an edge-centric workload orchestration approach that uses machine learning in a three-tier vehicular architecture (edge, cloud via roadside unit, or cloud via cellular base station).The orchestrator uses a wireless network at the edge to receive and send over-the-air requests from vehicles, considering a metropolitan network connects the entire edge structure to support the high mobility of devices, allowing vehicles to share information and resources.Additionally, suppose the edge is congested, or its resources are unavailable.In that case, cloud resources will be used via roadside or cellular networks using a wide-area network to meet the tasks' time constraints.Moreover, the proposed machine learning model uses variance-based sensitivity analysis to determine which inputs influence the model's final decision.The experiments performed on the EdgeCloudSim simulator are based on modelling computational and network resources besides the representation of vehicles.The results indicate that our approach best fits task offloading over the air, outperforming the comparative experiments between the one-stage(our approach) model against two-stage and random models.Furthermore, by using our one-stage model that outputs the average of the prediction interval and the variance of this interval, we can measure how confident our model is in its prediction.
A Decentralized Mnemonic Backup System for Non-custodial Cryptocurrency Wallets

When using non-custodial cryptocurrency wallets such as Exodus (Bitcoin), Metamask (Ethereum), and Keplr (Osmosis), the private keys are directly stored on the user’s device. Using such a wallet comes with the risk of losing all crypto assets when the device gets lost, stolen, or breaks down irremediably. Fortunately, most non-custodial wallets offer a way to recover the private keys by the mean of a “recovery phrase” also known as a “mnemonic seed phrase”. That phrase is usually between 12 and 24 words and it is generated when the user creates the wallet. Indeed, this mnemonic phrase is a really sensitive piece of information since anyone knowing that phrase can get full control of the crypto assets held by the wallet. Usually, it is recommended to write this passphrase down on a piece of paper and store it in a “safe place”. However, storing a physical object is still not ideal since it can get stolen, lost, or destroyed as well. In this paper, we propose a decentralized application that can be used to back up mnemonic phrases and recover them eventually using a simple email. This application is built on a privacy-preserving blockchain to store the confidential passphrase and protect the identity of its owner and the crypto assets holders.
A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

Asynchronous federated learning (AsynFL) can effectively mitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security. However, the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning (FL) algorithm uses synchronous or asynchronous aggregation. Therefore, there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction. This paper proposes a novel Fixed-point Asynchronous Federated Learning (FixedAsynFL) algorithm, which could mitigate the resource consumption caused by frequent data communication while alleviating the effect of device heterogeneity. FixedAsynFL uses fixed-point quantization to compress the local and global models in AsynFL. In order to balance energy consumption and learning accuracy, this paper proposed a quantization scale selection mechanism. This paper examines the mathematical relationship between the quantization scale and energy consumption of the computation/communication process in the FixedAsynFL. Based on considering the upper bound of quantization noise, this paper optimizes the quantization scale by minimizing communication and computation consumption. This paper performs pertinent experiments on the MNIST dataset with several edge nodes of different computing efficiency. The results show that the FixedAsynFL algorithm with an 8-bit quantization can significantly reduce the communication data size by 81.3% and save the computation energy in the training phase by 74.9% without significant loss of accuracy. According to the experimental results, we can see that the proposed AsynFixedFL algorithm can effectively solve the problem of device heterogeneity and energy consumption limitation of edge nodes.
Multi Language Recognition Translator App Design Using Optical Character Recognition (OCR) and Convolutional Neural Network (CNN)

A mobile translator is a Phone’s app that lets user to translate between languages. In this paper, we proposed multilanguage recognition translator (MLRT) mobile app to help the education system, especially for students who are new to Arabic, Malay, and English to learn the languages and translated to other languages. OCR Methodology has been chosen for this project because it is the most appropriate methodology to develop a mobile application. Data acquisition, pre-processing, segmentation, feature extraction, classification, and post-processing are the six phases for OCR methodology. The Convolutional Neural Network (CNN) algorithm is used by deep learning to identify objects in image and Optical Character Recognition (OCR) is used for feature extraction to process Arabic words and translate them into Malay. A system architecture has been created to provide an overview of how the application will run and the functionality and the framework of output to show the application works.
Potential Assessment of Rooftop Photovoltaic Power Generation in Wide Areas

The concept of low-carbon environmental protection is being taken into consideration by more and more countries and regions. As a clean renewable energy, technology of solar power generation has been developed rapidly. This paper proposed the method of the potential assessment of rooftop photovoltaic (PV) power generation in wide areas. The influence factors were analyzed with the actual data, including the planning site and weather condition. The rooftop area was calculated with consideration the rooftop area coefficient, available area coefficient and cell panel coverage coefficient. For different planning sites, the coefficients were different. Then, based on long-term historical data, the predicted value of power generation per unit area of photovoltaic panels is obtained. According to the total panel area data and the unit panel power generation data, the regional photovoltaic power generation is obtained. The method proposed in this paper is used to calculate the rooftop photovoltaic power generation in Hebei Province. The method proposed in this paper can be applied to a wide range of rooftop photovoltaic power generation potential assessments with different planning types.
Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

Smartphones have now become an integral part of our everyday lives. User authentication on smartphones is often accomplished by mechanisms (like face unlock, pattern, or pin password) that authenticate the user's identity. These technologies are simple, inexpensive, and fast for repeated logins. However, these technologies are still subject to assaults like smudge assaults and shoulder surfing. Users' touch behavior while using their cell phones might be used to authenticate them, which would solve the problem. The performance of the authentication process may be influenced by the attributes chosen (from these behaviors). The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of smartphones. We began by concentrating on feature selection methods utilizing the grey wolf optimization strategy. The random forest classifier is used to evaluate these tactics. The testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public dataset. It achieved a 97.89% accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly; processing power of the device and memory to validate individuals. Simultaneously, the findings revealed that our approach has a lower equal error rate (EER) of 0.5104, a false acceptance rate (FAR) of 1.00, and a false rejection rate (FRR) of 0.0209 compared to the methods discussed in the literature. These promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources.
Sign Language Detection Using Machine Learning

Sign language detection project is to detect the sign language hand gestures, which really helps the common people like is to understand what a deaf or mute people are trying to converse with us. The sign language detection translates the sign language, in which user forms a hand shape that is structured signs or gestures. In sign language, the configuration of the fingers, the orientation of the hand, and the relative position of fingers and hands to the body are the expressions of a deaf and mute person. Based on this application, the user must be able to capture images of the hand signs or gestures using web camera and they shall predict the hand signs or meaning of the sign and display the name of sign language on screen. At first, we will be taking sample images of different signs, for example, hello, eat, thankyou, etc. Then we are going to label the images with the LabelImg python application file, which is very helpful for object detection. The LabelImg application file develops an XML document for the corresponding image for the training process. In the training process, we have used TensorFlow object detection API to train our model. After training the model, we have detected the sign language or hand gestures in real time; with the help of OpenCV-python, we access the webcam and load the configs and trained model, so that we have detected the sign languages in real time.
About How to Write a Scientific Text: Bruno Latour’s Recommendations

The article raises the problem of the need to update the methodology of writing scientific texts in the context of the transformation of modern science. A solution to this problem is proposed using the theoretical and methodological capabilities of the actor-network theory. Based on the analysis of the works of the founder of this theory B. Latour, a system of methodological techniques is presented: the method of controversy, description, explanation and translation, which ensure the integration of the processes of conducting research and writing scientific texts. As a result, a conclusion is made about the need for consistent development of B. Latours methodology and its approbation in the preparation of scientific personnel.
Learning Permutation-Invariant Embeddings for Description Logic Concepts

Concept learning deals with learning description logic concepts from a background knowledge and input examples. The goal is to learn a concept that covers all positive examples, while not covering any negative examples. This non-trivial task is often formulated as a search problem within an infinite quasi-ordered concept space. Although state-of-the-art models have been successfully applied to tackle this problem, their large-scale applications have been severely hindered due to their excessive exploration incurring impractical runtimes. Here, we propose a remedy for this limitation. We reformulate the learning problem as a multi-label classification problem and propose a neural embedding model (NERO) that learns permutation-invariant embeddings for sets of examples tailored towards predicting $$F_1$$ scores of pre-selected description logic concepts. By ranking such concepts in descending order of predicted scores, a possible goal concept can be detected within few retrieval operations, i.e., no excessive exploration. Importantly, top-ranked concepts can be used to start the search procedure of state-of-the-art symbolic models in multiple advantageous regions of a concept space, rather than starting it in the most general concept $$\top $$ . Our experiments on 5 benchmark datasets with 770 learning problems firmly suggest that NERO significantly (p-value $$<1\%$$ ) outperforms the state-of-the-art models in terms of $$F_1$$ score, the number of explored concepts, and the total runtime. We provide an open-source implementation of our approach ( https://github.com/dice-group/Nero ).