Reviewerly/exHarmony huggingface dataset:
Acts of Commanding and Promising in Dynamified Common Sense Term-Sequence-Deontic-Alethic Logic

The language of propositional modal logic has been shown highly useful in developing logics of various specific speech acts. But it is also clear that we need more expressive language if we wish to say, for example, that, for any x and y, whenever x promises y to see to it that y is safe, it becomes obligatory for x to see to it that y is safe with respect to y in the name of x. We develop a dynamic modal predicate logic, $$\textbf{DCTSDAL}^{=}$$ (Dynamified Commonsense Term-Sequence-Deontic-Alethic Logic with equality), in which we can assert exactly the kind of things like this by extending a version of term-sequence-modal logic introduced in [7] with dynamic modalities that stand for acts of commanding and promising. We develop a semantics which incorporates the commonsense treatment of free variables introduced in [1] and combine it with the varying domain assumption as is done in [9]. The main contribution of this paper is the relative semantic completeness of $$\textbf{DCTSDAL}^{=}$$ with respect to its static base logic $$\textbf{CTSDAL}^{=}$$ via recursion axioms.
Gesture Recognition and Conversion to Speech for Specially Abled

There are around 1 million people who suffer from hearing and speech impairment. They might find it difficult to express their thoughts because of their limited capabilities. However, using the latest technologies we can overcome this problem and help the specially abled with their inabilities. The latest trends in human-computer interaction, artificial intelligence and machine learning made it possible to build a system which can act as a mediator for specially abled people. In this paper, a comparative analysis is provided with respect to few existing gesture recognition methods. The intent of this paper is to compare and analyse the existing methods of gesture recognition and conversion of text to speech to help find out the most efficient algorithm among the algorithms compared to other fellow researchers.
An Optimization Ontology for Goal Modelling Frameworks

Enterprises are driven by specific business objectives which should be delivered in the best possible manner within constrained business environments. In this paper, we propose a new ontology for goal modeling frameworks that enables enterprises to formally capture the business objectives and constraints associated with business goals as an optimization problem. We associate optimization problems with goals in such a manner that the solutions to the problems at the lower levels of the goal decomposition tree can be combined to obtain the solution for goals higher up in the tree. As a proof-of-concept, we have developed a web-based supply chain configuration management tool, deployed over the Ethereum blockchain, that relies on the Google OR-tools API to find optimal solutions. The interface allows the user to specify business objectives and constraints and then provides the best possible supply chain configuration that optimizes the business objectives.
Classification of Devanagari Characters Dataset Using Deep Learning Methods

Deep Learning is a growing set of approaches for extracting useful information and knowledge from large amounts of data. Deep Learning research and tools have focussed on commercial sector applications. Only a fewer Deep Learning research have focussed on scientific data. This paper highlights the Machine Learning techniques applied to handwriting character recognition using network models built by using Deep Learning Techniques. Handwriting Recognition is now getting attention for researchers to assist technology for visually impaired, blind and human-robots for business documents. We have experimented Devanagari Character database with two different architectures GoogLeNet and AlexNet to evaluate the performance and achieve highest accuracy.
Performance Analysis of Classification Algorithms

Classification issues are crucial in machine learning and data mining. Classification difficulties are utilised in medical diagnostics, bank customer estimation, medicinal investigations and emotion analysis. Many categorisation methods have been created with many different parameter inputs. Using hyperparameter optimisation algorithms, this work aims to improve classification success. The ‘heart and iris’ data sets have been classified using K-nearest neighbour, SVM, decision tree and gradient boost techniques. The hyperparameter optimisation algorithms grid search and random search are applied to these selected classification algorithms. Experiments have shown that using hyperparameter optimisation algorithms improves the performance of all classification algorithms. The best parameter values are shown.
Towards Developing a Deep Learning-Based Liver Segmentation Method

With continuous advancement of high dimensional imaging devices and the development of new image computing and visualization techniques, the world of interventional radiology and surgical care has achieved new heights. Medical images acquired before and during the intervention/surgery are being used to accurately delineate the boundaries of objects of interest or guide the interventional/surgical procedure. Generally, the neighboring organs such as kidney, heart, stomach, and muscles make the delineation of liver difficult due to similar intensity distributions on the radiological images. Thus, there have been several challenges arise due the variability in shape, size, and proximity to the other neighboring organs. This may also be mentioned that blurred liver edges and low contrast on the CT image make the segmentation further challenging. Additionally, patient movement during CT data acquisition along with spatial averaging lead to reconstruction artifacts; these are all reflected on the CT image that further complicates the segmentation task. In this paper, we have designed U-Net based fully automatic liver segmentation approach which accurately delineates the boundaries between the liver and other abdominal organs.
A Comparative Study on Sign Language Translation Using Artificial Intelligence Techniques

The mode of communication which employs gestures made manually and is primarily utilized by deaf-mutes is called Sign Language (SL). There arises a challenge when the deaf-and-dumb community tries to communicate with the normal community. The recent advancements in science and technology led to the invention of several systems based on artificial intelligence which translates the sign language to its predefined meaning. Here is a comparative analysis of various artificial intelligence techniques based on classification ac- curacy which has already been used for sign language translation by several authors. This observation will help recognize the flaws and techniques to improve the present technology.
Human Posture Monitoring

The problem this report sets out to serve is Human Posture Monitoring. Keeping good posture is very important because it helps in improving spine health. People who keep good posture are less likely to experience back and neck pain. The paper proposes a solution to these problems of fixing human posture. Performing different Asanas that can help fix posture is important. The posture monitoring while performing Asanas is important otherwise it could result in various problems. To keep these activities in check while performing them can yield better results in the long run. Keeping bad posture can result in some chronic problems as one grows old. This can be prevented in advance with practicing some daily Asanas, so the earlier the better. The objective is to monitor the posture of humans using technology like OpenCV and TensorFlow and make comparisons to see if the individual is following the right posture.
Blockchain-Based Agri-Food Supply Chain Management

Advancements in the supply chain involving automatic networks are becoming a significant wellspring of advantages in the contemporary environment. Consumers on the other hand are emphasized in the quality of food products. The proliferation in production and supply of unhealthy, preserved, and zero nutritional value food supply has completely restored the center around the well-being, quality, and approval of high nutritional value food products in the food supply chain. The booming concerns about the third-party involvement, food quality, user privacy, and transparency calls for an effective, reliable, and secure system that tackles all these existing issues. Blockchain is advancing as a decentralized and more secured technology that could reinstate the contribution of the third party and also verify the transactions within the network. The ultimate aim was to implement a blockchain based to keep a track on the food quality and the emergence of the supply chain. A Quick Response code, which contained the location of the case, was a reference to the virtual item in the network. In order to achieve transparency, interaction of all the actors within the system is necessary. Peers of the blockchain system validate each and every transaction that is product related. For every transaction, the product ownership was modified. The reputation of farmers with their products was stipulated based on token-based mechanisms. A confirmation demand with respect to the items can be put, and the farmers can be benefited with notoriety tokens for each affirmation by peers. In order to distinguish or identify every item inside the supply chain, a unique Quick Response code has been used to improve both security and productivity for area-based assistance in vehicular social networks (VSNs), because of its inborn decentralization, obscurity, and trust properties. Sadly, the current methodologies either need powerful verification, which is helpless against man-in-the-middle attack, or require an online certificate authority (CA), where regular communications with asset compelled vehicles are required. To address these challenging issues, in this paper, a lightweight threshold CA for public blockchain along with a privacy preserving location-based service is proposed.
Legal Aspects of Image Morphing and Manipulation Detection Technology

Manipulation Attack Detection (MAD) becomes increasingly important when relying on biometric information systems which serve a large variety of purposes, such as border checks, access control to digital or physical environments, and convenience, e.g., airline check-in. Besides technical aspects, legal aspects shall not be overlooked. When deploying MAD technology, responsible entities shall determine, inter alia, the objectives of the use of this technology (legitimacy and purpose limitation principle), the law and the legal ground on which the deployment of MAD technology will be based (legality and lawfulness), the information to be provided to individuals relating to data processing (transparency), and also how fair and accurate the processing is and shall be (fairness). Also, human oversight and intervention where automatic decision-making is in place is a must. This contribution delves into these principles and poses legal questions which arise in the context of MAD technology and practical applications.
Empirical Model for Estimating Measured Monthly Average Global Solar Radiation in Lawra using TMY Data

In this study, simple and multiple regression models were developed to estimate the monthly average daily global solar radiation in Lawra, Ghana using ground measurement of global horizontal irradiance (Nov 2020–May 2022) and typical meteorological year (TMY) data (Jan 2017–Dec 2019). Various predictor variables such as sunshine ratio, minimum relative humidity and maximum relative humidity ratio, minimum and maximum temperature ratio, etc. were correlated from the TMY data. Many model equations were developed with the variables ranging from one to eight. The best model from each category was chosen and compared using statistical indices to determine the overall best model. We used the JMP statistical software’s ‘All Possible Models’ functionality to select the best model from each category. The selected models where then compared using the adjusted R-squared, mean absolute percentage error, and the root mean square error statistical indices. The best model equation correlated with eight independent variables with adjusted R-squared of 0.99. The equation can be used to estimate monthly global solar radiation in Lawra and in locations with similar climatic conditions where ground measurement of radiation data is unavailable but have access to the National Solar Radiation Database’s (NSRDB) TMY data.
Verifiable External Blockchain Calls: Towards Removing Oracle Input Intermediaries

It is widely accepted that blockchain and other distributed ledgers cannot initiate requests for input from external systems and are reliant on oracles to provide such inputs. This belief is founded on the fact that each node has to reach a deterministic state. In this paper we show that this belief is a preconceived one by demonstrating a method that supports calls to external systems initiated from the blockchain itself.
Examining the effect of different knowledge aspects on information security awareness

Purpose The purpose of this paper is to identify the different aspects of knowledge and how they associate with information security awareness (ISA). The paper also explores how ISA differs based on demographic characteristics. Design/methodology/approach Survey data was collected from 609 respondents in Malaysia. Findings The results show that increasing access to informal, multimedia learning mediums, declarative, schematic and strategic knowledge positively impacts an individual's ISA, whereas textual learning medium decreases the ISA. Respondents with different education levels significantly prefer different types of knowledge. Males learn better for ISA with schematic and strategic knowledge compared to females. Practical implications The research provides implications for governments and organizations in designing effective ISA campaigns. Originality/value Studies show that ISA is crucial in improving information systems policy compliance behavior. The literature has examined various topics ranging from the factors influencing the ISA to how ISA impacts information security behavior. However, there is a lack of study on how different aspects of knowledge impact ISA. This study identified various knowledge aspects from the literature and grouped them into the source, type of knowledge, emotion toward knowledge and learning medium.
Grape: Efficient Hybrid Consensus Protocol Using DAG

One most potential solution to enhancing the performance of the Nakamoto consensus is to utilize the classic Byzantine fault-tolerant protocol running by a rolling committee. However, this hybrid consensus method still faces some challenges. One is that many hybrid consensus schemes use the Nakamoto single-chain, resulting in low throughput and poor scalability. The other is that the committee’s internal consensus process has to be interrupted when the committee rotates. To address these challenges, we propose Grape, an efficient hybrid consensus protocol using the Directed Acyclic Graph structure. We prove that Grape is secure when the adversary’s ratio of the mining power is less than 1/3. To demonstrate the feasibility of Grape, we implement a prototype and make the experimental evaluation. The result shows that Grape achieves high transaction throughput with instant confirmation.
A Game-Theoretic Analysis of Delegation Incentives in Blockchain Governance

The paper presents a mathematical description of the vote delegation incentive process for funding proposals in a decentralized governance system using a blockchain-based voting. Two models of bribing a delegate by a proposer submitting proposals for funding are considered: “Rational Delegates” and “Emotional Delegates”. In terms of parameters describing the voting process, a sufficient condition for a Nash equilibrium is found to be as follows: if both a proposer and a delegate do not intend to participate in bribery. Moreover, it is shown at what share of the briber’s stake this condition is satisfied. The main practical result of the paper is the possibility to define what kind of an attacker (in terms of the bribing capability) we will be able to resist under certain parameters.
Depth Control of a Biomimetic Manta Robot via Reinforcement Learning

This paper proposes a model-free biomimetic manta robot depth control method based on reinforcement learning. Different from the traditional control method, the reinforcement learning method does not need to establish a mathematical model of the control object, and autonomously learns the control law through data training. Based on the classical Q algorithm, the state space, the action space, and reward function of the depth control of the bionic manta robot are designed. The state-action function is trained offline using the experience replay mechanism and random sampling strategy. Finally, the trained function is transplanted to the biomimetic manta robot prototype to establish a controller. The effectiveness of the proposed control method is verified by experiments.
Incremental Quaternion Random Neural Networks

Quaternion, as a hypercomplex number with three imaginary elements, is effective in characterizing three- and four-dimensional vector signals. Quaternion neural networks with randomly generated quaternions as the hidden node parameters become attractive for the good learning capability and generalization performance. In this paper, a novel incremental quaternion random neural network trained by extreme learning machine (IQ-ELM) is proposed. To fully exploit the second-order Q-properness statistic of quaternion random variables, the augmented quaternion vector is further applied in IQ-ELM (IAQ-ELM) for hypercomplex data learning. The network is constructed by gradually adding the hidden neuron one-by-one, where the output weight is optimized by minimizing the residual error based on the fundamental of the generalized HR calculus (GHR) of quaternion variable function. Experiments on multidimensional chaotic system regression, aircraft trajectory tracking, face and image recognition are conducted to show the effectiveness of IQ-ELM and IAQ-ELM. Comparisons to two popular quaternion RNNs, Schmidt NN (SNN) and random vector functional-link net (RVFL), are also provided to show the feasibility and superiority of using quaternions in RNN for incremental learning.
Factors influencing Second Language Acquisition: The perspectives of EFL learners

There are a number of factors that influence the second language acquisition of a learner. This paper aims to investigate the most influential factors affecting SLA among English education students at the University of Lampung. The participants are 117 English education department students from batches 2018, 2019, and 2020 enrolled at the University of Lampung. The study employed a qualitative methodological approach. The data were descriptively analysed, which denotes that the data analysis took the form of a direct description of the data. The most influential factors in SLA, according to the findings, are internal motivation and instruction. In order for EFL learners to effectively acquire a language during the process of language acquisition, the educator plays a crucial role in assisting and facilitating them. Future research is suggested to investigate the causes of the differences in people's perceptions of the most influential factors in SLA, as well as the disparities in culture and educational level.
AUTHOR INDEX Volume 4 (2022)

International Journal of Big Data Mining for Global WarmingVol. 04, No. 02, 2299001 (2022) Free AccessAUTHOR INDEX Volume 4 (2022)https://doi.org/10.1142/S2630534822990017Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Vol. 04, No. 02 Metrics History PDF download
BURNOUT, ENGAGEMENT AND PROFESSIONAL BEHAVIOR OF BUSINESS MANAGERS

The article is devoted to the analysis of the leading symptoms of burnout, caused by the specifics of managerial activity in the business environment. A survey of 149 managers and owners of commercial organizations was conducted using the "Professional burnout" method, the Utrecht Scale of Work Engagement, and the "Work-related Behavior and Experiences" questionnaire. The obtained results reveal the prevalence and internal structure of burnout of business managers in Ukraine, which is characterized by manifestations of exhaustion and depersonalisation along with high self-esteem of personal achievements and involvement in work. Among the manifestations of burnout, signs of exhaustion turned out to be the most common: almost half of managers constantly or often experience a state of exhaustion of emotional, motivational, and physical resources. Depersonalisation of managers is not a natural consequence of exhaustion and not the reason for the reduction of personal achievements; it is accompanied by energy, initiative, absorption in activity. We observed many "highly cynical" respondents with the highest scores on the scales of professional efficiency and involvement in work. The intercorrelations of the burnout scales contradict the theoretical causal model: there is no significant relationship between the indicators of exhaustion and depersonalization, while there is a strong direct correlation of the scales of depersonalization and professional effectiveness. These data expand knowledge about the professionally important qualities of managers and clarify the three-component theoretical model of burnout. A well-founded view of professional cynicism as a protective strategy of self-preservation, which is related to the manifestations of burnout, but is not an unambiguously negative symptom. Depersonalisation contributes to the saving of internal resources of managers at a high level of energy costs and involvement in activities. The optimal level of psychological distancing from work and some objectification of employees ensures the effective performance of managerial functions in a business environment. This is a kind of personal adaptation to professional challenges, which has both favorable and destructive sides.
Self-Organizing Maps

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.