Reviewerly/exHarmony huggingface dataset:
Optimizing Space Utilization for More Effective Multi-Robot Path Planning

We perform a systematic exploration of the principle of Space Utilization Optimization (SUO) as a heuristic for planning better individual paths in a decoupled multi-robot path planner, with applications to both one-shot and life-long multi-robot path planning problems. We show that the decentralized heuristic set, SU-I, preserves single path optimality and significantly reduces congestion that naturally happens when many paths are planned without coordination. Integration of SU-I into complete planners brings dramatic reductions in computation time due to the significantly reduced number of conflicts and leads to sizable solution optimality gains in diverse evaluation scenarios with medium and large maps, for both one-shot and life-long problem settings.
Implementation of CNN and ANN for Fashion-MNIST-Dataset using Different Optimizers

Objectives:The paper presents application of convolution neural network and artificial neural network for image classification problem for clothing dataset along with their performance comparison against different optimizers.The major objective of this paper is to perform image classification on fashion-mnist clothing dataset images.Methods: The methods used here are, the traditional ANN and CNN.Here image classification is performed on Fashion-mnist, clothing dataset using CNN and ANN with different optimizers.The performance of the working of ANN and CNN in classifying images from fashion-mnist dataset is compared against different optimizers namely stochastic gradient Descent, Adagrad, RMS prop and Adam optimizer.Findings: The study found that CNN worked better than ANN yielding training accuracy of 95%, 93% and testing accuracy of 91%, 89% when used with Adam and RmsProp respectively.Novelty: The novelty of this work is to present a comparative study of image classification using CNN, ANN using different optimizers, since not many studies or research articles showed the performance comparison of traditional and convolution neural networks in image classification along with different optimizers.Since the real-world scenarios of today require enormous data to be processed, CNN can fit well to diversify applications since they highly reduce the number of parameters to be trained that speeds up the training process.Moreover, to be specific on image classification problems they require the best and most prominent features to be detected and uncovered; this can be achieved using CNN since it has the concept of convolution using filters at its Core.Hence, CNN is highly recommended for such image classification applications than the traditional artificial-neural-networks because of the aforementioned reasons.
Named Entity Recognition for Hadiyya Language using BiLSTM-CRF Model

Objective: This work aims to the development of Hadiyya language named entity recognition which is widely used in text summarization, machine translation, and information retrieval to categorizing and predicting tokens of a given corpus into predefined named entity classes Method : In this paper, a method combining Bidirectional Long Short-Term Memory neural network with Conditional Random Field (BiLSTM-CRF) is proposed to automatically recognize entities of Hadiyya language (Location, time, person, geography and other nonname entity) from annotated Hadiyya language corpus, the experiment in this work was conducted to discover the most suitable features for Hadiyya NER system.We have collected the data from Department of Hadiya Language & Literature (DHLL) at Wachemo University, Ethiopia.Hadiyya TV, and Hadiyya Media Network (HMN) Therefore, a newly annotated dataset having 5,148 instances is used for this study.We have used 70 % for training and 30% for testing Hadiyya NER system.Finding: after training and validating BiLSTM-CRF model using the collected dataset we have obtained a result of precision, recall and f1-measure values of 95.49%, 94.93%, and 95.21% respectively.Novelty: Finally, we have contributed by hybrid NER system in Hadiyya language to obtain state-of-the-art result which is independent of other natural language processing tasks.
Wheelchair Controlling by eye movements using EOG based Human Machine Interface and Artificial Neural Network

The use of vital signals as a connection interface between humans and computers has recently attracted a great deal of attention.The electro-oculogram (EOG) signal, which is due to eye potential, is one of these signals.More advanced, EOGbased Human-Machine Interfaces (HMIs) are widely investigated and considered to be a noble interface option for disabled people.Artificial neural networks were utilized in this study to detect eye movement from the EOG signal.Neural networks can detect and classify biological signals with nonlinear dynamics, including EOG signals, due to their ability to learn nonlinear dynamics and their pervasive approximation.In this study, two fundamentally distinct networks, MLP and ART, were used to detect sequential and random eye movements for controlling wheelchair.The results indicate that the MLP network could indeed detect consecutive eye movements with an accuracy of over 90%, although the accuracy of this network detection in the case of random movements is relatively poor.In the field of random eye movements, the greatest results are obtained using the ART2AE network, which allows having a diagnostic accuracy of over 70%.
Method and utility for minimizing bitsliced representations of 4×4 S-boxes

The article is devoted to methods and tools for generating bitsliced descriptions of bijective 4×4 S-Boxes with a reduced number of gates/instructions. Bitsliced descriptions generated by the proposed method make it possible to improve the security and performance of both software implementations of cryptoalgorithms using 4×4 S-Boxes on various processor architectures, as well as FPGA and ASIC based hardware. The paper develops a heuristic method of minimization that uses standard logical instructions AND, OR, XOR, NOT, which are available in most 8/16/32/64-bit processors. Due to the combination of different heuristic techniques (preliminary calculations, exhaustive search to a certain depth, DFS algorithm, refining search) in the method, it was possible to reduce the number of gates in bitsliced descriptions of S-Boxes compared to other known methods. The corresponding software in the form of a utility in the Python language was developed and its operation was tested on 225 S-Boxes of various cryptoalgorithms. It is found that the developed method generates a bitsliced description with a smaller number of gates in 57 % of cases compared to the best known methods implemented in the LIGHTER/Peigen utilities.
Automatic Emotion Modelling in Written Stories

Telling stories is an integral part of human communication which can evoke emotions and influence the affective states of the audience. Automatically modelling emotional trajectories in stories has thus attracted considerable scholarly interest. However, as most existing works have been limited to unsupervised dictionary-based approaches, there is no labelled benchmark for this task. We address this gap by introducing continuous valence and arousal annotations for an existing dataset of children's stories annotated with discrete emotion categories. We collect additional annotations for this data and map the originally categorical labels to the valence and arousal space. Leveraging recent advances in Natural Language Processing, we propose a set of novel Transformer-based methods for predicting valence and arousal signals over the course of written stories. We explore several strategies for fine-tuning a pretrained ELECTRA model and study the benefits of considering a sentence's context when inferring its emotionality. Moreover, we experiment with additional LSTM and Transformer layers. The best configuration achieves a Concordance Correlation Coefficient (CCC) of .7338 for valence and .6302 for arousal on the test set, demonstrating the suitability of our proposed approach. Our code and additional annotations are made available at https://github.com/lc0197/emotion_modelling_stories.
Comparative Study of Top Cloud Providers on basis of Service Availability and Cost

The term "cloud computing" refers to a place where we can store our important data and use pay-as-you-go computing and networking services without a physical environment. Today's cloud computing provides us with robust computing and storage, high availability and security, rapid accessibility and adaption, ensured scalability and interoperability, and cost and time efficiency. Users in a cloud environment assume that there are endless resources accessible, and they only pay for the resources they really utilize. The number of cloud service providers is growing quickly and they are adding new capabilities as technology advances. However, a cloud consumer may find it challenging to identify the best service provider for their needs. Three cloud service providers—Amazon Web Services, Google and Microsoft Azure—are explored in this study, and a comparison of these cloud service providers is provided. On the basis of service availability, price, pricing structure, data security, operating system, Windows support, free trial, and geographies, the cloud service providers were compared.
DLIQ: A Deterministic Finite Automaton Learning Algorithm through Inverse Queries

Automaton learning has attained a renewed interest in many interesting areas of software engineering including formal verification, software testing and model inference. An automaton learning algorithm typically learns the regular language of a DFA with the help of queries. These queries are posed by the learner (Learning Algorithm) to a Minimally Adequate Teacher (MAT). The MAT can generally answer two types of queries asked by the learning algorithm; membership queries and equivalence queries. Learning algorithms can be categorized into two broad categories: incremental and complete learning algorithms. Likewise, these can be designed for 1-bit learning or k-bit learning. Existing automaton learning algorithms have polynomial (atleast cubic) time complexity in the presence of a MAT. Therefore, sometimes these algorithms even become fail to learn large complex software systems. In this research work, we have reduced the complexity of the Deterministic Finite Automaton (DFA) learning into lower bounds (from cubic to square form). For this, we introduce an efficient complete DFA learning algorithm through Inverse Queries (DLIQ) based on the concept of inverse queries introduced by John Hopcroft for state minimization of a DFA. The DLIQ algorithm takes O(|Ps||F|+|Σ|N) complexity in the presence of a MAT which is also equipped to answer inverse queries. We give a theoretical analysis of the proposed algorithm along with providing a proof correctness and termination of the DLIQ algorithm. We also compare the performance of DLIQ with ID algorithm by implementing an evaluation framework. Our results depict that DLIQ is more efficient than ID algorithm in terms of time complexity.
A Formalization and Proof Checker for Isabelle’s Metalogic

Abstract Isabelle is a generic theorem prover with a fragment of higher-order logic as a metalogic for defining object logics. Isabelle also provides proof terms. We formalize this metalogic and the language of proof terms in Isabelle/HOL, define an executable (but inefficient) proof term checker and prove its correctness w.r.t. the metalogic. We integrate the proof checker with Isabelle and run it on a range of logics and theories to check the correctness of all the proofs in those theories.
Keyword-Enhanced Multi-Expert Framework for Hate Speech Detection

The proliferation of hate speech on the Internet is harmful to the psychological health of individuals and society. Thus, establishing and supporting the development of hate speech detection and deploying evasion techniques is a vital task. However, existing hate speech detection methods tend to ignore the sentiment features of target sentences and have difficulty identifying some implicit types of hate speech. The performance of hate speech detection can be significantly improved by gathering more sentiment features from various sources. In the use of external sentiment information, the key information of the sentences cannot be ignored. Thus, this paper proposes a keyword-enhanced multiexperts framework. To begin, the multi-expert module of multi-task learning is utilized to share parameters and thereby introduce sentiment information. In addition, the critical features of the sentences are highlighted by contrastive learning. This model focuses on both the key information of the sentence and the external sentiment information. The final experimental results on three public datasets demonstrate the effectiveness of the proposed model.
The Impact of Government Policies and Small Scale Enterprise Development Activities on Economic Growth Evidence from Nigeria

In Nigeria, government initiatives and ways to grow and sustain firms have been mapped out to enhance SME entrepreneurial development.This will help SSEs thrive and develop so they can address societal needs.Interest in SSE development, expansion, and promotion as a productive structure to promote economic growth and prosperity has developed in recent years.This research explores how government policies, SSE advancement, expansion, and promotion affect SSE performance and economic growth in Nigeria.The research contributes to various theoretical models, including the Organizational Theory of Innovation and the Schumpeterian Theory of Innovation.These models serve as the foundations for innovation performance, competitive advantage and organizational strategic management.It also promotes ethical decision-making in terms of policies, progress, expansion, promotion and enterprise/company performance.We used survey based research approach. .The study sampled a valid respondents of 250 to make a generalization using Yaro Yamane formulae.The study used Cronbach's Alpha, Pearson's Correlation, Durbin-Watson, ANOVA, and OLS Regression..The results reflects the level of significant impact of the regressed variables on the SSEs and economic growth at 10%, 1%, 5%, and 1%, significant P-values.This indicate that SSE progress, expansion and promotion are significant factors or elements that can impact positively on SSE development and economic growth (EG).We accept all hypotheses in this study due to their relevance to the measured variable.Effective government policy boosts SSE performance and economic growth.We agree that good government policy for SMEs is vital to most economies' long-term growth and prosperity.Effective government policies affect SSEs' long-term viability.The correlation analysis connected all variables positively.These issues may affect SSE's performance and economic growth.Effective government policies, SSE advancement, expansion, and promotion are combined efforts between the government and entrepreneurs.It will help firms or organizations to expand, find new possibilities, and improve.Besides, the ability of entrepreneurs to continue to develop and offer services depend on having access to financial resources to expand their businesses is a means to contribute to economic growth and development because the continual function of these SSEs will create employment, reduced unemployment and generate revenue through taxes for development intervention.Hence, a need for government to create the enabling environment for small scale business activities to thrive.This research informs the government to strengthen policies to support the private sector growth in Nigeria and elsewhere around the globe.
A Meta Heuristic Multi-View Data Analysis over Unconditional Labeled Material: An Intelligence OCMHAMCV

Artificial intelligence has been provided powerful research attributes like data mining and clustering for reducing bigdata functioning. Clustering in multi-labeled categorical analysis gives huge amount of relevant data that explains evaluation and portrayal of qualities as trending notion. A wide range of scenarios, data from many dimensions may be used to provide efficient clustering results. Multi-view clustering techniques had been outdated, however they all provide less accurate results when a single clustering of input data is applied. Numerous data groups are conceivable due to diversity of multi-dimensional data, each with its own unique set of viewpoints. When dealing multi-view labelled data, obtaining quantifiable and realistic cluster results may be challenge. This study provides unique strategy termed OCMHAMCV (Orthogonal Constrained Meta Heuristic Adaptive Multi-View Cluster). In beginning, OMF approach used to cluster similar labelled sample data into prototypes of dimensional clusters of low-dimensional data. Utilize adaptive heuristics integrate complementary data several dimensions complexity of computational analysis data representation data in appropriate orthonormality constrained viewpoint. Studies on massive data sets reveal that proposed method outperforms more traditional multi-view clustering techniques scalability and efficiency. The performance measures like accuracy 98.32%, sensitivity 93.42%, F1-score 98.53% and index score 96.02% has been attained, which was good improvement. Therefore it is proved that proposed methodology suitable for document summarization application for future scientific analysis.
An Exploratory Study of Cybersecurity in Working from Home: Problem or Enabler?

ABSTRACT This article explores the implications of a shift to working from home (WFH) in the context of the COVID-19 pandemic. The literature and news coverage of this topic focuses on rising concerns over cybersecurity. Based on in-depth exploratory interviews with cybersecurity experts, it is apparent that cybersecurity problems do arise, but the advances in cybersecurity have enabled this shift and the scaling up of WFH. This qualitative research suggests the need for survey research and selected case studies to gain a more empirically anchored perspective on the degree that cybersecurity has raised problems but also enabled WFH.
On intuitionistic fuzzy modal topological structures with modal operator of second type

Two new Intuitionistic Fuzzy Modal Feeble Topological Structures (IFMFTSs) are introduced of fifth and sixth types. Examples for these structures are given.
THE EFFECTIVENESS OF USING JIGSAW STRATEGY IN COMPARISON TO TRADITIONAL LECTURING IN ENHANCING READING COMPREHENSION SKILLS OF SAUDI EFL LEARNERS

This experimental study aims to investigate the effectiveness of using Jigsaw strategy in comparison to traditional lecturing in enhancing ICP EFL students’ reading comprehension skills. The experiment was conducted in an ICP EFL reading comprehension course, a five credit course, with five hours of instruction per week, over the first semester of 2021-2022 academic year. Forty ICP EFL students of Sciences and Arts College taking EFL reading comprehension course took part in this study, with 20 students in the experimental group and 20 in the control group. The experimental group received a Jigsaw strategy instruction, whereas the control group received a traditional lecturing instruction. The sole instrument used in this study was an identical pre-post EFL reading comprehension test. Both of the experimental and control groups were administered pre and post EFL reading comprehension tests. The pre- and post- scores from the EFL reading comprehension tests were calculated for descriptive statistics and compared using a t Test. The findings of the study indicated that there were statistically significant differences in favor of the effectiveness of using Jigsaw strategy instruction in enhancing ICP EFL students’ reading comprehension skills. The students’ scores in EFL reading comprehension were higher for the post-test than the pre-test after being subject to Jigsaw strategy instruction. Most literature published on using cooperative learning strategies in the Saudi higher education context did not focus on using Jigsaw strategy in teaching EFL reading comprehension for ICP EFL students. To the best of the researcher’s knowledge, this is the first paper to investigate the effectiveness of using Jigsaw strategy in teaching EFL reading comprehension for secondary school leavers without prior experience with cooperative learning strategies like ICP EFL students. In conclusion, EFL reading comprehension instructors are highly recommended to use Jigsaw strategy instruction in EFL reading comprehension courses.
A Dual Adaptive Interaction Click-Through Rate Prediction Based on Attention Logarithmic Interaction Network

Click-through rate (CTR) prediction is crucial for computing advertisement and recommender systems. The key challenge of CTR prediction is to accurately capture user interests and deliver suitable advertisements to the right people. However, there are an immense number of features in CTR prediction datasets, which hardly fit when only using an individual feature. To solve this problem, feature interaction that combines several features via an operation is introduced to enhance prediction performance. Many factorizations machine-based models and deep learning methods have been proposed to capture feature interaction for CTR prediction. They follow an enumeration-filter pattern that could not determine the appropriate order of feature interaction and useful feature interaction. The attention logarithmic network (ALN) is presented in this paper, which uses logarithmic neural networks (LNN) to model feature interactions, and the squeeze excitation (SE) mechanism to adaptively model the importance of higher-order feature interactions. At first, the embedding vector of the input was absolutized and a very small positive number was added to the zeros of the embedding vector, which made the LNN input positive. Then, the adaptive-order feature interactions were learned by logarithmic transformation and exponential transformation in the LNN. Finally, SE was applied to model the importance of high-order feature interactions adaptively for enhancing CTR performance. Based on this, the attention logarithmic interaction network (ALIN) was proposed for the effectiveness and accuracy of CTR, which integrated Newton's identity into ALN. ALIN supplements the loss of information, which is caused by the operation becoming positive and by adding a small positive value to the embedding vector. Experiments are conducted on two datasets, and the results prove that ALIN is efficient and effective.