Reviewerly/exHarmony huggingface dataset:
Comparison of Seventeen Missing Value Imputation Techniques

Copious data are collected and put away each day. That information can be utilized to extricate curiously designs. However, the information that we collect is ordinarily inadequate. Presently, utilizing that information to extricate any data may allow deceiving comes about. Utilizing that, we pre-process the information to exterminate the variations from the norm. In case of a low rate of lost values, those occurrences can be overlooked, but, in the case of huge sums, overlooking them will not allow wanted results. Many lost spaces in a dataset could be a huge issue confronted by analysts because it can lead to numerous issues in quantitative investigations. So, performing any information mining procedures to extricate a little good data out of a dataset, a few pre-processings of information can be done to dodge such paradoxes and, in this manner, move forward the quality of information. For handling such lost values, numerous methods have been proposed since 1980. The best procedure is to disregard the records containing lost values. Another method is ascription, which includes supplanting those lost spaces with a few gauges by doing certain computations. This would increment the quality of information and would extemporize forecast comes about. This paper gives an audit on methods for handling lost information like median imputation (MDI), hot (cold) deck imputation, regression imputation, expectation maximization (EM), support vector machine imputation (SVMI), multivariate imputation by chained equation (MICE), SICE technique, reinforcement programming, nonparametric iterative imputation algorithms (NIIA), and multilayer perceptrons. This paper also explores some good options of methods to estimate missing values to be used by other researchers in this field of study. Also, it aims to help them to figure out what method is commonly used now. The overview may also provide insight into each method and its advantages and limitations to consider for future research in this field of study. It can be a baseline to answer the questions of which techniques have been used and which is the most popular.
A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model

To address the problems of the traditional A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mi>∗</mi> </msup> </math> algorithm in solving paths with many expansion nodes, high memory overhead, low operation efficiency, and many path corners, this paper improved the traditional A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow /> <mi>∗</mi> </msup> </math> algorithm by combining jump point search strategy and adaptive arc optimization strategy. Firstly, to improve the safety of our paths, the risk area of the obstacles was expanded. Then, the A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow /> <mi>∗</mi> </msup> </math> algorithm was combined with the jump point search strategy to achieve the subnode jump search, reducing the calculation scale and memory overhead, and improving search efficiency. Considering the influence of the density of obstacles on search efficiency, the heuristic function was enhanced according to the special effects of the density of obstacles. Finally, the redundant jump point and adaptive arc optimization strategies were used to shorten the path length further and enhance the initial path’s smoothness. Simulation results showed that our algorithm outperforms traditional A <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>∗</mi> </math> and literature algorithms in path length, security, and smoothness, and then was further validated and applied in large-scale marine environments and realistic settings.
Onboard Sensor Systems for Automatic Train Operation

This paper introduces the specific requirements of the domain of train operation and its regulatory framework to the AI community. It assesses sensor sets for driverless and unattended train operation. It lists functionally justified ranges of technical specifications for sensors of different types, which will generate input for AI perception algorithms (i.e. for signal and obstacle detection). Since an optimal sensor set is the subject of research, this paper provides the specification of a generic data acquisition platform as a crucial step. Some particular results are recommendations for the minimal resolution and shutter type for image sensors, as well as beam steering methods and resolutions for LiDARs.
Composable end-to-end security of Gaussian quantum networks with untrusted relays

Gaussian networks are fundamental objects in network information theory. Here many senders and receivers are connected by physically motivated Gaussian channels while auxiliary Gaussian components, such as Gaussian relays, are entailed. Whilst the theoretical backbone of classical Gaussian networks is well established, the quantum analogue is yet immature. Here, we theoretically tackle composable security of arbitrary Gaussian quantum networks (quantum networks), with generally untrusted nodes, in the finite-size regime. We put forward a general methodology for parameter estimation, which is only based on the data shared by the remote end-users. Taking a chain of identical quantum links as an example, we further demonstrate our study. Additionally, we find that the key rate of a quantum amplifier-assisted chain can ideally beat the fundamental repeaterless limit with practical block sizes. However, this objective is practically questioned leading the way to new network/chain designs.
Examining the Export-Led Growth Hypothesis: Empirical Evidence from Sudan

The current study analyzes the relationship between Sudan's income growth and exports from 1970 to 2020. The system of equations using the Autoregressive Distributed Lag (ARDL) approach has been employed. The ARDL results showed that there exists a long-run relationship between the variables considered in the estimated model. The researchers observed a negative lagged error-correction term coefficient, which is highly significant in all cases supporting cointegration. The result reveals the existence of a long-run equilibrium relationship between GDP, export, import, labor force, and trade policy. This confirms that the export-led growth hypothesis is valid for Sudan. Thus, the most essential conclusion is that the economy’s export expansion strategy is completely dependent on the imports of raw materials and capital inputs and the kind of goods being exported. The coefficient of import is of significance, which offers strong support for the import compression hypothesis. The most important policy implication of the findings is the implementation of an appropriate and optimal approach that can boost exports to increase economic growth substantially. Policy-makers should focus on export diversification strategies and invest more in Sudan’s ability to provide value-added services to meet international export demand.
A Generative Approach for Detection-driven Underwater Image Enhancement

In this paper, we introduce a generative model for image enhancement specifically for improving diver detection in the underwater domain. In particular, we present a model that integrates generative adversarial network (GAN)-based image enhancement with the diver detection task. Our proposed approach restructures the GAN objective function to include information from a pre-trained diver detector with the goal to generate images which would enhance the accuracy of the detector in adverse visual conditions. By incorporating the detector output into both the generator and discriminator networks, our model is able to focus on enhancing images beyond aesthetic qualities and specifically to improve robotic detection of scuba divers. We train our network on a large dataset of scuba divers, using a state-of-the-art diver detector, and demonstrate its utility on images collected from oceanic explorations of human-robot teams. Experimental evaluations demonstrate that our approach significantly improves diver detection performance over raw, unenhanced images, and even outperforms detection performance on the output of state-of-the-art underwater image enhancement algorithms. Finally, we demonstrate the inference performance of our network on embedded devices to highlight the feasibility of operating on board mobile robotic platforms.
Error-Tolerant Integrated Optical Neural Network Processor based on Multi-Plane Light Conversion

We demonstrate integrated optical neural network processor with excellent error tolerance using multiport directional couplers. Thanks to the robust multi-plane light-conversion mechanism, high data-classifying accuracy over 95% is confirmed, insensitive to the exact coupling ratio.
A Brief Review of Domain Adaptation

Classical machine learning assumes that the training and test sets come from the same distributions. Therefore, a model learned from the labeled training data is expected to perform well on the test data. However, This assumption may not always hold in real-world applications where the training and the test data fall from different distributions, due to many factors, e.g., collecting the training and test sets from different sources, or having an out-dated training set due to the change of data over time. In this case, there would be a discrepancy across domain distributions, and naively applying the trained model on the new dataset may cause degradation in the performance. Domain adaptation is a sub-field within machine learning that aims to cope with these types of problems by aligning the disparity between domains such that the trained model can be generalized into the domain of interest. This paper focuses on unsupervised domain adaptation, where the labels are only available in the source domain. It addresses the categorization of domain adaptation from different viewpoints. Besides, It presents some successful shallow and deep domain adaptation approaches that aim to deal with domain adaptation problems.
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from retrieving text passages, potentially containing evidence. We obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks. Interestingly, we observe that the performance of this method significantly improves when increasing the number of retrieved passages. This is evidence that generative models are good at aggregating and combining evidence from multiple passages.
Cross-Cultural Emotion Recognition and Comparison Using Convolutional Neural Networks

The paper sets to define a comparison of emotions across 3 different cultures namely Canadian French, Italian, and North American. This was achieved using speech samples for each of the three languages subject to our study. The features used were MFCCs and were passed through convolutional neural network in order to verify their significance for the task of emotion recognition through speech. Three different systems were trained and tested, one for each language. The accuracy came to 71.10%, 79.07%, and 73.89% for each of them respectively. The aim was to prove that the feature vectors we used were representing each emotion well. A comparison across each emotion, gender and language was drawn at the end and it was observed that apart from the emotion neutral , every other emotion was expressed somewhat differently by each culture. Speech is one of the main vehicles to recognize emotions and is an attractive area to be studied with application to presenting and preserving different cultural and scientific heritage.
Development of entrepreneurial thinking of students of technical specialties based on the use of transdisciplinary didactic content of a special course in physics

Research problem. The activation of the processes of entrepreneurial specialization and updating the content of vocational education based on a transdisciplinary approach is a socio-economic order and an urgent problem of modernizing didactics. One of the philosophical prerequisites for transdisciplinarity is the philosophy of holism, which we applied to theoretically substantiate the coherence of physical and entrepreneurial thinking. The research problem is related to the need to develop transdisciplinary didactic content to improve the quality of entrepreneurial training of students. The purpose of the study is to develop a transdisciplinary didactic content of a special course in physics and fundamentals of economics for the development of entrepreneurial thinking among students of various specialties with an entrepreneurial specialization. Research methods. The study involved 156 students enrolled in the educational programs Instrument Engineering, Heat Power Engineering, Electric Power Engineering and Economics, with an entrepreneurial specialization. Determination of the level of educational achievements was carried out using online testing on the website https://dot.tou.edu.kz (Toraigyrov University) in the student's personal account and through https://docs.google.com/forms. Research results. The possibility of a convergent approach has been confirmed. This is evidenced by the results of the ascertaining experiment according to J. Bruner’s method. The hypothesis about the coherence of professional entrepreneurial and physical thinking has been confirmed. The calculation of the G-criterion of signs revealed (Gemp.&lt; Gcr. (0&lt;1)) that the use of the integrated didactic content of a special course in physics contributes to the development of entrepreneurial thinking. The coherence of physical and entrepreneurial thinking was verified based on the calculation of the Pearson correlation coefficient, which, with a sample of n=21 and a confidence probability of 0.05, is 0.7795. Measuring the correlation of the results of the analysis of educational achievements of the control and experimental groups made it possible to establish a close relationship between the criteria for the formation of professional entrepreneurial and physical thinking among students. Conclusion. It has been proven that the use of a transdisciplinary approach to the development of integrated didactic content in physics and the basics of economics contributes to the development of professional entrepreneurial thinking among students of technical specialties with entrepreneurial specialization.
Modelling Business Agreements in the Multimodal Transportation Domain through Ontological Smart Contracts

The blockchain technology provides integrity and reliability of the information, thus offering a suitable solution to guarantee trustability in a multi-stakeholder scenario that involves actors defining business agreements. The Ride2Rail project investigated the use of the blockchain to record as smart contracts the agreements between different stakeholders defined in a multimodal transportation domain. Modelling an ontology to represent the smart contracts enables the possibility of having a machine-readable and interoperable representation of the agreements. On one hand, the underlying blockchain ensures trust in the execution of the contracts, on the other hand, their ontological representation facilitates the retrieval of information within the ecosystem. The paper describes the development of the Ride2Rail Ontology for Agreements to showcase how the concept of an ontological smart contract, defined in the OASIS ontology, can be applied to a specific domain. The usage of the designed ontology is discussed by describing the modelling as ontological smart contracts of business agreements defined in a ride-sharing scenario.
Adversarial Attacks on Heterogeneous Multi-Agent Deep Reinforcement Learning System with Time-Delayed Data Transmission

This paper studies the gradient-based adversarial attacks on cluster-based, heterogeneous, multi-agent, deep reinforcement learning (MADRL) systems with time-delayed data transmission. The structure of the MADRL system consists of various clusters of agents. The deep Q-network (DQN) architecture presents the first cluster’s agent structure. The other clusters are considered as the environment of the first cluster’s DQN agent. We introduce two novel observations in data transmission, termed on-time and time-delay observations. The proposed observations are considered when the data transmission channel is idle, and the data is transmitted on time or delayed. By considering the distance between the neighboring agents, we present a novel immediate reward function by appending a distance-based reward to the previously utilized reward to improve the MADRL system performance. We consider three types of gradient-based attacks to investigate the robustness of the proposed system data transmission. Two defense methods are proposed to reduce the effects of the discussed malicious attacks. We have rigorously shown the system performance based on the DQN loss and the team reward for the entire team of agents. Moreover, the effects of the various attacks before and after using defense algorithms are demonstrated. The theoretical results are illustrated and verified with simulation examples.
Transfer Learning for Robust Low-Resource Children's Speech ASR with Transformers and Source-Filter Warping

Automatic Speech Recognition (ASR) systems are known to exhibit difficulties when transcribing children's speech.This can mainly be attributed to the absence of large children's speech corpora to train robust ASR models and the resulting domain mismatch when decoding children's speech with systems trained on adult data.In this paper, we propose multiple enhancements to alleviate these issues.First, we propose a data augmentation technique based on the source-filter model of speech to close the domain gap between adult and children's speech.This enables us to leverage the data availability of adult speech corpora by making these samples perceptually similar to children's speech.Second, using this augmentation strategy, we apply transfer learning on a Transformer model pre-trained on adult data.This model follows the recently introduced XLS-R architecture, a wav2vec 2.0 model pre-trained on several crosslingual adult speech corpora to learn general and robust acoustic frame-level representations.Adopting this model for the ASR task using adult data augmented with the proposed source-filter warping strategy and a limited amount of in-domain children's speech significantly outperforms previous state-of-the-art results on the PF-STAR British English Children's Speech corpus with a 4.86% WER on the official test set.
Exploring and Evaluating Different Game Mechanics for Anti-Phishing Learning Games

Anti-phishing learning games are a promising approach to teach end-users about phishing, as they offer a scalable and engaging environment for active learning. Existing games have been criticized for their limited game mechanics that do not allow for detailed assessment of the players' acquired knowledge, instead focusing mostly on factual and conceptual knowledge to remember or understand. To extend the research field, this paper presents the design and evaluation of two new anti-phishing learning games: The first game implements an extended classification mechanic to better assess the player's decision process, while the second game implements a different game mechanic, which requires players to combine URL parts to construct their own phishing URLs. We compare the games with each other and with a baseline implementation that uses binary decisions similar to existing games in a user study with 133 participants. The study shows, that while all three games lead to performance increases, none of the new games offer significant improvements over the baseline. Furthermore, results of a longitudinal test three months after playing the games show that knowledge can be retained as participants still perform significantly better than before playing either one of the games.
Sentence Level Amharic Word Sense Disambiguation

Lexical ambiguity, phonological ambiguity, structural ambiguity, referential ambiguity, semantic ambiguity, and orthographic ambiguity were all types of Amharic ambiguity. The other ambiguities were out of this research because the study focuses on lexical-semantic, orthographic, and semantic ambiguities. Until now, some experts have been researching the Amharic word sense disambiguation system. Recent research, on the other hand, did not take into account antonym, troponymy, holonomy, and homonym relationships in the WordNet; this problem was overcome by this study. Using a Deep Learning method, we are developing an Amharic word sense disambiguation model. We use a design science research strategy to close the gap, starting with problem identification and concluding with final communication. 159 ambiguous words, 1214 synsets, and 2164 sentence datasets were used to create three distinct Deep Learning algorithms in three separate experiments. Using the given dataset, the overall performance of the model is measured using performance metrics in precision, F-measure, and confusion matrix. In this study, LSTM, CNN, and Bi-LSTM obtained 94 percent, 95 percent, and 96 percent accuracy respectively in the third experiment, based on performance measurement.
Design of New Working Environment Based on Artificial Intelligence Algorithm

With the three industrial revolutions sweeping the world, especially since the third industrial revolution, the complexity of human work has greatly increased, and in the new era of technology and information, workers have new standards and new requirements for their work environment, and new form of work environment design has come into being. In this paper, a work environment system is designed by artificial intelligence algorithm to improve the workers’ work environment by assessing the good and bad degree of their natural and social work environment, and an intelligent service system is designed by using artificial intelligence algorithm, which can not only analyze and process the work environment assessment results but also execute the subjective and reasonable requirements made by the workers to help the workers maintain a good mood at work and improve their efficiency.
Rethinking organizational ecology in light of developments in cognitive science and natural language processing

Theory and research on the ecology of organizations has taken a cognitive turn over the past fifteen years. The most recent development in organizational ecology builds explicit bridges to cognitive science. This reconstructs the core of the original theories in light of the new knowledge. It builds on a probabilistic view of concepts and categories. It turns out that the cognitive-based reformulation effort reveals unexpected connections to language models and natural-language processing. In particular, the leading probabilistic classifier BERT provides new and powerful ways to measure core concepts.