Reviewerly/exHarmony huggingface dataset:
A Network Load Perception Based Task Scheduler for Parallel Distributed Data Processing Systems

In parallel distributed data processing frameworks like Spark and Flink, task scheduling has a great impact on cluster performance. Though task Scheduling has proven to be an NP-complete problem, a large number of researchers have proposed many heuristic rules to obtain approximate optimal solutions. But most of them ignore the fact that the resource requirements of tasks are dynamically changing during its runtime. Considering the overall task entire lives, the CPU utilization is often lower during the data transfer. Especially for most distributed data processing platforms, data transmission is time-consuming, which usually resulting in low overall CPU utilization. Similarly, network throughput during task calculations is also low in some cases. In this article, we propose a network load variation perception based heuristic task scheduling algorithm, and based on this implement a dual-phase pipeline task scheduler (D2PTS) from the perspective of dynamic resource requirements that aims at maximizing cluster resource utilization, as a supplement to existing data-parallel frameworks. D2PTS divides the states of task into two phases: network-intensive and network-free. To improve the overall resource utilities, this article proposes different algorithms to evaluate the execution time of network sensitive and network free phases respectively. When an executing task is in the network-free phase, D2PTS can additionally schedule a new network-intensive task at the right time. Under this scheduling policy, the two tasks sharing the same CPU core can be executed as a coarse-grained pipeline. This execution method can start tasks earlier and improve resource utilization. Finally, we have implemented our model prototype on Spark 2.4.3 and conducted a number of experiments to evaluate the performance of our model. Experimental results show that D2PTS can not only minimize application makespan, but also improve resource utilization.
Genetic Algorithm and Distinctiveness Pruning in the Shallow Networks for VehicleX

Building well-performing neural network models often require a large amount of real data. However, synthetic datasets are favoured as the collection of real data often brings up privacy and data security issues. This paper aims to build a shallow neural network model for the pre-trained synthetic feature dataset, VehicleX. Using genetic algorithm to reduce the dimensional complexity by randomly selecting a subset of features from before training. Furthermore, distinctiveness pruning techniques are used to reduce the network structure and attempt to find the optimal hidden neuron size. Both techniques improve the model performance in terms of test accuracy. The baseline model achieves 36.07% classification accuracy. Integrating genetic algorithm and the distinctiveness pruning achieve approximately 37.26% and 36.12% respectively while combining both methods achieve 37.31%.
Development of Evaluation Scale of Creativity and Personality for College Students Using Delphi Survey Method

The purpose of this study is to develop a college student’s creativity and personality evaluation scale using Delphi survey. For this study, 20 experts in the field of creativity and personality were selected as panels, and the fourth Delphi survey was used to develop the scale for the evaluation of creativity and character for college students. The Delphi survey was conducted three times to calculate the descriptive statistics of the mean, standard deviation, skewness, and quartiles. In addition, content validity, coefficient of variation, degree of consensus, and degree of convergence were derived to correct or delete the content of the evaluation scale based on the opinions of the expert panel. This study analyzed the opinions of the expert panel to derive two evaluation areas and five evaluation criteria consisting of creativity (cognitive factor, disposition factor, motivation factor) and personality (virtuality, and core competencies). In addition, through expansion of thinking, convergence of thinking, problem-solving ability, openness, independence, curiosity and interest, immersion, e.g., filial piety, honesty, responsibility, respect, consideration, communication, cooperation, knowledge of personality practice, communication skills, conflict resolution, a final draft of the creativity and personality scale for college students, consisting of 18 evaluation indicators and 90 evaluation questions, was developed. The creative and personality evaluation scale for college students developed through this study is expected to provide important implications for the academic development and qualitative improvement of the creative and personality areas.
SIMULATION OF PARTICLE SWARM OPTIMIZATION FOR INVESTMENTS ON STOCK MARKET

This work reports simulations performed using Particle Swarm Optimization (PSO) as applied to investments on the stock market. About 480 stocks belonging to the S&amp;P500 index have been taken into account. A naive approach has been developed in which one simulation step corresponded to one trading period. As a second ingredient of the investment strategy, the relative strength of an asset has been employed. The results are analyzed with respect to the parameters of PSO.
Governance and Privacy Issues from the Internet of Value

This chapter considers the challenges and risks arising from the Internet of Value (IoV) on governance and privacy that are apparent with the transfer of trust and risk. It further considers the current governance and privacy issues with the development of new digital services such as blockchain that underpin the IoV, and how these issues need to be addressed.
Decolonial Ethics in Training in Computational Engineering. A Qualitative Study of Professors' and Students' Perspectives

We present an exploratory study that aims to diagnose the Engineering in Computational Technologies students' level of awareness with respect to the impact of the accelerated use of technology and the exponential growth that private socio-digital platforms have in everyday life because of the COVID-19 confinement. We research possible deterministic postures in the students' positions that could impede their ethical analysis of the social and environmental effects of the advance of data capitalism, among which racialization, algorithmic sexualization, and discrimination, as well as the erosion of the Commons on the Internet, stand out. The theoretical approach comes from a decolonial ethical framework, and the methodology of this study is qualitative. It consists of, in the first phase of the implementation, two focus groups with engineering students between the ages of 19 and 22 from different semesters and genders. In a second phase, we include eight in-depth interviews with professors from the School of Engineering and the School of Humanities who teach disciplinary and general education subjects in two campuses of a private university in Mexico.
Design and Build a Smart Door Lock Using the Deep Learning Convolutional Neural Network Method

The world is currently being hit by the COVID-19 virus. In this New Normal era, a rule is enforced that everyone must wear a mask wherever we are. Checking masks and body temperature is still done manually or by human observation, thus allowing for inaccuracies in observing and checking temperature. The problem occurred at Trunojoyo Madura University which still uses a manual mask and body temperature checking system. So, for accuracy and to reduce the risk of contracting officers. A tool was created to detect the mask and temperature automatically. In this study using a camera, temperature sensor MLX90614, and proximity sensor using Raspberry Pi. This research uses a machine learning system with the Deep Learning Convolutional Neural Network (CNN) Single Shot Detector (SSD) method. From this study, the results of mask detection obtained a success percentage of 93.4% and an error percentage of 6.6% from the entire test and obtained an average detection time of 2.63 seconds. And the average time of the whole system is 3.8 seconds. In this study, there was a delay during detection due to the heavy computational load on the system, so for further research, use a mini pc that has better performance.
A Deep Learning Based Face Mask and Social Distance Monitoring Framework for Covid-19

Covid-19 has become one of the most dangerous diseases suddenly which is infecting the people in all over the world. It has created an impact on the lives of thousands of people all over the world. Governments of various countries are trying to control the wide spread of COVID-19 in our society. It is a danger to the human existence, so it is highly important to stop spreading the disease as it is deadly contagious. Spreading of virus can be prevented, by maintaining social distance and hygiene. The major transmission mode of COVID-19 is through saliva and nose discharge.It is highly important to know the necessity of wearing mask in the public. We need an automatic monitoring system for time being to monitor the public so as to avoid the situation of spreading of the disease for not wearing mask and not maintaining social distance. We have applied a deep learning technique to check whether the person is having a face mask or not. This work is aimed to identify the face mask in the public places which helps in the reducing of spread of the virus. CNN is used for the model. The proposed model recognizes the face region in the image given as input and extracts the necessary facial features to identify the face mask region.
Using Speech Enhancement to Realize Speech Synthesis of Low-Resource Dungan Languages

Deep learning-based speech synthesis has shown great potential in the quality of synthetic speech. However, as deep learning approaches need an abundant amount of training data, lacking training data will affect the quality of synthesized speech for low-resource Dungan languages since it is challenging to obtain high-quality training data. This paper realized a Dungan speech synthesis that uses a Speech Enhancement Generative Adversarial Network (SEGAN) to enhance the original training utterances for obtaining the clean training corpus of Dungan utterances. We firstly trained a Transformer-based front-end to realize Dungan's text-to-Pinyin conversion. At the same time, we adopt a SEGAN to enhance the 4616 Dungan utterances recorded by 5 Dungan speakers total 6-hours. Then we trained a Tacotron2-based acoustic model. Finally, we use the WaveNet as a vocoder for synthesizing Dungan speech. The experiment results show that the proposed Dungan speech synthesis can synthesize higher clarity and naturalness Dungan speech compared with other Dungan speech synthesis methods.
Comparative Analysis of Various Supervised Machine Learning Techniques Used for Sentiment Analysis on Tourism Reviews

On various social media sites, many tourists share their impressions and opinions in the form of text, photographs, and videos. To conduct sentiment analysis, these tourist reviews are collected and preprocessed. Text-based sentiment analysis currently relies on the creation of dictionaries and machine learning models to extract sentiments from text data. This form of sentiment analysis is commonly used in tourist satisfaction, tour recommendation, tourist place ranking, and decision-making. Traditional techniques in sentiment analysis in tourism domains are examined in this study. Using the Weka Tool, the experiment ran various machine learning algorithms on the tourist data collected from various websites and social media platforms. Lowercasing, lemmatization, stop word elimination, and nonword removal were all used as preprocessing techniques. The efficiency of classifiers was significantly improved as a result of these techniques. Sentiment processing was carried out using a supervised machine learning method in this experiment. This experiment's results are compared to one another. The outcomes are examined. As compared to J48, random forest, Logistic, and Naïve Bayes classifiers, LibSVM obtained the highest accuracy (92.5%) from the tourism data in this analysis.
Combining Mobile and Traditional Learning to Learn Mandarin Language

There are quite numbers of languages around the globe use by respective native. Among all, the growing popularity of Mandarin Language in numerous countries keeps increasing, partly because of China’s rapid economic growth. Up to date, the Mandarin Language itself is the second most-spoken language in the world, with more than one billion speakers all over the world. Several methods and ways are used by people out there to learn this Mandarin Language. With the current globalization and modern technology era, many smartphone applications offering Mandarin courses started to pop. Those application offers various features, such as (1) game-based application; and (2) teaching and learning application or traditional learning. Each method has their pros and cons. The problem with a game-based application is that users cannot discuss their concerns or ask questions. On the other hand, teaching and learning application relies on the availability of the tutors, therefore less flexible for the user. This paper aimed to develop an application that combines these two methods in the hope to help ease the complexity of learning the Mandarin Language. The result of this paper shows that it’s possible to combine the two methods, by focusing on a conversation-type application equipped with a game to help users with vocabularies.
Research and Application of Relation Extraction based on Triple Relation Graph Convolutional Networks

Abstract In recent years, data in non-Euclidean spaces is becoming more and more. Traditional methods cannot perform feature extraction on these data. Most of existing methods just extract contextual semantic features from relational instances. Their structural features in corpora are ignored. To solve this problem, the paper proposed a relation extraction method based on triple relation graph convolutional networks (TRGCN). Based on the extraction of semantic features of sentences using convolutional neural networks, this method used the concept of triple relation graphs to represent structural features. In other words, triple relation graphs were formed by considering triples formed by the relation between two entities in one sentence as nodes and triples with common entities and same relations as edges. Finally, multiple-layer graph convolutional networks were used for training. As shown by experimental results, the method proposed in this paper achieved an F1 value of 86.8% on the SemEval 2010 Tesk 8 data set, indicating that it is better than mainstream convolutional neural networks and recurrent neural networks.
Artificial Intelligence – Extending the Automation Spectrum

Recent advances in Artificial Intelligence (AI) is enabling automation of tasks that once seemed only humans could perform. This paper examines the effect of Artificial Intelligence on the spectrum of automation spanning automated, assisted/support, augmented, and extending into autonomous decision-making. At one point in time enterprises were able to automate structured decisions and made headway in automating semi-structured decisions. With AI, enterprises are now able to automate unstructured decisions as well. Furthermore, AI is now being used to augment the individual consumer’s routine and non-routine decision-making. In some cases, automation with AI does not even require the activation of capacity. Some researchers, companies, and policymakers are concerned about the replacement of human labour as they hypothesize irreversible negative consequences in terms of job reduction and social divide. A traditional view of automating decision-making is provided to frame recent perspectives of AI in the literature including those that are less pessimistic and a more proactive vision for the future in a variety of decision-making environment.
Thoreau’s Arboreal Encounters and Aphoristic Forest Thinking

Given the recent turn to literary and cultural plant studies and forest research that illuminates tree communication and even tree cognition, this paper re-evaluates Henry David Thoreau’s arboreal writing in his Journal as a model for thinking with the forest. I argue that Thoreau speculatively thinks and writes with trees in a way that promotes their agency in co-producing meaning. Drawing on the aphorism, not as a short, pithy statement of accepted truth, but rather as an open-ended method of thinking that uses ambiguity to demand constant reinterpretation, this paper suggests that many of Thoreau’s entries in the Journal, particularly where he discusses trees, may be productively read as aphoristic. Ultimately, I claim that Thoreau’s aphoristic writing communicates an affective arboreal encounter to the reader, thus modelling a more sustainable speculative practice for forest thinking.
History-Aware Expansion and Fuzzy for Query Reformulation

Query reformulation is the task of rewriting users’ query to predict their information need. A user often struggles to modify a query by adding or removing terms when interacting with search engines. To address this issue, we propose a history-aware expansion and fuzzy model for query reformulation that improves follow-up queries based on successful history click-through logs. A probabilistic model is thus presented to calculate term weight in history and expand meaningful terms or fuzz trivial terms to follow-up query. Experimental results show that reformulated query can improve search engine results on low-frequency and long-tailed queries.
An Affect as Interaction Approach for Stress Management Among Paramedics

Stress is a leading cause of errors and mental health issues for Emergency Medical Service providers, and most mitigation strategies have not always been successful. We describe our planned research to engage affect as interaction to design a system that promotes reflection and awareness to build resilience towards stress and negative emotions among paramedics. Also, although successfully implemented, affect as interaction has received far less attention in research. Therefore, we propose an affect as interaction mobile application that leverages the camaraderie nature of paramedics and the Alcoholic Anonymous (AA) framework to present paramedics and sponsors with paramedic&#x0027;s electrodermal activity data and users proffered contextual information for an individual or team review, discussion, and interpretation.
A Novel Machine Learning-Based Load-Adaptive Power Supply System for Improved Energy Efficiency in Datacenters

Power Supplies are a key part of the modern Internet and Communications Technologies (ICT) industry. Modern Uninterruptible Power Supply (UPS) systems are modular and as such, consists of several Power Supply Units (PSUs). Various PSU designs are used to optimize efficiency of operation at specific loading conditions, but this engenders inefficient operation at other loading conditions. To optimize the energy efficiency in various loading conditions, this paper proposes a novel power supply multiplexing system engaging different combinations of PSUs, controlled through machine learning techniques to maximize efficiency depending on the loading conditions. Each PSU combination is given a state number. Because of the vast number of combinations (States) that can occur in such systems and due to the redundancy requirements, that need to be met during multiplexing, machine learning techniques are adopted. It is shown that through the novel proposed system an efficiency improvement of over 78% can be achieved in low loading conditions, and an average 5.23% efficiency improvement in all loading conditions.
Ensemble Learning to Perform Instance Segmentation over Synthetic Data

Recently, deep neural networks have led the progress of instance segmentation. There are several models of neural networks, such as the Mask R-CNN network, that perform this task with good results. However, it is possible to improve the results of the Mask R-CNN network if several models of it are combined, and their results are merged using Ensemble Learning. We propose the algorithm “Simple Instance Segmentation Ensemble” that is capable of assembling the results of two Mask R-CNN networks to produce better results. In our experiments, we train several Mask R-CNN networks with synthetic images of machinery objects. In addition, these Mask R-CNN networks have different backbones and different sizes of kernels for the Gaussian Blur filter applied to the synthetic machinery images used during training. We tested the performance of these networks by predicting real images of machinery. Besides, we propose the SISE algorithm to assemble the predictions of two previously trained Mask R-CNN networks, and we obtained better results than those of the individual Mask R-CNN networks. In particular, our best result is an ensemble that has one Mask R-CNN trained with synthetic images smoothed by Gaussian Blur filter with a kernel size of 7 \(\times \) 7, and another network with a kernel size of 3 \(\times \) 3. Both networks have as backbone a ResNeXt 101 with FPN (Feature Pyramid Network). This ensemble has a bounding box mAP of 89.42% and a segmentation mAP of 88.34% in the real machinery test images.
Probability-based Path Planning for Multi-Robot Systems with Stochastic Behavior in a Grid Map

For the multi-robot path planning on a grid map, the widely adopted robot model assumes that its motion is deterministic once a path has been decided. However, this assumption is not quite realistic because some interferences such as noise, friction and inaccurate control input could disturb the robot motion, leading to a stochastic behavior. In this paper, we tackle the problem of planning a multi-robot path based on the robot probabilistic motion model. At the beginning, we model the robot action with several probability distributions, where the basic actions include going forward, turning left/right, going backward, and wait. We then extend A-star algorithm incorporating these actions such that an optimal path can be planned for a single robot. Based on this result, we apply conflict-based search to optimally plan path for a multi-robot system. Because probability calculation demands too much computation we simplify the conflict detection scheme and make it applicable for online practice.
Modality-shared MRI Image Translation Based on Conditional GAN

Multimodal MRI images are often necessary for precise clinical diagnosis and the development of high-performance intelligent medical image systems. However, it is expensive and often difficult to obtain sufficient registered multimodal MRI images. One way to alleviate this issue is MRI image translation which aims to generate images of another modality based on images of one modality. This paper proposes a novel image translation framework that can translate images of any MRI modality to any other one. The core part of the framework is a conditional generative adversarial network (CGAN), where unlike commonly used conditions such as one-hot vectors, an image-specific condition can be generated based on the gradient-weighted class activation mapping (Grad-CAM), which can highlight the parts of the input image that need more attention during the translation process. In addition, to translate the lesion information in the MRI images, a modality-shared segmentation model is proposed to extract the lesion information, which is then novelly embedded into the image translation process. Comprehensive experiments on different multi-modality MRI datasets demonstrated the effectiveness of the proposed translation approach, achieving better performance compared to state-of-the-art methods.