Reviewerly/exHarmony huggingface dataset:
Text Classification Method Based On Semi-Supervised Transfer Learning

Semi-supervised transfer learning is an effective technique to improve the performance of few-shot learning. In order to solve the difficulty of obtaining a large amount of labeled data, this paper proposes a text classification method based on semi-supervised transfer learning (TC_SSTL). TC_SSTL makes full use of readily available unlabeled data to assist the model in learning text features through its underlying information. Firstly, TC_SSTL uses data augmentation on unlabeled data. Then augmented data, unlabeled data and labeled data are sent into the pre-training model together, using pseudo-label technology to conduct semi-supervised training on the unlabeled data and augmented data. At the same time, the pre-training model is fine-tuned using discriminative fine-tuning. On short text classification task, TC_SSTL can achieve the best performance using only 1000 labeled data.
How Innovation and Ownership Concentration Affect the Financial Sustainability of Energy Enterprises: Evidence from a Transition Economy

The energy sector in Vietnam has been developed rapidly as the country is transitioning to renewable energy. Moreover, the Vietnamese government also motivates the engagement of private investors in the energy sector to enhance the competitiveness of the energy market. Therefore, this paper investigates how innovation investment and ownership concentration affect the financial sustainability of 103 energy companies in Vietnam from January 2007 to December 2020. We employ the Fixed Effect Model and Generalized Method of Moments estimations to analyze a sample of 600 firm-year observations. The empirical findings show that innovations and block-holders support firms to grow sustainably. The positive relationship between innovation investment and financial sustainability is robust even when we employ alternative proxies of innovation investment. Our study indicates that block-ownership affects sustainable developments of smaller energy firms, while innovation investment significantly improves the sustainability of larger energy firms. Finally, our study reports that the Covid 19 pandemic adversely affects the financial sustainability of energy firms. Our findings recommend that energy firms increase innovation investments such as training and R&D activities to grow sustainably. In addition, the Vietnamese government should encourage small energy firms to attract block-holders to improve financial sustainability. Our findings align with agency theory, resource-based theory, and prior literature.
Improving Deep Reinforcement Learning With Mirror Loss

Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL) in various artificial intelligence applications, but the training process needs a very large amount of samples and huge computational costs. To alleviate the low sample efficiency issue, one feasible solution is to improve the state representation learning. We uncover that the agents, trained by the original DRL algorithm, face severe performance degradation in mirrored game environments. As mirror symmetry is an important property of the environment, the poor performance in the mirrored situation indicates that the agents are not fully aware of the essence of the environment. In order to handle this problem and make use of the property to attain better state representation, we propose a mirror loss, which serves as an auxiliary module to bring mirror symmetry representation to the DRL agent. It is model-agnostic and prompts the DRL agent to make logically consistent mirrored actions in the mirrored environment. We conduct experiments on OpenAI Gym Atari environments and a more complex reinforcement learning task, Mahjong AI, and the results demonstrate the efficiency and versatility of our method.
Solar Radiation Variability Between Coastal and Inland Qatar

An accurate assessment of solar radiation resources is crucial in supporting the management of solar energy-based projects. When available, high-quality ground radiometric measurements present the best data source for resource assessment and variability analysis due to their low error rate. Qatar is blessed with high solar radiation levels. However, significant aerosol loads in the atmosphere constitute unknown challenges for the performance of solar systems. A thorough assessment of the impact of aerosols on solar yield is necessary to establish the severity of these challenges. This paper presented a study of solar resource variation in Qatar aimed at understanding the impact of aerosols, based on measurements from two monitoring stations, one inland and one closer to the coastline. The inland station is at the site of the 800 MW solar power plant under construction in Al Kharsaah. The station closer to the coastline is at the HBKU Research Complex in Doha. The analysis of solar radiation components (direct, diffuse, global) from data collected at these stations shows higher solar radiation inland than in areas closer to the coastline. The ensuing results provide valuable insights on solar resource variability for a more effective placement of solar plants in Qatar.
Cyberbullying detection system focusing on the isiXhosa language

Due to the Covid19 pandemic, and the restrictions placed in social interactions, there has been an upsurge in the use of social networks, such as Facebook, WhatsApp, Instagram, Telegram, Twitter, and others. As more people turn to the social networks for social interaction, there has been increased occurrences of cyberbullying. Cyberbullying is a type of bullying that occurs through online technology, whereby harmful texts and pictures are shared through social networks. This research project aimed to develop a system that can detect cyberbullying on social networks such as Twitter focusing on the IsiXhosa language. Machine learning algorithms were applied to Twitter feeds in order to detect cyberbullying. The project will help law enforcement to apprehend and prosecute cyberbullies that make threats using isiXhosa. The methodology used incorporated machine learning algorithms to fully implement the cyberbullying detection system. It starts with collecting the data from Twitter using Python, cleaning the data followed by testing the data. The results show that the implementation successfully collected the desired data from Twitter and the data was then pre-processed and prepared to be tested using the different algorithms mentioned in the paper.
Global and Local Feature Based Deep Cross-Modal Hashing

In recent years, with the increasing number of different modal data, cross-modal retrieval has come to be an important research task. Deep cross-modal hashing receives increasing attention due to its combination of low storage cost, high search efficiency and strong capability of feature extraction of neural networks. Most of the existing deep cross-modal hashing methods extract the global semantic features of the images and texts to generate hash codes through two independent networks. In this work, we propose a novel method called Global and Local Feature based Deep Cross-Modal Hashing (GLFH). GLFH not only extracts the global features of the images and texts, but also extracts the features of regions containing objects in images and the features of words in texts by introducing the object detection method and the recurrent neural network, respectively. Then the local features of the images and texts are obtained by fusing the region features and the word features with the adaptively learned attention matrix. Finally, the global features and the local features are fused to obtain the richer features of image and text to generate the more robust hash codes. The experimental results on three widely used datasets are significantly surpass other methods, verifying the effectiveness of GLFH.
The Fusion of Bearing and Frequency Measurements from Multi-sensor Arrays for Underwater Target Tracking

Estimation is the process of generating information that is not directly available from raw measurements to obtain different target motion parameters. To fulfil this aim, different measurements from various sensors are fused to obtain a more accurate and reliable estimate of the target state. This task is more complex when the sensors operate in passive mode. In this research work, passive measurements i.e., bearings from an array mounted on the hull (HMA) and array sensors towed behind the observer along with doppler shifted frequency measurements from the towed array are fused to obtain kinematic parameters of the desired target. The sub-optimal filter, Unscented Kalman filter is considered for the performance analysis. The results are compared to that of simple doppler-bearing tracking using measurements from HMA. The simulation is carried in MATLAB software using the Monte-Carlo simulation method for better analysis of the results.
Evaluating Adversarial Robustness of Secret Key-Based Defenses

The vulnerability of neural networks to adversarial attacks has inspired the proposal of many defenses. Key-based input transformation techniques are the recently proposed methods that make use of gradient obfuscation to improve the adversarial robustness of models. However, most gradient obfuscation techniques can be broken by adaptive attacks that consider the knowledge of the new defense; thus, defenses that rely on gradient obfuscation require a thorough evaluation to identify their effectiveness. Block-wise transformation and randomized diversification are the two recently proposed key-based defenses that claim adversarial robustness. In this study, we developed adaptive attacks and used preexisting attacks against key-based defenses to show that they are still vulnerable to adversarial attacks. Our experiments demonstrate that for a block-wise transformation defense on the CIFAR-10 dataset with the block size of 4, our work can reduce the accuracy of pixel-shuffling to 7.45%, bit-flipping to 4.20% and feistel-based encryption to 9.45%, as compared to previous work that claims high adversarial robustness. In addition to block-wise transformation, we reduced the accuracy of the randomized diversification method by 25.30% on CIFAR-10.
Movie Recommendation System by Feed Forward Deep Neural Network

Movie recommendation is powerful in providing improved entertainment. It can suggest a set of movies to users on basis of their interests. In this paper we propose a movie compilation program that has the power to recommend movies to the user on the basis of his interests, preferences and past behaviour. This paper makes use of deep learning techniques for data analysis. Many companies are recognizing the incredible power of neural networks that can come from exposing this wealth of information and are becoming increasingly accustomed to AI systems for automated support. In this paper, the proposed approach is based on neural networks and experimental results are produced based on the dataset MovieLens.
Fast Frequent Patterns Mining by Multiple Sampling With Tight Guarantee Under Bayesian Statistics

Sampling from large dataset is commonly used in the frequent patterns (FPs) mining. To tightly and theoretically guarantee the quality of the FPs obtained from samples, current methods theoretically stabilize the supports of all the patterns in random samples, despite only FPs do matter, so they always overestimate the sample size. We propose an algorithm called multiple sampling-based FPs mining (MSFP). The MSFP first generates the set of approximate frequent items ( AFI ), and uses the AFI to form the set of approximate FPs without supports ( AFP* ), where it does not stabilize the value of any item's or pattern's support, but only stabilizes the relationship ≥ or < between the support and the minimum support, so the MSFP can use small samples to successively obtain the AFI and AFP* , and can successively prune the patterns not contained by the AFI and not in the AFP* . Then, the MSFP introduces the Bayesian statistics to only stabilize the values of supports of AFP* 's patterns. If a pattern's support in the original dataset is unknown, the MSFP regards it as random, and keeps updating its distribution by its approximations obtained from the samples taken in the progressive sampling, so the error probability can be bound better. Furthermore, to reduce the I/O processes in the progressive sampling, the MSFP stores a large enough random sample in memory in advance. The experiments show that the MSFP is reliable and efficient.
Design of a Data Storage System for Students' Physical Fitness Monitoring Based on Artificial Intelligence Technology

research-article Share on Design of a Data Storage System for Students' Physical Fitness Monitoring Based on Artificial Intelligence Technology Author: Song Yan Bohai University, China Bohai University, ChinaView Profile Authors Info & Claims ICASIT 2021: 2021 International Conference on Aviation Safety and Information TechnologyDecember 2021 Pages 519–522https://doi.org/10.1145/3510858.3511000Online:04 March 2022Publication History 0citation13DownloadsMetricsTotal Citations0Total Downloads13Last 12 Months13Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Path Planning of E-puck Mobile Robots Using Braitenberg Algorithm

In this paper an efficient method is proposed and tested to navigate a mobile robot to its goal point safely by passing through different types of obstacles. For the obstacle avoidance a Braitenberg vehicle method is used. This method can be implemented without using much expensive equipment or any complex structures. For the mobile robot, E-puck robot is being used for this paper. The robot should have ability to locate its goal point and move towards it. There are various ways to do it, but in this paper a new technique is being studied and implemented by considering the coordinates of the robot and the destination point. The robot’s efficiency has been tested and simulated at webots software in different environments. Some problems were identified during the simulation, but it has been effectively resolved. The paths followed by the robot during the simulations are presented.
AI as a Tool in Internal Communication to Reduce Employee Attrition Rate in India

As employees are representatives of their business to the outside world, a structured communication strategy can bridge the knowledge gap and create an informed workforce. This research takes into account the role of artificial intelligence in internal communication that delves into the benefits that arise when employees are fully involved in the organisation’s ethos, values and functioning (Aristotle and Smith in Prior analytics. Hackett Pub. Co., Indianapolis, 1989). The objective of this research is to understand the role of AI in increasing employee engagement; application of AI in tandem with work stress for employees; ease of AI usage with respect to up-skilling/cross-skilling, switching organisations, etc. The methodology implemented is a mixed method approach. The primary data collection consisted of a pilot study through a structured questionnaire among 60 working professionals that includes 50 employees and ten industry experts as respondents for the quantitative study. For qualitative study, in-depth interviews with five industry experts included two corporate communication professionals, two human resources professionals and one person working for artificial intelligence. The timeline for this research was across three months, i.e., December 2018–February 2019, and the sample size of 60 people was selected purposively.
Deep Multi-View Subspace Clustering Based on Intact Space Learning

Since single-view data is not enough to describe objects comprehensively, how to make full use of multi-view data has become a popular research direction in the field of machine learning and pattern recognition. In this paper, we mainly focus on multi-view subspace clustering. We propose a deep multi-view subspace clustering based on intact space learning model (DMVSC-ISL). Our model contains an intact space learning module and a subspace clustering module. The intact space learning module utilizes an auto-encoder networks to reduce the redundant and noise information to obtain the latent representation of each view and utilizes a degradation networks to obtain the intact space representation based on these different latent representations. Comparing single-view, the obtained intact space representation contains richer multi-view information. The subspace clustering module utilizes a self-expression layer to calculate the reconstruction coefficient matrix of the intact space representation. Through calculating intact space representation, we integrate the intact space learning and subspace clustering into one train network. Through considering their interactions, our model has good clustering property. By comparing the existing clustering methods on four multi-view data sets, experimental results show the superiority of DMVSC-ISL clustering performance.
Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning

We present a novel method to reconstruct a spectral central view and its aligned disparity map from spatio-spectrally coded light fields. Since we do not reconstruct an intermediate full light field from the coded measurement, we refer to this as principal reconstruction. We show that the direct estimation is superior to a full light field reconstruction and subsequent disparity estimation. The coded light fields correspond to those captured by a light field camera in the unfocused design with a spectrally coded microlens array. In this application, the spectrally coded light field camera can be interpreted as a single-shot spectral depth camera. We investigate several multi-task deep learning methods and propose a new auxiliary loss-based training strategy to enhance the reconstruction performance. The results are evaluated using a synthetic as well as a new real-world spectral light field dataset that we captured using a custom-built camera. The results are compared to state-of-the art compressed sensing reconstruction and disparity estimation. We achieve a high reconstruction quality for both synthetic and real-world coded light fields. The disparity estimation quality is on par with or even outperforms state-of-the-art disparity estimation from uncoded RGB light fields.
A fault diagnosis model for the power transformer based on the enhanced association rule mining algorithm

The association rule mining methods are commonly utilized to analyze the dissolved gas which is applied to diagnose the power transmission fault events. For the purpose of further improving the performance, this paper proposes a diagnosis method for power transformer fault events based on the enhanced association rule mining model. Firstly, the conditional and adjustable significance measurements are deployed to assess the different input features, and to incorporate the high impact low probability data. Thus, all the potential extreme circumstances in reality can be considered. Next, the risk weights of input features are generated through their likelihood of causing a fault rather than their statistical distribution. Therefore, the impact of each feature can be measured more precisely. Finally, the Relim algorithm is applied to raise the efficiency of data mining. The consequences of the empirical study show that the proposed method is more pinpoint, realizable and efficient compared with the methods with the fixed significance measurements, the conventional risk weight estimator, and the Apriori algorithm.
A Hybrid Model of Query Expansion using Word2Vec

Query expansion method is one of the most popular methods to reduce the vocabulary mismatch in Information retrieval tasks. Traditional methods of query expansion that use Pseudo relevance feedback are not much efficient for document retrieval from a large collection of documents. In the proposed method we will try to minimize the vocabulary mismatch using a hybrid method of query expansion using the word embedding technique. The proposed method uses both Word2Vec and a local method to predict the expansion terms. The mean average precision of the proposed method is 0.2992. The proposed model also compares with the original query and BM25 model.
Dual Layered Authentication Security for Sensitive Healthcare Data by Hadoop MapReduce

The healthcare industry is developing smart ways or systems to monitor one's own health through Big Data generated by Internet of Things (IoT) devices. The Internet of Things (IoT) provides a myriad of objectives that enable quick advancement in the healthcare field. Data created by those gadgets about a patient's ailment and diagnoses has become extremely sensitive, and its security has become a major concern for data professionals. Patients' data security has become increasingly and extremely relevant, allowing patients and other fitness-oriented individuals to freely use online gadgets in health care. For the security of sensitive Big Data, multiple symmetric encryption algorithms are used. In the near future, the role and significance of IoT devices in people's everyday lives will be greatly expanded. This study discusses a mechanism that provides double-step security by using a symmetric encryption technique that takes less time while encrypting and decrypting sensitive data. In addition, how is symmetric encryption used in healthcare to provide authentication security benefits to patients? IoT devices provide precise diagnosis of patients and patients in remote locations.
A Comparative Study of Regression Analysis for Modelling and Prediction of Bitcoin Price

The appraisal of Bitcoin’s price-changing characteristics is extremely difficult because of the nonlinear, nonstationary, effect of multiple uncontrollable factors, and volatile nature. The conventional approaches to machine learning categorization failed to yield accurate results. Additionally, the performance of the presented regression model strategies was evaluated in terms of the mean absolute percentage error (MAPE) between predicted actual values and expected values, as well as the root mean square error (RMSE). These two performance indicators are insufficient to demonstrate the efficacy of Bitcoin price prediction. The aim of this paper is to propose six regression models for Bitcoin price prediction based on historical data from 2014 to 2020. We employ six different regression models. They are CatBoost regressor, gradient boosting regressor, extra tree regressor, AdaBoost regressor, K-nearest neighbor regressor, and the Theil-Sen regressor. Coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), root mean squared logarithmic error (RMSLE), and mean absolute percentage error (MAPE) were used to examine the models’ performance. The experimental results indicated that the extra tree had the highest MAE of 3.1447, RMSLE of 0.553, and MAPE of 0.4783 while the gradient boosting regressor had the highest MSE of 5.4842 and RMSE of 7.4055. Theil-Sen regressor model produced the highest R2 value of 0.4533.