Reviewerly/exHarmony huggingface dataset:
Secure Chess-Based Data Exchange and User Validation

In each and every organization, data communication is the essential part. On the daily basis, the data is exchanged within the organization as well as outside the organization. The passwords and OTPs play a critical role in securing the data. Now, how long and how well these OTP will work on, will depend on the strength of these passwords and OTPs. In the proposed work, we have used the novel concept of applying the chess game in the process of the OTP or password generation. Here, we have made use of the four-player chess, with the dynamic movement of the Kings, Bishops and other components. The formation of the OTP for the user authentication as well as data sending is done on the basis of the movement of the moveable objects of the chess. For testing the strength of the pattern, we have tested it using the various entropy checking tools and the results are quite satisfactory comparing to the results of the other.
Heterogeneous hybrid extreme learning machine for temperature sensor accuracy improvement

High-accuracy temperature sensors usually come at a high cost, making them cost-ineffective solutions for mass-produced commercial products. The overall product cost can be one of the decisive factors for its success (e.g., commercial freezers). On the other hand, low-cost temperature sensors are viable options, but they suffer from accuracy issues. This paper presents a system aimed at improving the accuracy of a low-cost temperature sensor using a hybrid machine learning algorithm. The system utilizes a low-cost single-board computer connected to a series of sensors to collect data. These data are then used as an input vector to the proposed linear regression heterogeneous hybrid extreme learning machine (LR-HE-HyELM), a supervised learning algorithm responsible for increasing the sensors’ accuracy. The proposed linear regression heterogeneous extreme learning machine (LR-HE-HyELM) combines linear regression with the self-adaptive heterogeneous hybrid extreme learning machine (SA-He-HyELM) algorithm. SA-He-HyELM is an extreme learning machine (ELM) based algorithm that utilizes a genetic algorithm (GA) for the creation of an optimized single-layer neural network (SLNN) having different combinations of custom-created neurons at its hidden layer. The proposed training algorithm has all the advantages of SA-He-HyELM. These advantages include utilizing low-order and higher-order neurons in the same network while retaining ELM’s simplicity by making all GA parameters self-adaptive. The utilization of low-order and higher-order unit types in the same network resulted in optimized neural networks with better generalization ability than those with the same neuron types in their hidden layer. The creation of SA-He-HyELM was nature-inspired since the human brain contains different neuron types. The proposed system is tested with eight existing methods, with most of them having different neuron types in the hidden layer. The experimental results involved using a custom-created regression dataset having measurements taken from a commercial fridge. The proposed method managed to get the lowest mean square error (MSE) compared to the other eight methods.
Self-Sovereign Identity in University Context

A user-centred identifier enables verifiable and de-centralized digital identity, and lead users to control and to generate their own identifiers using systems they trust. This is how Self-Sovereign Identity works. This paper presents the case of universities, where several different agents need their own identifier and shows a digital identity mathematical model for the academic context. Moreover, the Alastria model for the same context is detailed for a specific case of students’ mobility.
Smart Communication System Using Sign Language Interpretation

Although sign language has become more widely used in recent years, establishing effective communication between mute/deaf people and non-signers without a translator remains a barrier. There have been multiple methods proposed in the literature to overcome these challenges with the help of Sign Language Recognition (SLR) using methods based on arm sensors, data glove and computer vision. However, the sensor-based methods require users to wear additional devices such as arm bands and data-glove. The sensor-free vision-based methods are computationally intensive and sometimes less accurate as compared to the wearable sensor-based methods. In this paper, we propose a vision-based light weight web-based sign-language interpretation system. It provides two-way communication for all classes of people (deaf-and-mute, hard of hearing, visually impaired, and non-signers) and can be scaled commercially. The proposed method uses Mediapipe to extract hand features from the input image/video and then uses a light weight random forest classifier to classify the signs based on the extracted features with the accuracy of 94.69 %. The proposed model is trained on alphabets from American Sign Language. We developed a web-based user interface to remove for ease of deployment. It is equipped with text-to-speech, speech-to-text and auto-correct features to support communication between deaf-and-mute, hard of hearing, visually impaired and non-signers.
A deep map transfer learning method for face recognition in an unrestricted smart city environment

Face images often change in posture, Angle, expression, makeup, aging, lighting, and other aspects. The performance of many models with high accuracy in restricted scenes decreases sharply in unrestricted scenes. Therefore, the study of face recognition in unrestricted scenes is of great significance. In this paper, we proposed a face recognition algorithm based on depth map transfer learning to effectively recognize face images taken in unrestricted environment. Firstly, the deep network encoding and decoding structure based on dense feature fusion is used to extract depth information from face images. Secondly, depth information for migration learning is fused to improve the feature dimension of small sample data, thereby improving the problem of insufficient feature extraction training due to a small number of samples. Finally, in order to improve the classifier recognition performance in small sample machine learning, central loss is introduced into the traditional Softmax loss to improve the punishment supervision ability of the classification loss function.
Harr-NMF Feature Extraction for Multilevel Educational Technology Teaching Big Data System

An essential role in improving educational informatization and encouraging new approaches to education is played by educational technology, a specialist field that supports both theorising and doing in this area. When it comes to learning educational technology, students must have excellent professional knowledge and abilities, as well as a thorough understanding of data. Harr-NMF feature extraction for educational technology teaching large data systems is the focus of this project. With the help of big data technology, the construction of a teaching resource sharing system provides useful solutions for the storage and sharing of current and future massive teaching resources, enables fast and accurate analysis and retrieval of massive teaching resources and data, improves the security and scalability of the system while maintaining system stability, and promotes education teaching informatization to a new level. The system can be used to analyze and retrieve data quickly and accurately. By mapping different types of Harr-NMF features into a gradient matrix using grayscale projection, pattern classification is performed through the simplified gradient matrix as features. The algorithm complexity of feature extraction is eventually reduced to a great extent by the fast advantage of feature extraction by this algorithm. The Harr-NMF algorithm and the similarity relation matrix are used to obtain the optimal parameter values and the corresponding affiliation matrix and class centers, thus providing effective initial values for Harr-NMF, which effectively solves the problems of slow convergence and easy to fall into local minima of the Harr-NMF algorithm. The algorithm of this paper is compared with several improved algorithms of Harr-NMF in terms of convergence, recognition rate, and other performance to verify the feasibility and effectiveness of this paper. With a combination of qualitative and quantitative approaches, the data literacy of teaching educational technology is dissected and constructed to enhance its data literacy and improve the quality of education technology discipline personnel training.
Evaluating push-down on NoSQL data sources

Reducing execution time of data integration processes is a challenging and still only partially solved research and technological problem. A few optimization techniques have been developed and applied in practice. One of them is called push-down. It consists in moving the execution of some data processing tasks into a data source. This technique has been developed and evaluated on relational data sources. In this paper, we report our evaluation of push-down on NoSQL data sources, i.e., MongoDB and Cassandra, deployed on a micro-cluster.
Uncertainty-Based Network for Few-Shot Image Classification

The transductive inference is an effective technique in the few-shot learning task, where query sets update prototypes to improve themselves. However, these methods optimize the model by considering only the classification scores of the query instances as confidence while ignoring the uncertainty of these classification scores. In this paper, we propose a novel method called Uncertainty-Based Network, which models the uncertainty of classification results with the help of mutual information. Specifically, we first data augment and classify the query instance and calculate the mutual information of these classification scores. Then, mutual information is used as uncertainty to assign weights to classification scores, and the iterative update strategy based on classification scores and uncertainties assigns the optimal weights to query instances in prototype optimization. Extensive results on four benchmarks show that Uncertainty-Based Network achieves comparable performance in classification accuracy compared to state-of-the-art methods.
Roadmap for development of skills in Artificial Intelligence by means of a Reinforcement Learning model using a DeepRacer autonomous vehicle

Using Deepracer, through experimentation and simulation, theoretical concepts can be applied to a practical application of reinforcement learning (RL) in a real-life problem. It was considered as a highly useful tool to develop many direct and transversal competences that students need to work within the field of artificial intelligence (AI). Nowadays the combination of Hardware, Computer Vision methods and Machine Learning (ML) algorithms for the development of controllers for vehicle driving automation have facilitated the development of solutions for this problem. The intention of this work is to show a Roadmap that was formulated to learn AI, ML and RL competencies required to prepare undergraduate students for the industry of this area, following a structured mostly practical learning plan using Deepracer AWS platform and local alternatives for training; and a physical vehicle as primary tools that have made an incredibly compact setup process and reduced complexity in the educational researching field related to learning autonomous vehicles (AV) software development process. The AWS DeepRacer framework includes all needed hardware based on a two front-camera vehicle for stereo vision and a LiDAR sensor, besides it provides a powerful computation computer for high performance in scale autonomous vehicles. The roadmap was executed testing and comparing different RL models over the modalities that Deepracer provides (by comparing both local and AWS console training) documentation of the execution of the generated Roadmap is shown to ensure that should be considered as a stable learning system that could be followed by college programs. Finally, models were tested on a physical track built, and limitations, considerations and improvements for this Roadmap are explained as a contribution for future work.
Policing the smart home: The internet of things as ‘invisible witnesses’1

In this paper, we develop the concept of smart home devices as ‘invisible witnesses’ in everyday life. We explore contemporary examples that highlight how smart devices have been used by the police and unpack the socio-technical implications of using these devices in criminal investigations. We draw on several sociological, computing and forensics concepts to develop our argument. We consider the challenges of obtaining and interpreting trace evidence from smart devices; unpack the ways in which these devices are designed to be ‘invisible in use’; and reflect on the processes by which they become domesticated into everyday life. We also analyse the differentiated levels of control occupants have over smart home devices, and the surveillance impacts of making everyday life visible to third parties, particularly the police.
KGDetector: Detecting Chinese Sensitive Information via Knowledge Graph-Enhanced BERT

The Bidirectional Encoder Representations from Transformers (BERT) technique has been widely used in detecting Chinese sensitive information. However, existing BERT-based frameworks usually fail to emphasize key entities in the texts that contribute significantly to knowledge inference. To meet this gap, we propose a BERT and knowledge graph-based novel framework to detect Chinese sensitive information (named KGDetector). Specifically, we first train a pretrained knowledge graph-based Chinese entity embedding model to characterize entities in the Chinese textual inputs. Finally, we propose an effective framework KGDetector to detect Chinese sensitive information, which employs the knowledge graph-based embedding model and the CNN classification model. Extensive experiments on our crafted Chinese sensitive information dataset demonstrate that KGDetector can effectively detect Chinese sensitive information, outperforming existing baseline frameworks.
Generating a trading strategy in the financial market from sensitive expert data based on the privacy-preserving generative adversarial imitation network

Applying a deep learning algorithm to real financial trading records can effectively establish a profitable trading strategy, but leaking of private information during the process is a concern. To solve this problem, previous studies have proposed a privatized data "release" mechanism, but this mechanism is unable to truly protect privacy. To preserve privacy while still being able to learn useful information, we proposed a privacy-preserving generative adversarial imitation network (PPGAIN), a novel deep learning algorithm that imitates behaviors of general financial investors while preserving individual privacy. The core concept of PPGAIN is to provide class-ambiguous outputs to preserve privacy while generating sequences of trading behavior that are similar to a real investor's behavior. We used an auxiliary classifier as an adversary to ensure that the generated results are class ambiguous. We also proposed weakness replay to enforce the training of a discriminator. The PPGAIN model was trained in two stages, in the first stage, the model was trained to be realistic. In the second stage, it was trained against the auxiliary classifier to ensure that the outputs were highly realistic but could not be easily classified as an imitation from any specific individual to preserve privacy. Using real and synthetic trading records in TAIEX, we proved that our proposed method (PPGAIN) can achieve a generation quality similar to that obtained using conditional variational autoencoder models and demonstrated that PPGAIN provides better class ambiguity to preserve individual privacy than privacy-preserving adversarial network.
Video captioning using Semantically Contextual Generative Adversarial Network

In this work, we propose a Semantically Contextual Generative Adversarial Network (SC-GAN) for video captioning. The semantic features extracted from a video are used in the discriminator to weigh the word embedding vectors. The weighted word embedding vectors along with the visual features are used to discriminate the ground truth descriptions from the descriptions generated by the generator. The manager in the generator uses the features from the discriminator to generate a goal vector for the worker. The worker is trained using: a goal based reward and a semantics based reward in generating the description. The semantics based reward ensures that the worker generates descriptions that incorporate the semantic features. The goal based reward calculated from discriminator features ensures the generation of descriptions similar to the ground truth descriptions. We have used MSVD and MSR-VTT datasets to demonstrate the effectiveness of the proposed approach to video captioning.
Herding in different states and terms: evidence from the cryptocurrency market

Abstract In this paper, we provide an in-depth analysis of the herding nature in the cryptocurrency market. We use the first 200 crypto coins data ranked based on market capitalization on January 1, 2020, to show the analysis. We illustrate the crypto investors' herding nature and intensity in different terms (by using daily, weekly, and monthly frequency data) and various states (high vs. low EPU states and high vs. low VIX states). We also demonstrate the magnitude of the herding effect on the next day's market returns in the cryptocurrency market.
Smart Campus Geographic Information System (GIS) Mobile Application Reporting System

Abstract The Geographical Information System (GIS) mobile application helps the communities in a specific area, the agency related to the maintenance and solving problems related to infrastructure. The purpose of this study is to enhance the location and visualization of the current reporting system. The methodology used in this study starts with a preliminary study to understand the issue, data collection, data processing, and system design using ArcGIS for Desktop, ArcGIS App Studio to design the application, ArcGIS Online to create the computer dashboard, and finished by testing the system. As a result, a mobile geographical information system based on the maintenance and solving problems related to infrastructure was created as a mobile application. Some people tested the Smart Campus GIS Mobile Application Reporting System in the specified area to verify the capabilities. In conclusion, the positive feedback received from the respondent is used to validate the mobile application and is ready to be used by the people in the target area.
Visibility enhancement and dehazing: Research contribution challenges and direction

Image Dehazing is a fast growing research area with several practical applications. Dehazing improves the image quality that has been affected due to the scattering phenomenon caused by air or water. Extensive work has been carried out in this field. But a significant research gap exists when it comes to large-scale performance evaluation covering a variety of dehazing approaches, datasets, and Image Quality Assessment (IQA) metrics. In this work, we try to fill this gap by reviewing more than 100 methods from different categories. We present a complete taxonomy for image dehazing methods. Haze and IQA datasets were discussed and compared. In addition, we also contribute a real-world dense-to-low high and low-resolution hazy image dataset. Our real hazy images dataset (D2L-Haze) contains 107 natural outdoor dense and moderate haze images. Qualitative and Quantitative evaluation of dehazing approaches allows us to identify their strength and limitations. Several shortcomings of the existing techniques and some open challenges are discussed. To the best of our knowledge, this is the first extensive review that covers all the aspects of Daytime image enhancement and dehazing.
GVC-Net: Global Vascular Context Network for Cerebrovascular Segmentation Using Sparse Labels

Cerebrovascular disease is a serious threat to human health. Because of its high mortality and disability rate, early diagnosis and prevention are very important. The performance of existing cerebrovascular segmentation methods based on deep learning depends on the integrity of labels. However, manual labels are usually of low quality and poor connectivity at small blood vessels, which directly affects the cerebrovascular segmentation results. In this paper, we propose a new segmentation network to segment cerebral vessels from MRA images by using sparse labels. The long-distance dependence between vascular structures is captured by the global vascular context module, and the topology is constrained by the hybrid loss function to segment the cerebral vessels with good connectivity. Experiments show that our method performed with a sensitivity, precision, dice similarity coefficient, intersection over union and centerline dice similarity coefficient of 61.24%, 75.58%, 67.66%, 51.13% and 83.79% respectively. The obtained results reveal that the proposed cerebrovascular segmentation network has better segmentation performance for cerebrovascular segmentation under sparse labels, and can suppress the noise of background to a certain extent.
Developing momentum in vanishing index photonics

Refractive index invariably describes the speed at which light passes through materials, and subsequently its perceived momentum. But what happens to these quantities as the index becomes zero? A new work explores this question, highlighting how momentum in near-zero-index materials affects linear optical processes.
A Novel Data-Driven Method to Estimate Invisible Solar Power Generation: A Case Study in Taiwan

As the penetration of photovoltaic (PV) solar generation increases, numerous residential and commercial solar PV systems without meters are being installed. The majority of these systems, however, are not monitored by power system operators. Therefore, the uncertainty of net load owing to these invisible solar power generation will raise additional challenges for power system operation. To reduce the above-mentioned impact, this work proposes a novel method to estimate the total solar power generation in a large region from a small representative subset. The proposed method is capable of capturing all relevant information that assists in the identification of representative subsets. Moreover, different optimization algorithms are utilized and evaluated to select the optimal number of clusters and representative subsets. As a case study, the power generation of 166 PV sites in Taiwan was collected and analyzed. The proposed method demonstrates a significant improvement in estimating the aggregated power generation compared to other existing studies.