Reviewerly/exHarmony huggingface dataset:
Are El Salvador’s Volcanoes Being Harnessed to Power Bitcoin Mining?

On october 1th, 2021, international outlets reported El Salvador had just started mining Bitcoin using energy from volcanoes. CNBC published that “El Salvador had mined 0.0059918 Bitcoin, or about US${\$}$269.00 at the time, with power harnessed from a volcano”. Just three months before, president Nayib Bukele had instructed state-owned geothermal electric company, LaGeo S.A. de C.V., to “put up a plan to offer facilities for Bitcoin mining with very cheap, 100% clean, 100% renewable, 0 emissions energy from our volcanoes.” But how true it was to put up a geothermal electric power plant in three months? Was it profitable to mine Bitcoin from a geothermal source? This has not been the kind of transparent government, information has been hard to acquire. However its need for publicity and its desire to appear as a technology savvy government made them show some details of their mining operation. An early analysis has shown up there was no new power plant nor profit on mining crypto. Bitcoin mining bet appears to be backfiring, showing the gap between false promises of producing crypto money and economic realities.
Mobile Platform for Livestock Monitoring and Inspection

Livestock keepers acquire and manage information (e.g. identification numbers, images, etc.) about livestock to identify and keep track of livestock using systems with capabilities to extract such information. Examples of such systems are Radio Frequency Identification (RFID) systems which are used to collect and transmit livestock’s information to host devices. Sophisticated RFID readers are very expensive, and more functional than the cheap ones whose use are mostly limited to reading and transmission of tag IDs. Cross-platform mobile applications will allow monitoring of livestock irrespective of the platform on which mobile devices are being operated. Farmers’ secured access to records via web services is not limited to a device as they can login on any mobile device with the installed application. In this work, a mobile platform which consists of a cross-platform mobile application, webservice and database is developed to cost-effectively manage and exploit records of livestock acquired using a cheap RFID reader. The mobile application was developed using a Xamarin form framework. The programming language and development environment used are C# and Visual studio respectively. Records of livestock were acquired, posted, updated, deleted and retrieved from the database via a web service. Additional advantages offer by the solution implemented include, exporting of animals’ records via email and SMS, viewing of animal’s record by scanning their tags or QR code of animals’ passports, and login system to sign users in and out of the application. Development of RFID readers with sensors to acquire health-related parameters for health monitoring is recommended.
State-of-the-Art Review of Taxonomies for Quality Assessment of Intelligent Software Systems

As the number of AI-based software projects is increasing drastically, frequent use and dependency on these projects raise an alarm to look into the quality of such systems which is essential for their practical usage in the industry as well as in the academy. In addition, deeper look into this field will provide researchers the opportunity to develop new methods and metrics to effectively and efficiently assess the quality of such systems. However, this area of development is considered to be relatively new and emerging, thus, it hasn’t well-explored and therefore creates a large demand for collaborative researches in this area. This preliminary review of existing focuses on the study of quality assessment metrics and methods for AI-based systems, identifying attributes and properties of intelligent software projects that play an important role in assessing the quality of such systems.
Automated Sinhala Voice Assistant to Manage Tasks using Natural Language Processing - ශ්‍රී Voice

Voice assistants are programs on digital devices that listen and respond to verbal commands. In this dynamic world, users can use these voice assistants to manage daily tasks, plan their day, get answers to problems, and for entertainment purpose. Most of existing voice assistant applications functioned using the English language. Since Sinhala is the native language in Sri Lanka, it is not recognized internationally as well as within Sri Lanka for technical applications. As Sri Lankans are more inclined to use the Sinhala language, it is expected to develop this for the benefit of all the Sri Lankans despite their age and to use their native language through a technical application. Furthermore, the lack of English knowledge will lead to the decline of Information technology literacy. This project expects to take the Sinhala language forward to a standard where it is recognized locally and internationally. Thus, building a mobile application that supports Sinhala voice commands will solve the above-mentioned issues. It will facilitate the individuals to do daily activities efficiently and effortlessly within a less time. Machine Learning and Natural Language Processing are the man technologies used in this project. A computer vision-based algorithm from face detection technology is trained. This application is capable of answering questions and following the instructions for daily tasks and also can be used for entertainment. Apart from the mentioned specialty, it also supports and responds to the Sinhala language that will be shown off on a display.
Opinion Mining Using LSTM Networks Ensemble for Multi-class Sentiment Analysis in E-commerce

As technology conquers more sectors and industries, the burden of protruding falls heavy on stakeholders, especially with the rising popularity of blogging and social media. The outpour of views and opinions, accompanied by the recent trend of influencers and content creators, inspired various advances in natural language processing. These advances aim to harvest reviews and experiences of consumers to start, improve, and expand businesses. The recent surge in textual data has also inspired many large-scale data analyses powered by artificial intelligence. In this study, we investigate some of these analyses and their applications. We also implement state-of-the-art LSTM models to explore different attributes of text. Based on the relevancy of these attributes, we suggest an ensemble model of LSTM neural networks that utilize word-level and character-level tokenizers to convey helpful knowledge in sentiment or opinion classification. For training, validating, and testing the presented model, we accumulated a large dataset of 602,202 product reviews from Turkish e-commerce websites. The study establishes an 84.7% accuracy of the proposed ensemble model when applied to raw data.
The Use of Biharmonic Dejdamrong Surface in Gray Image Enlargement Process

In this given study, the image enlargement method was investigated from the perspective of making use of biharmonic cubic Dejdumrong surfaces. It is common practice to resize a picture by upsampling or downsampling in order to accommodate a tiny image on a bigger screen (full-screen mode) or to decrease a high-quality image to a lower resolution. In contrast, this study will focus on picture enlargement utilizing a scaling factor of two owing to certain limitations. Biharmonic cubic Dejdumrong was employed in the investigation under various four-boundary curve conditions. This research looked at the effectiveness of the recommended approach by applying it to well-known grayscale test photos and calculating the peak signal-to-noise ratio (PSNR), which is an indication of how well the strategy performed.
Privacy Preserving Enhancing Model for Multiple-sensitive Attributes

Recently, there is a demand of publishing sensitive data about individuals for many research reasons. However, this will compromise the privacy of the individuals. Therefore, anonymity concept needed to be applied on the data before publishing it. The problem is that most of anonymity models will have information leak when dealing with multiple sensitive attributes. In this paper, a privacy preserving enhancing model for multiple sensitive attributes is presented based on the Anatomy. This model can be achieved by clustering the data based on the Anatomy principles on sensitive attributes together using Single Sensitive Bucketization SSB. M-Buckets algorithm is presented and compared with Anatomy algorithm. Results show better privacy preserving for the proposed algorithm.
Statistical Features and PageRank Scoring Fusion for Arabic Text Summarization

online text content is increasing daily, making it a big challenge for readers to find the required information. So, providing a gist of the available text content can help those readers save their time and effort. This operation is completed by Automatic Text Summarization (ATS), which creates a summary for the entire text. Automatic Text Summarization can be achieved by many techniques, such as graph theory, statistics, machine learning, and clustering methods. Each of the above techniques takes care of a specific issue of text which other techniques do not. Text summarization for the Arabic language suffers from a lack of resources and language complexity. So, it is worthwhile to take the advantages of two or more text summarization techniques and fuse them together to improve their performance and enhance their summary quality. This study proposes a method that combines two of the ATS methods, which are the statistical based method and the graph-based modified PageRank algorithm. Graph-based techniques take care of relationships among document sentences, whereas statistical-based techniques take care of signalizing important sentences. Experiment results show that proposed method achieved a good performance. The Essex Arabic Summaries Corpus (EASC) was used as evaluation data.
An Augmented Reality-based System for Fashion and Beauty Makeover

With the large-scale virtualization of essential and non-essential services in today’s world, it has become imperative to develop an efficient and all-round online shopping system which stimulates the shopping experience while avoiding physical contact. Especially given the recent pandemic situation, such an online shopping system which incorporates the experience of physical shopping would turn out to become extremely useful. This paper thus proposes various methodologies and technologies which we have used to effectively implement the said system in the fashion industry using various augmented reality and artificial intelligence techniques for a holistic user experience.
DDCNet: A Lightweight Network with Variable Receptive Field for Real-Time Portrait Segmentation in Complex Environment

Due to the complex and diverse structure of the portrait boundary, it is a great challenge to segment the portrait from the natural background efficiently and accurately. We propose a new lightweight real-time semantic segmentation network-DDCNet, for portrait segmentation in complex background. Firstly, we propose a deformable depthwise separable convolution block, which combines deformable convolution with depthwise separable convolution, so that the network can fully obtain global and local information while reducing time consumption, and effectively reduce the complexity of the model. Secondly, we propose a detail selection block, which improves the segmentation accuracy of the network by selecting the information supplemented by skip connection. Finally, we propose a novel loss to improve the robustness of portrait segmentation in natural background. Our model has few parameters (0.122M) and FLOPs (0.092G). Experimental results show that our method could efficiently obtain the accurate segmentation image in real-time and achieve state-of-the-art comprehensive performance on the public datasets EG1800 and Conference Video Segmentation Dataset. User study shows that our method is favored by the most testers.
MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis

Deep learning models have been widely studied and have achieved expert-level performance in medical imaging tasks such as diagnosis. Recent research also considers integrating data from various sources, for instance, chest X-rays (CXR) radiographs and electronic medical records (EMR), to further improve the performance. However, most existing methods ignore the intrinsic relations among different sources of data, thereby lack interpretability. In this paper, we propose a framework for pulmonary disease diagnosis that combines deep learning and domain-knowledge reasoning. We first formalize the standard medical guidelines into formal-logic rules, and then learn the weights of the rules from medical data, integrating multimodal data for pulmonary disease diagnosis. We verify our method on a real dataset collected from a hospital, and the experimental results show that the proposed method outperforms the previous state-of-the-art multi-modal baselines.
Anomaly Electrocardiograms Automatic Detection with Unsupervised Deep Learning Methods

Anomaly detection is an important problem in various fields of technology and industry, such as malicious intrusions into computer systems, finance and banking, health monitoring, etc. Currently, deep learning methods have achieved significant success in anomaly detection. Methods of detecting anomalies in a set of electrocardiograms containing normal ECG signals and ECG signals with various cardiovascular diseases have been investigated. To detect abnormal electrocardiograms, an autoencoder model in the form of a deep neural network with several fully connected layers was developed. Also, a method of selecting a threshold to separate abnormal ECG signals from normal ones was proposed, which consists in optimizing the ratio of performance indicators of the autoencoder model. A comparative computer analysis of the effectiveness of applying the proposed autoencoder model and other machine learning models, such as the support vector method, isolation forest, and random forest, to solve the problem of detecting abnormal ECG signals was carried out. For this purpose, metrics such as accuracy, recall, completeness, and f-score were used. The results showed that the proposed model surpassed other models with accuracy of 98.8%, precision of 95.75%, recall of 99.12%, f1-score of 98.75%.
Tarragona Graph Database for Machine Learning Based on Graphs

Attributed graphs are commonly used to represent structured objects ranging from images or chemical compounds to social networks, among others. In the last years, graphs have become a powerful tool in machine learning in different fields. For instance, graph generation (i.e. drug design), graph analysis (i.e. marked prediction) or graph matching (i.e. hand written character recognition). When new algorithms in these fields are presented, it is necessary to consciously test them in different situations and applications to evaluate their quality. With this concept in mind, we present a new repository of ten graph datasets, which has the unique characteristic that registers are composed of pairs of classified graphs with their node-to-node ground-truth correspondences. These datasets were previously used in state-of-the-art articles but we have given them a unified format and they are ready to be used for new algorithm-testing purposes.
An Efficient Dense Depth Map Estimation Algorithm Using Direct Stereo Matching for Ultra-Wide-Angle Images

We present an efficient dense depth map estimation algorithm using patch-based direct stereo matching for ultra-wide-angle images. Our algorithm takes account of the fact that the neighboring pixels inside a local patch are likely to lie on the same plane. Our algorithm propagates the “good” initial guesses to the neighboring pixels by spatial propagation, followed by a random refinement process. Therefore, this allows finding precise depth value for each point in an infinite space using a random search strategy. Our algorithm can be used to perform 3D reconstruction using the dense depth maps directly generated from ultra-wide-angle images, especially from stereo camera pairs.
Face Detection Algorithm in Classroom Scene Based on Deep Learning

With informatisation progresses in education, the application of artificial intelligence technology in education is increasing day by day. Although some breakthroughs have been made, the application in specific scenes (such as classroom scenes) faces many difficulties, such as small target detection, severe occlusion, etc. We propose a target detection algorithm based on video data for the classroom scene, which combines the optical flow information to improve the accuracy and alleviate the impact of occlusion. We also put forward the counting method suitable for classroom scenes, which can be used as the evaluation standard of attendance rate, head-up rate, and other indicators in classroom quality evaluation.
Wider and Higher: Intensive Integration and Global Foreground Perception for Image Matting

This paper reviews recent deep-learning-based matting research and conceives our wider and higher motivation for image matting. Many approaches achieve alpha mattes with complex encoders to extract robust semantics, then resort to the U-net-like decoder to concatenate or fuse encoder features. However, image matting is essentially a pixel-wise regression, and the ideal situation is to perceive the maximum opacity correspondence from the input image. In this paper, we argue that the high-resolution feature representation, perception and communication are more crucial for matting accuracy. Therefore, we propose an Intensive Integration and Global Foreground Perception network (I2GFP) to integrate wider and higher feature streams. Wider means we combine intensive features in each decoder stage, while higher suggests we retain high-resolution intermediate features and perceive large-scale foreground appearance. Our motivation sacrifices model depth for a significant performance promotion. We perform extensive experiments to prove the proposed I2GFP model, and state-of-the-art results can be achieved on different public datasets.
Research Genealogy Knowledge Base

Genealogy metrics are developed for analyzing the properties of the Research Genealogy. For Consistency, a minimal set of information attributes are defined. For Correctness, feasible biological age ranges are tested for age at marriage, at birth of children, and at death. For Closure, the genealogy is partitioned into extended families and checks are made to ensure paths can be found linking any two persons. For Connectivity, methods are described to evaluate global connections (ascents), local connections (own cousin relationship), and ancestral fraction distributions. For Completeness, links are shown to a Unifying Ancestry. For external Coherence, links are made to history books. For internal Coherence, the information content is analyzed to determine the number of generations needed to find progenitors for the national community.
Implementation of Machine Learning Methods on Data to Analyze Emotional Health

The design and implementation of the intelligent model based on machine learning methodologies on datasets to analyze emotional and mental health, majorly concentrating to measure, cognize, simulate, and react to human emotions. The data captured by the apparatus is always available to the user and provides contact to interact with patients in case of Emergency. It stores data for future analysis and for pattern identification, Experts will be able to use Artificial Intelligent based machines to augment the human capacity to detect, diagnose, and treat mental health issues. Machines have a significantly higher computational capacity than humans, creating assessments from large pools of patient’s data that will eventually make informed clinical decisions. This article will examine AI applications on emotional and mental health, It also highlights the implications of using emotional AI in identifying, diagnosing, and improving mental health. We focused on the conceptual and simulation model for result analysis where data stored in the cloud is retrieved as per the requirement and analyzed. Different machine learning algorithms are implemented on the dataset and results are identified through graphical analysis.
Cryptographic Toolkit

In a typical IoT system, we have a single IoT server working as a supervisor for multiple IoT devices.
An Efficient Key Recovery Attack Against NTRUReEncrypt from AsiaCCS 2015

At AsiaCCS 2015, Nu $$\tilde{n}$$ ez et al. proposed a NTRU-based proxy re-encryption (PRE) scheme, called NTRUReEncrypt. A complete PRE scheme permits the sender to encrypt messages to the proxy, and allows the receiver to decrypt the ciphertexts re-encrypted by the proxy. At PQCrypto 2019, Liu et al. provided cryptanalysis of the scheme based on decryption failures and statistical analysis, both of which need huge amount of ciphertexts. For instance, for ees1171ep1 parameter set, the number of ciphertexts required are $$4.68 \cdot 10^{17}$$ and $$4.83 \cdot 10^{17}$$ respectively. In this paper we point out that the security of NTRUReEncrypt would be impacted by an efficient key recovery attack based on linearization technique, it can reduce the number of required ciphertexts drastically. To be specific, two parties sending and receiving messages can recover the other’s private key by communicating $$O(N+[\frac{N}{2}])$$ times, where N is an odd prime in the ring $$\mathcal {R}=\mathbb {Z}[x] /\left( x^{N}-1\right) $$ . For specific scheme on parameter sets ees1087ep1, ees1171ep1, ees1499ep1, where N equals 1087, 1171 and 1499 respectively, the amount of ciphertexts used in our attack is only on the order of $$10^3$$ , and our experiments are all completed within one hour on PC. Moreover, we discuss the NTRUReEncrypt instantiated with the NTRU parameter sets in the third round of NIST-PQC competition and give the theoretical analysis.
Interaction-Aware Temporal Prescription Generation via Message Passing Neural Network

In recent years, deep learning techniques have been applied with great success in the healthcare industry, such as disease prediction and drug recommendation. However, existing works on drug recommendation either do not take the critical impact of doses on treatment outcomes into account, or neglect the patient’s personalized history and drug-drug interactions, resulting in suboptimal recommendation results. To fill these gaps, we propose a novel Temporal Prescription Generation (TPG) model in this paper. Specifically, we first utilize a message passing neural network to capture the interaction between drugs, combined with a decomposed long and short-term memory (LSTM-DE) to characterize patient’s general information. Then, we design a prescription generator based on recurrent neural network to generate drugs and estimate doses simultaneously. Experimental results on a real-world dataset clearly demonstrate that the proposed model is superior to the best existing models in terms of drug recommendation and dose estimation.