Reviewerly/exHarmony huggingface dataset:
Research on Named Entity Recognition in Judicial Field Based on ERNIE-Gram

Named Entity Recognition is a key and fundamental task in natural language processing and can benefit many downstream tasks such as knowledge graph construction, question answering system, machine reading, etc. In view of the contradiction between the rapidly increasing of judgement documents and the low efficiency of manual analysis in the judicial field, we propose a NER model by using a combination of ERNIE-Gram, BiGRU, and CRF. ERNIE-Gram adopts multi-granularity n-gram language learning mechanism to learn the semantic of n-grams more adequately. Thus, we first employ the ERNIE-Gram to capture rich language representation, then we feed them into the BiGRU to obtain more important text features, finally, we use the CRF to decode and output optimal labeling sequence. We conduct experiments on an open dataset of the 2021 “Challenge of AI in Law” information extraction subtask and compare our model with currently familiar models for NER task. Experimental results demonstrate that our proposed model achieves a F1-score of 87.01%, and outperforms all the baseline models.
A Quantum Computing Arithmetic-logic Unit

We show that a quantum version of a classical arithmetic-logic unit (ALU) can be implemented on a quantum circuit. It would perform the same functions as a classical ALU, with the possibility of adding quantum functions in conjunction. To create the quantum ALU, we utilized IBM's Python package Qiskit and JupyterLab. We believe that a quantum ALU has the potential to be faster than its classical counterpart and the ability to calculate quantum specific operations. The simple classical functions translated to a quantum circuit show a promising future for the development of a full quantum ALU with unique quantum operations.
Continuous epoch distance integration for unsupervised person re-identification

Unsupervised person re-identification aims to learn discriminative feature representations for person retrieval from unlabeled datasets. Clustering-based methods achieve state-of-the-art performance in this research direction. However, the noisy pseudo-labels generated during the clustering process and the interference caused by background noise limit the performance of the model. To this end, this paper proposes continuous epoch distance integration for unsupervised person re-identification, which performs clustering by integrating the distance matrix between two consecutive epochs to generate reliable pseudo-labels. In addition, the attention module of spatial structure and channel dimension is also proposed, so that the model pays more attention to the person itself and eliminates the interference of the background. The effectiveness of this method is verified on Market-1501, DukeMTMC-ReID, and MSMT17. The experimental results show that this method is superior to the current mainstream unsupervised person re-identification method.
Exploring Node Classification Uncertainty in Graph Neural Networks

In order to represent and investigate interconnected data, Graph Neural Networks (GNN) offer a robust framework that deftly combines Graph theory with Machine learning. Most of the studies focus on performance but uncertainty measurement does not get enough attention. In this study, we measure the predictive uncertainty of several GNN models, to show how high performance does not ensure reliable performance. We use dropouts during the inference phase to quantify the uncertainty of these transformer models. This method, often known as Monte Carlo Dropout (MCD), is an effective low-complexity approximation for calculating uncertainty. Benchmark dataset was used with five GNN models: Graph Convolutional Network (GCN), Graph Attention Network (GAT), Personalized Propagation of Neural Predictions (PPNP), PPNP's fast approximation (APPNP) and GraphSAGE in our investigation. GAT proved to be superior to all the other models in terms of accuracy and uncertainty both in node classification. Among the other models, some that fared better in accuracy fell behind when compared using classification uncertainty.
Cyberattack Repository: A Web Application for the Selection and Composition of Cyberattack Models

In the area of research and the process of being innovative, researchers know that sometimes the tools that are needed are something other than what can be attained off the shelf. During research conducted in the selection and composition of cyberattack models, a prototype application was built to assist in searching for cyberattack information against specific systems. This process of selection is only one step towards learning more about cyberattacks and how organizations can defend against them. An initial prototype web application was developed, and this project has expanded the functionality of that web application to not only allow the selection of cyberattack models but also to compose them. The composition is based on two approaches, sequential or parallel. The application has also been expanded to automate the process of assembling models. A description of the design specifications of the application and additional development plans are shown.
A 3D Object Detection Algorithm Based on Improved EPNet

Early 3D object detection algorithms focused on using a single sensor. However, with the development of target detection technology, the target detection algorithm based on multi-sensor fusion has gradually entered people's field of vision. In this paper, we improve the EPNet algorithm based on image and point cloud fusion. In its image stream branch, due to its relatively simple image upsampling method, most of the image information will be lost, which reduces the detection accuracy. Therefore, the method of combining the feature pyramid network with the coordinate attention mechanism is used to make up for the problem. Extensive experiments on the KITTI datasets demonstrate this method is good.
Smart Contract-Based Social Recovery Wallet Management Scheme for Digital Assets

Social recovery schemes enable the recovery of decentralized digital assets like Bitcoin and Ethereum through a social network. These schemes suffer from security issues and limitations including centralization, a conspiracy of one's network, large transaction fees for the user and network, and verifiable connections between the user and the network. To mitigate these issues, the work proposes and implements a novel social recovery scheme based on verifiable secret sharing, smart contract code, encrypted communication, and biometric encryption. The security and performance analysis versus key existing implementations is evaluated in terms of security features and gas fees.
A Comparison of Two Approaches to Support Methods Time Measurement in an Automotive Factory

In a manufacturing factory, eliminating seconds off a task that is repeated often can reduce the overall manufacturing time and improve the profitability of each unit produced. It is advantageous to understand the average amount of time required to complete each task. In order to scientifically determine the desired time required to complete a task, the individual subtasks also can be timed. This can be made more efficient by automating the process of recording the basic physical motions of factory workers that are involved in a task. Our paper shows how it is possible to use a machine learning based approach to classify the basic motions. This paper describes two approaches that we implemented in order to automate the process of motion classification. We contrast the two approaches and analyze the tradeoffs between each approach. The context for the application of our project is Mercedes-Benz US International, a large automotive manufacturing facility in the Southeastern United States. Additionally, we discuss the limitations of the two approaches and future work that can address these issues.
Comparison of Long Short-Term Memory Networks and Temporal Convolutional Networks for Sentiment Analysis

The use of AI to detect human emotion is growing rapidly with the need for human-like customer service without constant human interaction, conducting market research, monitoring opinions on social media, and so on. One model that is often used for this sentiment detection is a recurrent neural network with long short-term memory. Another, newer model that can be used is a temporal convolutional network, but the differences between these two models are under-researched. The goal of this project is to apply these models and compare their performance when detecting sentiment. This will provide some guidance to programmers, allowing them to be more aware of the implications of implementing one model over the other. We find that overall, a temporal convolutional network outperforms the recurrent neural network with long short-term memory for sentiment analysis; our temporal convolutional network achieves 72% accuracy when detecting sentiment.
Modeling Particle Randomness: A Pseudorandom Number Generator

This paper discusses the development of a new pseudorandom number generator (PRNG) based on chaotic billiards and particle randomness. In this new system, two massless particles bounce, teleport, and collide with each other inside of the classic Sinai Billiard. Random sequences are generated based on the collision coordinates of two particles as they bounce off of the circular center billiard wall. Three statistical tests conducted on the generated sequences indicate the generator produces sequences comparable to truly random sequences. In addition to discussing the results of the three statistical analysis, this paper details how the model is set up and how the pseudorandom sequence is produced.
Re-evaluating Keystroke Dynamics for Continuous Authentication

Today we use smartphones for banking, shopping, and monitoring our health. These applications store sensitive data in the smartphone, making reliable authentication a crucial element in mobile devices. However, current mobile authentication systems such as pin codes, passwords, pattern locks, fingerprints, and face IDs have security vulnerabilities as they are one-time authentication systems. In contrast, behavioural biometric-based Continuous Authentication (CA) focuses on continuously authenticating the user while using the device. Among behavioural biometrics, keystroke dynamics is an efficient and well-researched behavioural biometric. Keystrokes dynamics refers to the unique typing patterns of the user. However, despite many frameworks proposed for mobile CA using keystroke dynamics, they are only evaluated for their discriminative power using traditional metrics such as Equal Error Rate. However, these evaluations are unable to capture temporal performance. Hence, in this work, we evaluate the state-of-the-art keystroke dynamics systems using continuous evaluation metrics. Our analysis indicates that these systems perform differently with traditional and continuous evaluation metrics, stressing the importance of evaluating CA systems using traditional and continuous evaluation metrics for a holistic assessment. Further, our analysis highlights how different models perform differently when evaluated with CA evaluation metrics highlighting the need to carefully select appropriate evaluation schemes based on the requirements such as security and usability.
Swa-Bhasha: Romanized Sinhala to Sinhala Reverse Transliteration using a Hybrid Approach

With the social and technological revolution, the usage of social media platforms and instant message services strengthens native language compatibility in the digital arena. The Sinhala and the Romanized Sinhala became the prominent typing languages among the general Sri Lankan community. Informal short-hand-based typing and short net acronyms were used for easier Sinhala typing. But Typing Romanized Sinhala using ad-hoc transliterations and getting the expected output in native Sinhala is less accurate and time-consuming. Therefore, this study aims to introduce a novel reverse transliterator which can back transliterate and suggest Romanized Sinhala to Sinhala words. The Transliterator has been modelled using the Statistical approach with Trigram and Rule-based model for back transliteration purposes and Knowledge-based with a Trie data structure for suggesting purposes. The proposed solution is capable of transliterating both formal and informal shorthand Romanized Sinhala. This hybrid model used in the study is capable of efficient transliteration with the word level accuracy of 0.84. This proposed model can be used in digital platforms to enhance the usability of native Sinhala communication in a much more efficient way.
Tamil Grammarly – A Typing Assistant for Tamil Language using Natural Language Processing

Tamil is one of the ancient and most convoluted languages in the world. Although it is being the official language of many Asian countries, even native speakers tend to find difficulties in writing Tamil due to its morphologically rich nature. While there are various studies focusing on automatically identifying and correcting a specific typing error, very limited effort has been made to develop a comprehensive solution to assist the native and non-native writers of Tamil. In this paper, we propose a typing assistant tool Tamil Grammarly using Natural Language Processing (NLP) techniques. Specifically, the tool aims to aid the user to fix grammatical errors and spelling errors and recommend the next words and synonyms of the current word in real-time while typing. The NLP-based typing assistant functions of Tamil Grammarly were developed using a transformer-based model, LSTM model, and Word2Vec model. Extensive evaluation performed shows that our tool can assist the users in real-time with an accuracy of 73% - 93% within 0.4 to 5.3 seconds.
TacoSi: A Sinhala Text to Speech System with Neural Networks

A piece of software that turns text into speech is referred to as text to speech or speech synthesize software. There are vast number of TTS tools available for various languages including English and Chinese. Sinhala is the first language of Sri Lanka and speaks by over 16 million people which are the major ethnic group in the country. Few researchers have tried to develop Sinhala-dependent TTS systems using traditional methods, but they required time-consuming, manually generated features, and doesn't produce natural-sounding speech. The main aim of this study is to develop a human quality Sinhala TTS tool without relying on hand-crafted features. The proposed solution TacoSi, is an algorithm based on Tacotron and was trained with raw text and audio pairs. By using the raw text as an input, TacoSi can produce human-like speeches in Sinhala. Based on 10 respondents’ ratings for generated 10 voice recordings, the proposed technique achieved a 4.39 MOS. TacoSi's intelligibility has been tested by SUS sentences-based technique and was able to achieve 84% of intelligibility score which is significantly higher than existing systems. More importantly, TacoSi can pronounce rare words (never seen in training) and comprehends most common symbols, numerical values, and abbreviations used in written Sinhala.
Developing a GUI Application: GPU-Accelerated Malicious Domain Detection

Our study uses the power of a graphics processing unit (GPU) to run malicious domain detection algorithms quickly and efficiently. We have developed a graphical user interface-based system that allows users to upload datasets (malicious domains) into a local database and then run tests with a list of domains to identify whether they are malicious. We have collected real malicious domain data from malicious domain websites and tested the five most widely used string-matching algorithms (Naïve, Levenshtein distance, Hamming distance, KMP and Rabin Karp), which allow users to compare the speeds of different string algorithms with varying time complexities against the number of domains both on the GPU (or the CPU) and our sample. On a CPU, this task becomes slower as our dataset grows. On a GPU, however, these algorithms can be run on any dataset size within the limit of the GPU's capacity with consistent performance.
Reconstruct Damaged Sinhala Handwritten Characters by Guided Edge Linking Approach

The Sinhala language is considered a low-resource language in the computing domain. Before reconstructing damaged characters, damaged areas should be identified. The fundamental process in the damaged Sinhala handwritten character reconstruction process is to identify features of the damaged end tip pixels and character segments. Discovering suitable end tip pixel couples and linking them via appropriate linking shapes (straight or curved) is di cult because most Sinhala characters consist of curved shapes compared to other language characters like English. The most suitable end tips are discovered by considering both the minimum distance end tips and the direction of each end tip in the damaged Sinhala handwritten character. Edges of the damaged Sinhala handwritten characters are linked through local and global edge connecting using straight-line end tip linking and the Bezier curve end tip linking approaches, respectively. The results show that the proposed direction-based nearest end- tip coupling approach provides 96.2% accuracy, while coupled end-tip linking approach gives an accuracy of 94.2%.
Damaged Sinhala Handwritten Character Location-Identification using Neighbour Mapping

In Sri Lanka, the Sinhala language is the major language among other languages. Identification of the damaged Sinhala handwritten characters from the degraded Sinhala handwritten document is the primary goal of this research. Although mean-based threshold and character width and height are used to classify the damaged and non-damaged characters in English, they cannot apply to the Sinhala language because Sinhala characters consist of different widths and heights. Therefore, classifying the damaged and non-damaged characters in a degraded Sinhala handwritten document is di cult. The gradient-based clustering technique is the main approach for clustering the character segment pixels based on their gradient values. Boundary drawing around the damaged zone cluster approach leads to identifying the damaged locations of the character foreground areas and classifying the character as damaged or non-damaged. Thinning-based neighbouring pixel mapping technique determines that a character has exactly damaged end tips. The accuracy of the output images is calculated using a confusion matrix by considering both damaged and non-damaged characters. The results show that the boundary-overlapping approach outputs 98.5% accuracy while the thinning-based neighbouring pixel mapping approach provides 93.6% accuracy.
An Efficient Knock Code Decoding Approach to assist the People with Communication Impairments

Communicating with communication impaired people is a challenging task for doctors and caretakers and hence computer based automated assistant systems are important to identify their needs in real-time. An efficient automated assistant system should be simple, fast and easy to use. To achieve these objectives, we have proposed a novel communication assistant approach based on the Knock code. In the proposed approach, communication impaired people are able to encode their messages into knock sounds. Then the sound patterns are decoded as the English words by processing the corresponding waveform images using a sequence of image processing algorithms. Finally, the decoded text is displayed to the patient and a voice message is also produced. Based on the evaluation studies, proposed approach is able to recognize the English words with accuracy of 94.11 percentage in real-time.
Ontology Based Restaurant Recommendation Approach

As the world moves forward, the restaurant industry is rapidly expanding. Customers may never physically evaluate a restaurant based on its services until that customer has practical experience with it. A better recommendation mechanism can always direct the customer to the correct location, resulting in a positive outcome. The paper discusses an approach of a context rich chatbot that can identify the customer’s mode of thinking using a restaurant ontology that suggests relevant restaurants and foods. Most importantly the defined methodology will use a hybrid version of knowledge bases along with a two-way bind to the primary knowledge base. The chatbot will proceed to find relationships in the ontology by tracing concept definitions and properties while feeding information from the database. The main components related to the proposed system are Natural Language Understanding (NLU) pipeline, dialog management module, action server, knowledge query module, and data repository (MongoDB). This mechanism was evaluated through information retrieval measures.
Automated Radiology Report Generation Using Transformers

Given the rapid increase of respiratory illnesses in recent times, the demand for medical report writing for chest X- Rays (CXR) has significantly increased. In practice, a specialized medical expert has to go through an X-Ray image to compile the accompanying report, which is tedious, not scalable, and potentially prone to human error. Therefore, automatic medical report generation (AMRG) solutions for CXR as a diagnostic assistance tool could play an important role in lowering the burden on radiologists, making them more productive. However, current AMRG solutions are still lagging far behind the performance of human experts due to the reasons such as the inability to extract the most relevant features to be used for the compilation of the report. We address this by proposing MERGIS: MEdical Report Generation using the Image Segmentation approach. MERGIS is a modern transformer-based encoder-decoder model that leverages image segmentation to improve the accuracy of automatic report generation. In this approach, the CXR images are segmented before feeding into the model, enabling the encoder to extract relevant visual features of the medical image resulting in more accurate radiography reports. The proposed model outperforms the current state-of-the-art model for report generation on the MIMIC-CXR dataset with performance scores: BLUE-1 = 0.296, METEOR = 0.128, ROUGE L = 0.335, and CIDEr = 1.150.
Description of Image Using Deep Learning

Computer vision and natural language processing (NLP) are used in artificial intelligence (AI) to automatically create the description of contents of an image. A regenerative neuronal model is developed for this purpose. It depends on machine translation and computer vision. This model is designed to produce natural sentences that finally explain about the image. Convolution neural networks (CNN) and recurrent neural networks (RNN) make up this model. The CNN is used to extract picture features, and the RNN is used to generate sentences. The model has been trained to produce descriptions that almost exactly describe the given input image. Various datasets are used to test the model's precision, smoothness, and linguistic proficiency after learning from image descriptions. These tests demonstrate that the model frequently describes an input image accurately.