Reviewerly/exHarmony huggingface dataset:
Exploring Real-Time Sentiment Analysis Prototype for Retail Industry

The rise of social media platforms has revolutionized the way consumers interact with retailers and express their opinions on products and services. Online retailers particularly need to keep a close eye on customer sentiment in real-time to make informed decisions about their offerings and improve customer satisfaction. However, efficiently analysing large volumes of unstructured text data from social media in real-time poses a significant challenge. This research aimed to develop a scalable, real-time sentiment analysis system tailored for online retailers using Reddit as the data source. The system comprises three main components: a data extraction and streaming pipeline, a sentiment analysis model, and a web application with real-time analytics. To address the data extraction challenge, a job queue-based system was implemented using Node.js, ‘BullMQ’, and Redis to create and manage campaigns for data streaming from Reddit. The data was streamed using Kafka, a distributed streaming platform, to enable efficient real-time processing. The sentiment analysis model was developed using a Naive Bayes classifier after experimenting with other machine learning and deep learning techniques. In the conducted study, the sentiment analysis model's performance was evaluated using standard metrics tailored to the context of online retail sentiment analysis. An accuracy of 0.6737 was achieved, reflecting the model's ability to correctly classify approximately 67.37 per cent of the sentiments in the test data. Concurrently, an F1 score of 0.7894 was recorded and the Area Under the Curve (AUC) value on the test data was measured at 0.5468, a metric that, while acceptable, suggests room for further refinement in the model's discriminatory ability between classes. The integration of the Data Version Control (DVC) system provided a mechanism for fine-tuning the model according to specific data requirements of various tenants. These results, taken together, not only validate the feasibility of employing a Naive Bayes classifier for real-time sentiment analysis in the retail context, but also provide a baseline for future research aimed at enhancing both the accuracy and efficiency of sentiment classification. The project’s evaluation focused on the performance of the sentiment analysis model, the efficiency of the Kafka streaming and real-time Spark pre-processing pipeline, and the backend infrastructure, including the job queuing system and WebSocket implementation. Various evaluation techniques, such as graphs and literature comparisons, were used to assess the system’s performance. In conclusion, this project successfully demonstrated the feasibility of a scalable, real-time sentiment analysis system for online retailers using Reddit data. The system has the potential to help retailers better understand customer opinions and make data-driven decisions for their businesses. Future work could include exploring alternative data sources, experimenting with more advanced sentiment analysis techniques, and enhancing the web application’s user interface and analytics capabilities.
Unleash the Power of Deep Neural Networks and Transfer Learning for Enhanced Face Recognition

Face recognition has become a crucial technology with diverse applications in various domains such as biometric security, healthcare, personal identification, and law enforcement. It stands out due to its high recognition rate, the richness of features, its universality, and its uniqueness. Having said that, challenges such as varying illumination, poses, facial expressions, face transformations, and backgrounds have hindered recognition accuracy, especially when using traditional techniques that rely on explicit feature extraction before the actual classification. The emergence of deep learning algorithms, such as Convolutional Neural Networks (CNNs), has exhibited prominent potential in image recognition capabilities, but building effective face recognition systems from scratch demands substantial labeled data and computational resources. To overcome these challenges, this paper proposes a novel approach that utilizes transfer learning and a pre-trained AlexNet CNN to improve face recognition capabilities. The model is trained with three different deep-learning optimizers, and the most suitable one is selected for the proposed system. For experiments, we created a custom database with images of five distinct individuals, generating over 1260 face images with each person having at least 250 samples. The dataset was further enriched using data augmentation technique. Using MATLAB with the Deep Learning Toolbox, the results demonstrate the model's recognition rate, peaking up to 100 per cent when the SGD optimizer is employed, 97.88 per cent when RMSProp is the optimizer, and 99.2 per cent for the Adam optimizer while using the same set of hyperparameters. These findings showcase the effectiveness of the approach and open doors for more efficient and accurate face recognition systems in real-world scenarios with large datasets.
Machine learning as a feedback tool for the gestalt principle of proximity

When teaching students about the underlying structure of images, the Gestalt Principles are frequently used. Students and teachers may not always agree on the Gestalt Principles that an image contains, making assessment activities focusing on this topic very subjective. When working with large class sizes or when using multiple markers, this subjectivity becomes more problematic. A DBSCAN-based feature generation process and a multi-layer perceptron-based model are suggested in this study for categorising images as exhibiting the principle of proximity. When classifying images as containing aspects of proximity, the model displays a high level of accuracy. The model was integrated as a component of a larger process that alters the original input images to highlight areas that demonstrate proximity. The use of these annotated images can aid in giving students feedback. However, as the model was trained on a synthetic data set, its accuracy on real-world student submissions is yet to be tested.
A Convolutional Neural Network Model for Setswana Named Entity Recognition

Named entity recognition (NER) is a key component of the core task of natural language processing (NLP). In order to represent language, neural networks have been used starting in the 2000s, which enhanced entity recognition outcomes. The Setswana language, in contrast, has never been used with neural networks, in particular convolutional neural networks (CNN). Recently, problems with NLP have been addressed using CNNs, and the results have been quite interesting. CNNs are frequently used in NLP due to their ease of training and reputation as the best in sequence labelling. They depict the interdependence of all conceivable word combinations. Given the difficulties in identifying named entities for South African languages, including Setswana, and the inadequacy of resources, this research proposes the use of CNN model to identify named entities for Setswana. The results obtained are benchmarked with traditional methods such as Conditional random fields (CRF). The performance metrics such as F1-Score are explored in establishing the magnitude of trust and reliability of the proposed model. The model is evaluated using data from the South African Centre for Digital Language Resources' Setswana NER dataset. Compared to the present CRF model, which had an F-score performance of 78.0%, the testing results demonstrate that the model performs 94.0% better.
Abstractive Text Summarisation using Recurrent Neural Networks at the Paragraph Level

Continuous production of information has been facilitated by the easy access to new technology. This has made it difficult for many users to find relevant information, which are sometimes buried deeply inside mass-produced content. Without the development of new tools and technology to make this data more accessible, it potential remain unexploited. Abstractive text summarisation aims to extract the key points of the document. Because text generating techniques are still in their early stage, it has received little attention in the past. Recently, the application of recurrent neural network models has made significant progress in abstractive sentence summarisation. Despite the improvement in results, these models still tend to produce grammatical errors. Unfortunately, attempts in abstractive document summarisation are still in their early phases, and evaluation outcomes on benchmark datasets are noticeably inferior to human summarisation. In this study we propose a data-driven for abstractive document. Each word generated in the summary use an attention-based technique depending on the input paragraph. According to experimental findings, our model generates higher-quality summaries, achieving ROUGE-1 score of 44.44, ROUGE-2 score of 22.50, and ROUGEL score of 45.15 on the document understanding conference 2004 datasets.
INDONESIAN SYNTACTIC INTERFERENCE IN ENGLISH ESSAYS OF ENGLISH EDUCATION PROGRAM STUDENTS FACULTY OF EDUCATION AND TEACHER TRAINING UMSU

In formal educational institutions, mastering English as a second language is a must, especially for students majoring in English. Language mastery often poses challenges because each language has different elements. Proficiency in a foreign language such as English involves mastering four language skills, including writing essays. This study presents research results regarding Indonesian syntactic interference in English essays. This study used a descriptive qualitative method. The analysis begins by reviewing all available data from respondents, and the steps are 1) Collecting exam data, 2) Identifying errors due to syntactic interference, 3) Classifying errors according to indicators of syntactic interference, and 4) Explaining student errors in English essays. Research findings show that, although many students can write English essays with correct grammar, students, especially in their fourth semester at FKIP UMSU, still show the influence of Indonesian in the sentence structure of their English essays.
Students Obstacles in Listening Comprehension in English Learning

In developing a person's life, understanding various aspects of life comes from language learning. One aspect of language learning that plays a vital role is listening to conversations spoken by readers. This research has a qualitative descriptive pattern using selected literature reports for systematic reviews and meta-analysis methods. The source of the literature search was articles indexed on the DOAJ portal from 2016-2022, while the data collected was in the form of articles related to students' difficulties in learning to listen in EL. Document analysis techniques are used to solve data analysis problems. The results show that the factors that cause obstacles in learning listening are students' low attitudes and willingness to learn listening subjects, and the teacher's strategy in teaching the material also creates significant obstacles in students' abilities. Moreover, it is not overlooked that the recording media used also severely influences students' ability to capture verbal messages in listening activities.
The Influence of YouTube on Listening Skills

Listening is a crucial proficiency that learners who desire to acquire English must master. This research focused on the utilization of YouTube as a medium for improving listening proficiency. The approach in this study is a literature search process, while the literature search source is from the DOAJ portal. The data are articles about using YouTube to improve listening proficiency published in 2022-2023. The research results show that all studies conclude a significant increase in the application of YouTube media in improving students' abilities in listening. Future research regarding the use of technology in the Listening Skills aspect is needed.
The Influence of Internal Control, User Capability and Information Technology on the Quality of the Accounting Information System at PT VK

This study aims to determine the effect of internal control, user capabilities, and information technology utilization on the quality of accounting information systems at PT VKP. This study uses a quantitative method. The data were obtained by distributing questionnaires to the company’s employees and then analyzed using multiple linear regression as a statistical analysis tool. The sample was 50 employees of PT VKP, those working in the Bureau of Finance, Bureau of Human Resources, General Affairs and Information Technology, Bureau of Internal Control System, Bureau of Marketing, Bureau of Operational, and Bureau of Engineering and Management System. The results of this study indicate that both partially and simultaneously, the variables of internal control, user capabilities, and information technology utilization have a positive and significant impact on the quality of accounting information systems. This shows that the company has well-executed internal control, quality user capabilities, and sophisticated technology that can help a company improve the quality of the accounting information systems used. The value of the coefficient of determination is 0.604, indicating that 60.4% of the quality of accounting information systems (Y) is influenced by internal control (X1), user capabilities (X2), and information technology utilization (X3), while the remaining 39.6% influenced by other variables not examined here. It suggests that further research includes other variables, such as the developments in information technology and top management support.
AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation

Leveraging knowledge from multiple tasks through introducing a small number of task specific parameters into each transformer layer, also known as adapters, receives much attention recently. However, adding an extra fusion layer to implement knowledge composition not only increases the inference time but also is non-scalable for some applications. To avoid these issues, we propose a two-stage knowledge distillation algorithm called AdapterDistillation. In the first stage, we extract task specific knowledge by using local data to train a student adapter. In the second stage, we distill the knowledge from the existing teacher adapters into the student adapter to help its inference. Extensive experiments on frequently asked question retrieval in task-oriented dialog systems validate the efficiency of AdapterDistillation. We show that AdapterDistillation outperforms existing algorithms in terms of accuracy, resource consumption and inference time.
Domain Generalization with Vital Phase Augmentation

Deep neural networks have shown remarkable performance in image classification. However, their performance significantly deteriorates with corrupted input data. Domain generalization methods have been proposed to train robust models against out-of-distribution data. Data augmentation in the frequency domain is one of such approaches that enable a model to learn phase features to establish domain-invariant representations. This approach changes the amplitudes of the input data while preserving the phases. However, using fixed phases leads to susceptibility to phase fluctuations because amplitudes and phase fluctuations commonly occur in out-of-distribution. In this study, to address this problem, we introduce an approach using finite variation of the phases of input data rather than maintaining fixed phases. Based on the assumption that the degree of domain-invariant features varies for each phase, we propose a method to distinguish phases based on this degree. In addition, we propose a method called vital phase augmentation (VIPAug) that applies the variation to the phases differently according to the degree of domain-invariant features of given phases. The model depends more on the vital phases that contain more domain-invariant features for attaining robustness to amplitude and phase fluctuations. We present experimental evaluations of our proposed approach, which exhibited improved performance for both clean and corrupted data. VIPAug achieved SOTA performance on the benchmark CIFAR-10 and CIFAR-100 datasets, as well as near-SOTA performance on the ImageNet-100 and ImageNet datasets. Our code is available at https://github.com/excitedkid/vipaug.
Attention-Enhanced Reservoir Computing

Photonic reservoir computing has been recently utilized in time series forecasting as the need for hardware implementations to accelerate these predictions has increased. Forecasting chaotic time series remains a significant challenge, an area where the conventional reservoir computing framework encounters limitations of prediction accuracy. We introduce an attention mechanism to the reservoir computing model in the output stage. This attention layer is designed to prioritize distinct features and temporal sequences, thereby substantially enhancing the forecasting accuracy. Our results show that a photonic reservoir computer enhanced with the attention mechanism exhibits improved forecasting capabilities for smaller reservoirs. These advancements highlight the transformative possibilities of reservoir computing for practical applications where accurate forecasting of chaotic time series is crucial.
Rational Economic Behaviours in the Bitcoin Lightning Network

The Bitcoin Lightning Network (LN) is designed to improve the scalability of blockchain systems by using off-chain payment paths to settle transactions in a faster, cheaper, and more private manner. This work aims to empirically study LN's fee revenue for network participants. Under realistic assumptions on payment amounts, routing algorithms and traffic distribution, we analyze the economic returns of the network's largest routing nodes which currently hold the network together, and assess whether the centralizing tendency is incentive-compatible from an economic viewpoint. Moreover, since recent literature has proved that participation is economically irrational for the majority of large nodes, we evaluate the long-term impact on the network topology when participants start behaving rationally.
Exploring intra-task relations to improve meta-learning algorithms

Meta-learning has emerged as an effective methodology to model several real-world tasks and problems due to its extraordinary effectiveness in the low-data regime. There are many scenarios ranging from the classification of rare diseases to language modelling of uncommon languages where the availability of large datasets is rare. Similarly, for more broader scenarios like self-driving, an autonomous vehicle needs to be trained to handle every situation well. This requires training the ML model on a variety of tasks with good quality data. But often times, we find that the data distribution across various tasks is skewed, i.e.the data follows a long-tail distribution. This leads to the model performing well on some tasks and not performing so well on others leading to model robustness issues. Meta-learning has recently emerged as a potential learning paradigm which can effectively learn from one task and generalize that learning to unseen tasks. In this study, we aim to exploit external knowledge of task relations to improve training stability via effective mini-batching of tasks. We hypothesize that selecting a diverse set of tasks in a mini-batch will lead to a better estimate of the full gradient and hence will lead to a reduction of noise in training.
LIP-Loc: LiDAR Image Pretraining for Cross-Modal Localization

Global visual localization in LiDAR-maps, crucial for autonomous driving applications, remains largely unexplored due to the challenging issue of bridging the cross-modal heterogeneity gap. Popular multi-modal learning approach Contrastive Language-Image Pre-Training (CLIP) has popularized contrastive symmetric loss using batch construction technique by applying it to multi-modal domains of text and image. We apply this approach to the domains of 2D image and 3D LiDAR points on the task of cross-modal localization. Our method is explained as follows: A batch of N (image, LiDAR) pairs is constructed so as to predict what is the right match between N X N possible pairings across the batch by jointly training an image encoder and LiDAR encoder to learn a multi-modal embedding space. In this way, the cosine similarity between N positive pairings is maximized, whereas that between the remaining negative pairings is minimized. Finally, over the obtained similarity scores, a symmetric cross-entropy loss is optimized. To the best of our knowledge, this is the first work to apply batched loss approach to a cross-modal setting of image & LiDAR data and also to show Zero-shot transfer in a visual localization setting. We conduct extensive analyses on standard autonomous driving datasets such as KITTI and KITTI-360 datasets. Our method outperforms state-of-the-art recall@1 accuracy on the KITTI-360 dataset by 22.4%, using only perspective images, in contrast to the state-of-the-art approach, which utilizes the more informative fisheye images. Additionally, this superior performance is achieved without resorting to complex architectures. Moreover, we demonstrate the zero-shot capabilities of our model and we beat SOTA by 8% without even training on it. Furthermore, we establish the first benchmark for cross-modal localization on the KITTI dataset.
Adaptive Flip Graph Algorithm for Matrix Multiplication

This study proposes the "adaptive flip graph algorithm", which combines adaptive searches with the flip graph algorithm for finding fast and efficient methods for matrix multiplication. The adaptive flip graph algorithm addresses the inherent limitations of exploration and inefficient search encountered in the original flip graph algorithm, particularly when dealing with large matrix multiplication. For the limitation of exploration, the proposed algorithm adaptively transitions over the flip graph, introducing a flexibility that does not strictly reduce the number of multiplications. Concerning the issue of inefficient search in large instances, the proposed algorithm adaptively constraints the search range instead of relying on a completely random search, facilitating more effective exploration. Numerical experimental results demonstrate the effectiveness of the adaptive flip graph algorithm, showing a reduction in the number of multiplications for a $4\times 5$ matrix multiplied by a $5\times 5$ matrix from $76$ to $73$, and that from $95$ to $94$ for a $5 \times 5$ matrix multiplied by another $5\times 5$ matrix. These results are obtained in characteristic two.