Reviewerly/exHarmony huggingface dataset:
Development of Indonesian Language Intelligent Chatbot for Public Services in JAKI Application

Jakarta as a city with a vision to advance its city and make its people happy is always creating innovations in achieving its goals. One important indicator in achieving this goal is the provision of interactive services to the community. Jakarta Kini (JAKI) is a product of the City of Jakarta that provides various services that can be accessed by the community. One of the JAKI features intended to expand the affordability of use is a chatbot. Chatbot provides an alternative form of interaction that is more flexible by natural human conversation. However, before this research, Jakarta Smart City (JSC) as the developer of JAKI did not have its chatbot model. Therefore, an intelligent chatbot model is needed that can understand various requests or questions from the public based on the Indonesian language with flexibility. This study aims to build an Indonesian-language chatbot in the public service domain in the JAKI application. Experiments will include the use of various embeddings and classifiers in the intent classification pipeline model. Embeddings tried to include Word2Vec, GloVe, FastText, and BERT. The classifiers used were the built-in Rasa framework DIET Classifier and SVM as the mathematical model. The best model is obtained by a pipeline model that uses a DIET classifier with IndoBERT contextual embedding witan h F1-score performance of 0.93.
NFC Based Digital Prescription for Improving Patient Care and Reduce Pharmacy Losses

Managing prescription information and expiry dates is a challenging task, which often involves multiple caretakers to make sure right medication is administered to the patient. Moreover, pharmacies incur heavy losses due to missing ex-piry date information on pieces of medicine strips. This work presents a solution to manage prescription information with the expiry date towards improving Digital Healthcare. Near-field communication (NFC) Tags are evaluated for storing and keeping track of the IDs and expiry date. Optical Character Recognition (OCR) is explored to automate reading the expiry dates and reduce any manual errors. An android application is demonstrated to capture, write and read the expiry date and prescription information on these tags.
EV Charger Placement Optimization using D-Wave Quantum Computing Solvers

With the increased adoption of electric vehicles contributing to the dynamic change in transportation infrastructure in the city of Dubai, this paper proposes an optimization model to determine the appropriate placement of new EV charging stations, further expanding on, and enhancing the existing network of EV charging stations in the city. The factors invoked in computing the optimization algorithm can be used in any level of EVCP use case.
Dual Semantic Enhanced Event Causality Identification with Derivative Temporal Prompt

Event causality identification (ECI) is a crucial task in natural language processing (NLP) that aims to identify the causal relation between a pair of events in a sentence. However, the existing methods still face two main challenges: a deficiency in causal reasoning capabilities, particularly in detecting implicit causality, and a scarcity of adequately annotated data for training causal relation patterns. In this paper, we propose an innovative approach to address these challenges. On the one hand, we introduce a derivative temporal relation identification task to enhance the model's capability to capture temporal information, which can be utilized to improve the identification of implicit causality. On the other hand, our method incorporates dual semantic information to enhance the representation of events. Firstly, we leverage semantic knowledge relevant to event pairs from external knowledge bases. Secondly, we utilize a dependency-based semantic enhancement module to extract comprehensive semantic information from events within the sentence. We evaluate our method on two benchmark datasets, and the results demonstrate its superior performance compared to previous state-of-the-art methods.
Time Series Anomaly Detection Based on Data Stream Clustering

With the development of network-based software system, companies and operators pay more and more attention to the analysis of key performance indicators (KPI), such as network traffic and user browsing time. The challenges of anomaly detection of KPI data include the lack of labels and the unexpected changes of time series data distribution, named concept drift. Stream data clustering method is also used to deal with the above situation of stream data. An anomaly detection method based on stream data clustering is introduces in this paper to deal with the challenges above. This algorithm is called similarity based stream clustering (SBSC), which aims to deal with seasonal time series and concept drift. By using the density based stream data clustering framework based on similarity as a distance measure for time series, SBSC can deal with seasonal time series well and detect the concept drift phenomenon of time series. Compared with several common unsupervised anomaly detection algorithms, the algorithm is more efficient at identifying seasonal time series anomalies on real KPI data, and it is easier to detect concept drift and subsequence anomalies.
Data Uncertainty Learning in Breast Cancer Recognition

Breast cancer has emerged as the most prevalent malignancy, occupying the top position in both incidence and mortality among cancers in women. Distinguishing between benign and malignant breast tumors plays a crucial role in achieving early detection and treatment of breast cancer. As breast biopsy is a critical diagnostic evidence for confirming the malignant tumors of the breast, using its pathological images to diagnose the benign or malignant nature of breast tumors by deep learning models has become an important approach in breast cancer recognition. The noise in the digitization process of biopsy samples' pathological images will lead to decision uncertainty. Nevertheless, most existing deterministic models fail to accurately represent the uncertainty in the data, leading to incorrect breast cancer diagnoses. Misdiagnosis comes at a high cost, as it can result in patients missing the optimal treatment window or receiving unnecessary treatments, etc. To solve this problem, we introduce data uncertainty learning, an effective method for representing noise, into breast cancer recognition for the first time. Then, we propose a data uncertainty learning model for breast cancer recognition task, which learns the mean to recognize breast cancer and variance to represent uncertainty of samples. Specifically, we design a variance regularizer to guide the model adjusting optimization weights of the samples based on the uncertainty and enable the model to focus more on high-quality samples while suppressing the influence of noise, which enhances the performance of breast cancer recognition. Finally, insightful evaluation demonstrates the effectiveness of our model.
Voltage Transformer Anomaly Detection Using LSTM Based Autoencoder

Potential transformers (PTs) are essential measurement equipment in power systems. Developing monitoring techniques for them is crucial to enable measurement-based applications, where the challenge lies in establishing continuous online monitoring without relying on standard PTs. Since the temporal-spatial correlation among multiple PTs in the same substation can be leveraged, detecting abnormal changes in measurement data can be used for PT monitoring. In this study, we propose a long short-term memory autoencoder (LSTM-AE) based approach for anomaly identification in multivariate time-series measurement data of PTs. Generally, the LSTM-AE is trained to learn the normal patterns exhibited by the measurement data of PTs. Then the mean square error of the reconstruction serves as a metric for detecting anomalies in a substation with multiple PTs. To evaluate the effectiveness of the proposed method, we conduct experiments using both artificial and real-world datasets. Experimental results demonstrate the success in anomaly detection, indicating the capability of our approach for PT monitoring.
A Private Blockchain Based P2P Energy Trading Platform for Energy Users

With the rapid rise of renewable energy sources (RES) in energy grids, the need for secure peer-to-peer (P2P) energy trading platforms are increasing. Such platforms should allow energy users to buy/sell energy, participate in bids, and negotiate prizes. The decentralized blockchain architecture makes it suitable for building an efficient energy trading platform. In this paper, we propose a private blockchain-based energy trading platform using a proof of authority (PoA) consensus algorithm for energy users within a neighbourhood or a microgrid. The private Ethereum blockchain is configured using three Raspberry Pi 4 models, which act as three nodes on the network. The trading process is implemented using a smart contract deployed on the nodes by configuring each node as a buyer and seller. The nodes can add offers, choose offers, and trade energy within the nodes in the network. All the transactions are recorded on the blockchain. For ease of demonstration, Python Flask and HTTP-based interaction environment is developed.
Implementation of an Adaptive Five-Factor Authentication Scheme for Online Banking Services in South Africa

Online banking services are one of the fastest growing platforms for customer service delivery. However, the escalating attacks is forcing the banks to constantly improve their online authentication methods. To reduce these attacks, this study developed a secure adaptive Five-Factor Authentication (5FA) scheme. The 5FA used five mix modalities for user authentication: the Username, Password and Personal Identification Number (PIN), One-Time Pin (OTP), registered devices, fingerprints biometric and the Time-Based location. One of the standout solutions is the integration of active time and location to monitor and authenticate users with minimum user intervention. This resulted in a robust Multi-Factor Authentication (MFA) scheme with a good User Experience (UX). This study through the design survey research methodology; two 5FA prototypes were simulated and tested against the present MFA in use for online banking, to evaluate the proposed 5FA's security and performance features. To conclude the evaluation, this study through a short survey of active online banking users, assessed the usability aspects of the proposed scheme. The results showed the acceptable levels of performance in terms of throughput, security and usability.
A Deep Learning Prediction Approach for Machine Workload in Cloud Computing

Accurate workload prediction for cloud computing clusters is essential to ensure Quality of Service (QoS), meet Service Level Agreements (SLAs), and minimize energy consumption. In a cloud computing environment, cloud servers collect and store large sets of time series data, including metrics such as CPU usage, network traffic, and disk I/O at each point in time. The fluctuations in these metrics show noticeable temporal correlations. Therefore, these data can be used in time series data prediction models to predict upcoming workload scenarios. However, traditional statistical methods have limitations such as requiring manual feature extraction, high data requirements, and limited generalizability, leading to inaccurate predictions. In addition, most standard deep learning models ignore the problem of sequence noise. To overcome these hurdles, we have created a hybrid deep learning model utilizing advanced techniques. This model integrates Time Convolutional Networks (TCN), Gated Recurrent Units (GRU), and self-attention mechanism to achieve a more accurate workload prediction. Additionally, we perform a comparative analysis of different methods during the preprocessing stage and select the optimal one to effectively remove noise from the original sequences. Experimental results demonstrate that our proposed model outperforms other workload prediction algorithms in terms of prediction accuracy.
Feasibility Study of Improving Blockchain-Based Self-Sovereign Identity Security using Artificial Intelligence and Lightweight Cryptography

In this paper, we focus on blockchain-based mechanisms for securing self-sovereign digital identities of people (physical and legal). These solutions almost all depend on the near-daily use of Internet of Things (IoT) devices that themselves produce huge amounts of data while being resource constrained. We support the idea of integrating artificial intelligence as a mandatory step in the blockchain management process and the use of lightweight cryptography in zero-knowledge proofs. In essence, we identify at least eight attack vectors on self-sovereign identities and classified how these vectors could be secured using the two techniques.
Design of a Methodological Framework for Community Based Digital Literacy: A Kenyan Case Study

To participate effectively in today's society, it is becoming critical that everyone is digitally literate. This can be viewed as the fundamental level of computer science education. However, enabling this particularly in developing counties where a person's priority might be on ensuring they have enough food, water and or shelter for the night is a challenge. Several frameworks have been developed relating to digital literacy and what this comprises, but much less attention has been given to developing a framework on how to embed this within a community. Many governments in developing countries have digital technologies as a core part of their strategy, with an accompanying plan to upskill their citizens. But this usually means in practice that a centralized organisation is charged with delivering digital literacy to the community, with little involvement or understanding of that community in the design and roll out of this. This often results in low uptake/involvement by the community. The aim of this study is to explore how to change this approach by developing a methodological framework that puts the community at the centre of a digital literacy development programme. The researchers have designed this framework based on their experience of working with the N dhiwa rural community in Kenya. This paper outlines the issues with current practice, identifies the key evidence to inform the design and development of the framework and then presents the framework itself. This methodological approach enables those looking to develop digital literacy within different types of communities to consider the needs of each community and adapt the delivery accordingly.
Relation Extraction Model Based on Semantic Enhancement Mechanism

Relational extraction is one of the basic tasks related to information extraction in natural language processing, and is an important link and core task in the fields of information extraction, natural language understanding, and information retrieval. None of the existing relation extraction methods can effectively solve the problem of triple overlap. The CasAug model proposed in this paper based on the CasRel framework combined with the semantic enhancement mechanism can solve this problem to a certain extent. The CasAug model enhances the semantics of the identified possible subjects by adding a semantic enhancement mechanism, First, based on the semantic coding of possible subjects, pre-classify the possible subjects, and then combine the subject lexicon to calculate the semantic similarity to obtain the similar vocabulary of possible subjects. According to the similar vocabulary obtained, each word in different relations is calculated through the attention mechanism. For the contribution of the possible subject, finally combine the relationship pre-classification results to weight the enhanced semantics of each relationship to find the enhanced semantics of the possible subject, and send the enhanced semantics combined with the possible subject to the object and relationship extraction module. Complete the final relation triplet extraction. The experimental results show that, compared with the baseline model, the CasAug model proposed in this paper has improved the effect of relation extraction, and CasAug's ability to deal with overlapping problems and extract multiple relations is also better than the baseline model, indicating that the semantic enhancement mechanism proposed in this paper It can further reduce the judgment of redundant relations and alleviate the problem of triple overlap.
Comparative Study of Leveraging Big Data Processing Techniques for Sentiment Analysis

Sentiment analysis, an essential task in natural language processing, plays a pivotal role in understanding sentiment and opinions expressed in textual data. However, with the exponential growth of social media and online platforms, the sheer volume of textual data presents challenges for efficient processing. Traditional approaches struggle to cope with the increased data size, necessitating the adoption of big data processing techniques. This study presents a comparative performance analysis of sentiment analysis, evaluating the utilization of a big data processing framework. The study compares three machine learning algorithms for sentiment analysis with and without the implementation of big data processing techniques, focusing on model training efficiency. Additionally, two textual feature extraction techniques are examined to assess their impact on the results. Evaluation of the models' performance is based on the average execution time for training. The study's findings indicate that SparkML's Random Forest significantly outperforms the traditional sci-kit learn's Random Forest in terms of training time.
Combining conditional random fields and word embeddings to improve Amazigh part-of-speech Tagging

Part-of-speech tagging is considered a key task in natural language processing projects. Part-of-speech taggers that use classical conditional random fields perform very well in well-studied and rich language reaching 97%. However, in low-resourced language like Amazigh language, the accuracy of the conditional random fields model with features extraction is less than 90% in tagging. In this paper, we present a new part-of-speech tagging model using conditional random fields with the integration of word-embeddings. This method uses word-embeddings as features instead of the function of extracting features that is usually used in conditional random fields models. Because the Amazigh language suffers from the absence of pretrained word-embeddings, we designed a word-embedding layer that is trained gradually as the model. We used an existing dataset of 60k tokens and a tagset of 28 tags to evaluate our model. The accuracy of our proposed model reaches 96.78 %. The result is compared with other state-of-art Amazigh part-of-speech taggers that used conditional random fields. This comparison shows that our approach outperforms the other techniques in terms of accuracy. In the future, will continue enriching the Amazigh language with more natural language processing applications and datasets.
Maximum Likelihood Estimation of the Gamma and Gamma Mixture Distribution

Parametric mixture models are frequently used in fields such as computer vision, communications and data science; and model parameters are commonly estimated via maximum-likelihood estimators. There are challenges in the estimation of these model parameters, for example, truncation-bias, computational efficiency, ill-conditioning; and numeric precision and instability. In this paper we derive the maximum-likelihood estimator for the gamma mixture, and we optimize the parameter set in a Gauss-Seidel framework that employs a nested Newton-Raphson estimator. We use this derived theoretical framework, to investigate the reasons for the above challenges in mixture estimation. We also suggest solutions and future research that may address these challenges.
Optimization of Feature Selection to Improve Order Transaction Performance in the Manufacturing Industry

The development of business processes that occur in Industry 4.0 requires the optimization of processes in the system, especially in the process of order transactions. To optimize the transaction process, the assistance of the rank and weight system on dataset features, called the feature selection process, can be employed. Feature selection aims to improve the elimination of redundant and irrelevant features, which can reduce computation time and improve accuracy. In this research using multivariate datasets. Feature selection is used to eliminate some attributes that are irrelevant to the label class, resulting in improved performance of the classification algorithm. The author compares three algorithms: information gain, ratio gain, and chi-square. These are algorithms to find the rank and weight of data. In this research to measure feature selection performance using the Naïve Bayes classifier algorithm. The evaluation is performed by observing the level of classification accuracy generated without feature selection versus the classification accuracy generated after the implementation of feature selection. The experimental results showed that 14 features were included in the selection of features, and four relevant and best features were produced using the Chi-square binary classification algorithm. Classification using naïve Bayes algorithm reached 97.56%, while using ratio gain and information gain resulted in classification values of 75.57% and 92.69%, respectively.
Large Language Models (LLMs): Representation Matters, Low-Resource Languages and Multi-Modal Architecture

Large Language Models (LLMs) have recently burst into popularity via contemporary chatbots and are being considered for information retrieval as well as reasoning tasks by ordinary citizens. Examples from ongoing research are presented in an attempt to highlight the chasm between high and low-resource languages. The importance of representation as well as multi-modality, well known in the research community for decades, is highlighted and brought to focus. We argue that those pillars are likely key to making the new generation of systems more powerful, usable, accessible and utile for all.
A Data Management Model for Malaria Control: A Case of Rwanda

We are in an era when the diagnosis of diseases benefits greatly from digitizing their control. On the other hand, malaria is a long-standing yet one of the deadliest diseases. Its diagnosis processes still use old mechanisms, even when we have embarked on the Fourth Industrial Revolution. It is, therefore, more important than ever to digitize its control to enhance its diagnosis. This paper studies a digital data management model for malaria control in the context of a typical African country, Rwanda. Existing data management models are revisited, exploited, and contextualized to suit the Rwandan scenario, with the aim of their adoption. The proposed model in this study is the first of its kind for the developing world.