Reviewerly/exHarmony huggingface dataset:
Arabic Sentiment Analysis and Sarcasm Detection Using Probabilistic Projections-Based Variational Switch Transformer

Text classification is a common task in natural language processing (NLP), where the objective is to assign predefined categories or labels to a given text. Detecting sarcasm and classifying sentiment and dialect in NLP has practical applications, including spam detection, topic classification, and sentiment analysis. However, sarcasm and sentimental expressions, such as irony, humor, or criticism, can be difficult to identify through traditional NLP methods due to their implicit nature. To address this, we propose a Modified Switch Transformer (MST) for detecting sarcasm and classifying sentiment and dialect in Arabic text data. Our approach includes two key contributions: Variational Enmesh Expert’s Routing ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V E<sub>e</sub>R</i> ) and Probabilistic Projections ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϕ</sub> ). The switch transformer model incorporates probabilistic projections using a Variational Spatial Gated Unit-MLP to enhance the embedding generation mechanism. This updated mechanism introduces a variational aspect, providing dynamic control over the flow of information in the network, in contrast to the simpler embedding generation phase used in the original switch transformer. Moreover, we incorporate Variational Enmesh Expert’s Routing, which utilizes a hierarchical set of Variational experts, where each expert is a small and variational-directed acyclic graph network. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V E<sub>e</sub>R</i> routing technique allows the network to dynamically choose which path to take at each layer based on the input, using a set of weights learned during training to determine the best route for a given input. Instead of optimizing route paths deterministically, we utilize Variational Inference and model each route as a random variable from a distribution. Our study evaluates the effectiveness of the Modified Switch Transformer (MST) model on the ArSarcasm Dataset, which includes Arabic language data related to sarcasm, dialect, and sentiments. We compare the performance of our proposed model with existing state-of-the-art models in the literature. The results show that the switch transformer outperforms other models in detecting sarcasm and also performs well in classifying sentiment and dialect.
Federated Learning for Activity Recognition: A System Level Perspective

The past decade has seen substantial growth in the prevalence and capabilities of wearable devices. For instance, recent human activity recognition (HAR) research has explored using wearable devices in applications such as remote monitoring of patients, detection of gait abnormalities, and cognitive disease identification. However, data collection poses a major challenge in developing HAR systems, especially because of the need to store data at a central location. This raises privacy concerns and makes continuous data collection difficult and expensive due to the high cost of transferring data from a user’s wearable device to a central repository. Considering this, we explore the adoption of federated learning (FL) as a potential solution to address the privacy and cost issues associated with data collection in HAR. More specifically, we investigate the performance and behavioral differences between FL and deep learning (DL) HAR models, under various conditions relevant to real-world deployments. Namely, we explore the differences between the two types of models when (i) using data from different sensor placements, (ii) having access to users with data from heterogeneous sensor placements, (iii) considering bandwidth efficiency, and (iv) dealing with data with incorrect labels. Our results show that FL models suffer from a consistent performance deficit in comparison to their DL counterparts, but achieve these results with much better bandwidth efficiency. Furthermore, we observe that FL models exhibit very similar responses to those of DL models when exposed to data from heterogeneous sensor placements. Finally, we show that the FL models are more robust to data with incorrect labels than their centralized DL counterparts.
Compact Quantum Circuit Design of PUFFIN and PRINT Lightweight Ciphers for Quantum Key Recovery Attack

Quantum computing plays a vital role in the next generation computing platforms as researchers have achieved quantum supremacy by proving that quantum computers can outperform classical computers.These high performance computers will pose a serious threat to the security of the conventional cryptographic algorithms.The secret key of the conventional cryptographic algorithms when implemented by quantum circuits can easily be recovered with the help of Grover key search algorithm.The Grover's algorithm requires low cost quantum implementation of cryptographic algorithms in order to mount the quantum key recovery attack successfully.Hence the low cost quantum implementation of conventional cryptographic algorithms to mount quantum key recovery attack using Grover search algorithm is an active area of research.This work proposes a novel quantum circuit implementation of two lightweight block ciphers namely PUFFIN and PRINT and for the first time in literature In-place method is used to optimize the quantum resources in these two ciphers which helps to build compact quantum circuits without extra ancilla inputs.The performance metrics considered in this work to quantify the quantum resources of the proposed circuits are number of quantum gates, quantum cost, latency and number of qubits.In addition, the quantum resources are also estimated to mount the quantum key recovery attacks on the proposed quantum circuit implementations of PUFFIN and PRINT using Grover-based key search algorithm.
MACGAN: An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GAN

Various vision-based tasks suffer from inaccurate navigation and poor performance due to inevitable problems, such as adverse weather conditions like haze, fog, rain, snow, and clouds affecting ground and aerial navigation, as well as underwater images being degraded with blue-green tones and mud affecting marine navigation. While most techniques in the literature are designed to restore only one or a few degradations using a single model, using separate architectures for each restoration process is computationally expensive. To address this, an all-in-one Multidomain Attention-based Conditional Generative Adversarial Network (MACGAN) is proposed to improve scene visibility for optimal ground, aerial, and marine navigation, using the same set of parameters across all domains. The MACGAN is a lightweight network with four encoder and decoder blocks and multiple attention blocks in between, which enhances the image restoration process by focusing on the most important features. The performance of MACGAN is compared qualitatively and quantitatively with various state-of-the-art image-to-image translation models, all-in-one adverse weather removal models, and single-effect removal models. Further, MACGAN is tested on real-world unseen image domains, such as smog, dust, fog, rain, snow, and lightning. The results demonstrate that MACGAN can improve scene visibility and has good generalizability. An ablation study is also conducted to assess the impact of the discriminator and attention blocks in MACGAN, and the results confirm the effectiveness of the proposed architecture, which includes a discriminator and three attention blocks.
Pseudo-label self-training model for transfer learning algorithm

Abstract When aligning joint distributions between domains, the existing transfer learning algorithms usually assign pseudo labels due to the lack of labels in target domain. However, the noise in pseudo labels will affect the performance of transfer learning. Pseudo-label self-training for transfer learning (PST-TL) model is proposed to generate reliable pseudo labels for target domain and have a wide range of applications in existing algorithms. Pseudo labels are predicted by an ensemble classifier using absolute majority vote, and labels predicted successfully are considered to be high confidence. The training of ensemble classifier applies the self-training of joint pseudo labels strategy, adding strongly stable data to training set of the classifier. The semi-supervised and unsupervised transfer learning tasks in experiment show that the existing transfer learning algorithm can significantly improve the transfer performance after embedded by PST-TL model.
Privacy-protected aggregation in federated learning based on semi-homomorphic encryption

As people pay more and more attention to privacy protection, federated learning with privacy protection ability comes into being, and gradually becomes one of the research hotspots in the field of machine learning. As a distributed machine learning framework, federated learning can prevent data from being directly leaked, but some private information can still be derived from the gradient. Therefore, some cryptographic schemes are used to solve this privacy leakage. In this paper, Paillier algorithm in the semi-homomorphic encryption scheme is introduced to encrypt the gradient uploaded by clients in federated learning, which is aggregated by the server and sent back to each client for model update. Relevant experiments are also designed to verify the efficiency of Paillier algorithm through the change of the computing cost with the increase of the number of customers.
VTGAN: hybrid generative adversarial networks for cloud workload prediction

Abstract Efficient resource management approaches have become a fundamental challenge for distributed systems, especially dynamic environment systems such as cloud computing data centers. These approaches aim at load-balancing or minimizing power consumption. Due to the highly dynamic nature of cloud workloads, traditional time series and machine learning models fail to achieve accurate predictions. In this paper, we propose novel hybrid VTGAN models. Our proposed models not only aim at predicting future workloads but also predicting the workload trend (i.e., the upward or downward direction of the workload). Trend classification could be less complex during the decision-making process in resource management approaches. Also, we study the effect of changing the sliding window size and the number of prediction steps. In addition, we investigate the impact of enhancing the features used for training using the technical indicators, Fourier transforms, and wavelet transforms. We validate our models using a real cloud workload dataset. Our results show that VTGAN models outperform traditional deep learning and hybrid models, such as LSTM/GRU and CNN-LSTM/GRU, concerning cloud workload prediction and trend classification. Our proposed model records an upward prediction accuracy ranging from $$95.4\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>95.4</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> to $$96.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>96.6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> .
The StoryMapper: Piloting a Traveling Placemaking Interface for Inclusion and Emplacement

As aresponse to traditional (top‐down) urban planning processes, placemaking engages local citizens in the process of shaping the form, social activity, and meaning of places around them. However, placemaking practices similarly face political challenges regarding inclusion and emplacement. These challenges relate to who participates, facilitation through linguistic discourse, and place engagement itself. Attempting to address these challenges, this article (based on a pilot study) reports on the design and deployment of the StoryMapper, a traveling placemaking interface that uses a participant‐driven “chain of engagement” recruiting process to invite participants to create emplaced “morphings” (i.e., visually produced stories superimposed on public space) to spark dialogue on a digitally facilitated living map. This pilot study took place within a larger placemaking project that engages citizens to share their ideas regarding the reconversion of a community church. Plugging the Storymapper into this larger project, we discuss preliminary findings relating to the role of placemaking facilitators in citizen‐driven recruitment and the role of multimodality in placemaking processes. This pilot study suggests that inclusion should not only be evaluated based on who participates and who does not, but also on how the tool itself, in its capacity to engage participants to visualize complex emplaced ideas, may facilitate inclusion of different publics.
Latent Topic Analysis of the Post Property for Sales to Predict a Selling Price of Second-Hand Condominiums

Abstract This research objective is to study the latent topics analysis in selling post of real estate of second-hand condominium by using Latent Dirichlet Allocation (LDA) and build a price prediction model of second-hand condominium using multiple linear regression and artificial neural networks by measuring and comparing the performance of the second hand condominium price prediction model with root mean square error (RMSE). This experiment included four variables are room size, number of bathroom, number of bedroom and latent topics from LDA. The result of LDA indicated that selling post of real estate can be separated into 4 topics, in which finding the factors that affect the price use the regression analysis method to get five variables are room size, number of bathroom, floors, topic 2 and topic 4. The RMSE based on the multiple linear regression analysis was 1.349, while the RMSE based on artificial neural network was 1.156. Thus, it can be concluded that the predictive model using the artificial neural networks is superior to multiple linear regression.
Detection Model for URL Phishing with Comparison Between Shallow Machine Learning and Deep Learning Models

In the report on trends in phishing activity released by the Anti-Phishing Working Group (APWG), global phishing cases continued to increase throughout 2021 to the first quarter of 2022.This study compares shallow machine learning algorithms that have been used by governments with deep learning in classifying URLs.Phishing.From the data as many as 30,047 URLs consisting of 15,022 phishing URLs and 15,025 legal URLs, the distribution was carried out for training data and test data.URL phishing modeling uses deep learning algorithms LSTM and GRU as well as the best shallow machine learning algorithms from research conducted by Rao et.al, namely Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT).Modeling is done based on URL characteristics, text structure, and a combination of URL characteristics with text structure.Based on URL characteristics, the model with the best accuracy from the shallow machine learning algorithm is Random Forest at 97.4%, while the deep learning algorithm is LSTM at 96.7%.Based on the structure of the text, the best deep learning algorithm is the GRU of 97.8%.While the combination model using 2 deep learning algorithms LSTM and GRU get an accuracy of 98.1%.Furthermore, the combination model as the best model is implemented in the form of a website using the Flask framework with the classification results in the form of a URL probability score that is detected as a phishing URL.
SaNDA: A small and iNcomplete dataset analyser

In personalised health, small datasets with missing data are quite common. Current Machine Learning methods are unable to process such datasets in a meaningful way due to the huge data volume requirement. To address this problem, we propose a new Small and iNcomplete Dataset Analyser (SaNDA) to process such datasets in a meaningful way. Due to the characteristics of these datasets and the criticality of the domain, an explainable method is mandatory to facilitate decision-making interpretation. Thus, SaNDA prioritises explainability over efficiency by design. We evaluated our proposal against Random Forest as a baseline for explainable methods, and against gcForest as state-of-the-art for small datasets. We observed that our proposal outperforms Random Forest when there is more missing data and/or lower number of entries in the dataset, obtaining less favourable results over larger, well-curated datasets. It is also preferable than gcForest due to its explainability and privacy protection capabilities. Given the difficulties in obtaining complete, reliable data in the healthcare field, we consider that our proposal could be useful for practitioners.
Power analysis for causal discovery

Abstract Causal discovery algorithms have the potential to impact many fields of science. However, substantial foundational work on the statistical properties of causal discovery algorithms is still needed. This paper presents what is to our knowledge the first method for conducting power analysis for causal discovery algorithms. The power sample characteristics of causal discovery algorithms typically cannot be described by a closed formula, but we resolve this problem by developing a new power sample analysis method based on standardized in silico simulation experiments. Our procedure generates data with carefully controlled statistical effect sizes in order to enable an accurate numerical power sample analysis. We present that method, apply it to generate an initial power analysis table, provide a web interface for searching this table, and show how the table or web interface can be used to solve several types of real-world power analysis problems, such as sample size planning, interpretation of results, and sensitivity analysis.
MRLCC: an adaptive cloud task scheduling method based on meta reinforcement learning

Abstract Task scheduling is a complex problem in cloud computing, and attracts many researchers’ interests. Recently, many deep reinforcement learning (DRL)-based methods have been proposed to learn the scheduling policy through interacting with the environment. However, most DRL methods focus on a specific environment, which may lead to a weak adaptability to new environments because they have low sample efficiency and require full retraining to learn updated policies for new environments. To overcome the weakness and reduce the time consumption of adapting to new environment, we propose a task scheduling method based on meta reinforcement learning called MRLCC. Through comparing MRLCC and baseline algorithms on the performance of shortening makespan in different environments, we can find that MRLCC is able to adapt to different environments quickly and has a high sample efficiency. Besides, the experimental results demonstrate that MRLCC can maintain a high utilization rate over all baseline algorithms after a few steps of gradient update.
Forecasting Meteorological Solar Irradiation Using Machine Learning and N-BEATS Architecture

For developing nations like Bangladesh, the Calamity of Energy, one of the most important warnings experienced in the modern world, is a significant problem. Solar energy is a great solution for the future. When solar panels are used to create electricity, no greenhouse gas emissions are produced. Solar energy is essential to the shift to the production of clean energy because the sun generates more electricity than people could possibly need. In this study, for better planning and decision making for solar energy consumption, we propose a forecasting model based on Bangladeshi data. We collected the data from NSRDB(National Solar Radiation Database). Using meteorological data of 4 Regions of Bangladesh - Chittagong(CTG), Khulna(KHU), Sylhet(SYL), Rajshahi(Raj). Firstly, we find the most important feature for radiation prediction and we conduct a regression analysis based on the selected feature, then for time series analysis, we use state-of-the-art N-BEATS architecture, which gives us impressive results with very low computational cost and time. N-BEATS outperformed other popular models like LSTM and SARIMA.
Hate Speech Detection on Indonesian Social Media: A Preliminary Study on Code-Mixed Language Issue

Nowadays, social media becomes an important media for online communication, facilitating its users to publish content and providing a medium to express their opinions and feelings about anything. At the same time, abusive language is becoming a relevant problem on social media platforms such as Facebook and Twitter. Geographically, Indonesia consists of several regions with their own local languages. A recent report shows 718 local languages used by different regions and tribes in Indonesia. Indonesian tend to use a mix of their own local language and Bahasa to communicate on social media platforms, such as Twitter. Similar to other languages, code-mixed is also becoming the main issue and challenge of detecting hate speech in Indonesian social media. In this study, we conduct a preliminary experiment to study the detection of hate speech in Indonesian social media, specifically Twitter. Our experiment used 6,115 tweets in Indonesian-Javanese code-mixed and 2,945 tweets in Indonesian-Sundanese code-mixed. The overall results show that the traditional machine learning model with lexical-based features obtained the best performance in Javanese-Indonesian, while the LSTM network achieved the best performance in Sundanese-Indonesian. We also found that translating the code-mixed data into more resource-rich languages could not help to improve the classification performance.
Contextualised Modelling for Effective Citation Function Classification

Citation function classification is an important task in scientific text mining. The past two decades have witnessed many computerised algorithms working on various citation function datasets tailored to various annotation schemes. Recently, deep learning has pushed the state of the art by a large margin. Several pitfalls exist. Due to annotation difficulty, data sizes, especially the minority classes, are often not big enough for training effective deep learning models. Being less discussed, most state-of-the-art deep learning solutions in fact generate a feature representation for the citation sentence or context, instead of modelling individual in-text citations. This is conceptually flawed as it is common to see multiple in-text citations with different functions in the same citation sentence. In addition, existing deep learning studies have only explored a rather limited design space of encoding citation and its surrounding context. This paper explored a wide range of modelling options based on SciBERT, the popular cross-disciplinary pre-trained scientific language model, and their performances on citation function classification, for the purpose of determining the most effective way of modeling citation and its context. To deal with the data size issue, we created a large-scale citation function dataset by mapping, merging and re-annotating six publicly available datasets from the computational linguistics domain by adapting Teufel et al.'s 12-class scheme. The best F1 scores we achieved were around 66.16%, 71.39% and 73.56% on a 11-class annotation scheme slightly adapted from Teufel et al.'s 12-class scheme, a reduced 7-class scheme by merging comparison functions, and Jurgens et al.'s 6-class scheme respectively. A useful observation is that there is no single best model that is superior for all functions, therefore the trained model variants allow for applications which emphasise on a specific type of or a specific group of citation functions.