Reviewerly/exHarmony huggingface dataset:
CPU Signal Rank-Based Disaggregation in Cloud Computing Environments

Cloud computing is a critical aspect of IT services, but fluctuating workloads of virtual machines (VMs) present challenges to achieving quality service for clients. Accurate workload prediction is necessary for efficient cloud resource management. However, due to Central Limit Theorem, workload prediction is expected to be more accurate for the aggregated signal of virtual machines instead of singular ones. As a result, the demand for disaggregation mechanisms rises to realize increased determinism of aggregated signals for forecasting purposes. This paper reviews several state-of-the-art CPU workload disaggregation methods and proposes data pre-processing that improves disaggregation feasibility. The data used in this research are obtained from providers of web store hosting, cloud resources optimization, retail, computing platforms, and cloud service sharing. Data were analyzed and processed using change-point detection algorithms to ensure the stability of the signal’s statistical properties. The authors present insights into designing and optimizing machine learning models for CPU workload disaggregation for effective cloud resource management.
Automation of Deanonymization Queries for the Bitcoin Investigations

The pseudo-anonymous nature of Bitcoin makes it possible to assign addresses to clusters, and in some cases to identify an entity to which the cluster belongs. Searching for these connections manually is a tedious and time-consuming activity. The increasing number of commercial tools supporting de-anonymization, due to the high cost, does not always mean their greater availability among law enforcement authorities. So there is a real need to create an open source and free solutions to support this process. This article presents the assumptions for the proposed solution, shows its implementation and principle of operation, and finally provides a script $$^6$$ that automates queries to selected web services which enable the deanonymization of BTC addresses.
Line Segmentation of Handwritten Documents Using Direct Tensor Voting

A great number of historical documents are kept in archives and libraries throughout the world. In many cases, they are hardly available. The digitization process can make access to them easier. However, even digitized, they can not be searched, so the important task is to convert them to the computer-readable form. A lot of the records are written by hand and the handwriting recognition process must be performed on them. The initial stage of this process involves dividing the document into lines. This article presents a solution to this issue by utilizing tensor voting. The algorithm begins by conducting voting directly on the binary image. The lines of text are then tracked and labeled using the local maxima found in the tensor field produced during the voting process. The performance of the algorithm is checked on the data-set and using the procedure proposed by the organizers of the ICDAR2009 competition.
Transformer-based cynical expression detection in a corpus of Spanish YouTube reviews

Consumers of services and products exhibit a wide range of behaviors on social networks when they are dissatisfied.In this paper, we consider three types of cynical expressionsnegative feelings, specific reasons, and attitude of being right -and annotate a corpus of 3189 comments in Spanish on car analysis channels from YouTube.We evaluate both token classification and text classification settings for this problem, and compare performance of different pre-trained models including BETO, Span-BERTa, Multilingual Bert, and RoBERTuito.The results show that models achieve performance above 0.8 F1 for all types of cynical expressions in the text classification setting, but achieve lower performance (around 0.6-0.7 F1) for the harder token classification setting.
GSAC: A Gujarati Sentiment Analysis Corpus from Twitter

Sentiment Analysis is an important task for analysing online content across languages for tasks such as content moderation and opinion mining.Though a significant amount of resources are available for Sentiment Analysis in several Indian languages, there do not exist any large-scale, open-access corpora for Gujarati.Our paper presents and describes the Gujarati Sentiment Analysis Corpus (GSAC), which has been sourced from Twitter and manually annotated by native speakers of the language.We describe in detail our collection and annotation processes and conduct extensive experiments on our corpus to provide reliable baselines for future work using our dataset.
How to Control Sentiment in Text Generation: A Survey of the State-of-the-Art in Sentiment-Control Techniques

Recent advances in the development of large Pretrained Language Models, such as GPT, BERT and Bloom, have achieved remarkable performance on a wide range of different NLP tasks.However, when used for text generation tasks, these models still have limitations when it comes to controlling the content and style of the generated text, often producing content that is incorrect, irrelevant, or inappropriate in the context of a given task.In this survey paper, we explore methods for controllable text generation with a focus on sentiment control.We systematically collect papers from the ACL Anthology, create a categorisation scheme based on different control techniques and controlled attributes, and use the scheme to categorise and compare methods.The result is a detailed and comprehensive overview of state-of-the-art techniques for sentiment-controlled text generation categorised on the basis of how the control is implemented and what attributes are controlled and providing a clear idea of their relative strengths and weaknesses. 1
A Comparative Study of Topic Modeling Methods for Document Retrieval

In information retrieval, topic modeling is used to predict hidden subjects from a text corpus. As a result, it offers a system for organizing, comprehending, and summarising vast amounts of text data automatically. Topic modeling can also be used to provide a document representation that may be interpreted in a broad spectrum of natural-language processing (NLP) applications Techniques for modeling span from probabilistic graphs models to neural models. This research looks at subject models from a variety of angles. The first element categorizes subject modeling strategies as algebraic, fuzzy, probabilistic, or neural. We investigate enormous number of handy models in every group, emphasize contrasts and similarities among models and model types from a unifying standpoint, examine the properties and limits of these models, and debate the correctness of the application. Another factor to consider is a review of datasets and benchmarks. We talk about studies done on datasets to evaluate topic models along stated measures. The study focuses on the contrast between both models and their rightness for miscellaneous applications.
3D Object Encryption Through a Disruptive Rotational Symmetry Transformation

The need to secure 3D objects that are exchanged over unsecured channels is unprecedented. In this paper, we propose a simple 3D object encryption scheme. The scheme is based on a rotational symmetry transformation that disrupts the pixel locations. Each 3D object is first sliced into a number of 2D images, then the disruptive transformation is applied on sets of 3 pixels of each 2D image at a time. The proposed scheme is shown to exhibit good security properties, in terms of visual analysis, statistical analysis (MSE, PSNR and SSIM), as well as robustness to brute force attacks.
Novel framework for Detecting Multiple Sclerosis using Hybrid models

The central nervous system (CNS) lesions that characterize the chronic inflammatory disease known as multiple sclerosis (MS) can cause significant physical or cognitive impairments as well as neurological problems. Segmentation of Magnetic Resonance Imaging (MRI) is one of the most important challenges in the process of detection and segmentation of MS. The process of segmentation of MRI means dividing the medical images into the disjoint part for making the process of analyzing and diagnosis too easy for clinicians. This paper presents a novel framework for the segmentation of the MRI to detect MS. The framework contains two stages; the first stage is about removing different types of noise such as rician and speckle noise; The framework uses different modified methods for removing rician and speckle noise such as uses Vibrational Mode Decomposition (VMD) along with Block-matching and 3D filtering (Bm3D)and (Conv-AE-Net, D-U-Net, Br-U-Net, DGan-Net, and DeRNet) for removing the rician noise. (DeRNet), (Dilated Convolution Auto encoder Denoising Network (Di-Conv-AE-Net), and Denoising Network (DGAN-Net) for removing the speckle noise. The second step in the framework is the segmentation of the most free-noise images using Residual U-net. The framework has achieved an accuracy of nearby 96.66 in dice score in the process of segmentation before removing noise and 96.89 after removing different types of noises.
Investigation of Local Interpretable Model-Agnostic Explanations (LIME) Framework with Multi-Dialect Arabic Text Sentiment Classification

Sentiment Analysis (SA) aims at determining the positive, negative, or neutral polarity in unstructured data, which is mostly social media data such as tweets, blogs, or online reviews. Various Machine Learning (ML) algorithms are used for social text polarity classification, however without providing sufficient explanation for the obtained decisions. Explainable Artificial Intelligence (XAI) is an emergent field of AI that helps providing more visibility and explanations that leads to more understanding of the ML models’ output. This paper presents an investigation study on the effectiveness of well-known supervised ML algorithms for sentiment classification of Arabic social media text (tweets in the Arabic language) concerning different topics. That has been achieved through utilizing the Interpretable Model-Agnostic Explanations (LIME) XAI framework to attain reasonable explanation of the models’ performance based on highlighting the features that the model considered necessary while making the decision. Two benchmark publicly available social media text datasets of Modern Standard Arabic (MSA) and dialectal Arabic are used to evaluate the performance of the classifiers. Experimental results show that the implemented ML models trained using a Random Forest (RF) classifier with the Hotel Arabic Reviews dataset (HARD) dataset attains a significant accuracy of 93.40 % and F-score values of 96 % and 97.20 %, considering positive and negative polarity sentences, respectively.
Effect of Annual Ambient Temperature Increase on Solar PV plant performance: Case Study of 300MW, Sakaka Saudi Arabia

The 300 MW Sakaka Independent Power Plant (IPP) is the first plant in the Saudi national renewable energy program. The plant, located at Al Jouf, covered an area of 6 square kilometers. Sakaka IPP harnesses solar energy by utilizing photovoltaic (PV) technology.The power plant, which is connected to the national electricity grid, is supplying clean energy to power more than 75,000 Saudi Arabian households and avoid the production of more than 430,000 t of carbon dioxide (CO2) a year.This paper investigates the effects of the annual ambient temperature increase on solar PV panels performance. A JKMS225PP-60B solar panel has been used as reference model. The simulation results show that the temperature impacts solar panels critical parameters such as open circuit voltage, short circuit current and fill factor. Although the performance ratio during cold ambient temperature was better than hot temperature, the energy production was less due to low irradiance values during the cold weather conditions.
Predicting Solar Irradiance in Saudi Arabia via Machine Learning &amp; Artificial Neural Networks for Efficient Grid Integration

Solar energy is becoming an essential part of the energy mix of several counties. However, there are challenges with implementing such renewable sources including intermittency, difficulty in managing energy flux, integrating & operating the power grid, and the implicit relationship between weather parameters and solar irradiance requiring data analysis techniques to be implemented. These techniques would uncover the hidden patterns and correlations between weather features (e.g., humidity, temperature and solar irradiance) to enhance solar irradiance prediction accuracies for efficient planning of electricity production of solar panels connected to the grid. Saudi Arabian weather features will be employed for prediction assessments of machine learning & artificial neural network algorithms with multiple data sources & public domain entities including Photovoltaic Geographical Information System (PVGIS), King Abdullah City for Atomic and Renewable Energy (KACARE), and the National Renewable Energy Laboratory (NREL).
Locating Utility-Scale PV Plants in Power System: Case Study of Saudi Arabia

Numerous countries around the globe have decided to reduce their CO2 emissions by considering solar energy technologies for their power generation. Several large-scale PV (LSPV) power plant projects are growing every year. A gigawatt scale has now been introduced in China. Additionally, there is no standard yet found for the design of large-scale PV. One of the purposes of this study is to introduce the most important aspects of designing the LSPV. The aim of this study is to evaluate and identify the best location for large-scale PV plants by using a geographical information system (GIS) technique. Climatic, environmental, and spatial factor areas are considered for producing the GIS map. The result shows that there are huge areas that can be exploited for harvesting energy in Saudi Arabia. The GIS mapping method has many advantages for planning. It enables analysis of terrain for successful planning as well as helps to determine the most popular location for PV farms.
Deploying OWL ontologies for semantic mediation of mixed-reality interactions for human–robot collaborative assembly

For effective human–robot collaborative assembly, it is paramount to view both robots and humans as autonomous entities in that they can communicate, undertake different roles, and not be bound to pre-planned routines and task sequences. However, with very few exceptions, most of recent research assumes static pre-defined roles during collaboration with centralised architectures devoid of runtime communication that can influence task responsibility and execution. Furthermore, from an information system standpoint, they lack the self-organisation needed to cope with today’s manufacturing landscape that is characterised by product variants. Therefore, this study presents collaborative agents for manufacturing ontology (CAMO), which is an information model based on description logic that maintains a self-organising team network between collaborating human–robot multi-agent system (MAS). CAMO is implemented using the Web Ontology Language (OWL). It models popular notions of net systems and represents the agent, manufacturing, and interaction contexts that accommodate generalisability to different assemblies and agent capabilities. As a novel element, a dynamic consensus-driven collaboration based on parametric validation of semantic representations of agent capabilities via runtime dynamic communication is presented. CAMO is instantiated as agent beliefs in a framework that benefits from real-time dynamic communication with the assembly design environment and incorporates a mixed-reality environment for use by the operator. The employment of web technologies to project scalable notions of intentions via mixed reality is discussed for its novelty from a technology standpoint and as an intention projection mechanism. A case study with a real diesel engine assembly provides appreciable results and demonstrates the feasibility of CAMO and the framework.
Systematic review of natural language processing for recurrent cancer detection from electronic medical records

This systematic review was conducted to explore natural language processing (NLP) focusing on text representation techniques and algorithms used previously to identify recurrent cancer diagnoses from electronic medical records (EMR), and an assessment of their detection performance. Relevant studies were identified from PubMed, Scopus, ACM Digital Library, and IEEE databases since inception to August 18, 2022. Data, including text representation methods, model algorithms and performance, and type of clinical notes, were extracted from individual studies by two independent reviewers. Study risk of bias was assessed using the prediction model risk of bias assessment tool. Of the 412 studies identified, 17 were eligible for inclusion, with 15 representing models that were not externally validated. Three text representations were used: statistical, context-free, and contextual representations (bidirectional encoder representations from transformers (BERT) and its variants), from 12, 6, and 3 studies, respectively. The corresponding median harmonic precision and recall means (F1 scores) for these representations were 0.43, 0.87, and 0.72, respectively. The algorithms applied included rule-based, machine learning, and deep learning approaches with median F1 scores of 0.71, 0.43, and 0.76, respectively. In conclusion, this systematic review suggests that deep learning models that use PubMedBERT as a text representation perform best. These findings are clinically informative for the selection of appropriate approaches for the detection of recurrent cancer from electronic medical records.
Learn Branching Policies with an Attention-based Model

The branch-and-bound method is an exact tree search method in mixed integer linear programming. Recently, learning branching policies become a popular topic, but most existing machine learning methods only consider problems with similar structures, and pay little attention to heterogeneous problems. The parameterized search tree method demonstrates promising performance on heterogeneous problems, but there is still room for improvement when it generalizes to other problems. In this paper, we propose an attention-based branching framework, which can exploit information among candidate variables. In this way, we can take full advantage of the properties exploited from the parameterized branch-and-bound search tree, resulting in a better branching policy. Our proposed model is evaluated on multiple mixed integer linear programming instances and attains remarkable performance within a given time limit.
An Improved Lightweight Network Using Attentive Feature Aggregation for Object Detection in Autonomous Driving

Object detection, a more advanced application of computer vision than image classification, utilizes deep neural networks to predict objects in an input image and determine their locations through bounding boxes. The field of artificial intelligence has increasingly focused on the demands of autonomous driving, which require both high accuracy and fast inference speeds. This research paper aims to address this demand by introducing an efficient lightweight network for object detection specifically designed for self-driving vehicles. The proposed network, named MobDet3, incorporates a modified MobileNetV3 as its backbone, leveraging its lightweight convolutional neural network algorithm to extract and aggregate image features. Furthermore, the network integrates altered techniques in computer vision and adjusts to the most recent iteration of the PyTorch framework. The MobDet3 network enhances not only object positioning ability but also the reusability of feature maps across different scales. Extensive evaluations were conducted to assess the effectiveness of the proposed network, utilizing an autonomous driving dataset, as well as large-scale everyday human and object datasets. These evaluations were performed on NXP BlueBox 2.0, an advanced edge development platform designed for autonomous vehicles. The results demonstrate that the proposed lightweight object detection network achieves a mean precision of up to 58.30% on the BDD100K dataset and a high inference speed of up to 88.92 frames per second on NXP BlueBox 2.0, making it well-suited for real-time object detection in autonomous driving applications.
Distributed ItemCF Recommendation Algorithm Based on the Combination of MapReduce and Hive

The ItemCF algorithm is currently the most widely used recommendation algorithm in commercial applications. In the early days of recommender systems, most recommendation algorithms were run on a single machine rather than in parallel. This approach, coupled with the rapid growth of massive user behavior data in the current big data era, has led to a bottleneck in improving the execution efficiency of recommender systems. With the vigorous development of distributed technology, distributed ItemCF algorithms have become a research hotspot. Hadoop is a very popular distributed system infrastructure. MapReduce, which provides massive data computing, and Hive, a data warehousing tool, are the two core components of Hadoop, each with its own advantages and applicable scenarios. Scholars have already utilized MapReduce and Hive for the parallelization of the ItemCF algorithm. However, these pieces of literature make use of either MapReduce or Hive alone without fully leveraging the strengths of both. As a result, it has been difficult for parallel ItemCF recommendation algorithms to feature both simple and efficient implementation and high running efficiency. To address this issue, we proposed a distributed ItemCF recommendation algorithm based on the combination of MapReduce and Hive and named it HiMRItemCF. This algorithm divided ItemCF into six steps: deduplication, obtaining the preference matrixes of all users, obtaining the co-occurrence matrixes of all items, multiplying the two matrices to generate a three-dimensional matrix, aggregating the data of the three-dimensional matrix to obtain the recommendation scores of all users for all items, and sorting the scores in descending order, with Hive being used to carry out steps 1 and 6, and MapReduce for the other four steps involving more complex calculations and operations. The Hive jobs and MapReduce jobs are linked through Hive’s external tables. After implementing the proposed algorithm using Java and running the program on three publicly available user shopping behavior datasets, we found that compared to algorithms that only use MapReduce jobs, the program implementing the proposed algorithm has fewer lines of source code, lower cyclomatic complexity and Halstead complexity, and can achieve a higher speedup ratio and parallel computing efficiency when processing all datasets. These experimental results indicate that the parallel and distributed ItemCF algorithm proposed in this paper, which combines MapReduce and Hive, has both the advantages of concise and easy-to-understand code as well as high time efficiency.