Reviewerly/exHarmony huggingface dataset:
Web Scrapping Tools and Techniques: A Brief Survey

Web scraping can be done using many languages such as C++, Java, JavaScript, PhP, Python, Ruby, etc. Among them, Python stands to be the most powerful language with lots of inbuilt libraries that supports web scraping, extensive support for third-party open-source libraries, and higher speeds compared to other languages. Python libraries for web scraping are designed for fast and highly accurate data extraction. There are many libraries available for web scraping and the developer can choose the respective library in accordance with their scraping application. This paper focuses on the study of several web scraping tools and techniques and analyze the performance of those tools and present the statistical significance of the results.
Friend Recommendation System Using Map-Reduce and Spark: A Comparison Study

Connecting with other people online is common practice and huge amounts of data are generated each day by increasing web activity. This type of data collection can be used to recommend friends on social media. We have utilized Map Reduce and Spark to analyze the vast amount of data. A Friend Recommendation system has been implemented using Map Reduce and Spark. Furthermore, we compared both Distributed Computation techniques in order to determine the optimum solution. We found that spark computation is 16 times faster than Hadoop Map-Reduce computation for Friend Recommendation System. Spark proves to be more efficient than map-reduce in terms of time efficiency.
IoT-enabled Contactless Doorbell with Facial Recognition

The COVID-19 epidemic has altered lifestyles all across the globe, causing people to take additional safety precautions and make using a face mask a requirement. Face masks are becoming more popular, making it occasionally challenging for people to recognize other people. Children and the elderly in particular would have trouble identifying their masked guests, which poses a serious hazard because thieves or burglars would take advantage of the situation. In this study, a system was created using IoT and deep learning technologies that works as a unit to offer a contactless solution to the ongoing COVID-19 pandemic while also enabling home owners to keep track of their visitors and receive notifications when someone comes over. The contactless doorbell was created with the help of a Raspberry Pi and a modified ResNet-50 model using ArcFace loss as the feature extractor to efficiently extract visible features from a masked face and support very accurate recognition. Due to the lack of a real masked face dataset with sufficient data, this study used a data augmentation method to add masks to face images from a dataset. The model was able to achieve a recognition accuracy of 98.27% when evaluated using a masked LFW dataset. Furthermore, testing the face recognition model in real-time with limited users, each with and without a mask yielded an accuracy of 100% in unmasked facial recognition and 90% on masked facial recognition.
Collaborative Filtering Recommender System for Semantic Model Refinement

In order to prepare data for a semantic data integration (e.g., into knowledge graphs), a semantic mapping in the form of a semantic model between data and the corresponding ontology is necessary. Manual mapping, although a time-consuming process, is often required as the models created by automated approaches still need improvements. For supporting this refinement process, we propose a semi-automatic modeling process that includes the use of a recommender system. This recommender system supports the modeler in the context of a manual refinement process by suggesting possible adjustments consisting of new nodes and corresponding relations to the model. In our approach, we use random forest classifiers and graph neural networks to generate recommendations for model modifications following a collaborative filtering idea and using information from existing semantic models. We evaluated our approach on different public datasets. We achieve an MRR of 0.69 and Hits@3 of 0.86 for our predictions showing that the approach is able to provide suitable recommendations to support the modeler based on the characteristics of the training data.
Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance

Accurately extracting driving events is the way to maximize computational efficiency and anomaly detection performance in the tire frictional nose-based anomaly detection task. This study proposes a concise and highly useful method for improving the precision of the event extraction that is hindered by extra noise such as wind noise, which is difficult to characterize clearly due to its randomness. The core of the proposed method is based on the identification of the road friction sound corresponding to the frequency of interest and removing the opposite characteristics with several frequency filters. Our method enables precision maximization of driving event extraction while improving anomaly detection performance by an average of 8.506%. Therefore, we conclude our method is a practical solution suitable for road surface anomaly detection purposes in outdoor edge computing environments.
SentiMap: Domain-Adaptive Geo-Spatial Sentiment Analysis

Location-based sentiment analysis has numerous applications, but suffers from both location uncertainty and lack of domain specificity. We propose an approach to automatically build domain-adaptive lexicons for region-specific sentiment analysis. For location estimation, we collect an initial lexicon using topic modeling on a collection of news articles about the target domain. For sentiment estimation we start with a preexisting lexicon. Both initial lexicons are then expanded recursively through employment of a word embedding trained on social media messages from the target area. The final location lexicon is used for location estimation, the final sentiment lexicon for automatically annotating data that is used to fine-tune a BERT transformer network on the task of sentiment estimation. We validate our approach by using the city of Amsterdam as a case study, and show that both the automatically expanded lexicons and the fine-tuned network outperform their respective baselines. This illustrates that with little manual input, our system improves through adapting to the domain.
Explaining BERT model decisions for near-duplicate news article detection based on named entity recognition

While it is a straight-forward task to detect lexically identical documents, it is challenging to do this on documents that are semantically similar. This work deals with the detection of near-duplicate news articles on various websites. The detection of documents of related content is especially interesting for search engines and recommender systems. Search engines aim at eliminating duplicates to prevent the user from seeing the same document in different versions. Recommender systems filter out items that are too similar to those the user already knows to tackle the problem of overspecialization. A near-duplicate is in some cases a mere paraphrasing of a previous article, in other cases it contains corrections or added content as a follow-up article. In this work, we investigate, among other things, the extent to which named entities such as people, places, and organisations that appear in the article text can be used to detect near-duplicates. Evaluating a fine-tuned BERT model on common named entities in an article pair, performance measures greater than 97 % are achieved. The SHAP library is then used to get insight into the model decisions and feature importance, which is the individual words in a text document. Overall, the results show that named entities are very suitable for the detection of near-duplicates. Furthermore, the explanations are in agreement with an intuitive human interpretation.
Multilayer CARU Model for Text Summarization

With the large amount of data available today, text summaries have become very important to get the right amount of information from a large amount of text. As can be seen, the articles in this review present different approaches to creating long summaries. Various studies have investigated different methods for summarizing text. In most cases, the methods described in this paper produce summaries or excerpt summaries of text documents, and a query-based summarization technique is also described. These techniques are structure and semantic based methods for summarizing text documents. Experimental results on the DUC-2002 dataset show that our system outperforms state of-the-art extractive and abstractive baselines on the ROUGE evaluation metric.
DAG-GCN: Directed Acyclic Causal Graph Discovery from Real World Data using Graph Convolutional Networks

Causal discovery has been challenging since the search space of directed acyclic graphs super-exponentially grows with respect to the number of nodes. Previously constraint-based and score-based methods have been used. In recent studies, a continuous optimization method has reached a high score, but the problem is still harsh in real-world observational data. Motivated by the success of recent GNN models, we extended previous methods to be suitable to actual world data. Our model is based on the DAG-GNN model, uses GCN, and tries to learn an adjacency matrix set as a model parameter. To solve the vanishing adjacency matrix problem, we use the He-Initialization method with Leaky ReLU and the batch normalization technique. We demonstrate our model on real-world data sets. Compared to the state-of-the art results, our proposed method reaches acceptable results.
Text Augmentation Based on Integrated Gradients Attribute Score for Aspect-based Sentiment Analysis

One of the factors that determine the effectiveness of deep learning models for sentiment analysis is the availability of high-quality training data. Data augmentation is a strategy to increase the amount of training data by applying semantically specified adjustments to training data. Such technique has been widely adopted in computer vision tasks, however, it has not been adequately addressed for aspect-based sentiment analysis (ABSA) tasks. ABSA is a text analysis method that discovers aspects of sentences as well as their polarity. In this paper, we investigate the effect of data augmentation on the hybrid approach for aspect-based sentiment analysis (HAABSA) model. We propose an extension of easy data augmentation (EDA) by combining the effectiveness of part-of-speech tagging, word sense disambiguation, and feature importance selection. We apply our proposed technique to the SemEval 2015 and SemEval 2016 datasets and compare it to existing approaches. Experimental results demonstrate that when compared to a model trained without data augmentation, our method is able to improve the accuracy between 0.6 and 3.4 percentage points. Furthermore, we show that an augmentation method that does an informed selection is more effective than the randomized ones. Moreover, we show that the combination of our techniques improves the accuracy and quality of generated sentences.
A Novel Framework for Constructing Multimodal Knowledge Graph from MuSe-CaR Video Reviews

The significance of the Knowledge Graph (KG) is rising across various industries. A KG is a powerful tool used to manage knowledge from vast amounts of resources effectively. In this paper, we propose an autonomous multimodal system for building a KG on the MuSe-CAR dataset. The system extracts several features of an entity from heterogeneous streams of the video, i.e., text and images, and represents them as fused Multi-Modal KG. We use the extracted features to explore the video content and learn to estimate queries and relationships between nodes of different modalities. This approach aims to enable users to perform a wider range of queries involving text and image data streams. The observation shows that multimodality either compensates for or corroborates knowledge in one stream with the other and allows the user to perform more queries. We evaluate the proposed system using a set of quantitative queries involving different data streams. The results of these queries can be used to gauge the system’s effectiveness. Based on these evaluations, it is possible to understand how well the system can extract knowledge from the dataset and how useful it is for downstream applications such as querying.
AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities

Openness and intelligence are two enabling features to be introduced in next generation wireless networks, for example, Beyond 5G and 6G, to support service heterogeneity, open hardware, optimal resource utilization, and on-demand service deployment. The open radio access network (O-RAN) is a promising RAN architecture to achieve both openness and intelligence through virtualized network elements and well-defined interfaces. While deploying artificial intelligence (AI) models is becoming easier in O-RAN, one significant challenge that has been long neglected is the comprehensive testing of their performance in realistic environments. This article presents a general automated, distributed and AI-enabled testing framework to test AI models deployed in O-RAN in terms of their decision-making perfor-mance, vulnerability and security. This framework adopts a master-actor architecture to manage a number of end devices for distributed testing. More importantly, it leverages AI to automatically and intelligently explore the decision space of AI models in O-RAN. Both software simulation testing and software-defined radio hardware testing are supported, enabling rapid proof of concept research and experimental research on wireless research platforms.
Distributed Learning Meets 6G: A Communication and Computing Perspective

With the ever improving computing capabilities and storage capacities of mobile devices in line with evolving telecommunication network paradigms, there has been an explosion of research interest toward exploring distributed learning (DL) frameworks to realize stringent key performance indicators (KPIs) that are expected in next-generation/6G cellular networks. In conjunction with edge computing, federated learning (FL) has emerged as the DL architecture of choice in prominent wireless applications. This article provides an outline of how DL in general and FL-based strategies specifically can contribute toward realizing part of the 6G vision and strike a balance between communication and computing constraints. As a practical use case, we apply multi-agent reinforcement learning within the FL framework to the dynamic spectrum access (DSA) problem and present preliminary evaluation results. Top contemporary challenges in applying DL approaches to 6G networks are also highlighted.
Artificial Intelligence Augmentation for Channel State Information in 5G and 6G

In this article, we present an artificial intelligence (AI) augmentation framework for physical layer communication applicable to both 5G and future 6G networks. The framework classifies the channel state information (CSI), and uses the classified CSI knowledge to adapt transmission configurations, perform resource optimization, and improve essential signal processing modules such as channel estimation. We demonstrate feasibility and benefits of the proposed AI augmentation in different use cases in the 5G NR context, such as beamforming mode adaptation, reference signal resource optimization, and link adaptation, as well as channel estimation. The framework also allows extension to resolve future 6G challenges.
A Graph-to-Sequence Model for Joint Intent Detection and Slot Filling

Effectively decoding semantic frames in task-oriented dialogue systems remains a challenge, which typically includes intent detection and slot filling. Although RNN-based neural models show promising results by joint learning of these two tasks, dominant RNNs are primarily focusing on modeling sequential dependencies. Rich graph structure information hidden in the dialogue context is seldom explored. In this paper, we propose a novel Graph-to-Sequence model to tackle the spoken language understanding problem by modeling both temporal dependencies and structural information in a conversation. We introduce a new Graph Convolutional LSTM (GC-LSTM) encoder to learn the semantics contained in the dialogue dependency graph by incorporating a powerful graph convolutional operator. Our proposed GC-LSTM can not only capture the spatio-temporal semantic features in a dialogue, but also learn the co-occurrence relationship between intent detection and slot filling. Furthermore, an LSTM decoder is utilized to perform final decoding for both slot filling and intent detection, which mutually improves both tasks through global optimization. Experiments on benchmark ATIS and Snips datasets (English) show that our model achieves state-of-the-art performance and outperforms existing models.
Machine learning-based prediction for 30-day unplanned readmission in all-types cancer patients

Unplanned readmission is a key indicator of quality of medical care and effectiveness of healthcare resource management. We aimed to develop the prediction model of 30-day unplanned readmission of cancer patients that can be applied to cancer hospitals. We obtained 15,877 patients and 4,437 variables obtained from the Clinical Data Warehouse (CDW) in the National Cancer Center in Korea and used conventional machine learning (ML) models to predict unplanned readmission. XGBoost showed the best performance with Fl-Score (0.74), Accuracy (0.84), Precision (0.85), AUROC (0.90), AUPRC (0.86) respectively. With XGBoost, we conducted additional experiments on the test set by reducing the number of top features to develop a general model with common features. Using the top 30 features, the performance of Fl-score (0.72) is equivalent to using all variables. The 30 variables are generally obtainable from EHR of cancer hospitals. Our model can be expected to apply to EHR of other cancer hospitals. Our result may help minimize the risk of unplanned readmission and improve the quality of care for cancer patients.
Chaos to Clarity with Semantic Inferencing for Python Source Code Snippets

Our work investigates using semantic indicators in source code to generate descriptions of its functionality. We applied natural language processing (NLP) techniques to improve the performance of a neural machine translation (NMT) model on source code. We trained the model on 20,000 code-comment pairs from the Source Code Analysis Dataset (SCAD) using a custom Python source code tokenizer and a learning model with attention, RNNs, LSTMs, and auto-encoders. We evaluated the model’s performance on a hold-out dataset using several established translation metrics, and found that our methods outperform prior work with a mean Bilingual Evaluation Understudy (BLEU) score of 0.47.
Protect the System Call, Protect (Most of) the World with BASTION

System calls are a critical building block in many serious security attacks, such as control-flow hijacking and privilege escalation attacks. Security-sensitive system calls (e.g., execve, mprotect), especially play a major role in completing attacks. Yet, few defense efforts focus to ensure their legitimate usage, allowing attackers to maliciously leverage system calls in attacks.
MP-Rec: Hardware-Software Co-design to Enable Multi-path Recommendation

Deep learning recommendation systems serve personalized content under diverse tail-latency targets and input-query loads. In order to do so, state-of-the-art recommendation models rely on terabyte-scale embedding tables to learn user preferences over large bodies of contents. The reliance on a fixed embedding representation of embedding tables not only imposes significant memory capacity and bandwidth requirements but also limits the scope of compatible system solutions. This paper challenges the assumption of fixed embedding representations by showing how synergies between embedding representations and hardware platforms can lead to improvements in both algorithmic- and system performance. Based on our characterization of various embedding representations, we propose a hybrid embedding representation that achieves higher quality embeddings at the cost of increased memory and compute requirements. To address the system performance challenges of the hybrid representation, we propose MP-Rec — a co-design technique that exploits heterogeneity and dynamic selection of embedding representations and underlying hardware platforms.
Analysis of real-time operating systems’ file systems: Built-in cameras from vehicles

The vehicles we use daily record enormous information via the embedded system that is installed. This recorded information identifies traffic accidents and might reveal the truth of crimes committed using the vehicle. However, as the embedded systems mounted on vehicles must maintain real-time performance, they often have a real-time operating system (RTOS)-dedicated file system structure. Analysis of such systems is becoming a new challenge for digital forensics. This study analyzed the real-time operating system-dedicated file system of a vehicle's built-in camera. When the built-in camera of the vehicle operates normally, there are various ways to acquire video data. However, when the built-in cam circuit is damaged, the only available method is to extract the onboard memory. To analyze the dedicated file system, we analyzed the driver file in the system area using the reverse engineering technique. We could analyze various log files and user-setting files in multiple partitions stored in the analyzed memory. In addition, we proved that more video frames can be restored by extracting the unallocated area of the video storage partition. In the future, this method can be applied to analyze various RTOS and dedicated file systems installed in the vehicle.
Database memory forensics: A machine learning approach to reverse-engineer query activity

Memory analysis allows forensic investigators to establish a more complete timeline of system activity using a snapshot of main memory (i.e., RAM). Investigators may rely on such analysis to detect malicious activity and understand the scope of what data was exfiltrated. This is of particular interest in the presence of incomplete or untrusted logs, where a privileged user (or an attacker with such capabilities) can altogether bypass or disable logging. In such instances, a forensic investigator can still rely on the fact that data must ultimately be processed in memory, regardless of the information that is recorded in audit logs. In this work, we propose methods to reverse-engineer query activity from a database management system (DBMS) process snapshot. Since DBMSes are used to manage and store an organization's most sensitive data, they are of particular concern for data exfiltration. A DBMS processes queries using a series of operations, such as index sort, file sort, or joins, which produce their own set of distinct forensic artifacts in memory. Our methods use these artifacts to make conclusions about recent query activity even in the presence of untrusted or incomplete logs. Our methods use a supervised learning based model using support vector machines (SVM) to approximate recently executed queries given these memory artifacts. We extract feature vectors from the byte frequencies in a special area of the DBMS process called the sort area fragment, and use SVM to predict the type of the query operation under supervised learning. We demonstrate the capabilities and the accuracy of our methods for two representative DBMSes, PostgreSQL and MySQL. Experimental results show that, our model achieved an accuracy of 92% and 90% on MySQL and PostgreSQL datasets, respectively.