Reviewerly/exHarmony huggingface dataset:
Web content topic modeling using LDA and HTML tags

An immense volume of digital documents exists online and offline with content that can offer useful information and insights. Utilizing topic modeling enhances the analysis and understanding of digital documents. Topic modeling discovers latent semantic structures or topics within a set of digital textual documents. The Internet of Things, Blockchain, recommender system, and search engine optimization applications use topic modeling to handle data mining tasks, such as classification and clustering. The usefulness of topic models depends on the quality of resulting term patterns and topics with high quality. Topic coherence is the standard metric to measure the quality of topic models. Previous studies build topic models to generally work on conventional documents, and they are insufficient and underperform when applied to web content data due to differences in the structure of the conventional and HTML documents. Neglecting the unique structure of web content leads to missing otherwise coherent topics and, therefore, low topic quality. This study aims to propose an innovative topic model to learn coherence topics in web content data. We present the HTML Topic Model (HTM), a web content topic model that takes into consideration the HTML tags to understand the structure of web pages. We conducted two series of experiments to demonstrate the limitations of the existing topic models and examine the topic coherence of the HTM against the widely used Latent Dirichlet Allocation (LDA) model and its variants, namely the Correlated Topic Model, the Dirichlet Multinomial Regression, the Hierarchical Dirichlet Process, the Hierarchical Latent Dirichlet Allocation, the pseudo-document based Topic Model, and the Supervised Latent Dirichlet Allocation models. The first experiment demonstrates the limitations of the existing topic models when applied to web content data and, therefore, the essential need for a web content topic model. When applied to web data, the overall performance dropped an average of five times and, in some cases, up to approximately 20 times lower than when applied to conventional data. The second experiment then evaluates the effectiveness of the HTM model in discovering topics and term patterns of web content data. The HTM model achieved an overall 35% improvement in topic coherence compared to the LDA.
A Model-based, Quality Attribute-guided Architecture Re-Design Process at Google

Communicating and justifying design decisions are difficult, especially when the architecture design has to evolve. In this paper, we report our experiences of using formal but lightweight design models to communicate, justify, and analyze the quality trade-offs of an architecture revision plan for Monarch, a large-scale legacy system from Google. We started from a few critical user scenarios and their associated quality attribute scenarios, which makes these models lightweight and concise, expressing high-level abstractions only. We also separated static views from dynamic views so that each diagram can be precise and suitable for analyzing different types of quality attributes respectively. The combination of scenarios, quality attributes, and lightweight modeling was well accepted by the team as an effective way to analyze and communicate the trade-offs. A few days after we presented and shared this process, two new projects within the Monarch team adopted component and sequence diagrams in their design documents, and two other product areas within Google started to learn and to adopt the process as well. Our experience indicates that these architecture modeling and analysis techniques can be integrated into software development process to communicate and assess features, quality attributes, or design decisions continuously and iteratively.
Robust Motion Recognition Using Gesture Phase Annotation

Robust gesture recognition is key to multimodal language understanding as well as human-computer interaction. While vision-based approaches to gesture recognition rightly focus on detecting hand poses in a single frame of video, there is less focus on recognizing the distinct "phases" of gesture as used in real interaction between humans or between humans and computers. Following the semantics of gesture originally outlined by Kendon, and elaborated by many such as McNeill and Lascarides and Stone, we propose a method to automatically detect the preparatory, "stroke," and recovery phases of semantic gestures. This method can be used to mitigate errors in automatic motion recognition, such as when the hand pose of a gesture is formed before semantic content is intended to be communicated and in semi-automatically creating or augmenting large gesture-speech alignment corpora.
Blockchain for Food Supply Chain: Trust, Traceability, and Transparency Enhancement, How Can Africa Benefit?

To put it simply, blockchain is a digital ledger that records the transactions of a decentralized network in which all participants control the databases, and no one can alter the transaction history. From a technical standpoint, a blockchain is a chain of data blocks connected by cryptographic hashes; these blocks contain transaction information and are themselves composed of a header and body; the header stores data from the previous block as well as a timestamp, a Nonce, and the Merkle root, while the body stores confirmed transactions. Trust, transparency, decentralization, and security are just a few of the numerous features of blockchain that make it an innovative and precise technology capable of addressing a wide range of issues. This chapter makes use of an experimental design and a comprehensive literature review to investigate the role of blockchain technology in the food supply chain. The study analyzed how Africa could benefit from the use of blockchain technology to increase trust, traceability, and transparency in the food supply chain. Finally, suggestions for what the African government can do to make use of blockchain technology are rounded up in this chapter.
Evaluating Interaction Challenges of Head-Mounted Device-Based Augmented Reality Applications for First-Time Users at Museums and Exhibitions

Many museums are seeking opportunities to apply head-mounted device (HMD)-based augmented reality (AR) technology to their exhibitions. However, due to the novel interaction method of HMDs, visitors are usually first-time users who find it difficult to gain enjoyment from using HMD-based AR applications directly. Thus, we argue that it is crucial to understand what has prevented visitors from experiencing the convenience of HMD-based AR applications. There are two main contributions to computer science in this paper: first, we introduced an HMD-based AR prototype with three interaction patterns that are made based on a natural science exhibition; Second, we proposed a usability test method to evaluate the HMD-based AR prototype to identify potential interaction problems in the exhibition sector. The results show that the suggested usability test method is effective in identifying interaction challenges and it helps media developers to improve AR experiences for first-time users at exhibitions and museums.
Estimation of one-minute direct normal irradiance using a deep neural network for five climate zones

Due to its high cost and significance for solar thermal technologies, the calculation of direct normal irradiance has dominated the modelling of solar radiation components. There are three methods described in the literature to calculate direct normal irradiance: regression analysis, probabilistic approach, and application of machine learning models. To demonstrate that machine learning models can achieve sufficient generality for solar radiation data with good statistical results, a new methodology is proposed with three main goals: that required only one measure data as input, trained a deep neural network model using only one station referred as the reference station, and evaluate the direct normal irradiance estimated by the model with the measured values from others stations of the same climate zone. The results then were compared against the most prominent separation models in the literature. The proposed methodology shows that the model learn the radiative characteristics of the reference station and is a feasible method to estimate the component of solar radiation in others location. Therefore, the proposed approach is a viable methodology that has the ability to reach the sought-after quasi-universal model by researchers, as demonstrated by the neural network’s similar or superior performance compared to the separation models.
User-aware multilingual abusive content detection in social media

Despite growing efforts to halt distasteful content on social media, multilingualism has added a new dimension to this problem. The scarcity of resources makes the challenge even greater when it comes to low-resource languages. This work focuses on providing a novel method for abusive content detection in multiple low-resource Indic languages. Our observation indicates that a post's tendency to attract abusive comments, as well as features such as user history and social context, significantly aid in the detection of abusive content. The proposed method first learns social and text context features in two separate modules. The integrated representation from these modules is learned and used for the final prediction. To evaluate the performance of our method against different classical and state-of-the-art methods, we have performed extensive experiments on SCIDN and MACI datasets consisting of 1.5M and 665K multilingual comments, respectively. Our proposed method outperforms state-of-the-art baseline methods with an average increase of 4.08% and 9.52% in the F1-score on SCIDN and MACI datasets, respectively.
Transforming spatio-temporal self-attention using action embedding for skeleton-based action recognition

Over the past few years, skeleton-based action recognition has attracted great success because the skeleton data is immune to illumination variation, view-point variation, background clutter, scaling, and camera motion. However, effective modeling of the latent information of skeleton data is still a challenging problem. Therefore, in this paper, we propose a novel idea of action embedding with a self-attention Transformer network for skeleton-based action recognition. Our proposed technology mainly comprises of two modules as, (i) action embedding and (ii) self-attention Transformer. The action embedding encodes the relationship between corresponding body joints (e.g., joints of both hands move together for performing clapping action) and thus captures the spatial features of joints. Meanwhile, temporal features and dependencies of body joints are modeled using Transformer architecture. Our method works in a single-stream (end-to-end) fashion, where multiple-layer perceptron (MLP) is used for classification. We carry out an ablation study and evaluate the performance of our model on a small-scale SYSU-3D dataset and large-scale NTU-RGB+D and NTU-RGB+D 120 datasets where the results establish that our method performs better than other state-of-the-art architectures.
Harmful Terms in Computing: Towards Widespread Detection and Correction

Modern-day software development and use is a product of decades of advancement and evolution. Over time as new technologies and concepts emerged, so did new terminology to describe and discuss them. Most terminology used in computing is harmless, however, some are rooted in historically discriminatory, and potentially harmful, terms. While the landscape of individuals who develop technology has diversified over the years, the terminology has become a normalized part of modern software development and computing jargon. Despite organizations such as the ACM raising awareness of the potential harm certain terms can do and companies like GitHub working to change the systemic use of harmful terms in computing, it is still not clear what the landscape of harmful terminology in computing really is and how we can support the widespread detection and correction of harmful terminology in computing artifacts. To this end, we conducted a review of existing work and efforts at curating, detecting, and removing harmful terminology in computing. Combining and building on these prior efforts, we produce an extensible database of what we define as harmful terminology in computing and describe an open source proof-of-concept tool for detecting and replacing harmful computing-related terminology.
StreamAI: Dealing with Challenges of Continual Learning Systems for Serving AI in Production

How to build, deploy, update & maintain dynamic models which continuously learn from streaming data? This paper covers the industrialization aspects of these questions in production systems. In today’s fast-changing environments, organizations are faced with the crucial challenge of predictive analytics in online fashion from big data and deploying Artificial Intelligence models at scale. Applications include cyber-security, cloud infrastructure, social networks and financial markets. Online learning models that learn continuously and adapt to the potentially evolving data distributions have demonstrated efficiency for big data stream learning. However, the challenges of deploying and maintaining such models in production (serving) have stalled their adoption. In this paper, we first categorize key challenges faced by the R&D, MLOps and governance teams for deploying automated and self-training AI models in production. Next, we highlight the challenges related to stream-based online machine-learning systems. Finally, we propose StreamAI, a technology-agnostic architecture to deal with the MLOps journey (learning, serving, maintenance) of online models in production. We conclude with open research questions for AI, MLOps and software engineering to bridge the gaps between industry needs and research-oriented development.
Establishing Interoperability in Blockchain Enabled Interconnected Smart Microgrids Using Ignite CLI

Application of Blockchain Technology (BCT) in the energy industry in Blockchain Enabled Interconnected Smart Microgrids (BSMGs) is on the rise as it can automate local energy markets; execute energy trading; and implement market operations and management. However, they are limited by their scalability and low transactions rate. Also, with the increase in adoption of BSMGs, different types of BCT platforms will emerge, creating heterogeneity in the system. This shortcoming may lead to a monopoly of certain platforms over the rest. These drawbacks can be overcome by establishing interoperability between heterogeneous blockchains. Interoperability can also enable inter-microgrid transactions which will be hindered if there is no inter-chain communication and transaction. Cosmos is a network of blockchains which allows blockchains, applications and services to be interconnected through Inter Blockchain Communication (IBC) protocol. Ignite CLI is an open-source, command line interface which easily creates modular and customizable blockchains which are inherently connected to Cosmos through IBC. In this paper, Ignite CLI is used to create blockchains for BSMGs which exchange data and calls for inter-microgrid transactions via IBC. The procedure to establish inter-chain communication is defined. Interoperability between BSMGs is explored and demonstrated through different examples, for the first time in the energy domain. The execution times for different cases and varying numbers of information packets have been observed.
Conceptual Review on Number Theoretic Transform and Comprehensive Review on Its Implementations

The Number Theoretic Transform (NTT) is a powerful mathematical tool that has become increasingly important in developing Post Quantum Cryptography (PQC) and Homomorphic Encryption (HE). Its ability to efficiently calculate polynomial multiplication using the convolution theorem with a quasi-linear complexity <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> log <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ) has made it a key component in modern cryptography. NTT is particularly useful in lattice-based cryptography, which relies on the hardness of certain mathematical problems to ensure security. Its importance in these fields continues to grow as quantum computing technology advances and traditional encryption methods become vulnerable. In this report, we discuss the mathematical concepts of polynomial multiplications using NTT and provide a comprehensive review of the latest implementation and state-of-the-art of NTT in both PQC and HE schemes.
SMET: Semantic Mapping of CVE to ATT&amp;CK and Its Application to Cybersecurity

Cybercriminals relentlessly pursue vulnerabilities across cyberspace to exploit software, threatening the security of individuals, organizations, and governments. Although security teams strive to establish defense measures to thwart attackers, the complexity of cyber defense and the magnitude of existing threats exceed the capacity of defenders. Therefore, MITRE took the initiative and introduced multiple frameworks to facilitate the sharing of vital knowledge about vulnerabilities, attacks, and defense information. The Common Vulnerabilities and Exposures (CVE) program and ATT&CK Matrix are two significant MITRE endeavors. CVE facilitates the sharing of publicly discovered vulnerabilities, while ATT&CK collects and categorizes adversaries' Tactics, Techniques, and Procedures (TTP) and recommends appropriate countermeasures. As CVE yields a low-level description of the vulnerability, ATT&CK can complement it by providing more insights into that vulnerability from an attacking perspective, thereby aiding defenders in countering exploitation attempts. Unfortunately, due to the complexity of this mapping and the rapid growth of these frameworks, mapping CVE to ATT&CK is a daunting and time-intensive undertaking. Multiple studies have proposed models that automatically achieve this mapping. However, due to their reliance on annotated datasets, these models exhibit limitations in quality and coverage and fail to justify their decisions. To overcome these challenges, we present SMET—a tool that automatically maps CVE entries to ATT&CK techniques based on their textual similarity. SMET achieves this mapping by leveraging ATT&CK BERT, a model that we trained using the SIAMESE network to learn semantic similarity among attack actions. In inference, SMET utilizes semantic extraction, ATT&CK BERT, and a logistic regression model to map CVE entries to ATT&CK techniques. As a result, SMET has demonstrated superior performance compared to other state-of-the-art models.
Cross-Domain Few-Shot classification via class-shared and class-specific dictionaries

In Cross-Domain Few-Shot Classification, researchers mainly utilize models which trained with source domain tasks to adapt to the target domain with very few samples, thus causing serious class-difference-caused domain differences. Although researchers have proposed methods to minimize the domain differences, the existing methods have the following drawbacks: 1) most models do not utilize the common knowledge between the source and target domains, and 2) require additional labeled samples from the target domain for finetuning or domain alignment, which is hard to obtain in reality. To address the problem mentioned above, we propose a class-shared and class-specific dictionaries (CSCSD) learning method. To make better utilization of the common knowledge, we apply a class-shared dictionary which is learned to represent the generality of source and target domain. Moreover, class-specific dictionaries are applied to represent the class-specific knowledge that can't be represented in the class-shared dictionary. Furthermore, unlike most other models, our CSCSD does not require additional target domain samples to meta-train or finetune. With the dictionaries, CSCSD can obtain more distinguishable collaborative representations of samples with the origin representations extracted with the model. To evaluate the effectiveness of CSCSD, we utilize larger datasets, e.g., MiniImageNet and TieredImageNet as source domains and fine-grained datasets, e.g., CUB, Cars, Places, and Plantae as target domains. With our CSCSD, the Cross-Domain Few-Shot accuracy exceeds most domain adaptive Few-Shot which utilizes additional training set in target domains.
Are crowd-sourced CTI datasets ready for supporting anti-cybercrime intelligence?

Cyber crimes rapidly increased over the past years, with attackers performing large-scale activities, using sophisticated and complex tactics and techniques, that have targeted governments, companies, and even strategic infrastructures. To tackle these attacks, the cyber-security community usually shares Cyber Threat Intelligence (CTI) that includes the collected Indicators of Compromise (IoC) using several open or private sharing platforms. In this paper, we study the informativeness and relevance of the IoCs related to cyber crimes following a major real-world event such as the war in Ukraine, which started in February 2022. To this end, we analyze different kinds of attacks available in a crowd-sourced dataset of Cyber Threat Intelligence (CTI) reports. Our analysis shows that while this data is able to capture major trends such as the ones following major events, the degree of miscellaneous information inside the reports makes it difficult to discern the association of a specific trace unequivocally.
Resolution enhancement processing on low quality images using swin transformer based on interval dense connection strategy

The Transformer-based method has demonstrated remarkable performance for image super-resolution in comparison to the method based on the convolutional neural networks (CNNs). However, using the self-attention mechanism like SwinIR (Image Restoration Using Swin Transformer) to extract feature information from images needs a significant amount of computational resources, which limits its application on low computing power platforms. To improve the model feature reuse, this research work proposes the Interval Dense Connection Strategy, which connects different blocks according to the newly designed algorithm. We apply this strategy to SwinIR and present a new model, which named SwinOIR (Object Image Restoration Using Swin Transformer). For image super-resolution, an ablation study is conducted to demonstrate the positive effect of the Interval Dense Connection Strategy on the model performance. Furthermore, we evaluate our model on various popular benchmark datasets, and compare it with other state-of-the-art (SOTA) lightweight models. For example, SwinOIR obtains a PSNR of 26.62 dB for $$\times $$ 4 upscaling image super-resolution on Urban100 dataset, which is 0.15 dB higher than the SOTA model SwinIR. For real-life application, this work applies the lastest version of You Only Look Once (YOLOv8) model and the proposed model to perform object detection and real-life image super-resolution on low-quality images. This implementation code is publicly available at https://github.com/Rubbbbbbbbby/SwinOIR .