Reviewerly/exHarmony huggingface dataset:
Personnel Feature Modeling and Application Based on Deep Neural Network Technology

The human resources management department of State Grid needs to match the personnel with relevant characteristics according to the characteristics of the project. However, it is difficult to achieve accurate matching by using the database to manually set query conditions for personnel allocation. The human resources department urgently needs to improve the process of PI management. Therefore, this paper models and applies human characteristics based on deep neural network (DNN). This paper first describes the two basic concepts of database structure and multi-task attribute recognition on personnel feature (HF) modeling, and then builds a DNN model through data preprocessing, HF classification and model building process design, and finally builds a personnel model. Feature recognition performance and local branching are analyzed.
Spider4SPARQL: A Complex Benchmark for Evaluating Knowledge Graph Question Answering Systems

With the recent spike in the number and availability of Large Language Models (LLMs), it has become increasingly important to provide large and realistic benchmarks for evaluating Knowledge Graph Question Answering (KGQA) systems. So far the majority of benchmarks rely on pattern-based SPARQL query generation approaches. The subsequent natural language (NL) question generation is conducted through crowdsourcing or other automated methods, such as rule-based paraphrasing or NL question templates. Although some of these datasets are of considerable size, their pitfall lies in their pattern-based generation approaches, which do not always generalize well to the vague and linguistically diverse questions asked by humans in real-world contexts. In this paper, we introduce Spider4SPARQL -a new SPARQL benchmark dataset featuring 9,693 previously existing manually generated NL questions and 4,721 unique, novel, and complex SPARQL queries of varying complexity. In addition to the NL/SPARQL pairs, we also provide their corresponding 166 knowledge graphs and ontologies, which cover 138 different domains. Our complex benchmark enables novel ways of evaluating the strengths and weaknesses of modern KGQA systems. We evaluate the system with state-of-the-art KGQA systems as well as LLMs, which achieve only up to 45% execution accuracy, demonstrating that Spider4SPARQL is a challenging benchmark for future research.
Multilingual Hate Speech Detection: Comparison of Transfer Learning Methods to Classify German, Italian, and Spanish Posts

With the increase of digital communication, a surge in online hate speech can be witnessed. Recent studies have concentrated on automated supervised detection of hate speech. However, there remains limited understanding of an effective strategy for identifying multilingual hate speech in social media posts. This study introduces an innovate experimental design for multilingual hate speech detection. It compares different approaches to automatically detect multilingual hate speech through a series of experiments and creates a classification algorithm for hate speech in German, Italian and Spanish text-based social media content. The study creates monolingual, multilingual, and translated datasets specific to the language triplet. Subsequently, the research explores suitable models for multilingual hate speech detection, evaluating a total of seven transformer-based models along with corresponding SVM models on the constructed datasets. The findings indicate that all chosen transformer-based models outperform the baseline SVM models. The research highlights the superiority of a multilingual approach, utilizing XLM-RoBERTa as a classifier model, over monolingual, multilingual, and translation-based approaches. Furthermore, the study demonstrates that translation-based methods in connection to the model DistillBERT can serve as viable alternatives to the multilingual XLM-RoBERTa approach, particularly in scenarios where computational resources are restricted and processing speed is of importance.
Toward Efficient and Incremental Spectral Clustering via Parametric Spectral Clustering

Spectral clustering is a popular method for effectively clustering nonlinearly separable data. However, computational limitations, memory requirements, and the inability to perform incremental learning challenge its widespread application. To overcome these limitations, this paper introduces a novel approach called parametric spectral clustering (PSC). By extending the capabilities of spectral clustering, PSC addresses the challenges associated with big data and real-time scenarios and enables efficient incremental clustering with new data points. Experimental evaluations conducted on various open datasets demonstrate the superiority of PSC in terms of computational efficiency while achieving clustering quality mostly comparable to standard spectral clustering. The proposed approach has significant potential for incremental and real-time data analysis applications, facilitating timely and accurate clustering in dynamic and evolving datasets. The findings of this research contribute to the advancement of clustering techniques and open new avenues for efficient and effective data analysis. We publish the experimental code at https://github.com/109502518/PSC_BigData.
C-PATR-IoT: A Hierarchical Classified Digital Forensics Framework for IoT

This paper presents a framework for digital forensics of the Internet of Things (IoT) called Preservation, Acquisition, Technical-analysis, and Reporting IoT (C-PATR-IoT) Digital Forensics Framework. The C-PATR-IoT framework lays out a systematic methodology that deals with the enormous scale and the many challenges and factors IoT presents to digital investigators. The process uses a classified, hierarchical, and hybrid approach that considers scale, privacy, ownership, cooperation assumptions, cost, device, Network, and cloud forensics. The methodology simplifies subsequent steps of the selected preservation, acquisition, analysis, and presentation of identified and collected evidence.
A High-Resolution DOA Estimation Method Based on the Subarray Combination Configuration

The paper proposes a direction of arrival(DOA) estimation method based on the subarray combination. The method utilizes the beamforming design and the spatial filtering on a specified spatial region by partitioned and overlapping subarrays in the array to suppress interference signals and improve the signal-to-noise ratio (SNR) of the desired signal. By applying the spatial spectral estimation methods to the outputs of each subarray, the DOA estimation of the desired signal is implemented based on the positional relationship between the subarrays. Simulation experiments demonstrate that compared to the conventional spectral estimation methods, the DOA estimation method based on the subarray combination effectively reduces the impact of interference and noise signals on the DOA estimation. As a result, the accuracy and anti-interference capability of the signal direction finding in complex electromagnetic environments are further improved.
Beyond the Screen: An Exploration of Theoretical Foundations and Paradigms in Human-Computer Interface Design

This paper delves into the multifaceted landscape of Human-Computer Interface (HCI) design, meticulously exploring the foundational theories and cutting-edge paradigms that form the backbone of the digital user experience. It skillfully synthesizes profound insights gleaned from seminal works, including Norman's cognitive model and Fitts' law, as well as delving into socio-technical theories such as Activity Theory and Distributed Cognition. These collective theories illuminate the intricate tapestry of human cognition, motor control, and the dynamic interactions between individuals, technology, and their environment. Furthermore, this paper rigorously analyzes the frontiers of HCI through emerging paradigms like natural user interfaces and affective computing, underlining the paramount importance of user-centered design principles in fostering empathy, inclusivity, and accessibility. By comprehensively exploring and elucidating the theoretical foundations of HCI, this work aspires to invigorate innovative, user-centric design approaches, thus enriching the future of human-computer interactions and enhancing the overall user experience within the digital realm.
Late Fusion-based Distributed Multimodal Learning

Multimodal artificial intelligence promises deeper insights by analyzing data from diverse sources such as text, images, audio and more. However, efficiently processing and fusing large multimodal datasets remains an open challenge. This paper presents a Spark-based approach to parallelize multimodal encoding tasks. A key aspect is the use of late fusion with frozen backbone encoders, allowing encodings to be processed independently across cluster nodes. The encoded vectors can then be used for a variety of supervised and unsupervised tasks, regardless of whether they are gradient-based or not. Experimental results on image, text and audio datasets show that Spark clusters can offer competitive performance compared to GPUs, especially for I/O-intensive modalities. While GPUs outperform the Spark cluster when sufficient CPU cores are available, Spark makes it possible to use already available commodity hardware. The presented architecture demonstrates how distributed computing platforms like Spark can be effectively “repurposed” for multimodal AI, enhancing scalability and making such systems more accessible.
Failure Mode Extraction via Vectorization and Clustering: A Case Study on Amazon Reviews

Due to the fact that consumers have mostly preferred e-commerce sites as a purchasing channel in recent years, it is necessary to focus on the online marketing processes for the manufacturers and get more benefit from it. Thus, product reviews on online sites provide great feedbacks for companies to improve their products or services and for potential customers on purchase decisions. However, it is not possible to evaluate every single feedback for a specific product by a company, due to the immense amount of post-sale reviews for a certain product. In that study, negative categorical customer reviews are crawled for a specific product from amazon.com, then reviews are converted into vectors using Doc2Vec and finally, reviews are clustered by using k-means, and DBSCAN algorithms. As a result of clustering and cluster labeling, the most frequent topics (failure modes) are extracted along with pointless reviews. The result of the clustering presented useful managerial insights for product improvement processes and a beneficial perspective for potential customers on purchasing decisions concerning the topic frequency and mean star values. Besides, the proposed pipeline can be used as a topic modeling approach in terms of its promising results.
A Parallel Connected Region Algorithm Optimized for CPU-GPU Heterogeneous Platforms

In recent years, GPU has become an important component of the system by accelerating applications with data parallelism. While traditional discrete GPU cooperates with CPU over I/O bus, more recent processor design trends feature fine-grained synchronization and inexpensive data sharing between CPU and GPU processors by tightly coupling them at a cache level. However, designing algorithms that effectively exploit these new architectural features has proved challenging. In this paper we present a novel parallel algorithm called Parafill that identifies connected regions of arbitrary topolo-gies in an image very efficiently on CPU -GPU heterogeneous platforms. Parafill is a greedy algorithm using dynamic programming memoization techniques. Essential to its efficiency is a data structure called the workload manager, which dynamically manages the complexity of work in each pass. We evaluate the performance of Parafill across two different heterogeneous platforms: a traditional discrete GPU system where the CPU and GPU are connected through I/O bus, and a single-chip processor where the CPU and GPU are tightly coupled at a cache level. We also compare Parafill to a CPU-optimized connected region algorithm using applications with different parallelism to quantify its scalability. Our experimental results show that minimizing GPU overhead and managing pass-by-pass workload are especially important for sparsely parallel applications. Tightly coupled GPUs, although less powerful than discrete GPUs, com-pete well in sparsely parallel applications because of their reduced overhead. Finally, we present two applications of Parafill: traditional flood filling and the absorption of unconnected interior regions into a containing connected region.
Exploration of the Teaching Reform Model for Computer Public Courses Based on Intelligent Algorithms

This article proposes a teaching reform model for computer public courses based on intelligent algorithms, aiming to improve the teaching quality of computer public courses and the learning effectiveness of students. This mode utilizes intelligent algorithms to optimize course content and teaching methods, achieving reasonable allocation of course resources and personalized teaching, and improving students' learning enthusiasm and grades. The experimental results indicate that this model has good feasibility and effectiveness, providing new ideas and methods for the teaching reform of computer public courses. And the learning efficiency of students has doubled compared to traditional methods, reaching 95%.
Heterogeneous Domain Adaptation with Positive and Unlabeled Data

Heterogeneous unsupervised domain adaptation (HUDA) is the most challenging domain adaptation setting where the feature spaces of source and target domains are heterogeneous, and the target domain has only unlabeled data. Existing HUDA methods assume that both positive and negative examples are available in the source domain, which may not be satisfied in some real applications. This paper addresses a new challenging setting called positive and unlabeled heterogeneous unsupervised domain adaptation (PU-HUDA), a HUDA setting where the source domain only has positives. PU-HUDA can also be viewed as an extension of PU learning where the positive and unlabeled examples are sampled from different domains. A naive combination of existing HUDA and PU learning methods is ineffective in PU-HUDA due to the gap in label distribution between the source and target domains. To overcome this issue, we propose a novel method, predictive adversarial domain adaptation (PADA), which can predict likely positive examples from the unlabeled target data and simultaneously align the feature spaces to reduce the distribution divergence between the whole source data and the likely positive target data. PADA achieves this by a unified adversarial training framework for learning a classifier to predict positive examples and a feature transformer to transform the target feature space to that of the source. Specifically, they are both trained to fool a common discriminator that determines whether the likely positive examples are from the target or source domain. We experimentally show that PADA outperforms several baseline methods, such as the naive combination of HUDA and PU learning.
A Comparison of Different Representations of Ordinal Patterns and Their Usability in Data Analysis

We describe and analyze different approaches to represent ordinal patterns. All of these can be found in the literature. The most important representations (plus sub-classes) are compared in terms of their applicability from different angles. Namely we consider digital implementation, inverse patterns and ties between values. At the end we provide a guideline on which occasions which representation should be used.
Harvesting Large Textual and Multimedia Data to Detect Illegal Activities on Dark Web Marketplaces

During the last decades, the dark web has become the ground for criminal activities, enabling for illegal content sharing, as well as marketplaces selling drugs and firearms. The 2023 Internet Organised Crime Threat Assessment (IOCTA) of Europol’s European Cybercrime Centre (EC3) presented the dark web as the one of the top crime ecosystems. Analyzing the .onion sites hosting marketplaces is of interest to law enforcement, security researchers, and big data analysts. The capability of automatically harvesting web content from web servers enables Law Enforcement Agencies (LEAs) to collect and preserve data prone to serve as potential clues or evidence in investigations. The use of sophisticated protocols and the inherent complexity of the Dark Web makes it difficult for security agencies to identify and investigate these activities through conventional methods. The sheer size, unpredictable ecosystem, and anonymity provided by the Dark Web are the essential confrontations to trace the criminals. Therefore, it is a crucial step to discover the potential solutions towards cyber-crimes evaluating the sailing Dark Web crime threats. In this paper, we devise Artificial Intelligence and Big Data processing to extract insights and investigate how the Dark Web facilitates crime and dynamically maintains marketplaces with illegal goods exchange. The scientific contribution of this paper entails novel textual and multimedia analytics over large collections of data to collect evidence for further investigations and support visual reporting and alerting mechanisms. The conclusions include practical implications of Dark Web content retrieval and archival, such as investigation clues and evidence, and related future research topics.
Electronic Voting System Based on the Blockchain Technology

This paper presents an alternative to classical voting. An Electronic Voting System Based on Blockchain Technology (EVSBT), by design and purpose, should be a secure and transparent voting system designed to ensure the integrity and immutability of election results. By leveraging the decentralized nature of blockchain technology, EVSBT could provide a reliable and tamper-proof platform for conducting elections. This paper aims to present the fundamentals that should be present in any blockchain voting system.
LogGPT: Log Anomaly Detection via GPT

Detecting system anomalies based on log data is important for ensuring the security and reliability of computer systems. Recently, deep learning models have been widely used for log anomaly detection. The core idea is to model the log sequences as natural language and adopt deep sequential models, such as LSTM or Transformer, to encode the normal patterns in log sequences via language modeling. However, there is a gap between language modeling and anomaly detection as the objective of training a sequential model via a language modeling loss is not directly related to anomaly detection. To fill up the gap, we propose LogGPT, a novel framework that employs GPT for log anomaly detection. LogGPT is first trained to predict the next log entry based on the preceding sequence. To further enhance the performance of LogGPT, we propose a novel reinforcement learning strategy to finetune the model specifically for the log anomaly detection task. The experimental results on three datasets show that LogGPT significantly outperforms existing state-of-the-art approaches.
RAxC: Reflexivity-based Approximate Computing techniques for efficient remote sensing

Hyperspectral images (HSI) have a huge size, which makes their processing through neural networks cumbersome. We propose novel approximate computing techniques that leverage physical properties of the reflectance spectra to accelerate the processing of HSI images. This makes the images interpretable across various applications. We propose three spectral dimensionality reduction techniques. These techniques use spectral clustering methods that rely on reflectance values to capture inherent characteristics from hyperspectral images across diverse domains. We also evaluate existing spatial dimension reduction techniques and a combination of spatial + spectral dimension reduction techniques. We conduct extensive experiments on three real-world open-source datasets, encompassing urban and rural landscapes. Our techniques reduce the training time by up to 8x and inference time by up to 5x, while reducing the model size by up to 3x. Our techniques have a negligible impact on accuracy. By contrast, PCA and MNF techniques incur 3X higher pre-processing latency overheads than our techniques and also degrade the accuracy. Our techniques are promising for addressing the computational challenges of HSI processing.
Analysis of the Performance of Photovoltaic System Through Different Angles of Tilt and Orientation in Equatorial Latitude

The amount of irradiance received by the photovoltaic panel has a direct impact on electricity generation. The article presents the analysis of the performance of an isolated photovoltaic system, based on the ability to periodically collect enough energy at an optimal angle in order to obtain energy results by decreasing the cost of energy and achieving a greater implementation of the technology. It should be noted that dusty environments can lead to PV energy losses of up to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> for a slightly dirty panel and up to 40% for a very dirty panel. Several angles of inclination of the photovoltaic panel have been studied in optimal and non-optimal conditions in which the energy losses caused by deviations of the angle of inclination and orientation with respect to the ideal position are evaluated. For this analysis, the Victron Connect application has been used where consumption, power and battery data are monitored in real time. The results show that the optimal azimuth and inclination angles are 00 and 00, with a power of 179 W, in addition to climate changes and dust influence its productivity.
Anaphoric Ambiguity Resolution in Software Requirement Texts

In requirements engineering (RE), anaphoric ambiguity is a frequent cause of misunderstandings. It can have a detrimental effect on the quality of requirements and jeopardize the success of a project. If stakeholders of the system, such as testers, developers, or customers, have different understandings or interpretations of software requirements, the system may not be accepted during customer validation. Despite its significance, there has been limited investigation into anaphoric ambiguity in RE. However, focusing on both recognizing and solving uncertainty can be more advantageous than just identifying it. Therefore we investigated the effectiveness of various QA learning techniques including encoder-based and text generation-based NLP models for two goals. We conduct detailed numerical experiments using various transformer models on two public requirements datasets and one generic dataset. Our results indicated that our QA architecture exhibits superior performance compared to baseline models in detecting ambiguity as well as resolving anaphora in contrast to other baseline approaches. We showed that our developed architecture can automatically support requirement development to minimize interpretation risk between stakeholders.