Reviewerly/exHarmony huggingface dataset:
Design of a Real-Time-Integrated System Based on Stereovision and YOLOv5 to Detect Objects

Real-time object detection represents a major part in the development of advanced driver assistance systems (ADAS). Pedestrian detection has become one of the most important tasks in the field of object detection due to the increasing number of road accidents. This study concerns the design and implementation of a Raspberry Pi 4-based embedded stereovision system to detect 80 object classes including persons and estimate 3D distance for traffic safety. Stereo camera calibration and deep learning algorithms are discussed. The study shows the system's design and a custom stereo camera designed and built using 3D printer as well as the implementation of YOLOv5s in the Raspberry Pi 4. The object detector is trained on the context object detection task (COCO) 2020 dataset and was tested using one of the two cameras. The Raspberry Pi displays a live video including bounding boxes and the number of frames per second (FPS).
IFML-Based Graphical User Interfaces Generated From BPMN up to PSM Level

The model-driven architecture (MDA) approach revolves around the development of multiple models, including the computation independent model (CIM), the platform independent model (PIM), and the platform specific model (PSM). Web applications have gained popularity for their capabilities. To address the need for robust user interfaces independent of technical details, the interaction flow modeling language (IFML) was introduced. This study focuses on model transformations within MDA, specifically from CIM to PSM via PIM. Metamodels for BPMN and IFML were created using Eclipse, and shift rules were applied with ATL. Webratio, an IFML implementation tool, was used to generate GUIs. A case study on after-sales service with CRUD features demonstrated the practical application of MDA. This research enhances understanding of MDA in web application development, enabling developers to create user-friendly interfaces. It serves as a valuable resource for software engineering professionals, providing insights into MDA's practical implementation and impact on web application development.
Intelligent Watermarking for Data Security

Artificial intelligence has become widely and increasingly used in various advanced applications, notably classification, optimization, object recognition, and segmentation. Recently, it has been extended into watermarking techniques. It brings some approaches implying innovative security means, which are adjusted to new communications and information technologies. As it generally believed that the use of artificial intelligence in digital watermarking schemes could revolutionize the way digital data is protected. This chapter is about an overview of recent developments in artificial intelligence techniques utilized for watermarking. It begins with the watermarking background. Next, it represents a review of machine and deep learning watermarking techniques followed by a delineation of their advantages and disadvantages. In this light, the main problems are pinpointed with a suggestion of some possible discussed and highlighted solutions. The last point of this chapter is about outlining future research directions.
A Deep Dive into Cobalt Strike Tool

Cobalt Strike is a popular commercial penetration testing tool that has also been widely used in cyber attacks. This paper provides a review of the Cobalt Strike and its use in cyber attacks, including an analysis of the tactics, techniques, and trends associated with its use. Conducted a literature review and data analysis of academic research, industry reports, and news articles on Cobalt Strike and its use in cyber attacks, as well as case studies of specific attacks involving Cobalt Strike. This research is based on cases in which Cobalt Strike has been used in a wide range of attacks: ransomware attacks, espionage campaigns, and advanced persistent threats. Attackers using Cobalt Strike tend to be highly sophisticated and motivated by a range of factors, including financial gain, political espionage, and cyber warfare. The tool's flexibility and adaptability make it a formidable threat to organizations seeking to defend against cyber attacks. Our research highlights key features and explains thoroughly the logical structure of the Cobalt Strike.
Contemporary High-Performance Computing for Big Data Applications

High-performance computing (HPC) involves leveraging parallel data processing to enhance computer performance and handle difficult tasks. HPC meets these aims by pooling computing capacity, enabling efficient, reliable, and prompt execution of even complex programs according to user demands and expectations. The rapid growth of HPDA in many sectors has led to the extension of the HPC market into new territory. HPC as well as Big Data systems differ not just in terms of technology but also in ecosystems. Extensive research in this sector has led to the emergence of various Big Data analytics models in recent years. As Big Data analytics spreads across several fields, new challenges about the usefulness of analytical paradigms also emerge. This article discusses the key analytical models, as well as the difficulties and challenges associated with high-performance data analytics. This research work aims to identify the factors influencing the integration of HPC with big data, including present and future trends. The study also proposes an architecture for big data with HPC convergence based on design principles.
Towards Generative Search and Recommendation: A keynote at RecSys 2023

The emergence of large language models (LLM's), especially ChatGPT, has for the first time make AI known to almost everyone and affected every facet of our society. The LLMs have the potential to revolutionize the ways we seek and consume information. This has stemmed the recent trends in both academia and industry to develop LLM-based generative AI systems for various applications with enhanced capabilities. One such systems is the generative search and recommender system, which is capable of performing content retrieval, content repurposing, content creation and their integration to meet users' information needs. However, before such systems can be widely used and accepted, we need to address several challenges. The primary challenge is the trust and safety in the generated content as we expect the LLM's to make mistakes with hallucination. This is because of the quality of data being used for their training is often erroneous and biased. The other challenges in the search and recommendation domain include: how to teach the system to be pro-active in anticipating the needs of users and in directing the conversation towards a fruitful direction; as well as the integration of retrieved and generated content. This keynote presented a generative information seeking paradigm, and discuss key research towards a trustable generative system for search and recommendation. Date : 21 September 2023.
Generating Seed Mappings for Machine Learning-Based Code-to-Architecture Mappers Using InMap

Software architecture consistency checking (SACC) is a popular method to detect architecture degradation. Most SACC techniques require software engineers to manually map a subset of entities of a system's implementation onto elements of its intended software architecture. Manually creating such a "seed mapping" for complex systems is a time-consuming activity. The objective of this paper is to investigate if creating seed mappings semi-automatically based on mapping recommendations for training automatic, machine learning-based mappers can reduce the effort for this task. To this end, we applied InMap, a highly accurate, interactive code-to-architecture mapping approach, to create seed mappings for five opensource systems with known architectures and mappings. Three different machine learning-based mappers were trained with these seed mappings and analysed regarding their predictive performance. We then compared the manual effort involved in using the combination of InMap and the most accurate automatic mapper and the manual effort of mapping the systems solely with InMap. The results suggest that InMap, with a minor adaption, can be used to seed an accurate mapper at a size of 20--25% based on Naive Bayes. A full mapping with only InMap though turns out to involve slightly less manual effort on average; this is, however, not consistent across all systems. These results give evidence that more advanced ways of combining automatic mappers with InMap may further reduce that effort.
Report on the 10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS 2023) at ACM RecSys 2023

The 10th edition of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems was held as part of the 17th ACM Conference on Recommender Systems (RecSys), the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. The workshop was organized as a hybrid event: the physical session took place on September 18th at the venue of the main conference, Singapore, with the possibility for authors to present remotely. The IntRS workshop brings together an interdisciplinary community of researchers and practitioners who share research on new recommender systems (informed by psychology), including new design technologies and evaluation methodologies, and aim to identify critical challenges and emerging topics in the field. This year we focused particularly on topics related to Human-Centered AI, Explainability of decision-making models, User-adaptive XAI systems, which are becoming more and more popular in the last years, especially in domains where recommended options might have ethical and legal impacts on users. The integration of XAI with recommender systems is crucial for enhancing their transparency, interpretability, and accountability. This topic attracted a lot of interest from the community, as demonstrated by the fact that several workshop papers proposed methods for recommendation explanations. Date : 18 September 2023. Website : https://intrs2023.wordpress.com.
Report on The Web Search Revolution: Symposium

This report describes a one-day symposium held at RMIT University that celebrated two milestone anniversaries in Web search: the launch in 1993 of JumpStation, the first modern web search engine; and the 25 th anniversary since Google was incorporated as a company in 1998. The report describes the keynote presentations from Microsoft Research leader, Susan Dumais and University of Washington professor, Chirag Shah as well as three panel sessions involving leading researchers across Australia in both academia and industry. The symposium explored the impact and future directions of the web search revolution. Date : 17 August 2023. Website : https://www.admscentre.org.au/event/web-search-revolution/.
Large Search Model: Redefining Search Stack in the Era of LLMs

Modern search engines are built on a stack of different components, including query understanding, retrieval, multi-stage ranking, and question answering, among others. These components are often optimized and deployed independently. In this paper, we introduce a novel conceptual framework called large search model , which redefines the conventional search stack by unifying search tasks with one large language model (LLM). All tasks are formulated as autoregressive text generation problems, allowing for the customization of tasks through the use of natural language prompts. This proposed framework capitalizes on the strong language understanding and reasoning capabilities of LLMs, offering the potential to enhance search result quality while simultaneously simplifying the existing cumbersome search stack. To substantiate the feasibility of this framework, we present a series of proof-of-concept experiments and discuss the potential challenges associated with implementing this approach within real-world search systems.
Report on the 13th Workshop on Temporal Web Analytics (TempWeb 2023) at WWW 2023

TempWeb is an established Workshop (series) with a long-standing tradition as a co-located event at The Web Conference. Considering the constantly evolving Web as a primary object of research, TempWeb provides a platform to present longitudinal studies on the Web (structure), its contents, and its communities, etc. by aiming at the investigation of infrastructures, scalable methods, and innovative software for aggregating, querying, and analyzing heterogeneous data at Web scale. Since longitudinal aspects in Web analytics is becoming more and more inter- and transdiciplinary relevant for analysts from various domains, the studies, tools, and demonstrations are not only limited to computer science, but also open to sociology, marketing, environmental studies, politics, etc., to name just a few. As such, TempWeb has developed as a forum for a community from academia and industry covering temporal Web analytics at all levels of granularity and across various disciplines. Date : 1 May 2023. Website : http://temporalweb.net/.
Report on the 3rd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023) at RecSys 2023

Evaluation is a central step when developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2023 workshop, held as part of the 17th ACM Conference on Recommender Systems (RecSys 2023), served as a forum where researchers from both academia and industry critically reflected on the evaluation of recommender systems. The goal of the PERSPECTIVES workshop series is to capture the current state of evaluation from different perspectives and discuss the different targets that recommender systems evaluation should strive for. In the third edition of the workshop, we discussed problems and lessons learned, and aimed to move the discourse forward within the community. Date : 19 September 2023. Website : https://perspectives-ws.github.io/2023/.
Evaluating Parrots and Sociopathic Liars: A keynote at ICTIR 2023

At ICTIR 2023, which took place on July 23, 2023 in Taipei, I talked about the dark sides of the Large Language Model (LLM) era, and a tentative framework for auditing LLM-based conversational search systems to protect the users from undesirable system responses. This extended abstract provides a short summary of the one-hour keynote and provides pointers to related resources. In addition, it mentions a few recent developments relevant to the talk. Date : 23 July 2023.
Report on the 14th Conference and Labs of the Evaluation Forum (CLEF 2023): Experimental IR Meets Multilinguality, Multimodality, and Interaction

This is a report on the fourteenth edition of the Conference and Labs of the Evaluation Forum (CLEF 2023), held on September 18--21, 2023, in Thessaloniki, Greece. CLEF was a four-day hybrid event combining a conference and an evaluation forum. The conference featured keynotes by Barbara Plank and Claudia Hauff, and presentation of peer-reviewed research papers covering a wide range of topics, in addition to many posters. The evaluation forum consisted of thirteen labs: BioASQ, CheckThat!, DocILE, eRisk, EXIST, iDPP, ImageCLEF, JokeR, LifeCLEF, LongEval, PAN, SimpleText, and Touché, addressing a wide range of tasks, media, languages, and ways to go beyond standard test collections. Date : 18--21 September, 2023. Website : https://clef2023.clef-initiative.eu/.
Report on the Workshop on Learning and Evaluating Recommendations with Impressions (LERI) at RecSys 2023

The Workshop on Learning and Evaluating Recommendations with Impressions (LERI) was held in conjunction with the 17th ACM Conference on Recommender Systems (RecSys 2023). The program included a keynote, a panel discussion and 7 paper presentations. The proceedings of the workshop are available online.1 The LERI workshop focused on all aspects related to the use of impressions for recommendation with the aim to bring the community together and share experience and perspectives. Recommender systems typically rely on past user interactions as the primary source of information for making predictions. Impressions are an important source of information that indicate the items displayed on screen when the user interacted (or not) with them, and have the potential to impact the field of recommender systems in several ways. Impressions present new research questions and opportunities, but also bring new challenges. Date : 19 September 2023. Website : https://recsyspolimi.github.io/leri2023/.
Report on the 13th Italian Information Retrieval Workshop (IIR 2023)

The 13th Italian Information Retrieval Workshop is the thirteenth edition of the annual conference of the Italian information retrieval and recommender systems communities. The two days of the conference gathered interesting studies and research work on a wide range of topics on information retrieval, recommender systems, and natural language processing, such as Search and Ranking, Recommendation, Content Analysis, and Classification, Artificial Intelligence, NLP, Semantics, and Dialog, Domain-Specific Applications, Human Factors and Interfaces, and Evaluation. It was hosted by the National Research Council (CNR) of Italy and the University of Pisa in a conference facility in Pisa, Italy. Date : 8--9 June 2023. Website : http://iir2023.isti.cnr.it/.
Report on the 1st Symposium on NLP for Social: Good (NSG 2023)

Artificial intelligence (AI), specifically, Natural Language Processing (NLP) is being hailed as a new breeding ground for immense innovation potential. Researchers believe that NLP-based technologies could help to solve societal issues such as equality and inclusion, education, health, and hunger, climate action etc., and many more. Tackling these questions requires a concerted, collaborative effort across all sectors of society. The first Symposium on NLP for Social Good (NSG) was a novel effort that aimed to enable NLP researchers and scholars from inter-disciplinary field who want to think about the societal implications of their work for solving humanitarian and environmental challenges. The objective of the symposium was to support fundamental research and engineering efforts and empower the social sector with tools and resources, while collaborating with partners from all sectors to maximise effect in solving problems within public health, nature &amp; society, accessibility, crisis response etc. In its inception, we invited speakers from academia and industry to provide an overview of some areas from NLP applications such as education, healthcare and legal domains in order to provide a platform to stimulate discussion regarding the current state of NLP in these varied fields. Date : 8--9 June 2023. Website : https://nlp4social.github.io/nlp4socialgood/.
Report on the 1st Workshop on Implicit Author Characterization from Texts for Search and Retrieval (IACT 2023) at SIGIR 2023

The first edition of the International Workshop on Implicit Author Characterization from Texts for Search and Retrieval (IACT'23) was held on July 27 th , 2023, in conjunction with the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) in Taipei, Taiwan. To support both online and in-person participation, the workshop was held as a hybrid event. Online participation was allowed using the EventX platform. During the course of the day, about 15 attendees had the opportunity to follow up and discuss the recent advances in topics related to identifying and extracting implicit information about authors (e.g., human or AI) from texts and using it in IR tasks. The workshop program included two invited keynotes, four paper presentations, and the report of the ColiE competition which was held as a part of the workshop1. The proceedings of the workshop are available online at https://ceur-ws.org/Vol-3477/. Date : 27 July 2023. Website : https://en.sce.ac.il/news/iact23.
Report on the 1st Workshop on Generative Information Retrieval (Gen-IR 2023) at SIGIR 2023

The first edition of the workshop on Generative Information Retrieval (Gen-IR 2023) took place in July 2023 in a hybrid fashion, co-located with the ACM SIGIR Conference 2023 in Taipei (SIGIR 2023). The aim was to bring information retrieval researchers together around the topic of generative AI that gathered attention in 2022 and 2023 with large language models and diffusion models. Given the novelty of the topic, the workshop was focused around multi-sided discussions, namely panels and poster sessions of the accepted proceedings papers. Two main research outcomes are the proceedings of the workshop1 and the potential research directions discussed in this report. Date : 27 July 2023. Website : https://coda.io/@sigir/gen-ir.
Tasks, Copilots, and the Future of Search: A Keynote at SIGIR 2023

Search is far from being a solved problem. While search engines may cope well with simple tasks, searchers and systems struggle as task complexity increases. Task is central to the search process, motivating the search and driving search behavior. Complex search tasks require more than support for rudimentary fact finding or re-finding. Various support options have been offered by search systems over time (e.g., query suggestions, contextual search) to help search engine users more effectively tackle complex tasks. The recent emergence of generative artificial intelligence (AI) and the arrival of assistive agents, or copilots , based on this technology, has the potential to offer further assistance to searchers, especially those engaged in complex tasks. The implications from these advances for the design of intelligent systems and for the future of search itself are significant. This overview of the keynote that I gave at the 2023 ACM SIGIR Conference introduces AI copilots and briefly presents some of the challenges and opportunities for researching, developing, and deploying search copilots. Date : 26 July 2023.
Analysis of Multi-Class Sentiment on Indonesian Twitter Using Support Vector Machine Classification Algorithm with Particle Swarm Optimization

With the rapid evolution of the internet, widespread interactions occur across diverse media platforms, including Twitter. Tweets shared on Twitter often need to be categorized into different sentiment polarities, often referred to as multi-class classification. This research aims to enhance the accuracy of sentiment classification utilizing the Support Vector Machine (SVM) algorithm through the application of Particle Swarm Optimization (PSO). PSO is employed to determine optimal hyperparameter values that maximize the performance of the SVM classification model. Two models were employed for testing: SVM classification alone and SVM classification integrated with PSO optimization. The accuracy of the SVM classification yielded 63.7%, while the combined SVM-PSO approach achieved 65.8%. Consequently, this approach led to a 2.1% accuracy enhancement following optimization through the PSO algorithm.