OpenRaiser/Intern-Atlas huggingface dataset:
Single-Server Pliable Private Information Retrieval with Identifiable Side Information
Venue: arXiv cs.IT 2024
Year: 2024

In Pliable Private Information Retrieval (PPIR) with a single server, messages are partitioned into $Γ$ non-overlapping classes. The user wants to retrieve a message from its desired class without revealing the identity of the desired class to the server. In S. A. Obead, H. Y. Lin and E. Rosnes, Single-Server Pliable Private Information Retrieval With Side Information, arXiv:2305.06857, authors consider the problem of PPIR with Side Information (PPIR-SI), where the user now has side information. The user wants to retrieve any new message (not included in the side information) from its desired class without revealing the identity of the desired class and its side information. A scheme for the PPIR-SI is given by Obead et al. for the case when the users side information is unidentified, and this case is referred to as PPIR with Unidentifiable SI (PPIR-USI). In this paper, we study the problem of PPIR for the single server case when the side information is partially identifiable, and we term this case as PPIR with Identifiable Side Information (PPIR-ISI). The user is well aware of the identity of the side information belonging to $η$ number of classes, where $1\leq η\leq Γ$. In this problem, The user wants to retrieve a message from its desired class without revealing the identity of the desired class to the server. We give a scheme for PPIR-ISI, and we prove that having identifiable side information is advantageous by comparing the rate of the proposed scheme to the rate of the PPIR-USI scheme given by Obead et al. for some cases. Further, we extend the problem of PPIR-ISI for multi-user case, where users can collaborately generate the query sets, and we give a scheme for this problem.
A Data-to-Product Multimodal Conceptual Framework to Achieve Automated Software Evolution for Context-rich Intelligent Applications
Venue: arXiv cs.SE 2024
Year: 2024

While AI is extensively transforming Software Engineering (SE) fields, SE is still in need of a framework to overall consider all phases to facilitate Automated Software Evolution (ASEv), particularly for intelligent applications that are context-rich, instead of conquering each division independently. Its complexity comes from the intricacy of the intelligent applications, the heterogeneity of the data sources, and the constant changes in the context. This study proposes a conceptual framework for achieving automated software evolution, emphasizing the importance of multimodality learning. A Selective Sequential Scope Model (3S) model is developed based on the conceptual framework, and it can be used to categorize existing and future research when it covers different SE phases and multimodal learning tasks. This research is a preliminary step toward the blueprint of a higher-level ASEv. The proposed conceptual framework can act as a practical guideline for practitioners to prepare themselves for diving into this area. Although the study is about intelligent applications, the framework and analysis methods may be adapted for other types of software as AI brings more intelligence into their life cycles.
GATlab: Modeling and Programming with Generalized Algebraic Theories
Venue: arXiv cs.LO 2024
Year: 2024

Categories and categorical structures are increasingly recognized as useful abstractions for modeling in science and engineering. To uniformly implement category-theoretic mathematical models in software, we introduce GATlab, a domain-specific language for algebraic specification embedded in a technical programming language. GATlab is based on generalized algebraic theories (GATs), a logical system extending algebraic theories with dependent types so as to encompass category theory. Using GATlab, the programmer can specify generalized algebraic theories and their models, including both free models, based on symbolic expressions, and computational models, defined by arbitrary code in the host language. Moreover, the programmer can define maps between theories and use them to declaratively migrate models of one theory to models of another. In short, GATlab aims to provide a unified environment for both computer algebra and software interface design with generalized algebraic theories. In this paper, we describe the design, implementation, and applications of GATlab.
AI for DevSecOps: A Landscape and Future Opportunities
Venue: arXiv cs.SE 2024
Year: 2024

DevOps has emerged as one of the most rapidly evolving software development paradigms. With the growing concerns surrounding security in software systems, the DevSecOps paradigm has gained prominence, urging practitioners to incorporate security practices seamlessly into the DevOps workflow. However, integrating security into the DevOps workflow can impact agility and impede delivery speed. Recently, the advancement of artificial intelligence (AI) has revolutionized automation in various software domains, including software security. AI-driven security approaches, particularly those leveraging machine learning or deep learning, hold promise in automating security workflows. They reduce manual efforts, which can be integrated into DevOps to ensure uninterrupted delivery speed and align with the DevSecOps paradigm simultaneously. This paper seeks to contribute to the critical intersection of AI and DevSecOps by presenting a comprehensive landscape of AI-driven security techniques applicable to DevOps and identifying avenues for enhancing security, trust, and efficiency in software development processes. We analyzed 99 research papers spanning from 2017 to 2023. Specifically, we address two key research questions (RQs). In RQ1, we identified 12 security tasks associated with the DevSecOps process and reviewed existing AI-driven security approaches, the problems they addressed, and the 65 benchmarks used to evaluate those approaches. Drawing insights from our findings, in RQ2, we discussed state-of-the-art AI-driven security approaches, highlighted 15 challenges in existing research, and proposed 15 corresponding avenues for future opportunities.
Unveiling Decentralization: A Comprehensive Review of Technologies, Comparison, Challenges in Bitcoin, Ethereum, and Solana Blockchain
Venue: arXiv cs.CR 2024
Year: 2024

Bitcoin stands as a groundbreaking development in decentralized exchange throughout human history, enabling transactions without the need for intermediaries. By leveraging cryptographic proof mechanisms, Bitcoin eliminates the reliance on third-party financial institutions. Ethereum, ranking as the second-largest cryptocurrency by market capitalization, builds upon Bitcoin's groundwork by introducing smart contracts and decentralized applications. Ethereum strives to surpass the limitations of Bitcoin's scripting language, achieving full Turing-completeness for executing intricate computational tasks. Solana introduces a novel architecture for high-performance blockchain, employing timestamps to validate decentralized transactions and significantly boosting block creation throughput. Through a comprehensive examination of these blockchain technologies, their distinctions, and the associated challenges, this paper aims to offer valuable insights and comparative analysis for both researchers and practitioners.
How Many Languages Make Good Multilingual Instruction Tuning? A Case Study on BLOOM
Venue: arXiv cs.CL 2024
Year: 2024

Instruction tuning a large language model with multiple languages can prepare it for multilingual downstream tasks. Nonetheless, it is yet to be determined whether having a handful of languages is sufficient, or whether the benefits increase with the inclusion of more. By fine-tuning large multilingual models on 1 to 52 languages, we present a case study on BLOOM to understand three pertinent factors affecting performance: the number of languages, language exposure, and similarity between training and test languages. Overall we found that 1) expanding language coverage in multilingual instruction tuning proves to be beneficial; 2) accuracy often significantly boots if the test language appears in the instruction mixture; 3) languages' genetic features correlate with cross-lingual transfer more than merely the number of language but different languages benefit to various degrees.
Contextual Chart Generation for Cyber Deception
Venue: arXiv cs.LG 2024
Year: 2024

Honeyfiles are security assets designed to attract and detect intruders on compromised systems. Honeyfiles are a type of honeypot that mimic real, sensitive documents, creating the illusion of the presence of valuable data. Interaction with a honeyfile reveals the presence of an intruder, and can provide insights into their goals and intentions. Their practical use, however, is limited by the time, cost and effort associated with manually creating realistic content. The introduction of large language models has made high-quality text generation accessible, but honeyfiles contain a variety of content including charts, tables and images. This content needs to be plausible and realistic, as well as semantically consistent both within honeyfiles and with the real documents they mimic, to successfully deceive an intruder. In this paper, we focus on an important component of the honeyfile content generation problem: document charts. Charts are ubiquitous in corporate documents and are commonly used to communicate quantitative and scientific data. Existing image generation models, such as DALL-E, are rather prone to generating charts with incomprehensible text and unconvincing data. We take a multi-modal approach to this problem by combining two purpose-built generative models: a multitask Transformer and a specialized multi-head autoencoder. The Transformer generates realistic captions and plot text, while the autoencoder generates the underlying tabular data for the plot. To advance the field of automated honeyplot generation, we also release a new document-chart dataset and propose a novel metric Keyword Semantic Matching (KSM). This metric measures the semantic consistency between keywords of a corpus and a smaller bag of words. Extensive experiments demonstrate excellent performance against multiple large language models, including ChatGPT and GPT4.
A Bird-Eye view on DNA Storage Simulators
Venue: arXiv cs.IT 2024
Year: 2024

In the current world due to the huge demand for storage, DNA-based storage solution sounds quite promising because of their longevity, low power consumption, and high capacity. However in real life storing data in the form of DNA is quite expensive, and challenging. Therefore researchers and developers develop such kind of software that helps simulate real-life DNA storage without worrying about the cost. This paper aims to review some of the software that performs DNA storage simulations in different domains. The paper also explains the core concepts such as synthesis, sequencing, clustering, reconstruction, GC window, K-mer window, etc and some overview on existing algorithms. Further, we present 3 different softwares on the basis of domain, implementation techniques, and customer/commercial usability.
Optimizing Information Propagation for Blockchain-empowered Mobile AIGC: A Graph Attention Network Approach
Venue: arXiv cs.CR 2024
Year: 2024

Artificial Intelligence-Generated Content (AIGC) is a rapidly evolving field that utilizes advanced AI algorithms to generate content. Through integration with mobile edge networks, mobile AIGC networks have gained significant attention, which can provide real-time customized and personalized AIGC services and products. Since blockchains can facilitate decentralized and transparent data management, AIGC products can be securely managed by blockchain to avoid tampering and plagiarization. However, the evolution of blockchain-empowered mobile AIGC is still in its nascent phase, grappling with challenges such as improving information propagation efficiency to enable blockchain-empowered mobile AIGC. In this paper, we design a Graph Attention Network (GAT)-based information propagation optimization framework for blockchain-empowered mobile AIGC. We first innovatively apply age of information as a data-freshness metric to measure information propagation efficiency in public blockchains. Considering that GATs possess the excellent ability to process graph-structured data, we utilize the GAT to obtain the optimal information propagation trajectory. Numerical results demonstrate that the proposed scheme exhibits the most outstanding information propagation efficiency compared with traditional routing mechanisms.
The Spatial Structures in the Austrian COVID-19 Protest Movement: A Virtual and Geospatial Twitter User Network Analysis
Venue: arXiv cs.SI 2024
Year: 2024

The emergence of the COVID-19 pandemic, followed by policy measures to combat the virus, evoked public protest movements world-wide. These movements emerged through virtual social networks as well as local protest gatherings. Prior research has studied such movements solely in the virtual space through social network analysis, thereby disregarding the role of local interaction for protest. This study, however, recognizes the importance of the geo-spatial dimension in protest movements. We therefore introduce a large-scale spatial-social network analysis of a georeferenced Twitter user network to understand the regional connections and transnational influences of the Austrian COVID-19 protest movement through the social network. Our findings reveal that the virtual network is distinctly structured along geographic and linguistic boundaries. We further find that the movement is clearly organized along national protest communities. These results highlight the importance of regional and local influencing factors over the impact of transnational influences for the protest movement.
The Impact of Virtual Laboratories on Active Learning and Engagement in Cybersecurity Distance Education
Venue: arXiv cs.CY 2024
Year: 2024

Virtual Laboratories (V Labs) have in the recent past become part and parcel of remote teaching in practical hands-on approaches, particularly in Cybersecurity distance courses. Their potential is meant to assist learners with hands-on practical laboratory exercises irrespective of geographical location. Nevertheless, adopting V Labs in didactic approaches in higher education has seen both merits and demerits. Based on this premise, this study investigates the impact of V Labs on Active Learning (AL) and engagement in cybersecurity distance education. A survey with a limited number of learners and educators who have had an experience with cybersecurity distance courses that leveraged V Labs in their practical Lab assignment, was conducted at Blekinge Tekniska Högskola, Sweden, to assess the impact of V Labs on AL and engagement in Cybersecurity Distance Education. 29% and 73% of the learners and educators, respectively responded to the survey administered remotely and with good internal consistency of questionnaires based on the Cronbalch Alpha; the results showed that learners and educators had a positive perception of using V Labs to enhance AL in cybersecurity distance education. The key concentration of the study was on AL and engagement and problem-solving abilities when V Labs are used. Both the learners and educators found the V Labs to be engaging, interactive, and effective in improving their understanding of cybersecurity concepts.
SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) are at the forefront of NLP achievements but fall short in dealing with shortcut learning, factual inconsistency, and vulnerability to adversarial inputs.These shortcomings are especially critical in medical contexts, where they can misrepresent actual model capabilities. Addressing this, we present SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for ClinicalTrials. Our contributions include the refined NLI4CT-P dataset (i.e., Natural Language Inference for Clinical Trials - Perturbed), designed to challenge LLMs with interventional and causal reasoning tasks, along with a comprehensive evaluation of methods and results for participant submissions. A total of 106 participants registered for the task contributing to over 1200 individual submissions and 25 system overview papers. This initiative aims to advance the robustness and applicability of NLI models in healthcare, ensuring safer and more dependable AI assistance in clinical decision-making. We anticipate that the dataset, models, and outcomes of this task can support future research in the field of biomedical NLI. The dataset, competition leaderboard, and website are publicly available.
Towards Developing Brain-Computer Interfaces for People with Multiple Sclerosis
Venue: arXiv cs.HC 2024
Year: 2024

Multiple Sclerosis (MS) is a severely disabling condition that leads to various neurological symptoms. A Brain-Computer Interface (BCI) may substitute some lost function; however, there is a lack of BCI research in people with MS. To progress this research area effectively and efficiently, we aimed to evaluate user needs and assess the feasibility and user-centric requirements of a BCI for people with MS. We conducted an online survey of 34 people with MS to qualitatively assess user preferences and establish the initial steps of user-centred design. The survey aimed to understand their interest and preferences in BCI and bionic applications. We demonstrated widespread interest for BCI applications in all stages of MS, with a preference for a non-invasive (n = 12) or minimally invasive (n = 15) BCI over carer assistance (n = 6). Qualitative assessment indicated that this preference was not influenced by level of independence. Additionally, strong interest was noted in bionic technology for sensory and autonomic functions. Considering the potential to enhance independence and quality of life for people living with MS, the results emphasise the importance of user-centred design for future advancement of BCIs that account for the unique pathological changes associated with MS.
How to characterize imprecision in multi-view clustering?
Venue: arXiv cs.LG 2024
Year: 2024

It is still challenging to cluster multi-view data since existing methods can only assign an object to a specific (singleton) cluster when combining different view information. As a result, it fails to characterize imprecision of objects in overlapping regions of different clusters, thus leading to a high risk of errors. In this paper, we thereby want to answer the question: how to characterize imprecision in multi-view clustering? Correspondingly, we propose a multi-view low-rank evidential c-means based on entropy constraint (MvLRECM). The proposed MvLRECM can be considered as a multi-view version of evidential c-means based on the theory of belief functions. In MvLRECM, each object is allowed to belong to different clusters with various degrees of support (masses of belief) to characterize uncertainty when decision-making. Moreover, if an object is in the overlapping region of several singleton clusters, it can be assigned to a meta-cluster, defined as the union of these singleton clusters, to characterize the local imprecision in the result. In addition, entropy-weighting and low-rank constraints are employed to reduce imprecision and improve accuracy. Compared to state-of-the-art methods, the effectiveness of MvLRECM is demonstrated based on several toy and UCI real datasets.
Reduction of Forgetting by Contextual Variation During Encoding Using 360-Degree Video-Based Immersive Virtual Environments
Venue: arXiv cs.HC 2024
Year: 2024

Recall impairment in a different environmental context from learning is called context-dependent forgetting. Two learning methods have been proposed to prevent context-dependent forgetting: reinstatement and decontextualization. Reinstatement matches the environmental context between learning and retrieval, whereas decontextualization involves repeated learning in various environmental contexts and eliminates the context dependency of memory. Conventionally, these methods have been validated by switching between physical rooms. However, in this study, we use immersive virtual environments (IVEs) as the environmental context assisted by virtual reality (VR), which is known for its low cost and high reproducibility compared to traditional manipulation. Whereas most existing studies using VR have failed to reveal the reinstatement effect, we test its occurrence using a 360-degree video-based IVE with improved familiarity and realism instead of a computer graphics-based IVE. Furthermore, we are the first to address decontextualization using VR. Our experiment showed that repeated learning in the same constant IVE as retrieval did not significantly reduce forgetting compared to repeated learning in different constant IVEs. Conversely, repeated learning in various IVEs significantly reduced forgetting than repeated learning in constant IVEs. These findings contribute to the design of IVEs for VR-based applications, particularly in educational settings.
A Hybrid Execution Environment for Computer-Interpretable Guidelines in PROforma
Venue: arXiv cs.SE 2024
Year: 2024

In this paper, we share our experience of developing a hybrid execution environment for computer-interpretable guidelines (CIGs) in PROforma. The proposed environment is part of the CAPABLE system which provides coaching for cancer patients and decision support for physicians. It extends a standard PROforma execution engine - Deontics Engine (DE) - with additional components that act as wrappers around DE, allow handling of non-standard tasks, and facilitate integration with the rest of the CAPABLE system. This yields a hybrid environment in which the standard engine and specialized components must be interfaced together by some intervening layer. In the CAPABLE system this has been achieved by defining a set of specialized meta-properties which are attached to data and tasks in the PROforma CIGs to specify the interface between engine and components.
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
Venue: arXiv cs.CV 2024
Year: 2024

In hematology, computational models offer significant potential to improve diagnostic accuracy, streamline workflows, and reduce the tedious work of analyzing single cells in peripheral blood or bone marrow smears. However, clinical adoption of computational models has been hampered by the lack of generalization due to large batch effects, small dataset sizes, and poor performance in transfer learning from natural images. To address these challenges, we introduce DinoBloom, the first foundation model for single cell images in hematology, utilizing a tailored DINOv2 pipeline. Our model is built upon an extensive collection of 13 diverse, publicly available datasets of peripheral blood and bone marrow smears, the most substantial open-source cohort in hematology so far, comprising over 380,000 white blood cell images. To assess its generalization capability, we evaluate it on an external dataset with a challenging domain shift. We show that our model outperforms existing medical and non-medical vision models in (i) linear probing and k-nearest neighbor evaluations for cell-type classification on blood and bone marrow smears and (ii) weakly supervised multiple instance learning for acute myeloid leukemia subtyping by a large margin. A family of four DinoBloom models (small, base, large, and giant) can be adapted for a wide range of downstream applications, be a strong baseline for classification problems, and facilitate the assessment of batch effects in new datasets. All models are available at github.com/marrlab/DinoBloom.