OpenRaiser/Intern-Atlas huggingface dataset:
Exact Error in Matrix Completion: Approximately Low-Rank Structures and Missing Blocks
Venue: arXiv cs.IT 2023
Year: 2023

We study the completion of approximately low rank matrices with entries missing not at random (MNAR). In the context of typical large-dimensional statistical settings, we establish a framework for the performance analysis of the nuclear norm minimization ($\ell_1^*$) algorithm. Our framework produces \emph{exact} estimates of the worst-case residual root mean squared error and the associated phase transitions (PT), with both exhibiting remarkably simple characterizations. Our results enable to {\it precisely} quantify the impact of key system parameters, including data heterogeneity, size of the missing block, and deviation from ideal low rankness, on the accuracy of $\ell_1^*$-based matrix completion. To validate our theoretical worst-case RMSE estimates, we conduct numerical simulations, demonstrating close agreement with their numerical counterparts.
A Survey of Personality, Persona, and Profile in Conversational Agents and Chatbots
Venue: arXiv cs.CL 2023
Year: 2023

We present a review of personality in neural conversational agents (CAs), also called chatbots. First, we define Personality, Persona, and Profile. We explain all personality schemes which have been used in CAs, and list models under the scheme(s) which they use. Second we describe 21 datasets which have been developed in recent CA personality research. Third, we define the methods used to embody personality in a CA, and review recent models using them. Fourth, we survey some relevant reviews on CAs, personality, and related topics. Finally, we draw conclusions and identify some research challenges for this important emerging field.
Towards Bridging the Gap between High-Level Reasoning and Execution on Robots
Venue: arXiv cs.AI 2023
Year: 2023

When reasoning about actions, e.g., by means of task planning or agent programming with Golog, the robot's actions are typically modeled on an abstract level, where complex actions such as picking up an object are treated as atomic primitives with deterministic effects and preconditions that only depend on the current state. However, when executing such an action on a robot it can no longer be seen as a primitive. Instead, action execution is a complex task involving multiple steps with additional temporal preconditions and timing constraints. Furthermore, the action may be noisy, e.g., producing erroneous sensing results and not always having the desired effects. While these aspects are typically ignored in reasoning tasks, they need to be dealt with during execution. In this thesis, we propose several approaches towards closing this gap.
Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond
Venue: arXiv cs.NI 2023
Year: 2023

A global effort has been initiated to reduce the worldwide greenhouse gas (GHG) emissions, primarily carbon emissions, by half by 2030 and reach net-zero by 2050. The development of 6G must also be compliant with this goal. Unfortunately, developing a sustainable and net-zero emission systems to meet the users' fast growing demands on mobile services, especially smart services and applications, may be much more challenging than expected. Particularly, despite the energy efficiency improvement in both hardware and software designs, the overall energy consumption and carbon emission of mobile networks are still increasing at a tremendous speed. The growing penetration of resource-demanding AI algorithms and solutions further exacerbate this challenge. In this article, we identify the major emission sources and introduce an evaluation framework for analyzing the lifecycle of network AI implementations. A novel joint dynamic energy trading and task allocation optimization framework, called DETA, has been introduced to reduce the overall carbon emissions. We consider a federated edge intelligence-based network AI system as a case study to verify the effectiveness of our proposed solution. Experimental results based on a hardware prototype suggest that our proposed solution can reduce carbon emissions of network AI systems by up to 74.9%. Finally, open problems and future directions are discussed.
An Interdisciplinary Survey on Information Flows in Supply Chains
Venue: arXiv cs.CR 2023
Year: 2023

Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010-2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points.
Security, extensibility, and redundancy in the Metabolic Operating System
Venue: arXiv cs.CR 2023
Year: 2023

People living with Type 1 Diabetes (T1D) lose the ability to produce insulin naturally. To compensate, they inject synthetic insulin. One common way to inject insulin is through automated insulin delivery systems, which use sensors to monitor their metabolic state and an insulin pump device to adjust insulin to adapt. In this paper, we present the Metabolic Operating System, a new automated insulin delivery system that we designed from the ground up using security first principles. From an architecture perspective, we apply separation principles to simplify the core system and isolate non-critical functionality from the core closed-loop algorithm. From an algorithmic perspective, we evaluate trends in insulin technology and formulate a simple, but effective, algorithm given the state-of-the-art. From a safety perspective, we build in multiple layers of redundancy to ensure that the person using our system remains safe. Fundamentally, this paper is a paper on real-world experiences building and running an automated insulin delivery system. We report on the design iterations we make based on experiences working with one individual using our system. Our evaluation shows that an automated insulin delivery system built from the ground up using security first principles can still help manage T1D effectively. Our source code is open source and available on GitHub (link omitted).
ALPC Is In Danger: ALPChecker Detects Spoofing and Blinding
Venue: arXiv cs.CR 2023
Year: 2023

The purpose of this study is to evaluate the possibility of implementing an attack on ALPC connection in the Windows operating system through the kernel without closing the connection covertly from programs and the operating system and to propose a method of protection against this type of attacks. Asynchronous Local Procedure Call technology (ALPC) is used in various Windows information protection systems, including antivirus systems (AV) and Endpoint Detection and Response systems (EDR). To ensure the concealment of malicious software, attackers need to disrupt the operation of AV, EDR tools, which in turn can be achieved by destructive impact on the components of the ALPC technology. Examples of such attacks already exist and are covered in this paper. To counteract such new threats, it is necessary to advance the improvement of information security systems and the ALPC security research was conducted. The most difficult case, Windows kernel driver attack, was considered. Three attacks on the ALPC connection were carried out, based on changing the ALPC structures in the kernel memory, which led to creation of illegitimate connections in the system and the disruption of correct connections. ALPChecker protection tool has been developed. The tool was successfully tested on three demonstrated attacks.
Using Terrestrial Laser Scanning, Unmanned Aerial Vehicles and Mixed Reality Methodologies for Digital Survey, 3D Modelling and Historical Recreation of Religious Heritage Monuments
Venue: arXiv cs.OH 2023
Year: 2023

Preserving and safeguarding the Cultural Heritage (CH) of our world from unforeseen hazards should be viewed as a collective responsibility for humanity. Consequently, there is a growing imperative for targeted measures aimed at conserving, rejuvenating, and safeguarding historical assets that carry cultural significance. In recent times, Terrestrial Laser Scanning (TLS), Unmanned Aerial Vehicle (UAV) Photogrammetry, and applications in Mixed Reality (MR) have assumed a pivotal role in the mapping, recording, preservation, and promotion of Cultural Heritage. This ar-ticle endeavors to present a comprehensive approach spanning from 3D surveying to the 3D representation and promotion of Religious Cultural Heritage, offering an overview of the applied methodologies. Through the integration of TLS and UAV photogrammetry techniques, a comprehensive digital record of Panagia Ekatontapyliani, the adjoining Church of Agios Nikolaos, and the Baptistery, along with their wall paintings (hagiographies) and natural surroundings, has been obtained. This record serves as the foundation for historical documentation and recreation using the HBIM concept, paving the way for the development of diverse Mixed Reality applications. These applications aim to enhance the visibility, accessibility, and visitability of the Monument.
Negation and Identity in a Modal Mode Theory
Venue: arXiv cs.LO 2023
Year: 2023

This piece threads substructurality and modality into a negation that activates the downside of equivalence and identity in a fibrational framework. The piece is a working through of negation and contradiction as type-theoretic/categorial objects, towards an immanent critique of the subtending univalent paradigm. Although this is not the terminus of the piece, i wish to try and delineate the epistemic and intra-mundane problematics intertwined therewith. The piece's terminus is a mode theory of an intuitionistic modal logic that internalises a restriction on the Double Negation Elimination rule.
Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks
Venue: arXiv cs.NE 2023
Year: 2023

Geometric Sensitive Hashing functions, a family of Local Sensitive Hashing functions, are neural network models that learn class-specific manifold geometry in supervised learning. However, given a set of supervised learning tasks, understanding the manifold geometries that can represent each task and the kinds of relationships between the tasks based on them has received little attention. We explore a formalization of this question by considering a generative process where each task is associated with a high-dimensional manifold, which can be done in brain-like models with neuromodulatory systems. Following this formulation, we define \emph{Task-specific Geometric Sensitive Hashing~(T-GSH)} and show that a randomly weighted neural network with a neuromodulation system can realize this function.
Trace and Edit Relation Associations in GPT
Venue: arXiv cs.CL 2023
Year: 2023

This study introduces a novel approach for analyzing and modifying entity relationships in GPT models, diverging from ROME's entity-focused methods. We develop a relation tracing technique to understand the influence of language model computations on relationship judgments. Using the FewRel dataset, we identify key roles of MLP modules and attention mechanisms in processing relationship information. Our method, tested against ROME on a new dataset, shows improved balance in specificity and generalization, underscoring the potential of manipulating early-layer modules for enhanced model understanding and accuracy.
Comparison of Microservice Call Rate Predictions for Replication in the Cloud
Venue: arXiv cs.DC 2023
Year: 2023

Today, many users deploy their microservice-based applications with various interconnections on a cluster of Cloud machines, subject to stochastic changes due to dynamic user requirements. To address this problem, we compare three machine learning (ML) models for predicting the microservice call rates based on the microservice times and aiming at estimating the scalability requirements. We apply the linear regression (LR), multilayer perception (MLP), and gradient boosting regression (GBR) models on the Alibaba microservice traces. The prediction results reveal that the LR model reaches a lower training time than the GBR and MLP models. However, the GBR reduces the mean absolute error and the mean absolute percentage error compared to LR and MLP models. Moreover, the prediction results show that the required number of replicas for each microservice by the gradient boosting model is close to the actual test data without any prediction.
Investigation of the efficiency of the moving least squares method in the reconstruction of a three-dimensional surface on a supercomputer
Venue: arXiv cs.DC 2023
Year: 2023

Currently, the area of geometric modeling and the construction of 3D models based on point clouds from laser sensors is actively developing. One of the basic tasks of geometric modeling is the reconstruction of a surface from a cloud of points. The aim of this work is to develop a parallel least-squares surface reconstruction method on a distributed memory supercomputer that allows achieving optimal results both in scalability and surface reconstruction quality. The focus of the work is on the surface reconstruction algorithm based on the least squares method, in connection with which the algorithm was called the moving least squares method.
Recent developments of selective laser processes for wearable devices
Venue: arXiv cs.HC 2023
Year: 2023

Recently, the growing interest in wearable technology for personal healthcare and smart VR/AR applications newly imposed a need for development of facile fabrication method. Regarding the issue, laser has long been proposing original answers to such challenging technological demands with its remote, sterile, rapid, and site-selective processing characteristics for arbitrary materials. In this review, recent developments in relevant laser processes are summarized in two separate categories. Firstly, transformative approaches represented by laser-induced graphene (LIG) are introduced. Apart from design optimization and alteration of native substrate, latest advancements in the transformative approach now enable not only more complex material compositions but also multilayer device configurations by simultaneous transformation of heterogeneous precursor or sequential addition of functional layers coupled with other electronic elements. Besides, more conventional laser techniques such as ablation, sintering and synthesis are still accessible for enhancing the functionality of the entire system through expansion of applicable materials and adoption of new mechanisms. Various wearable device components developed through the corresponding laser processes are then organized with emphasis on chemical/physical sensors and energy devices. At the same time, special attention is given to the applications utilizing multiple laser sources or multiple laser processes, which pave the way towards all-laser fabrication of wearable devices.
A Holistic Approach on Smart Garment for Patients with Juvenile Idiopathic Arthritis
Venue: arXiv cs.HC 2023
Year: 2023

Juvenile Idiopathic Arthritis (JIA) is a widespread and chronic condition that affects children and adolescents worldwide. The person suffering from JIA is characterized by chronic joint inflammation leading to pain, swelling, stiffness, and limited body movements. Individuals suffering from JIA require ongoing treatment for their lifetime. Beyond inflammation, JIA patients have expressed concerns about various factors and the lack of responsive services addressing their challenges. The implementation of smart garments offers a promising solution to assist individuals with Juvenile Idiopathic Arthritis in performing their daily activities. These garments are designed to seamlessly integrate technology and clothing, providing not only physical support but also addressing the psychological and emotional aspects of living with a chronic condition. By incorporating sensors, these smart garments can monitor joint movement, detect inflammation, and provide real-time feedback to both patients and healthcare providers. To tackle these comprehensive challenges, the research aims to offer a solution through the design of a smart garment, created with a holistic approach. This smart garment is intended to improve the overall well-being of JIA patients by enhancing their mobility, comfort, and overall quality of life. The integration of technology into clothing can potentially revolutionize the way JIA is managed, allowing patients to better manage their condition and minimize its impact on their daily lives. The synergy between healthcare and technology holds great potential in addressing the multifaceted challenges posed by Juvenile Idiopathic Arthritis patients. Through innovation and empathy, this research aims to pave the way for a brighter future for individuals living with Juvenile Idiopathic Arthritis.
U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural Accelerators
Venue: arXiv cs.AR 2023
Year: 2023

Architectures that incorporate Computing-in-Memory (CiM) using emerging non-volatile memory (NVM) devices have become strong contenders for deep neural network (DNN) acceleration due to their impressive energy efficiency. Yet, a significant challenge arises when using these emerging devices: they can show substantial variations during the weight-mapping process. This can severely impact DNN accuracy if not mitigated. A widely accepted remedy for imperfect weight mapping is the iterative write-verify approach, which involves verifying conductance values and adjusting devices if needed. In all existing publications, this procedure is applied to every individual device, resulting in a significant programming time overhead. In our research, we illustrate that only a small fraction of weights need this write-verify treatment for the corresponding devices and the DNN accuracy can be preserved, yielding a notable programming acceleration. Building on this, we introduce USWIM, a novel method based on the second derivative. It leverages a single iteration of forward and backpropagation to pinpoint the weights demanding write-verify. Through extensive tests on diverse DNN designs and datasets, USWIM manifests up to a 10x programming acceleration against the traditional exhaustive write-verify method, all while maintaining a similar accuracy level. Furthermore, compared to our earlier SWIM technique, USWIM excels, showing a 7x speedup when dealing with devices exhibiting non-uniform variations.
GeoAI in Social Science
Venue: arXiv cs.CY 2023
Year: 2023

GeoAI, or geospatial artificial intelligence, is an exciting new area that leverages artificial intelligence (AI), geospatial big data, and massive computing power to solve problems with high automation and intelligence. This paper reviews the progress of AI in social science research, highlighting important advancements in using GeoAI to fill critical data and knowledge gaps. It also discusses the importance of breaking down data silos, accelerating convergence among GeoAI research methods, as well as moving GeoAI beyond geospatial benefits.
The Key Artificial Intelligence Technologies in Early Childhood Education: A Review
Venue: arXiv cs.CY 2023
Year: 2023

Artificial Intelligence (AI) technologies have been applied in various domains, including early childhood education (ECE). Integration of AI educational technology is a recent significant trend in ECE. Currently, there are more and more studies of AI in ECE. To date, there is a lack of survey articles that discuss the studies of AI in ECE. In this paper, we provide an up-to-date and in-depth overview of the key AI technologies in ECE that provides a historical perspective, summarizes the representative works, outlines open questions, discusses the trends and challenges through a detailed bibliometric analysis, and provides insightful recommendations for future research. We mainly discuss the studies that apply AI-based robots and AI technologies to ECE, including improving the social interaction of children with an autism spectrum disorder. This paper significantly contributes to provide an up-to-date and in-depth survey that is suitable as introductory material for beginners to AI in ECE, as well as supplementary material for advanced users.
The Role of Generative AI in Global Diplomatic Practices: A Strategic Framework
Venue: arXiv cs.CY 2023
Year: 2023

As Artificial Intelligence (AI) transforms the domain of diplomacy in the 21st century, this research addresses the pressing need to evaluate the dualistic nature of these advancements, unpacking both the challenges they pose and the opportunities they offer. It has been almost a year since the launch of ChatGPT by OpenAI that revolutionised various work domains with its capabilities. The scope of application of these capabilities to diplomacy is yet to be fully explored or understood. Our research objective is to systematically examine the current discourse on Digital and AI Diplomacy, thus informing the development of a comprehensive framework for the role of Generative AI in modern diplomatic practices. Through the systematic analysis of 230 scholarly articles, we identified a spectrum of opportunities and challenges, culminating in a strategic framework that captures the multifaceted concepts for integration of Generative AI, setting a course for future research and innovation in diplomacy.
Trustworthy human-centric based Automated Decision-Making Systems
Venue: arXiv cs.CY 2023
Year: 2023

Automated Decision-Making Systems (ADS) have become pervasive across various fields, activities, and occupations, to enhance performance. However, this widespread adoption introduces potential risks, including the misuse of ADS. Such misuse may manifest when ADS is employed in situations where it is unnecessary or when essential requirements, conditions, and terms are overlooked, leading to unintended consequences. This research paper presents a thorough examination of the implications, distinctions, and ethical considerations associated with digitalization, digital transformation, and the utilization of ADS in contemporary society and future contexts. Emphasis is placed on the imperative need for regulation, transparency, and ethical conduct in the deployment of ADS.
The "Pac-Man'' Gripper: Tactile Sensing and Grasping through Thin-Shell Buckling
Venue: arXiv cs.RO 2023
Year: 2023

Soft and lightweight grippers have greatly enhanced the performance of robotic manipulators in handling complex objects with varying shape, texture, and stiffness. However, the combination of universal grasping with passive sensing capabilities still presents challenges. To overcome this limitation, we introduce a fluidic soft gripper, named the ``Pac-Man'' gripper, based on the buckling of soft, thin hemispherical shells. Leveraging a single fluidic pressure input, the soft gripper can encapsulate slippery and delicate objects while passively providing information on this physical interaction. Guided by analytical, numerical, and experimental tools, we explore the novel grasping principle of this mechanics-based soft gripper. First, we characterize the buckling behavior of a free hemisphere as a function of its geometric parameters. Inspired by the free hemisphere's two-lobe mode shape ideal for grasping purposes, we demonstrate that the gripper can perform dexterous manipulation and gentle gripping of fragile objects in confined environments. Last, we prove the soft gripper's embedded capability of detecting contact, grasping, and release conditions during the interaction with an unknown object. This simple buckling-based soft gripper opens new avenues for the design of adaptive gripper morphologies with applications ranging from medical and agricultural robotics to space and underwater exploration.