OpenRaiser/Intern-Atlas huggingface dataset:
Formalization of Biological Circuit Block Diagrams for formally analyzing Biomedical Control Systems in pHRI Applications
Venue: arXiv cs.LO 2024
Year: 2024

The control of Biomedical Systems in Physical Human-Robot Interaction (pHRI) plays a pivotal role in achieving the desired behavior by ensuring the intended transfer function and stability of subsystems within the overall system. Traditionally, the control aspects of biomedical systems have been analyzed using manual proofs and computer based analysis tools. However, these approaches provide inaccurate results due to human error in manual proofs and unverified algorithms and round-off errors in computer-based tools. We argue using Interactive reasoning, or frequently called theorem proving, to analyze control systems of biomedical engineering applications, specifically in the context of Physical Human-Robot Interaction (pHRI). Our methodology involves constructing mathematical models of the control components using Higher-order Logic (HOL) and analyzing them through deductive reasoning in the HOL Light theorem prover. We propose to model these control systems in terms of their block diagram representations, which in turn utilize the corresponding differential equations and their transfer function-based representation using the Laplace Transform (LT). These formally represented block diagrams are then analyzed through logical reasoning in the trusted environment of a theorem prover to ensure the correctness of the results. For illustration, we present a real-world case study by analyzing the control system of the ultrafilteration dialysis process.
Optimizing Speech-Input Length for Speaker-Independent Depression Classification
Venue: arXiv cs.CL 2024
Year: 2024

Machine learning models for speech-based depression classification offer promise for health care applications. Despite growing work on depression classification, little is understood about how the length of speech-input impacts model performance. We analyze results for speaker-independent depression classification using a corpus of over 1400 hours of speech from a human-machine health screening application. We examine performance as a function of response input length for two NLP systems that differ in overall performance. Results for both systems show that performance depends on natural length, elapsed length, and ordering of the response within a session. Systems share a minimum length threshold, but differ in a response saturation threshold, with the latter higher for the better system. At saturation it is better to pose a new question to the speaker, than to continue the current response. These and additional reported results suggest how applications can be better designed to both elicit and process optimal input lengths for depression classification.
Optimal design of triply-periodic minimal surface implants for bone repair
Venue: arXiv cs.CE 2024
Year: 2024

This work proposes a gradient-based method to design bone implants using triply-periodic minimal surfaces (TPMS) of spatially varying thickness to maximize bone in-growth. Bone growth into the implant is estimated using a finite element based mechanobiological model considering the magnitude and frequency of in vivo loads, as well as the density distribution of the surrounding bone. The wall thicknesses of the implant unit cells are determined via linear interpolation of the thicknesses over a user defined grid of control points, avoiding mesh dependency and providing control over the sensitivity computation costs. The TPMS structure is modeled as a homogenized material to reduce computational cost. Local properties of the implant are determined at run-time on an element-by-element basis using a pre-constructed surrogate model of the TPMS's physical and geometric properties as a function of the local wall thickness and the density of in-grown bone. Design sensitivities of the bone growth within the implant are computed using the direct sensitivity method. The methodology is demonstrated on a cementless hip, optimizing the implant for bone growth subject to wall thickness constraints to ensure manufacturability and allow cell infiltration.
Toward Corpus Size Requirements for Training and Evaluating Depression Risk Models Using Spoken Language
Venue: arXiv cs.CL 2024
Year: 2024

Mental health risk prediction is a growing field in the speech community, but many studies are based on small corpora. This study illustrates how variations in test and train set sizes impact performance in a controlled study. Using a corpus of over 65K labeled data points, results from a fully crossed design of different train/test size combinations are provided. Two model types are included: one based on language and the other on speech acoustics. Both use methods current in this domain. An age-mismatched test set was also included. Results show that (1) test sizes below 1K samples gave noisy results, even for larger training set sizes; (2) training set sizes of at least 2K were needed for stable results; (3) NLP and acoustic models behaved similarly with train/test size variations, and (4) the mismatched test set showed the same patterns as the matched test set. Additional factors are discussed, including label priors, model strength and pre-training, unique speakers, and data lengths. While no single study can specify exact size requirements, results demonstrate the need for appropriately sized train and test sets for future studies of mental health risk prediction from speech and language.
Applying Graph Explanation to Operator Fusion
Venue: arXiv cs.LG 2024
Year: 2024

Layer fusion techniques are critical to improving the inference efficiency of deep neural networks (DNN) for deployment. Fusion aims to lower inference costs by reducing data transactions between an accelerator's on-chip buffer and DRAM. This is accomplished by grouped execution of multiple operations like convolution and activations together into single execution units - fusion groups. However, on-chip buffer capacity limits fusion group size and optimizing fusion on whole DNNs requires partitioning into multiple fusion groups. Finding the optimal groups is a complex problem where the presence of invalid solutions hampers traditional search algorithms and demands robust approaches. In this paper we incorporate Explainable AI, specifically Graph Explanation Techniques (GET), into layer fusion. Given an invalid fusion group, we identify the operations most responsible for group invalidity, then use this knowledge to recursively split the original fusion group via a greedy tree-based algorithm to minimize DRAM access. We pair our scheme with common algorithms and optimize DNNs on two types of layer fusion: Line-Buffer Depth First (LBDF) and Branch Requirement Reduction (BRR). Experiments demonstrate the efficacy of our scheme on several popular and classical convolutional neural networks like ResNets and MobileNets. Our scheme achieves over 20% DRAM Access reduction on EfficientNet-B3.
Leaf diseases detection using deep learning methods
Venue: arXiv cs.LG 2024
Year: 2024

This study, our main topic is to devlop a new deep-learning approachs for plant leaf disease identification and detection using leaf image datasets. We also discussed the challenges facing current methods of leaf disease detection and how deep learning may be used to overcome these challenges and enhance the accuracy of disease detection. Therefore, we have proposed a novel method for the detection of various leaf diseases in crops, along with the identification and description of an efficient network architecture that encompasses hyperparameters and optimization methods. The effectiveness of different architectures was compared and evaluated to see the best architecture configuration and to create an effective model that can quickly detect leaf disease. In addition to the work done on pre-trained models, we proposed a new model based on CNN, which provides an efficient method for identifying and detecting plant leaf disease. Furthermore, we evaluated the efficacy of our model and compared the results to those of some pre-trained state-of-the-art architectures.
BundleFit: Display and See-Through Models for Augmented Reality Head-Mounted Displays
Venue: arXiv cs.GR 2024
Year: 2024

The head-mounted display is a vital component of augmented reality, incorporating optics with complex display and see-through optical behavior. Computationally modeling these optical behaviors requires meeting three key criteria: accuracy, efficiency, and accessibility. In recent years, various approaches have been proposed to model display and see-through optics, which can broadly be classified into black-box and white-box models. However, both categories face significant limitations that hinder their adoption in commercial applications. To overcome these challenges, we leveraged prior knowledge of ray bundle properties outside the optical hardware and proposed a novel bundle-fit-based model. In this approach, the ray paths within the optics are treated as a black box, while a lightweight optimization problem is solved to fit the ray bundle outside the optics. This method effectively addresses the accuracy issues of black-box models and the accessibility challenges of white-box models. Although our model involves runtime optimization, this is typically not a concern, as it can use the solution from a previous query to initialize the optimization for the current query. We evaluated the performance of our proposed method through both simulations and experiments on real hardware, demonstrating its effectiveness.
Machine Learning and Deep Learning Techniques used in Cybersecurity and Digital Forensics: a Review
Venue: arXiv cs.CR 2024
Year: 2024

In the paced realms of cybersecurity and digital forensics machine learning (ML) and deep learning (DL) have emerged as game changing technologies that introduce methods to identify stop and analyze cyber risks. This review presents an overview of the ML and DL approaches used in these fields showcasing their advantages drawbacks and possibilities. It covers a range of AI techniques used in spotting intrusions in systems and classifying malware to prevent cybersecurity attacks, detect anomalies and enhance resilience. This study concludes by highlighting areas where further research is needed and suggesting ways to create transparent and scalable ML and DL solutions that are suited to the evolving landscape of cybersecurity and digital forensics.
LLM Based Input Space Partitioning Testing for Library APIs
Venue: arXiv cs.SE 2024
Year: 2024

Automated library APIs testing is difficult as it requires exploring a vast space of parameter inputs that may involve objects with complex data types. Existing search based approaches, with limited knowledge of relations between object states and program branches, often suffer from the low efficiency issue, i.e., tending to generate invalid inputs. Symbolic execution based approaches can effectively identify such relations, but fail to scale to large programs. In this work, we present an LLM-based input space partitioning testing approach, LISP, for library APIs. The approach leverages LLMs to understand the code of a library API under test and perform input space partitioning based on its understanding and rich common knowledge. Specifically, we provide the signature and code of the API under test to LLMs, with the expectation of obtaining a text description of each input space partition of theAPI under test. Then, we generate inputs through employing the generated text description to sample inputs from each partition, ultimately resulting in test suites that systematically explore the program behavior of the API. We evaluate LISP on more than 2,205 library API methods taken from 10 popular open-source Java libraries (e.g.,apache/commonslang with 2.6k stars, guava with 48.8k stars on GitHub). Our experiment results show that LISP is effective in library API testing. It significantly outperforms state-of-the-art tool EvoSuite in terms of edge coverage. On average, LISP achieves 67.82% branch coverage, surpassing EvoSuite by 1.21 times. In total, LISP triggers 404 exceptions or errors in the experiments, and discovers 13 previously unknown vulnerabilities during evaluation, which have been assigned CVE IDs.
Towards Applying Deep Learning to The Internet of Things: A Model and A Framework
Venue: arXiv cs.NI 2024
Year: 2024

Deep Learning (DL) modeling has been a recent topic of interest. With the accelerating need to embed Deep Learning Networks (DLNs) to the Internet of Things (IoT) applications, many DL optimization techniques were developed to enable applying DL to IoTs. However, despite the plethora of DL optimization techniques, there is always a trade-off between accuracy, latency, and cost. Moreover, there are no specific criteria for selecting the best optimization model for a specific scenario. Therefore, this research aims at providing a DL optimization model that eases the selection and re-using DLNs on IoTs. In addition, the research presents an initial design for a DL optimization model management framework. This framework would help organizations choose the optimal DL optimization model that maximizes performance without sacrificing quality. The research would add to the IS design science knowledge as well as the industry by providing insights to many IT managers to apply DLNs to IoTs such as machines and robots.
Energy Efficient Computation Offloading and Virtual Connection Control in Uplink Small Cell Networks
Venue: arXiv cs.NI 2024
Year: 2024

Nowadays, the use of soft computational techniques in power systems under the umbrella of machine learning is increasing with good reception. In this paper, we first present a deep learning approach to find the optimal configuration for HetNet systems. We used a very large number of radial configurations of a test system for training purposes. We also studied the issue of joint carrier/power allocation in multilayer hierarchical networks, in addition to ensuring the quality of experience for all subscribers, to achieve optimal power efficiency. The proposed method uses an adaptive load equilibrium model that aims to achieve "almost optimal" equity among all servers from the standpoint of the key performance indicator. Unlike current model-based energy efficiency methods, we propose a joint resource allocation, energy efficiency, and flow control algorithm to solve common nonconvex and hierarchical optimization problems. Also, by referring to the allocation of continuous resources based on SLA, we extended the proposed algorithm to common flow/power control and operational power optimization algorithm to achieve optimal energy efficiency along with ensuring user's throughput limitations. Also, simulation results show that the proposed controlled power/flow optimization approach can significantly increase energy efficiency compared to conventional designs using network topology adjustment capability.
Spin-Weighted Spherical Harmonics for Polarized Light Transport
Venue: arXiv cs.GR 2024
Year: 2024

The objective of polarization rendering is to simulate the interaction of light with materials exhibiting polarization-dependent behavior. However, integrating polarization into rendering is challenging and increases computational costs significantly. The primary difficulty lies in efficiently modeling and computing the complex reflection phenomena associated with polarized light. Specifically, frequency-domain analysis, essential for efficient environment lighting and storage of complex light interactions, is lacking. To efficiently simulate and reproduce polarized light interactions using frequency-domain techniques, we address the challenge of maintaining continuity in polarized light transport represented by Stokes vectors within angular domains. The conventional spherical harmonics method cannot effectively handle continuity and rotation invariance for Stokes vectors. To overcome this, we develop a new method called polarized spherical harmonics (PSH) based on the spin-weighted spherical harmonics theory. Our method provides a rotation-invariant representation of Stokes vector fields. Furthermore, we introduce frequency domain formulations of polarized rendering equations and spherical convolution based on PSH. We first define spherical convolution on Stokes vector fields in the angular domain, and it also provides efficient computation of polarized light transport, nearly on an entry-wise product in the frequency domain. Our frequency domain formulation, including spherical convolution, led to the development of the first real-time polarization rendering technique under polarized environmental illumination, named precomputed polarized radiance transfer, using our polarized spherical harmonics. Results demonstrate that our method can effectively and accurately simulate and reproduce polarized light interactions in complex reflection phenomena.
How Large Language Models (LLMs) Extrapolate: From Guided Missiles to Guided Prompts
Venue: arXiv cs.CY 2024
Year: 2024

This paper argues that we should perceive LLMs as machines of extrapolation. Extrapolation is a statistical function for predicting the next value in a series. Extrapolation contributes to both GPT successes and controversies surrounding its hallucination. The term hallucination implies a malfunction, yet this paper contends that it in fact indicates the chatbot efficiency in extrapolation, albeit an excess of it. This article bears a historical dimension: it traces extrapolation to the nascent years of cybernetics. In 1941, when Norbert Wiener transitioned from missile science to communication engineering, the pivotal concept he adopted was none other than extrapolation. Soviet mathematician Andrey Kolmogorov, renowned for his compression logic that inspired OpenAI, had developed in 1939 another extrapolation project that Wiener later found rather like his own. This paper uncovers the connections between hot war science, Cold War cybernetics, and the contemporary debates on LLM performances.
A Web-Based IDE for DevOps Learning in Software Engineering Higher Education
Venue: arXiv cs.CY 2024
Year: 2024

DevOps can be best explained as people working together to conceive, build and deliver secure software at top speed. DevOps practices enable software development (dev) and operations (ops) teams to accelerate delivery through automation, collaboration, fast feedback, and iterative improvement. It is now an integral part of the information technology industry, and students should be aware of it before they start their careers. However, teaching DevOps in a university curriculum has many challenges as it involves many tools and technologies. This paper presents an innovative online Integrated Development Environment (IDE) designed to facilitate DevOps learning within university curricula. The devised tool offers a standardized, accessible learning environment, equipped with devcontainers and engaging tutorials to simplify learning DevOps. Research findings highlight a marked preference among students for self-paced learning approaches, with experienced DevOps practitioners also noting the value of the tool. With barriers such as limited hardware/software access becoming evident, the necessity for cloud-based learning solutions is further underscored. User feedback emphasizes the tool's user-friendliness and the imperative of automated installation procedures. We recommend additional exploration into the tool's extensibility and potential for continuous improvement, especially regarding the development of Dev Containers. The study concludes by emphasizing the pivotal role of practical learning tools in the dynamic field of DevOps education and research.
What we learned while automating bias detection in AI hiring systems for compliance with NYC Local Law 144
Venue: arXiv cs.CY 2024
Year: 2024

Since July 5, 2023, New York City's Local Law 144 requires employers to conduct independent bias audits for any automated employment decision tools (AEDTs) used in hiring processes. The law outlines a minimum set of bias tests that AI developers and implementers must perform to ensure compliance. Over the past few months, we have collected and analyzed audits conducted under this law, identified best practices, and developed a software tool to streamline employer compliance. Our tool, ITACA_144, tailors our broader bias auditing framework to meet the specific requirements of Local Law 144. While automating these legal mandates, we identified several critical challenges that merit attention to ensure AI bias regulations and audit methodologies are both effective and practical. This document presents the insights gained from automating compliance with NYC Local Law 144. It aims to support other cities and states in crafting similar legislation while addressing the limitations of the NYC framework. The discussion focuses on key areas including data requirements, demographic inclusiveness, impact ratios, effective bias, metrics, and data reliability.
Energy-Constrained Information Storage on Memristive Devices in the Presence of Resistive Drift
Venue: arXiv cs.ET 2024
Year: 2024

In this paper, we examine the problem of information storage on memristors affected by resistive drift noise under energy constraints. We introduce a novel, fundamental trade-off between the information lifetime of memristive states and the energy that must be expended to bring the device into a particular state. We then treat the storage problem as one of communication over a noisy, energy-constrained channel, and propose a joint source-channel coding (JSCC) approach to storing images in an analogue fashion. To design an encoding scheme for natural images and to model the memristive channel, we make use of data-driven techniques from the field of deep learning for communications, namely deep joint source-channel coding (DeepJSCC), employing a generative model of resistive drift as a computationally tractable differentiable channel model for end-to-end optimisation. We introduce a modified version of generalised divisive normalisation (GDN), a biologically inspired form of normalisation, that we call conditional GDN (cGDN), allowing for conditioning on continuous channel characteristics, including the initial resistive state and the delay between storage and reading. Our results show that the delay-conditioned network is able to learn an energy-aware coding scheme that achieves a higher and more balanced reconstruction quality across a range of storage delays.
Assessment of the application of the Universal Competencies
Venue: arXiv cs.CY 2024
Year: 2024

Application of Universal Competencies in Russian educational institutions is very important. Based on them, educational standards are invented. However, there is no universal assessment of the application of the Universal Competencies in practice. The main idea of the research is a general assessment of the application of universal competencies. For this, the activity of the enterprise is modeled. The enterprise process model is combined with the Universal Competencies. Further, the measurement is made by a universal indicator. The analysis of the dynamics of the universal indicator proves the existence of an assessment of the application of the Universal Competencies at a production facility. The integral indicator is a universal assessment of the application of the Universal Competencies.