OpenRaiser/Intern-Atlas huggingface dataset:
Grasping, Part Identification, and Pose Refinement in One Shot with a Tactile Gripper
Venue: arXiv cs.RO 2023
Year: 2023

The rise in additive manufacturing comes with unique opportunities and challenges. Rapid changes to part design and massive part customization distinctive to 3D-Print (3DP) can be easily achieved. Customized parts that are unique, yet exhibit similar features such as dental moulds, shoe insoles, or engine vanes could be industrially manufactured with 3DP. However, the opportunity for massive part customization comes with unique challenges for the existing production paradigm of robotics applications, as the current robotics paradigm for part identification and pose refinement is repetitive, where data-driven and object-dependent approaches are often used. Thus, a bottleneck exists in robotics applications for 3DP parts where massive customization is involved, as it is difficult for feature-based deep learning approaches to distinguish between similar parts such as shoe insoles belonging to different people. As such, we propose a method that augments patterns on 3DP parts so that grasping, part identification, and pose refinement can be executed in one shot with a tactile gripper. We also experimentally evaluate our approach from three perspectives, including real insertion tasks that mimic robotic sorting and packing, and achieved excellent classification results, a high insertion success rate of 95%, and a sub-millimeter pose refinement accuracy.
An Experimental Study of Satisfaction Response: Evaluation of Online Collaborative Learning
Venue: arXiv cs.HC 2023
Year: 2023

On the one hand, a growing amount of research discusses support for improving online collaborative learning quality, and many indicators are focused to assess its success. On the other hand, thinkLets for designing reputable and valuable collaborative processes have been developed for more than ten years. However, few studies try to apply thinkLets to online collaborative learning. This paper introduces thinkLets to online collaborative learning and experimentally tests its effectiveness with participants' responses on their satisfaction. Yield Shift Theory (YST), a causal theory explaining inner satisfaction, is adopted. In the experiment, 113 students from Universities in Beijing, China are chosen as a sample. They were divided into two groups, collaborating online in a simulated class. Then, YST in student groups under online collaborative learning is validated, a comparison study of online collaborative learning with and without thinkLets is implemented, and the satisfaction response of participants are analyzed. As a result of this comparison, YST is proved applicable in this context, and satisfaction is higher in online collaborative learning with thinkLets.
Comparing Effectiveness and Efficiency of Interactive Application Security Testing (IAST) and Runtime Application Self-Protection (RASP) Tools in a Large Java-based System
Venue: arXiv cs.CR 2023
Year: 2023

Security resources are scarce, and practitioners need guidance in the effective and efficient usage of techniques and tools available in the cybersecurity industry. Two emerging tool types, Interactive Application Security Testing (IAST) and Runtime Application Self-Protection (RASP), have not been thoroughly evaluated against well-established counterparts such as Dynamic Application Security Testing (DAST) and Static Application Security Testing (SAST). The goal of this research is to aid practitioners in making informed choices about the use of Interactive Application Security Testing (IAST) and Runtime Application Self-Protection (RASP) tools through an analysis of their effectiveness and efficiency in comparison with different vulnerability detection and prevention techniques and tools. We apply IAST and RASP on OpenMRS, an open-source Java-based online application. We compare the efficiency and effectiveness of IAST and RASP with techniques applied on OpenMRS in prior work. We measure efficiency and effectiveness in terms of the number and type of vulnerabilities detected and prevented per hour. Our study shows IAST performed relatively well compared to other techniques, performing second-best in both efficiency and effectiveness. IAST detected eight Top-10 OWASP security risks compared to nine by SMPT and seven for EMPT, DAST, and SAST. IAST found more vulnerabilities than SMPT. The efficiency of IAST (2.14 VpH) is second to only EMPT (2.22 VpH). These findings imply that our study benefited from using IAST when conducting black-box security testing. In the context of a large, enterprise-scale web application such as OpenMRS, RASP does not replace vulnerability detection, while IAST is a powerful tool that complements other techniques.
Modeling arousal potential of epistemic emotions using Bayesian information gain: Inquiry cycle driven by free energy fluctuations
Venue: arXiv cs.AI 2023
Year: 2023

Epistemic emotions, such as curiosity and interest, drive the inquiry process. This study proposes a novel formulation of epistemic emotions such as curiosity and interest using two types of information gain generated by the principle of free energy minimization: Kullback-Leibler divergence(KLD) from Bayesian posterior to prior, which represents free energy reduction in recognition, and Bayesian surprise (BS), which represents the expected information gain by Bayesian prior update. By applying a Gaussian generative model with an additional uniform likelihood, we found that KLD and BS form an upward-convex function of surprise (minimized free energy and prediction error), similar to Berlyne's arousal potential functions, or the Wundt curve. We consider that the alternate maximization of BS and KLD generates an ideal inquiry cycle to approach the optimal arousal level with fluctuations in surprise, and that curiosity and interest drive to facilitate the cyclic process. We exhaustively analyzed the effects of prediction uncertainty (prior variance) and observation uncertainty (likelihood variance) on the peaks of the information gain function as optimal surprises. The results show that greater prediction uncertainty, meaning an open-minded attitude, and less observational uncertainty, meaning precise observation with attention, are expected to provide greater information gains through a greater range of exploration. The proposed mathematical framework unifies the free energy principle of the brain and the arousal potential theory to explain the Wundt curve as an information gain function and suggests an ideal inquiry process driven by epistemic emotions.
Conceptual Mutation Testing for Student Programming Misconceptions
Venue: arXiv cs.SE 2023
Year: 2023

Context: Students often misunderstand programming problem descriptions. This can lead them to solve the wrong problem, which creates frustration, obstructs learning, and imperils grades. Researchers have found that students can be made to better understand the problem by writing examples before they start programming. These examples are checked against correct and wrong implementations -- analogous to mutation testing -- provided by course staff. Doing so results in better student understanding of the problem as well as better test suites to accompany the program, both of which are desirable educational outcomes. Inquiry: Producing mutant implementations requires care. If there are too many, or they are too obscure, students will end up spending a lot of time on an unproductive task and also become frustrated. Instead, we want a small number of mutants that each correspond to common problem misconceptions. This paper presents a workflow with partial automation to produce mutants of this form which, notably, are not those produced by mutation-testing tools. Approach: We comb through student tests that fail a correct implementation. The student misconceptions are embedded in these failures. We then use methods to semantically cluster these failures. These clusters are then translated into conceptual mutants. These can then be run against student data to determine whether we they are better than prior methods. Some of these processes also enjoy automation. Knowledge: We find that student misconceptions illustrated by failing tests can be operationalized by the above process. The resulting mutants do much better at identifying student misconceptions. Grounding: Our findings are grounded in a manual analysis of student examples and a quantitative evaluation of both our clustering techniques and our process for making conceptual mutants. The clustering evaluation compares against a ground truth using standard cluster-correspondence measures, while the mutant evaluation examines how conceptual mutants perform against student data. Importance: Our work contributes a workflow, with some automation, to reduce the cost and increase the effectiveness of generating conceptually interesting mutants. Such mutants can both improve learning outcomes and reduce student frustration, leading to better educational outcomes. In the process, we also identify a variation of mutation testing not commonly discussed in the software literature.
Quantifying Policy Administration Cost in an Active Learning Framework
Venue: arXiv cs.CR 2023
Year: 2023

This paper proposes a computational model for policy administration. As an organization evolves, new users and resources are gradually placed under the mediation of the access control model. Each time such new entities are added, the policy administrator must deliberate on how the access control policy shall be revised to reflect the new reality. A well-designed access control model must anticipate such changes so that the administration cost does not become prohibitive when the organization scales up. Unfortunately, past Access Control research does not offer a formal way to quantify the cost of policy administration. In this work, we propose to model ongoing policy administration in an active learning framework. Administration cost can be quantified in terms of query complexity. We demonstrate the utility of this approach by applying it to the evolution of protection domains. We also modelled different policy administration strategies in our framework. This allowed us to formally demonstrate that domain-based policies have a cost advantage over access control matrices because of the use of heuristic reasoning when the policy evolves. To the best of our knowledge, this is the first work to employ an active learning framework to study the cost of policy deliberation and demonstrate the cost advantage of heuristic policy administration.
Realizing Open and Decentralized Marketplace for Exchanging Data of Expected IoT Behaviors
Venue: arXiv cs.CR 2023
Year: 2023

With rising concerns about the security of IoT devices, network operators need better ways to handle potential risks. Luckily, IoT devices show consistent patterns in how they communicate. But despite previous efforts, it remains unclear how knowledge of these patterns can be made available. As data marketplaces become popular in different domains, this paper1 proposes creating a special marketplace focused on IoT cybersecurity. The goal is to openly share knowledge about IoT devices' behavior, using structured data formats like Manufacturer Usage Description (MUD) files. To make this work, we employ technologies like blockchain and smart contracts to build a practical and secure foundation for sharing and accessing important information about how IoT devices should behave on the network. Our contributions are two-fold. (1) We identify the essential features of an effective marketplace for sharing data related to the expected behaviors of IoT devices. We develop a smart contract on the Ethereum blockchain with five concrete functions; and, (2) We implement a prototype of our marketplace in a private chain environment-our codes are publicly released. We demonstrate how effectively our marketplace functions through experiments involving MUD files from consumer IoT devices. Our marketplace enables suppliers and consumers to share MUD data on the Ethereum blockchain for under a hundred dollars, promoting accessibility and participation.
Multiform Evolution for High-Dimensional Problems with Low Effective Dimensionality
Venue: arXiv cs.NE 2023
Year: 2023

In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not significantly affect the objective function). To this end, an instantiation of the multiform optimization paradigm is presented, where multiple low-dimensional counterparts of a target high-dimensional task are generated via random embeddings. Since the exact relationship between the auxiliary (low-dimensional) tasks and the target is a priori unknown, a multiform evolutionary algorithm is developed for unifying all formulations into a single multi-task setting. The resultant joint optimization enables the target task to efficiently reuse solutions evolved across various low-dimensional searches via cross-form genetic transfers, hence speeding up overall convergence characteristics. To validate the overall efficacy of our proposed algorithmic framework, comprehensive experimental studies are carried out on well-known continuous benchmark functions as well as a set of practical problems in the hyper-parameter tuning of machine learning models and deep learning models in classification tasks and Predator-Prey games, respectively.
Addressing Trust Challenges in Blockchain Oracles Using Asymmetric Byzantine Quorums
Venue: arXiv cs.CR 2023
Year: 2023

Distributed Computing in Blockchain Technology (BCT) hinges on a trust assumption among independent nodes. Without a third-party interface or what is known as a Blockchain Oracle, it can not interact with the external world. This Oracle plays a crucial role by feeding extrinsic data into the Blockchain, ensuring that Smart Contracts operate accurately in real time. The Oracle problem arises from the inherent difficulty in verifying the truthfulness of the data sourced by these Oracles. The genuineness of a Blockchain Oracle is paramount, as it directly influences the Blockchain's reliability, credibility, and scalability. To tackle these challenges, a strategy rooted in Byzantine fault tolerance ϕ is introduced. Furthermore, an autonomous system for sustainability and audibility, built on heuristic detection, is put forth. The effectiveness and precision of the proposed strategy outperformed existing methods using two real-world datasets, aimed to meet the authenticity standards for Blockchain Oracles.
Real-Time and Security-Aware Precoding in RIS-Empowered Multi-User Wireless Networks
Venue: arXiv cs.IT 2023
Year: 2023

In this letter, we propose a deep-unfolding-based framework (DUNet) to maximize the secrecy rate in reconfigurable intelligent surface (RIS) empowered multi-user wireless networks. To tailor DUNet, first we relax the problem, decouple it into beamforming and phase shift subproblems, and propose an alternative optimization (AO) based solution for the relaxed problem. Second, we apply Karush-Kuhn-Tucker (KKT) conditions to obtain a closed-form solutions for the beamforming and the phase shift. Using deep-unfolding mechanism, we transform the closed-form solutions into a deep learning model (i.e., DUNet) that achieves a comparable performance to that of AO in terms of accuracy and about 25.6 times faster.
A Gamified Framework to Assist Therapists with the ABA Therapy for Autism
Venue: arXiv cs.HC 2023
Year: 2023

We present a framework to assist therapists and children with autism spectrum disorder in their Applied Behavioral Analysis (ABA) therapy. The framework was designed in collaboration with Spazio Autismo, an autism center in Mantova, Italy. The framework is a first step toward transitioning from the current paper-based to fully digital-supported therapy. We evaluated the framework over four months with 18 children diagnosed with classic autism, ranging from 4 to 7 years old. The framework integrates a mobile app that children and therapists use during the sessions with a backend for managing therapy workflow and monitoring progress. Our preliminary results show that the framework can improve the efficacy of the therapy sessions, reducing non-therapeutic time, increasing patient focus, and quickening the completion of the assigned objectives. It can also support therapists in preparing learning materials, data acquisition, and reporting. Finally, the framework demonstrated improved privacy and security of patients' data while maintaining reliability.
AI and Tempo Estimation: A Review
Venue: arXiv cs.SD 2023
Year: 2023

The author's goal in this paper is to explore how artificial intelligence (AI) has been utilised to inform our understanding of and ability to estimate at scale a critical aspect of musical creativity - musical tempo. The central importance of tempo to musical creativity can be seen in how it is used to express specific emotions (Eerola and Vuoskoski 2013), suggest particular musical styles (Li and Chan 2011), influence perception of expression (Webster and Weir 2005) and mediate the urge to move one's body in time to the music (Burger et al. 2014). Traditional tempo estimation methods typically detect signal periodicities that reflect the underlying rhythmic structure of the music, often using some form of autocorrelation of the amplitude envelope (Lartillot and Toiviainen 2007). Recently, AI-based methods utilising convolutional or recurrent neural networks (CNNs, RNNs) on spectral representations of the audio signal have enjoyed significant improvements in accuracy (Aarabi and Peeters 2022). Common AI-based techniques include those based on probability (e.g., Bayesian approaches, hidden Markov models (HMM)), classification and statistical learning (e.g., support vector machines (SVM)), and artificial neural networks (ANNs) (e.g., self-organising maps (SOMs), CNNs, RNNs, deep learning (DL)). The aim here is to provide an overview of some of the more common AI-based tempo estimation algorithms and to shine a light on notable benefits and potential drawbacks of each. Limitations of AI in this field in general are also considered, as is the capacity for such methods to account for idiosyncrasies inherent in tempo perception, i.e., how well AI-based approaches are able to think and act like humans.
How to Evaluate Coreference in Literary Texts?
Venue: arXiv cs.CL 2023
Year: 2023

In this short paper, we examine the main metrics used to evaluate textual coreference and we detail some of their limitations. We show that a unique score cannot represent the full complexity of the problem at stake, and is thus uninformative, or even misleading. We propose a new way of evaluating coreference, taking into account the context (in our case, the analysis of fictions, esp. novels). More specifically, we propose to distinguish long coreference chains (corresponding to main characters), from short ones (corresponding to secondary characters), and singletons (isolated elements). This way, we hope to get more interpretable and thus more informative results through evaluation.
The Art of Defending: A Systematic Evaluation and Analysis of LLM Defense Strategies on Safety and Over-Defensiveness
Venue: arXiv cs.CL 2023
Year: 2023

As Large Language Models (LLMs) play an increasingly pivotal role in natural language processing applications, their safety concerns become critical areas of NLP research. This paper presents Safety and Over-Defensiveness Evaluation (SODE) benchmark: a collection of diverse safe and unsafe prompts with carefully designed evaluation methods that facilitate systematic evaluation, comparison, and analysis over 'safety' and 'over-defensiveness.' With SODE, we study a variety of LLM defense strategies over multiple state-of-the-art LLMs, which reveals several interesting and important findings, such as (a) the widely popular 'self-checking' techniques indeed improve the safety against unsafe inputs, but this comes at the cost of extreme over-defensiveness on the safe inputs, (b) providing a safety instruction along with in-context exemplars (of both safe and unsafe inputs) consistently improves safety and also mitigates undue over-defensiveness of the models, (c) providing contextual knowledge easily breaks the safety guardrails and makes the models more vulnerable to generating unsafe responses. Overall, our work reveals numerous such critical findings that we believe will pave the way and facilitate further research in improving the safety of LLMs.
Understanding Driver Agency in RideSharing
Venue: arXiv cs.HC 2023
Year: 2023

Agency is an important human characteristic that users of automated complex technologies are usually denied. This affects the user's experience leading to decreased satisfaction and productivity. In this paper, we consider the ridesharing context and interviewed 7 drivers to understand the controls that would improve the agency they feel. The results show that they desire transparency, community and an effective ability to seek redress.
Argumentation in Waltz's "Emerging Structure of International Politics''
Venue: arXiv cs.CL 2023
Year: 2023

We present an annotation scheme for argumentative and domain-specific aspects of scholarly articles on the theory of International Relations. At argumentation level we identify Claims and Support/Attack relations. At domain level we model discourse content in terms of Theory and Data-related statements. We annotate Waltz's 1993 text on structural realism and show that our scheme can be reliably applied by domain experts enables insights on two research questions on justifications of claims.
DREAM: Debugging and Repairing AutoML Pipelines
Venue: arXiv cs.SE 2023
Year: 2023

Deep Learning models have become an integrated component of modern software systems. In response to the challenge of model design, researchers proposed Automated Machine Learning (AutoML) systems, which automatically search for model architecture and hyperparameters for a given task. Like other software systems, existing AutoML systems suffer from bugs. We identify two common and severe bugs in AutoML, performance bug (i.e., searching for the desired model takes an unreasonably long time) and ineffective search bug (i.e., AutoML systems are not able to find an accurate enough model). After analyzing the workflow of AutoML, we observe that existing AutoML systems overlook potential opportunities in search space, search method, and search feedback, which results in performance and ineffective search bugs. Based on our analysis, we design and implement DREAM, an automatic debugging and repairing system for AutoML systems. It monitors the process of AutoML to collect detailed feedback and automatically repairs bugs by expanding search space and leveraging a feedback-driven search strategy. Our evaluation results show that DREAM can effectively and efficiently repair AutoML bugs.