OpenRaiser/Intern-Atlas huggingface dataset:
Hypergraph reconstruction from noisy pairwise observations
Venue: arXiv cs.SI 2022
Year: 2022

The network reconstruction task aims to estimate a complex system's structure from various data sources such as time series, snapshots, or interaction counts. Recent work has examined this problem in networks whose relationships involve precisely two entities-the pairwise case. Here we investigate the general problem of reconstructing a network in which higher-order interactions are also present. We study a minimal example of this problem, focusing on the case of hypergraphs with interactions between pairs and triplets of vertices, measured imperfectly and indirectly. We derive a Metropolis-Hastings-within-Gibbs algorithm for this model and use the algorithms to highlight the unique challenges that come with estimating higher-order models. We show that this approach tends to reconstruct empirical and synthetic networks more accurately than an equivalent graph model without higher-order interactions.
SynchroSim: An Integrated Co-simulation Middleware for Heterogeneous Multi-robot System
Venue: arXiv cs.RO 2022
Year: 2022

With the advancement of modern robotics, autonomous agents are now capable of hosting sophisticated algorithms, which enables them to make intelligent decisions. But developing and testing such algorithms directly in real-world systems is tedious and may result in the wastage of valuable resources. Especially for heterogeneous multi-agent systems in battlefield environments where communication is critical in determining the system's behavior and usability. Due to the necessity of simulators of separate paradigms (co-simulation) to simulate such scenarios before deploying, synchronization between those simulators is vital. Existing works aimed at resolving this issue fall short of addressing diversity among deployed agents. In this work, we propose \textit{SynchroSim}, an integrated co-simulation middleware to simulate a heterogeneous multi-robot system. Here we propose a velocity difference-driven adjustable window size approach with a view to reducing packet loss probability. It takes into account the respective velocities of deployed agents to calculate a suitable window size before transmitting data between them. We consider our algorithm-specific simulator agnostic but for the sake of implementation results, we have used Gazebo as a Physics simulator and NS-3 as a network simulator. Also, we design our algorithm considering the Perception-Action loop inside a closed communication channel, which is one of the essential factors in a contested scenario with the requirement of high fidelity in terms of data transmission. We validate our approach empirically at both the simulation and system level for both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Our approach achieves a noticeable improvement in terms of reducing packet loss probability ($\approx$11\%), and average packet delay ($\approx$10\%) compared to the fixed window size-based synchronization approach.
Recognition of All Categories of Entities by AI
Venue: arXiv cs.AI 2022
Year: 2022

Human-level AI will have significant impacts on human society. However, estimates for the realization time are debatable. To arrive at human-level AI, artificial general intelligence (AGI), as opposed to AI systems that are specialized for a specific task, was set as a technically meaningful long-term goal. But now, propelled by advances in deep learning, that achievement is getting much closer. Considering the recent technological developments, it would be meaningful to discuss the completion date of human-level AI through the "comprehensive technology map approach," wherein we map human-level capabilities at a reasonable granularity, identify the current range of technology, and discuss the technical challenges in traversing unexplored areas and predict when all of them will be overcome. This paper presents a new argumentative option to view the ontological sextet, which encompasses entities in a way that is consistent with our everyday intuition and scientific practice, as a comprehensive technological map. Because most of the modeling of the world, in terms of how to interpret it, by an intelligent subject is the recognition of distal entities and the prediction of their temporal evolution, being able to handle all distal entities is a reasonable goal. Based on the findings of philosophy and engineering cognitive technology, we predict that in the relatively near future, AI will be able to recognize various entities to the same degree as humans.
Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging
Venue: arXiv cs.LG 2022
Year: 2022

Occupational pneumoconiosis (OP) staging is a vital task concerning the lung healthy of a subject. The staging result of a patient is depended on the staging standard and his chest X-ray. It is essentially an image classification task. However, the distribution of OP data is commonly imbalanced, which largely reduces the effect of classification models which are proposed under the assumption that data follow a balanced distribution and causes inaccurate staging results. To achieve accurate OP staging, we proposed an OP staging model who is able to handle imbalance data in this work. The proposed model adopts gray level co-occurrence matrix (GLCM) to extract texture feature of chest X-ray and implements classification with a weighted broad learning system (WBLS). Empirical studies on six data cases provided by a hospital indicate that proposed model can perform better OP staging than state-of-the-art classifiers with imbalanced data.
Development of Augmented Reality Application for Made-to-Order Furniture Industry in Pampanga, Philippines
Venue: arXiv cs.HC 2022
Year: 2022

The focus of the study was to develop a mobile application utilizing marker-less augmented reality for specific made-to-order products to support furniture and fixtures businesses. The study implemented mixed-methodology to properly identify the various stakeholders' considerations in developing the application. Interviews with key informants were conducted to ensure that the features were appropriate for the intended user needs, and selected ISO standards were used as evaluation criteria. The results indicate that the mobile application with marker-less AR technology was found to be highly acceptable by three evaluators (i.e., customers, owners, and IT experts). The study also highlighted the use of AR-related technology in this case, where marker-less has the potential to improve customer purchasing experience even further. Future studies may include using newer technologies to further improve the application. The study suggests that Augmented Reality technology could be used to connect specific businesses directly to consumers regardless of setting or context.
On the Formalization of the Heat Conduction Problem in HOL
Venue: arXiv cs.LO 2022
Year: 2022

Partial Differential Equations (PDEs) are widely used for modeling the physical phenomena and analyzing the dynamical behavior of many engineering and physical systems. The heat equation is one of the most well-known PDEs that captures the temperature distribution and diffusion of heat within a body. Due to the wider utility of these equations in various safety-critical applications, such as thermal protection systems, a formal analysis of the heat transfer is of utmost importance. In this paper, we propose to use higher-order-logic (HOL) theorem proving for formally analyzing the heat conduction problem in rectangular coordinates. In particular, we formally model the heat transfer as a one-dimensional heat equation for a rectangular slab using the multivariable calculus theories of the HOL Light theorem prover. This requires the formalization of the heat operator and formal verification of its various properties, such as linearity and scaling. Moreover, we use the separation of variables method for formally verifying the solution of the PDEs, which allows modeling the heat transfer in the slab under various initial and boundary conditions using HOL Light.
Neural Encoding of Songs is Modulated by Their Enjoyment
Venue: arXiv cs.HC 2022
Year: 2022

We examine user and song identification from neural (EEG) signals. Owing to perceptual subjectivity in human-media interaction, music identification from brain signals is a challenging task. We demonstrate that subjective differences in music perception aid user identification, but hinder song identification. In an attempt to address intrinsic complexities in music identification, we provide empirical evidence on the role of enjoyment in song recognition. Our findings reveal that considering song enjoyment as an additional factor can improve EEG-based song recognition.
Regression test of various versions of STRmix
Venue: arXiv cs.SE 2022
Year: 2022

STRmix has been in operational use since 2012 for the interpretation of forensic DNA profiles. During that time incremental improvements have been made to the modelling of variance for single and composite peaks, and drop-in. The central element of the algorithm has remained as the Markov chain Monte Carlo (MCMC) based on the Metropolis-Hastings algorithm. Regression experiments are reported comparing versions V2.3 to V2.9. There is a high degree of similarity in LRs for true contributors across multiple versions of STRmix that span 7 years of development (from V2.3 to V2.9). This is due to the fact that the underlying core models of STRmix that are used to describe DNA profile behaviour have remained stable across that time.
Virtual Reality Platform to Develop and Test Applications on Human-Robot Social Interaction
Venue: arXiv cs.RO 2022
Year: 2022

Robotics simulation has been an integral part of research and development in the robotics area. The simulation eliminates the possibility of harm to sensors, motors, and the physical structure of a real robot by enabling robotics application testing to be carried out quickly and affordably without being subjected to mechanical or electronic errors. Simulation through virtual reality (VR) offers a more immersive experience by providing better visual cues of environments, making it an appealing alternative for interacting with simulated robots. This immersion is crucial, particularly when discussing sociable robots, a subarea of the human-robot interaction (HRI) field. The widespread use of robots in daily life depends on HRI. In the future, robots will be able to interact effectively with people to perform a variety of tasks in human civilization. It is crucial to develop simple and understandable interfaces for robots as they begin to proliferate in the personal workspace. Due to this, in this study, we implement a VR robotic framework with ready-to-use tools and packages to enhance research and application development in social HRI. Since the entire VR interface is an open-source project, the tests can be conducted in an immersive environment without needing a physical robot.
Cryptanalyzing an Image Encryption Algorithm Underpinned by 2D Lag-Complex Logistic Map
Venue: arXiv cs.CR 2022
Year: 2022

This paper analyzes security performance of an image encryption algorithm using 2D lag-complex Logistic map (LCLM), which adopts it as a pseudo-random number generator, and uses the sum of all pixel values of the plain-image as its initial value to control the random combination of the basic encryption operations. However, multiple factors make the final pseudo-random sequences controlling the encryption process may be the same for different plain-images. Based on this point, we proposed a chosen-plaintext attack by attacking the six encryption steps with a strategy of divide and conquer. Using the pitfalls of 2D-LCLM, the number of required chosen plain-images is further reduced to $5\cdot\log_2(MN)+95$, where $\mathit{MN}$ is the number of pixels of the plain-image.
$β$-Divergence-Based Latent Factorization of Tensors model for QoS prediction
Venue: arXiv cs.LG 2022
Year: 2022

A nonnegative latent factorization of tensors (NLFT) model can well model the temporal pattern hidden in nonnegative quality-of-service (QoS) data for predicting the unobserved ones with high accuracy. However, existing NLFT models' objective function is based on Euclidean distance, which is only a special case of $β$-divergence. Hence, can we build a generalized NLFT model via adopting $β$-divergence to achieve prediction accuracy gain? To tackle this issue, this paper proposes a $β$-divergence-based NLFT model ($β$-NLFT). Its ideas are two-fold 1) building a learning objective with $β$-divergence to achieve higher prediction accuracy, and 2) implementing self-adaptation of hyper-parameters to improve practicability. Empirical studies on two dynamic QoS datasets demonstrate that compared with state-of-the-art models, the proposed $β$-NLFT model achieves the higher prediction accuracy for unobserved QoS data.
Improving Phishing Detection Via Psychological Trait Scoring
Venue: arXiv cs.SI 2022
Year: 2022

Phishing emails exhibit some unique psychological traits which are not present in legitimate emails. From empirical analysis and previous research, we find three psychological traits most dominant in Phishing emails - A Sense of Urgency, Inducing Fear by Threatening, and Enticement with Desire. We manually label 10% of all phishing emails in our training dataset for these three traits. We leverage that knowledge by training BERT, Sentence-BERT (SBERT), and Character-level-CNN models and capturing the nuances via the last layers that form the Phishing Psychological Trait (PPT) scores. For the phishing email detection task, we use the pretrained BERT and SBERT model, and concatenate the PPT scores to feed into a fully-connected neural network model. Our results show that the addition of PPT scores improves the model performance significantly, thus indicating the effectiveness of PPT scores in capturing the psychological nuances. Furthermore, to mitigate the effect of the imbalanced training dataset, we use the GPT-2 model to generate phishing emails (Radford et al., 2019). Our best model outperforms the current State-of-the-Art (SOTA) model's F1 score by 4.54%. Additionally, our analysis of individual PPTs suggests that Fear provides the strongest cue in detecting phishing emails.
Real-time Caller Intent Detection In Human-Human Customer Support Spoken Conversations
Venue: arXiv cs.AI 2022
Year: 2022

Agent assistance during human-human customer support spoken interactions requires triggering workflows based on the caller's intent (reason for call). Timeliness of prediction is essential for a good user experience. The goal is for a system to detect the caller's intent at the time the agent would have been able to detect it (Intent Boundary). Some approaches focus on predicting the output offline, i.e. once the full spoken input (e.g. the whole conversational turn) has been processed by the ASR system. This introduces an undesirable latency in the prediction each time the intent could have been detected earlier in the turn. Recent work on voice assistants has used incremental real-time predictions at a word-by-word level to detect intent before the end of a command. Human-directed and machine-directed speech however have very different characteristics. In this work, we propose to apply a method developed in the context of voice-assistant to the problem of online real time caller's intent detection in human-human spoken interactions. We use a dual architecture in which two LSTMs are jointly trained: one predicting the Intent Boundary (IB) and then other predicting the intent class at the IB. We conduct our experiments on our private dataset comprising transcripts of human-human telephone conversations from the telecom customer support domain. We report results analyzing both the accuracy of our system as well as the impact of different architectures on the trade off between overall accuracy and prediction latency.
Block Orthogonal Sparse Superposition Codes for Ultra-Reliable Low-Latency Communications
Venue: arXiv cs.IT 2022
Year: 2022

Low-rate and short-packet transmissions are important for ultra-reliable low-latency communications (URLLC). In this paper, we put forth a new family of sparse superposition codes for URLLC, called block orthogonal sparse superposition (BOSS) codes. We first present a code construction method for the efficient encoding of BOSS codes. The key idea is to construct codewords by the superposition of the orthogonal columns of a dictionary matrix with a sequential bit mapping strategy. We also propose an approximate maximum a posteriori probability (MAP) decoder with two stages. The approximate MAP decoder reduces the decoding latency significantly via a parallel decoding structure while maintaining a comparable decoding complexity to the successive cancellation list (SCL) decoder of polar codes. Furthermore, to gauge the code performance in the finite-blocklength regime, we derive an exact analytical expression for block-error rates (BLERs) for single-layered BOSS codes in terms of relevant code parameters. Lastly, we present a cyclic redundancy check aided-BOSS (CA-BOSS) code with simple list decoding to boost the code performance. Our experiments verify that CA-BOSS with the simple list decoder outperforms CA-polar codes with SCL decoding in the low-rate and finite-blocklength regimes while achieving the finite-blocklength capacity upper bound within one dB of signal-to-noise ratio.
Models of Music Cognition and Composition
Venue: arXiv cs.SD 2022
Year: 2022

Much like most of cognition research, music cognition is an interdisciplinary field, which attempts to apply methods of cognitive science (neurological, computational and experimental) to understand the perception and process of composition of music. In this paper, we first motivate why music is relevant to cognitive scientists and give an overview of the approaches to computational modelling of music cognition. We then review literature on the various models of music perception, including non-computational models, computational non-cognitive models and computational cognitive models. Lastly, we review literature on modelling the creative behaviour and on computer systems capable of composing music. Since a lot of technical terms from music theory have been used, we have appended a list of relevant terms and their definitions at the end.
TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval
Venue: arXiv cs.IR 2022
Year: 2022

Robust test collections are crucial for Information Retrieval research. Recently there is a growing interest in evaluating retrieval systems for domain-specific retrieval tasks, however these tasks often lack a reliable test collection with human-annotated relevance assessments following the Cranfield paradigm. In the medical domain, the TripClick collection was recently proposed, which contains click log data from the Trip search engine and includes two click-based test sets. However the clicks are biased to the retrieval model used, which remains unknown, and a previous study shows that the test sets have a low judgement coverage for the Top-10 results of lexical and neural retrieval models. In this paper we present the novel, relevance judgement test collection TripJudge for TripClick health retrieval. We collect relevance judgements in an annotation campaign and ensure the quality and reusability of TripJudge by a variety of ranking methods for pool creation, by multiple judgements per query-document pair and by an at least moderate inter-annotator agreement. We compare system evaluation with TripJudge and TripClick and find that that click and judgement-based evaluation can lead to substantially different system rankings.
Syntax-driven Data Augmentation for Named Entity Recognition
Venue: arXiv cs.CL 2022
Year: 2022

In low resource settings, data augmentation strategies are commonly leveraged to improve performance. Numerous approaches have attempted document-level augmentation (e.g., text classification), but few studies have explored token-level augmentation. Performed naively, data augmentation can produce semantically incongruent and ungrammatical examples. In this work, we compare simple masked language model replacement and an augmentation method using constituency tree mutations to improve the performance of named entity recognition in low-resource settings with the aim of preserving linguistic cohesion of the augmented sentences.
Deepfake Detection using ImageNet models and Temporal Images of 468 Facial Landmarks
Venue: arXiv cs.CV 2022
Year: 2022

This paper presents our results and findings on the use of temporal images for deepfake detection. We modelled temporal relations that exist in the movement of 468 facial landmarks across frames of a given video as spatial relations by constructing an image (referred to as temporal image) using the pixel values at these facial landmarks. CNNs are capable of recognizing spatial relationships that exist between the pixels of a given image. 10 different ImageNet models were considered for the study.
Cooperative and uncooperative institution designs: Surprises and problems in open-source game theory
Venue: arXiv cs.GT 2022
Year: 2022

It is increasingly possible for real-world agents, such as software-based agents or human institutions, to view the internal programming of other such agents that they interact with. For instance, a company can read the bylaws of another company, or one software system can read the source code of another. Game-theoretic equilibria between the designers of such agents are called \emph{program equilibria}, and we call this area \emph{open-source game theory}. In this work we demonstrate a series of counterintuitive results on open-source games, which are independent of the programming language in which agents are written. We show that certain formal institution designs that one might expect to defect against each other will instead turn out to cooperate, or conversely, cooperate when one might expect them to defect. The results hold in a setting where each institution has full visibility into the other institution's true operating procedures. We also exhibit examples and ten open problems for better understanding these phenomena. We argue that contemporary game theory remains ill-equipped to study program equilibria, given that even the outcomes of single games in open-source settings remain counterintuitive and poorly understood. Nonetheless, some of these open-source agents exhibit desirable characteristics -- e.g., they can unexploitably create incentives for cooperation and legibility from other agents -- such that analyzing them could yield considerable benefits.
Prospects of federated machine learning in fluid dynamics
Venue: arXiv cs.LG 2022
Year: 2022

Physics-based models have been mainstream in fluid dynamics for developing predictive models. In recent years, machine learning has offered a renaissance to the fluid community due to the rapid developments in data science, processing units, neural network based technologies, and sensor adaptations. So far in many applications in fluid dynamics, machine learning approaches have been mostly focused on a standard process that requires centralizing the training data on a designated machine or in a data center. In this letter, we present a federated machine learning approach that enables localized clients to collaboratively learn an aggregated and shared predictive model while keeping all the training data on each edge device. We demonstrate the feasibility and prospects of such decentralized learning approach with an effort to forge a deep learning surrogate model for reconstructing spatiotemporal fields. Our results indicate that federated machine learning might be a viable tool for designing highly accurate predictive decentralized digital twins relevant to fluid dynamics.