Reviewerly/exHarmony huggingface dataset:
Towards Efficient Trajectory Generation for Ground Robots beyond 2D Environment

With the development of robotics, ground robots are no longer limited to planar motion. Passive height variation due to complex terrain and active height control provided by special structures on robots require a more general navigation planning framework beyond 2D. Existing methods rarely considers both simultaneously, limiting the capabilities and applications of ground robots. In this paper, we proposed an optimization-based planning framework for ground robots considering both active and passive height changes on the z-axis. The proposed planner first constructs a penalty field for chassis motion constraints defined in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{R}^{3}$</tex> such that the optimal solution space of the trajectory is continuous, resulting in a high-quality smooth chassis trajectory. Also, by constructing custom constraints in the z-axis direction, it is possible to plan trajectories for different types of ground robots which have z-axis degree of freedom. We performed simulations and real-world experiments to verify the efficiency and trajectory quality of our algorithm.
FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation

Abstract We present FRMT, a new dataset and evaluation benchmark for Few-shot Region-aware Machine Translation, a type of style-targeted translation. The dataset consists of professional translations from English into two regional variants each of Portuguese and Mandarin Chinese. Source documents are selected to enable detailed analysis of phenomena of interest, including lexically distinct terms and distractor terms. We explore automatic evaluation metrics for FRMT and validate their correlation with expert human evaluation across both region-matched and mismatched rating scenarios. Finally, we present a number of baseline models for this task, and offer guidelines for how researchers can train, evaluate, and compare their own models. Our dataset and evaluation code are publicly available: https://bit.ly/frmt-task.
Stairway To Rainbow

A cryptanalytic time-memory trade-off is a technique introduced by M. Hellman in 1980 to perform brute-force attacks. It consists of a time-consuming precomputation phase performed and stored once and for all, which is then used to reduce the computation time of brute-force attacks. A variant, known as rainbow tables, introduced by Oechslin in 2003 is used by most of today’s off-the-shelf password-guessing tools. Precomputation of such tables is highly inefficient however, because much of the values computed during this task are eventually discarded. This paper revisits rainbow tables precomputation, challenging what has so far been regarded as an immutable foundation. The key idea consists in recycling values discarded during the precomputation phase, and adapting the brute force phase to make use of these recycled values. For a given memory and probability of success, the stepped rainbow tables thus created significantly reduce the workload induced by both the precomputation phase and the attack phase. The speedup obtained by using such tables is provided, and backed up by practical experiments.
A (machine-oriented) logic based on pattern matching

Abstract Robinson’s unification algorithm can be identified as the underlying machinery of both C. Meredith’s rule D ( condensed detachment ) in propositional logic and of the construction of principal types in lambda calculus and combinatory logic. In combinatory logic, it also plays a crucial role in the construction of Meyer, Bunder &amp; Powers’ Fool’s model . This paper now considers pattern matching, the unidirectional variant of unification, as a basis for logical inference, typing, and a very simple and natural model for untyped combinatory logic. An analysis of the new typing scheme will enable us to characterize a large class of terms of combinatory logic which do not change their principal type when being weakly reduced. We also consider the question whether the major or the minor premisse should be used as the fixed pattern.
Study on the Design of a ChatGPT-Based Metaverse Platform Model

Interactivity is the most critical factor in the success of metaverse platforms, which can provide a unique user experience through interaction with digital avatars and engagement in a shared space. As a conversational artificial intelligence (AI) service, ChatGPT is considered a promising approach to dramatically improve the quality of interactions with computational agents on existing metaverse platforms. The ability of large language models (LLMs) to understand and respond to human communication can be expected to enable more immersive and realistic experiences. Therefore, in this study, we developed a computational model to enhance interaction with AI agents on a virtual campus on the Korea Polytechnics University metaverse platform by using ChatGPT. In future research, we plan to create an LLM with unique domain data related to Korea Polytechnic University and the National Competency Standards (NCS) along with industrial and personal data and implement the model by fine‑tuning a specially designed architecture with a pretrained framework. We plan to apply this system to the virtual Polytech Metaverse Campus for testing and monitoring. We will also perform quantitative and qualitative analyses based on advice from technical experts and the results of objective evaluations.
Solving a new application of asymmetric TSP by modified migrating birds optimization algorithm

In this study, we first introduce a new application of the asymmetric traveling salesman problem which is about a small restaurant with one cook and a single stove. Once a meal has started cooking on the stove, the cook prepares the next meal on the table where the preparation time is dependent on the previous meal prepared. For the solution of this problem, besides several simple construction algorithms and a new version of the simulated annealing (SA) algorithm, we focus on enhanced versions of the recently introduced migrating birds optimization (MBO) algorithm. The original MBO algorithm might suffer from early convergence. Here we introduce several different ways of handling this problem. The extensive numerical experimentation conducted shows the superiority of the enhanced MBO over the original MBO (about 2.62 per cent) and over the SA algorithm (about 1.05 per cent).
Visual tracking using transformer with a combination of convolution and attention

For Siamese-based trackers in the field of single object tracking, cross-correlation operation plays an important role. However, the cross-correlation essentially uses target feature to locally linearly match the search region, which leads to insufficient utilization or even loss of feature information. To effectively employ global context and sufficiently explore the relevance of template and search region, a novel matching operator is designed inspired by Transformer, which uses multi-head attention and embed a designed modulation module across the inputs of operator. Meanwhile, we equip our tracker with a multi-scale encoder/decoder strategy to gradually make more precise tracking. Finally, a complete tracking framework is presented named VTTR. The tracker consists of a feature extractor, a multi-scale encoder based on depth-wise convolution, a modified decoder as the matching operator and a prediction head. The proposed tracker is tested on many benchmarks and achieve excellent performance while running with fast speed.
Multilevel Credit Card Fraud Detection Using Face Recognition and Machine Learning

Technology in today’s world has wholly modified payment methods from cash to digital or card payments. Therefore, in these situations, the authentication system is essential for guaranteeing the legitimacy of a transaction. This is because such payment systems are susceptible to fraud or false transactions. As a result, numerous techniques to authenticate the user’s credentials have been developed and practiced. We use machine learning to predict the fate of the transaction by training our model on past or existing transactional data. Despite the fact that these solutions boost security, they could not be more reliable. These systems may predict that the transaction is legitimate when it’s not. This paper intends to illustrate the idea that helps to increase the credibility of such a machine learning system with the help of integration with a face recognition system. The final decision of whether the transaction is fraudulent or not will be taken based on outputs obtained by the machine learning module and face recognition module. The face recognition module uses the LBPH algorithm. To decide the threshold in the face recognition module and test it, we experimented with 30 users for fraudulent and non-fraudulent face data. For the machine learning module, to select the algorithm to use in our system, we performed a comparative analysis between different algorithms and chose the one that had the best performance on the input dataset. In this way, a multilevel authentication system is proposed to help increase a transaction’s truthfulness.
Fuzzy Cognitive Networks in Diverse Applications Using Hybrid Representative Structures

Abstract Fuzzy cognitive networks (FCNs) arose from traditional fuzzy cognitive maps (FCMs) to have the advantage of guaranteed convergence to equilibrium points, thus being more suitable than conventional FCMs for a variety of pattern recognition and system identification tasks. Moreover, recent developments led to FCNs with functional weights (FCNs-FW), as a significant FCNs enhancement in terms of storage requirements, efficiency and less human intervention requirements. In this paper we proceed further by introducing hybrid deep learning structures, interweaving FCNs-FW with well established deep neural network (DNN) representative structures and apply the new schemes on a variety of pattern recognition and time series prediction tasks. More specifically, after discussing general issues related to the construction of deep learning structures using FCNs-FW we present three hybrid models, which combine the FCN-FW with convolutional neural networks (CNNs), echo state networks (ESNs) and AutoEncoder (AE) schemes, respectively. The hybrid schemes are tested on diverse benchmark data sets and prove that FCN-FW based hybrid schemes perform equally well or better than state-of-the-art DNN-based schemes, paving thus the way for using cognitive networks to deep learning representative structures.
Hybrid Defect Prediction Model Based on Counterfactual Feature Optimization

Abstract Software defect prediction is critical to ensuring software quality. Researchers have worked on building various defect prediction models to improve the performance of defect prediction. Existing defect prediction models are mainly divided into two categories: models constructed based on artificial statistical features and models constructed based on semantic features. DP-CNN [Li J, He P, Zhu J, et al. Software defect prediction via convolutional neural network. In: 2017 IEEE international conference on software quality, reliability and security (QRS). IEEE, 2017; 318–328.] is one of the best defect prediction models, because it combines both artificial statistical features and semantic features, so its performance is greatly improved compared to traditional defect prediction models. This paper is based on the DP-CNN model and makes the following two improvements: first, using a new Struc2vec network representation technique to mine existing information between software modules, which specializes in learning node representations from structural identity and can further extract structural features associated with defects. Let the DP-CNN model once again incorporate the newly mined structural features. Then, this paper proposes a feature selection method based on counterfactual explanations, which can determine the importance score of each feature by the feature change rate of counterfactual samples. The origin of these feature importance scores is interpretable. Under the guidance of these interpretable feature importance scores, better feature subsets can be obtained and used to optimize artificial statistical features within the DP-CNN model. Based on the above methods, this paper proposes a new hybrid defect prediction model DPS-CNN-STR. Evaluating our model on six open source projects in terms of F1 score in defect prediction. Experimental results show that DPS-CNN-STR improves the state-of-the-art method by an average of 3.3%.
Visual Argument Structure Tool (VAST) Version 1.0

We present the first version of the Visual Argument Structure Tool (VAST), which may be used for jointly visualizing the semantic, conceptual, empirical and reasoning relationships that constitute arguments. Its primary purpose is to promote exactness and comprehensiveness in systematic thinking. The system distinguishes between concepts and the words (“names”) that may be used to refer to them. It also distinguishes various ways in which concepts may be related to one another (causation, conceptual implication, prediction, transformation, reasoning), and all of these from beliefs as to whether something IS the case and/or OUGHT to be the case. Using these elements, the system allows for formalizations of narrative argument components at any level of vagueness vs. precision that is deemed possible and/or necessary. This latter feature may make the system particularly useful for attaining greater theoretical specificity in the humanities, and for bridging the gap between the humanities and the “harder” sciences. However, VAST may also be used outside of science, to capture argument structures in e.g., legal analyses, media reports, belief systems, and debates.
Optimizing Quantum Space Using Spooky Pebble Games

Pebble games are usually used to study space/time trade-offs. Recently, spooky pebble games were introduced to study classical space/quantum space/time trade-offs for simulation of classical circuits on quantum computers. In this paper, the spooky pebble game framework is applied for the first time to general circuits. Using this framework we prove an upper bound for quantum space in the spooky pebble game. Moreover, we present a solver for the spooky pebble game based on a SAT solver. This spooky pebble game solver is empirically evaluated by calculating optimal classical space/quantum space/time trade-offs. Within limited runtime, the solver could find a strategy reducing quantum space when classical space is taken into account, showing that the spooky pebble model is useful to reduce quantum space.
ML Models for Detecting QoE Degradation in Low-Latency Applications: A Cloud-Gaming Case Study

Detecting abnormal network events is an important activity of Internet Service Providers particularly when running critical applications (e.g., ultra low-latency applications in mobile wireless networks).Abnormal events can stress the infrastructure and lead to severe degradation of user experience.Machine Learning (ML) models have demonstrated their relevance in many tasks including Anomaly Detection (AD).While promising remarkable performance compared to manual or threshold-based detection, applying ML-based AD methods is challenging for operators due to the proliferation of ML models and the lack of well-established methodology and metrics to evaluate them and select the most appropriate one.This paper presents a comprehensive evaluation of eight unsupervised ML models selected from different classes of ML algorithms and applied to AD in the context of cloud gaming applications.We collect cloud gaming Key Performance Indicators (KPIs) time-series datasets in real-world network conditions, and we evaluate and compare the selected ML models using the same methodology, and assess their robustness to data contamination, their efficiency and computational complexity.In addition to the traditional F1-score performance metric used in anomaly detection, we use Matthews Coefficient Correlation (MCC) to better differentiate between models' efficiencies.Our proposed methodology relies on window-based anomaly detection techniques as they are more useful for network operators compared to single point detection approaches.However, we found most existing window-based approaches to lack in accuracy and may under or over-estimate a model's performance.Therefore, in this paper, we propose a novel Window Anomaly Decision (WAD) approach that overcomes these drawbacks.We leverage our experimental results to provide insights about the most relevant models for detecting QoE degradation and offer recommendations on their suitability for different application requirements.
Analysis of a Pairwise Dominance Coevolutionary Algorithm And DefendIt

While competitive coevolutionary algorithms are ideally suited to model adversarial dynamics, their complexity makes it difficult to understand what is happening when they execute. To achieve better clarity, we introduce a game named DefendIt and explore a previously developed pairwise dominance coevolutionary algorithm named PDCoEA. We devise a methodology for consistent algorithm comparison, then use it to empirically study the impact of population size, the impact of relative budget limits between the defender and attacker, and the impact of mutation rates on the dynamics and payoffs. Our methodology provides reliable comparisons and records of run and multi-run dynamics. Our supplementary material also offers enticing and detailed animations of a pair of players' game moves over the course of a game of millions of moves matched to the same run's populations' payoffs.
Implementing Graph Neural Networks Over Wireless Networks via Over-the-Air Computing: A Joint Communication and Computation Framework

A Graph Neural Network (GNN) conducts the graph convolution for structured data and obtains the weighted sum over the vertices according to its graph structure. However, in the context of a wireless network, the traditional separate implementation of a GNN usually requires the full channel state information, which is hard to obtain in practice, especially for the underlying interference channels. On the other hand, Over-the-Air Computing (OAC) is an efficient analog wireless technique in which the weighted sum can be simultaneously calculated over an equivalent wireless superposition channel. Since the main goal of a distributed learning-based system is the fulfillment of the overall learning task instead of its communication rate, OAC is of great potential for implementing such a system. In this article, we exploit some specific features of the wireless interference graphs and propose a novel task-ori-ented OAC-based framework to deploy GNNs more efficiently in wireless networks. In particu-lar, we take advantage of the structural similarity between OAC and the graph convolution oper-ation over an interference graph, and the chan-nel prediction procedure can be merged into the weight updating procedure. Moreover, the inher-ent noise tolerance of a neural network further ensures its convergence and performance. We also conduct case studies based on the proposed framework and discuss the comprehensive future research directions and open problems.
The Parameterized Complexity of Quantum Verification

We initiate the study of parameterized complexity of $\textsf{QMA}$ problems in terms of the number of non-Clifford gates in the problem description. We show that for the problem of parameterized quantum circuit satisfiability, there exists a classical algorithm solving the problem with a runtime scaling exponentially in the number of non-Clifford gates but only polynomially with the system size. This result follows from our main result, that for any Clifford + $t$ $T$-gate quantum circuit satisfiability problem, the search space of optimal witnesses can be reduced to a stabilizer subspace isomorphic to at most $t$ qubits (independent of the system size). Furthermore, we derive new lower bounds on the $T$-count of circuit satisfiability instances and the $T$-count of the $W$-state assuming the classical exponential time hypothesis ($\textsf{ETH}$). Lastly, we explore the parameterized complexity of the quantum non-identity check problem.
Exploitation of Vulnerabilities: A Topic-Based Machine Learning Framework for Explaining and Predicting Exploitation

Security vulnerabilities constitute one of the most important weaknesses of hardware and software security that can cause severe damage to systems, applications, and users. As a result, software vendors should prioritize the most dangerous and impactful security vulnerabilities by developing appropriate countermeasures. As we acknowledge the importance of vulnerability prioritization, in the present study, we propose a framework that maps newly disclosed vulnerabilities with topic distributions, via word clustering, and further predicts whether this new entry will be associated with a potential exploit Proof Of Concept (POC). We also provide insights on the current most exploitable weaknesses and products through a Generalized Linear Model (GLM) that links the topic memberships of vulnerabilities with exploit indicators, thus distinguishing five topics that are associated with relatively frequent recent exploits. Our experiments show that the proposed framework can outperform two baseline topic modeling algorithms in terms of topic coherence by improving LDA models by up to 55%. In terms of classification performance, the conducted experiments—on a quite balanced dataset (57% negative observations, 43% positive observations)—indicate that the vulnerability descriptions can be used as exclusive features in assessing the exploitability of vulnerabilities, as the “best” model achieves accuracy close to 87%. Overall, our study contributes to enabling the prioritization of vulnerabilities by providing guidelines on the relations between the textual details of a weakness and the potential application/system exploits.
The Diagnostic Cockpit of the Future

The integration of digital data from imaging, pathology, genomics, and other fields creates an opportunity to produce information-rich diagnoses, especially for complex patients. Technology exists today to create a "diagnostic cockpit" that can accept inputs from multiple sources, analyze them using artificial intelligence and other quantitative tools, and produce a precision diagnosis. Although barriers to creation and dissemination of such a cockpit exist, the metaphor provides a glimpse into the diagnostic processes of the future.
ADL: Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction

Large-scale commercial platforms usually involve numerous business scenarios for diverse business strategies. To provide click-through rate (CTR) predictions for multiple scenarios simultaneously, existing promising multi-scenario models explicitly construct scenario-specific networks by manually grouping scenarios based on particular business strategies. Nonetheless, this pre-defined data partitioning process heavily relies on prior knowledge, and it may neglect the underlying data distribution of each scenario, hence limiting the model's representation capability. Regarding the above issues, we propose Adaptive Distribution Learning (ADL): an end-to-end optimization distribution framework which is composed of a clustering process and classification process. Specifically, we design a distribution adaptation module with a customized dynamic routing mechanism. Instead of introducing prior knowledge for pre-defined data allocation, this routing algorithm adaptively provides a distribution coefficient for each sample to determine which cluster it belongs to. Each cluster corresponds to a particular distribution so that the model can sufficiently capture the commonalities and distinctions between these distinct clusters. Our results on both public and large-scale industrial datasets show the effectiveness and efficiency of ADL: the model yields impressive prediction accuracy with more than 50% reduction in time cost during the training phase when compared to other methods.