Reviewerly/exHarmony huggingface dataset:
Visual search for reach targets in actionable space is influenced by movement costs imposed by obstacles

Real world search tasks often involve action on a target object once it has been located. However, few studies have examined whether movement-related costs associated with acting on located objects influence visual search. Here, using a task in which participants reached to a target object after locating it, we examined whether people take into account obstacles that increase movement-related costs for some regions of the reachable search space but not others. In each trial, a set of 36 objects (4 targets and 32 distractors) were displayed on a vertical screen and participants moved a cursor to a target after locating it. Participants had to fixate on an object to determine whether it was a target or distractor. A rectangular obstacle, of varying length, location, and orientation, was briefly displayed at the start of the trial. Participants controlled the cursor by moving the handle of a robotic manipulandum in a horizontal plane. The handle applied forces to simulate contact between the cursor and the unseen obstacle. We found that search, measured using eye movements, was biased to regions of the search space that could be reached without moving around the obstacle. This result suggests that when deciding where to search, people can incorporate the physical structure of the environment so as to reduce the movement-related cost of subsequently acting on the located target.
MuseHash: Supervised Bayesian Hashing for Multimodal Image Representation

This paper presents a novel method for supporting multiple modalities in the field of image retrieval, called Multimodal Bayesian Supervised Hashing (MuseHash). The method takes into consideration the semantic information of the training data through the use of Bayesian regression to estimate the semantic probabilities and statistical properties in the retrieval process. MuseHash is an extension of the previously proposed Bayesian ridge-based Semantic Preserving Hashing (BiasHash) method. Experimentation on various domain-specific and benchmark datasets demonstrates that MuseHash outperforms seven existing state-of-the-art methods in image retrieval performance, regardless of the feature extractor type, code length, and visual or textual descriptors used. This highlights the robustness and adaptability of MuseHash, making it a promising solution for multimodal image retrieval.
Symbol Location-Aware Network for Improving Handwritten Mathematical Expression Recognition

Recently most handwritten mathematical expression recognition methods adopt the attention-based encoder-decoder framework, which generates LaTeX sequences from given images. However, the accuracy of the attention mechanism limits the performance of HMER models. Lacking global context information in the decoding process is also a challenge for HMER. Some methods adopt symbol-level counting to localize symbols for improving the model performance, while these methods cannot work well. In this paper, we propose a method named SLAN, shorted for a Symbol Location-Aware Network, to solve the HMER problem. Specifically, we propose an advanced relation-level counting method to detect symbols in the image. We solve the lacking global context problem with a new global context-aware decoder. For improving the accuracy of attention, we design a novel attention alignment loss function by the dynamic programming algorithm, which can learn attention alignment directly without pixel-level labels. We conducted extensive experiments on the CROHME dataset to demonstrate the effectiveness of each part of SLAN and achieved state-of-the-art performance.
New space-efficient quantum algorithm for binary elliptic curves using the optimized division algorithm

Abstract In the previous research on solving the elliptic curve discrete logarithm problem, quantum resources were concretely estimated. In Banegas et al. (IACR Trans Cryptogr Hardw Embed Syst 2021(1):451–472, 2020. https://doi.org/10.46586/tches.v2021.i1.451-472 ), the quantum algorithm was optimized for binary elliptic curves, with the main optimization target being the number of the logical qubits. The division algorithm was primarily optimized in Banegas et al. (2020) since every ancillary qubit is used in the division algorithm. In this paper, we propose a new quantum division algorithm on the binary field that uses fewer qubits. Specifically, for elements in a field of $$2^n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> </mml:math> , our algorithm saves $$n - 3\lfloor \log {n} \rfloor - 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> <mml:mn>3</mml:mn> <mml:mo>⌊</mml:mo> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> <mml:mo>⌋</mml:mo> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> qubits instead of using $$n^2 - 64n\lfloor \log (n) \rfloor + O(n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>-</mml:mo> <mml:mn>64</mml:mn> <mml:mi>n</mml:mi> <mml:mrow> <mml:mo>⌊</mml:mo> <mml:mo>log</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>⌋</mml:mo> </mml:mrow> <mml:mo>+</mml:mo> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> more Toffoli gates, which leads to a more space-efficient quantum algorithm for binary elliptic curves. For the small size n of 16, 127, 163, 233, 283 and 571, both the number of qubits and the number of Toffoli gates are actually reduced. When the size n is 571, the reduction in ancillary qubits amounts to approximately 23% compared to the previous algorithm.
Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, the development of Unmanned Aerial Vehicles (UAVs) is standing on a new footing. In particular, the Flight Management System (FMS) plays an essential role in UAV design. However, the trade-offs between performance and SWaP-C (Size, Weight, Power, and Cost) and reliability–efficiency are challenging to determine for such a complex system. To address these issues, the identification of a successful approach to managing heterogeneity emerges as the critical question to be answered. This paper investigates Heterogeneous Computing (HC) integration in FMS in the UAV domain from academia to industry. The overview of cross-layer FMS design is firstly described from top–down in the abstraction layer to left–right in the figurative layer. In addition, the HC advantages from Light-ML, accelerated Federated Learning (FL), and hardware accelerators are highlighted. Accordingly, three distinct research focuses detailed with visual-guided landing, intelligent Fault Diagnosis and Detection (FDD), and controller-embeddable Power Electronics (PE) to distinctly illustrate advancements of the next-generation FMS design from sensing, and computing, to driving. Finally, recommendations for future research and opportunities are discussed. In summary, this article draws a road map that considers the heterogeneous advantages to conducting the Flight-Management-as-a-Service (FMaaS) platform for UAVs.
Bayesian Safety Validation for Black-Box Systems

Accurately estimating the probability of failure for safety-critical systems is important for certification. Estimation is often challenging due to high-dimensional input spaces, dangerous test scenarios, and computationally expensive simulators; thus, efficient estimation techniques are important to study. This work reframes the problem of black-box safety validation as a Bayesian optimization problem and introduces an algorithm, Bayesian safety validation, that iteratively fits a probabilistic surrogate model to efficiently predict failures. The algorithm is designed to search for failures, compute the most-likely failure, and estimate the failure probability over an operating domain using importance sampling. We introduce a set of three acquisition functions that focus on reducing uncertainty by covering the design space, optimizing the analytically derived failure boundaries, and sampling the predicted failure regions. Mainly concerned with systems that only output a binary indication of failure, we show that our method also works well in cases where more output information is available. Results show that Bayesian safety validation achieves a better estimate of the probability of failure using orders of magnitude fewer samples and performs well across various safety validation metrics. We demonstrate the algorithm on three test problems with access to ground truth and on a real-world safety-critical subsystem common in autonomous flight: a neural network-based runway detection system. This work is open sourced and currently being used to supplement the FAA certification process of the machine learning components for an autonomous cargo aircraft.
Camouflaged Object Detection with a Feature Lateral Connection Network

We propose a new framework for camouflaged object detection (COD) named FLCNet, which comprises three modules: an underlying feature mining module (UFM), a texture-enhanced module (TEM), and a neighborhood feature fusion module (NFFM). Existing models overlook the analysis of underlying features, which results in extracted low-level feature texture information that is not prominent enough and contains more interference due to the slight difference between the foreground and background of the camouflaged object. To address this issue, we created a UFM using convolution with various expansion rates, max-pooling, and avg-pooling to deeply mine the textural information of underlying features and eliminate interference. Motivated by the traits passed down through biological evolution, we created an NFFM, which primarily consists of element multiplication and concatenation followed by an addition operation. To obtain precise prediction maps, our model employs the top-down strategy to gradually combine high-level and low-level information. Using four benchmark COD datasets, our proposed framework outperforms 21 deep-learning-based models in terms of seven frequently used indices, demonstrating the effectiveness of our methodology.
MTAtrack: Multilevel transformer attention for visual tracking

Compared with traditional trackers based on convolutional neural network, Transformer tracking algorithms achieve the state-of-the-art performance owing to its ability of capturing long-range global interactions of image features. The current trackers based on Transformer only exploits the highest attention features from the last encoder layer, resulting in losing some boundary information of object targets from the front attention features. In this work, we propose an effective multilevel Transformer attention tracking algorithm named MTAtrack for predicting the target position. In a Transformer-like module, the hierarchical global feature dependencies between target templates and search regions are modeled by self-attention mechanism, and are fused by mutual attention mechanism for learning robust representation of the target positions. Additionally, the low-level attention features are converted into spatial weights to further strengthen the boundary details of the targets. Comprehensive experiments prove that our algorithm can enrich the feature description of target appearances and improve the robustness of locating the tracked targets. The proposed tracker performs favorably against the state-of-the-art tracking algorithms on five challenging datasets, especially on large-scale GOT-10k, TrackingNet, and LaSOT in terms of accuracy and efficiency. The source codes are available at https://github.com/adda1221/MTAtrack.
Too Lucky to Be a Victim? An Exploratory Study of Online Harassment and Hate Messages Faced by Social Media Influencers

Influencers are persistently exposed through social media. Once almost unapproachable, celebrities are now open to daily interaction with the public. From comments, polls, emails, and even private messages, the public can engage with their celebrities with a mere click. While this engagement provides influencers with advantages, it also renders them particularly susceptible to online harassment and toxic critics. This paper investigates the characteristics, impact, and reactions to cyber victimisation among social media influencers. To accomplish this objective, the paper presents the findings of two studies: a self-reported online victimisation survey conducted among Spanish influencers and an online ethnography. The results indicate that over 70% of influencers have encountered some form of online harassment and toxic critics. Cyber victimisation, its effects, and reactions vary across socio-demographic characteristics and the influencers' profiles. Furthermore, the qualitative analysis of the online ethnography reveals that harassed influencers can be classified as non-ideal victims. The implications of these findings for the literature are discussed.
Modeling Context-Aware Events and Responses in an IoT Environment

The Internet of Things (IoT) involves the use of devices that exchange information about the state of things in the real world. In IoT, monitoring is regarded to be the most fully researched use case. However, research on the use and manipulation of control and maintenance applications has not yet been fully addressed. An important step forward in this direction may be provided by executing automatic context-aware actuations. These may be achieved by delivering responses based on the context gathered with components endowed in some device. In this paper, we propose a solution that uses ontological knowledge for this purpose, thus improving the interoperability of IoT devices. We focus on real-time data collection to fully automate monitoring, context gathering, and appropriate responses. Our proposal is illustrated via the lens of a railroad use case, where maintaining track safety is critical to avoid accidents.
Adversarial Learning Improves Vision-Based Perception from Drones with Imbalanced Datasets

This work proposes a vision-based perception algorithm that combines image-processing-based detection and tracking of aerial objects with convolutional neural networks (CNNs) integrated for classification of general aviation aircraft, multirotor small uncrewed aerial systems (SUAS), fixed-wing SUAS, and birds to enable improved onboard avoidance algorithm decision making. Furthermore, we integrate adversarial learning during the training of the CNNs and evaluate performance with class balanced and imbalanced datasets because this maximizes the utility of resource-expensive flight experiments to collect aviation datasets. We compare our proposed CNN with adversarial learning (CNN+ADVL) model with a state-of-the-art CNN as well as a you only look once (YOLO, v4) model retrained (YOLO v4 aircraft) on the same data. The CNN+ADVL trained on the imbalanced dataset achieves the highest 10-fold cross-validation classification accuracy of 76.2% for aircraft and birds for all ranges while achieving 87.0% aircraft classification accuracy, meeting proposed self-assurance separation distances derived from Federal Aviation Administration (FAA) guidelines. In comparison, the CNNs achieved 74.4% 10-fold cross-validation classification accuracy for aircraft and birds as well as 83.4% accuracy for the aircraft, meeting proposed self-assurance separation distances derived from FAA guidelines. Furthermore, we demonstrate that the integration of adversarial learning improves the classification performance for the perception of aerial objects using a class imbalanced dataset.
Extracting Event Data from Document-Driven Enterprise Systems

The preparation of input event data is one of the most critical phases in process mining projects. Different frameworks have been developed to offer methodologies and/or supporting toolkits for data preparation. One of these frameworks, called OnProm, relies on sophisticated semantic technologies to extract event logs from relational databases. The toolkit consists of a series of general steps, meant to work on arbitrary, legacy databases. However, in many settings, the input database is not a legacy one but is structured with conceptually understandable object types and relationships that can be effectively employed to support business users in the extraction process. This is, for example, the case for document-driven enterprise systems. In this paper, we focus on this class of systems and propose a guided approach, erprep, to support a group of business and technical users in setting up OnProm with minimal effort. We demonstrate the approach in a real-life use case.
Why Deep Learning’s Performance Data Are Misleading

This is a theoretical paper, as a companion paper of the keynote talk at the same conference AIEE 2023. In contrast to conscious learning, many projects in AI have employed so-called "deep learning" many of which seemed to give impressive performance. This paper explains that such performance data are deceptively inflated due to two misconducts: "data deletion" and "test on training set". This paper clarifies "data deletion" and "test on training set" in deep learning and why they are misconducts. A simple classification method is defined, called Nearest Neighbor With Threshold (NNWT). A theorem is established that the NNWT method reaches a zero error on any validation set and any test set using the two misconducts, as long as the test set is in the possession of the author and both the amount of storage space and the time of training are finite but unbounded like with many deep learning methods. However, many deep learning methods, like the NNWT method, are all not generalizable since they have never been tested by a true test set. Why? The so-called "test set" was used in the Post-Selection step of the training stage. The evidence that misconducts actually took place in many deep learning projects is beyond the scope of this paper.
Modeling Illumination Data with Flying Light Specks

A Flying Light Speck, FLS, is a miniature sized drone configured with light sources. Swarms of FLSs will illuminate an object in a 3D volume, an FLS display. These illuminations and their data models are the novel contributions of this paper. We introduce a conceptual model of drone flight paths to render static, slide, and motion illuminations. We describe a physical implementation of the conceptual model using bag files. We evaluate this implementation using different lossless compression techniques. A key finding is that our bag file implementation is very compact when compared with the original point clouds. While compression reduces the size of a bag file, a combination that includes the use of both internal bag file compression (lz4 with chunks) and Gzip is not necessarily the most compact representation. We open source our software and its point cloud sequence data for use by the scientific community, see https://github.com/flyinglightspeck/FLSbagfile.
Improved Teaching Learning Algorithm with Laplacian operator for solving nonlinear engineering optimization problems

Teaching Learning Algorithm (TLA) is a recently developed nature-inspired optimization technique applicable to complex optimization problems. This paper proposes an improved TLA version using the Laplacian operator of the Genetic Algorithm (GA), named LX-TLA. The proposed algorithm is tested on benchmark optimization problems, including unimodal and multimodal problems. The numerical results are obtained in the form of objective function values, and a t-test is applied to compare the performance of LX-TLA and basic TLA. Convergence plots are given to provide insight into the convergence behavior of LX-TLA. The results reveal that proposed algorithm provides effective and efficient performance in solving benchmark test functions. The proposed algorithm is also applied to engineering design problems, such as Tuned Mass Damper (TMD), truss structure, welded beam, tension string, and pressure vessel. The results obtained using LX-TLA are compared with other nature-inspired optimization algorithms. The results demonstrate that the proposed algorithm is a robust and effective tool for solving complex optimization problems.
Extractive Summarization and Multiple Choice Question Generation using XLNet

Automated Multiple-Choice Question (MCQ) generation is a rapidly growing field in Natural Language Processing (NLP) that aims to assist educators and trainers in creating high-quality, efficient, and effective assessment materials. This is achieved by analyzing large amounts of textual data, such as educational content, and identifying key concepts and relationships between them. The process of generating MCQs automatically can be broken down into several steps, including text summarization, keyword extraction, distractor generation, and sentence mapping. A new approach is proposed for Text summarization is based on NLP models like XLNet and YAKE is used for keyword extraction. Distractor generation is done using lexical databases such as ConceptNet and WordNet. Sentence mapping is used to identify the main concepts and relationships within a text, which can then be used to formulate questions and options for the multiple-choice questions. The output is a set of MCQs that are semantically related to the input text and can be used for educational and training purposes.
FaceLivePlus: A Unified System for Face Liveness Detection and Face Verification

Face verification is a trending way to verify someone's identity in broad applications. But such systems are vulnerable to face spoofing attacks via, for example, a fraudulent copy of a photo, making it necessary to include face liveness detection as an additional safeguard. Among most existing studies, the face liveness detection is realized in a separate machine learning model in addition to the model for face verification. Such a two-model configuration may face challenges when deployed onto platforms with limited computation power and storage (e.g. mobile phone, IoT devices), especially considering each model may have millions of parameters. Inspired by the fact that humans can verify a person's identity and liveness at a single glance from a face, we develop a novel system, named FaceLivePlus, to learn a single and universal face descriptor for the two tasks (face verification and liveness detection) so that the computational workload and storage space can be halved. To achieve this, we formulate the underlying relationship between the two tasks, and seamlessly embed this relationship in a distance ranking deep model. The model directly works on features rather than classification labels, which makes the system well generalized on unseen data. Extensive experiments show that our average half total error rate (HTER) has at least 15% and 8% improvement from the state-of-the-arts on two benchmark datasets. We anticipate this approach could become a new direction for face authentication.