Reviewerly/exHarmony huggingface dataset:
Three-Dimensional Path Planning Algorithm for Global Path of AUV Based on ROBL-MFO on Static Environment

In this research we study the optimization problem of path planning in an oceanic ecosystem with a Static current in the presences of various obstacles with the autonomy of Autonomous underwater vehicles (AUV). We introduce an improved Moth flame optimization algorithm called refraction principle and opposite-based-learning Moth flame optimization (ROBL-MFO) in a Static current environment. This algorithm addresses the differences between the traditional Moth flame optimization (MFO) algorithm. First, it adjusts the moth position update formula we deal with the term of the historically optimal flame average, and improves the convergence velocity of the algorithm. Secondly, it uses a random inverse learning strategy to narrow down the algorithm search. Finally, the principle of refraction provides the algorithm with the ability to jump from the local optima and help the algorithm avoid premature convergence. The numerical results show that the ROBL-MFO improves the classical MFO, in terms of optimization criteria.
Consensus CIF-Based IMM Filtering for Multiple-UAV Target Tracking

Aimed at requirements of high estimation accuracy and less consensus iterations for multiple UAVs target tracking, a hybrid consensus cubature information filter (CIF)-based interacting multiple-model (CCIF-IMM) filter is proposed. The CIF-IMM framework is employed for distributed estimation to increase the nonlinear estimation accuracy, and the measurements of adjacent UAVs are fully used to improve the global observability by the CCIF-IMM. Besides, the hybrid consensus on measurement and information is employed for the CCIF-IMM filter to achieve the global consistency with less iteration steps. Moreover, the simulation results illustrate that CCIF-IMM can accurately track the mode transition of the maneuvering target and achieves better estimation performance in multiple UAVs target tracking.
Improved A* and Fuzzy Dynamic Window Based Dynamic Trajectory Planning for an UAV

An improved A* and fuzzy dynamic window approach (DWA) based trajectory planning algorithm is proposed in this paper to realize dynamic trajectory planning for an unmanned aerial vehicle (UAV). Firstly, a 3D diagonal distance is designed as the heuristic function to improve the calculation efficiency of A*. Meanwhile, a path smoothness optimization method is introduced to extract key points of the path planned by improved A*. Then, a DWA is extended to 3D space for the UAV’s trajectory planning under dynamic obstacles. Its evaluation function is improved by adding a novel evaluation item for different kinds of dynamic obstacles, and a fuzzy logic is adopted to update the function’s weight coefficients dynamically. In addition, the attitude angles adjusting functions are proposed to eliminate the initial angular deviations between the UAV and the global optimal path. Finally, the proposed algorithm can not only realize dynamic obstacle avoidance effectively, but also shorten and smoothen the trajectory under the global optimal constraints.
A Comparative Eye-Tracking Study Between a Virtual Reality System and a Desktop Environment for Learning the Prepositions of Place in English

Virtual reality (VR) is gaining momentum in language education thanks to technological advances and technology at a cost that is affordable for deploying a learning experience. However, there is a need for more research to identify the topics that are most suitable to be taught using VR in English as a foreign language (EFL) courses. In order to close part of this gap, this article reports on the results of a comparative eye-tracking study conducted with 80 university students on the effectiveness of a VR system and a desktop application for learning English prepositions of place. The results show that students using the VR system significantly outperformed those using a 2D application for learning English prepositions of place. However, students spent more time and made more mistakes in the VR system. The easiest English preposition was “on” and the most difficult one was “below” for the desktop application, and “behind” for the VR system. Some implications and recommendations for teachers are provided based on the results.
Study of Multi-Modal Diary Service Using Spoken Dialogue Agent for Self-Care in Elderly People

With Japan's aging population and low birth rate, the self-care of older adults at home has become increasingly in demand. Although the rapid progress of AI-enabled healthcare is developing, diversity and reusability of in-home daily living data to be recorded and analyzed are still challenging points. In our previous study, we have developed a system utilizing spoken dialogue agents for listening to the minds of older adults. The purpose of this study is to propose a multi-modal diary service that records and analyzes dialogue data with a diversified viewpoint. Our key idea is to retrieve the text logs, audio, and images and execute multi-modal visualization. The proposed method describes the main steps, including heterogeneous data extraction, Web API development, multi-modal diary generation, and heterogeneous data analysis. Also, we have discussed the approach to extending heterogeneous data. Using the proposed method, older adults can recollect the past for their own better. In this way, the correct improvement of in-home care and health management for all individuals is promising.
EasyView: Enabling and Scheduling Tensor Views in Deep Learning Compilers

In recent years, memory-intensive operations are becoming dominant in efficiency of running novel neural networks. Just-in-time operator fusion on accelerating devices like GPU proves an effective method for optimizing memory-intensive operations, and suits the numerous varying model structures. In particular, we find memory-intensive operations on tensor views are ubiquitous in neural network implementations. Tensors are the de facto representation for numerical data in deep learning areas, while tensor views cover a bunch of sophisticated syntax, which allow various interpretations on the underlying tensor data without memory copy. The support of views in deep learning compilers could greatly enlarge operator fusion scope, and appeal to optimizing novel neural networks. Nevertheless, mainstream solutions in state-of-the-art deep learning compilers exhibit imperfections either in view syntax representations or operator fusion. In this article, we propose EasyView, which enables and schedules tensor views in an end-to-end workflow from neural networks onto devices. Aiming at maximizing memory utilization and reducing data movement, we categorize various view contexts in high-level language, and lower views in accordance with different scenarios. Reference-semantic in terms of views are kept in the lowering from native high-level language features to intermediate representations. Based on the reserved reference-semantics, memory activities related to data dependence of read and write are tracked for further compute and memory optimization. Besides, ample operator fusion is applied to memory-intensive operations with views. In our tests, the proposed work could get average 5.63X, 2.44X, and 4.67X speedup compared with the XLA, JAX, and TorchScript, respectively for hotspot Python functions. In addition, operation fusion with views could bring 8.02% performance improvement in end-to-end neural networks.
Analysis of The Impact of Software Detailed Design on Mobile Application Performance Metrics

The rapid development of mobile applications in recent years has forced developers to develop their applications quickly. Application quality must be one of the top concerns of developers since poor application design will affect the quality of the application. Performance is one of the important quality attributes that determine the quality of an application. One approach that can be used to overcome performance problems is the design pattern. However, as the research progressed, other approaches were discovered such as refactoring code smells and design principles. In this study, a detailed design analysis was carried out on the source code of the mobile application by applying design patterns, refactoring the code smells, and implementing design principles to determine their impact on the application's performance. To measure the application performance, Central Processing Unit (CPU) usage, memory usage, and frame rate metrics are used. Based on the implementation design patterns, refactoring the code smells, and applying design principles, the result found that design patterns can affect application performance depending on the design pattern used. The Strategy pattern and Visitor pattern optimize memory usage by 1%, while the Bridge pattern increases memory usage by 2%. Meanwhile, the result of refactoring the code smells can optimize CPU usage by 35% and memory usage by 2%, and design principles can optimize CPU usage by 25% and application frame rates by 5 frame per second (fps).
Nostradamus Goes Quantum

In the Nostradamus attack, introduced by Kelsey and Kohno (Eurocrypt 2006), the adversary has to commit to a hash value y of an iterated hash function $$\textsf{H}$$ such that, when later given a message prefix $$P$$ , the adversary is able to find a suitable “suffix explanation” $$S$$ with $$\textsf{H}(P\left. \right\| {S})=y$$ . Kelsey and Kohno show a herding attack with $$2^{2n/3}$$ evaluations of the compression function of $$\textsf{H}$$ (with n bits output and state), locating the attack between preimage attacks and collision search in terms of complexity. Here we investigate the security of Nostradamus attacks for quantum adversaries. We present a quantum herding algorithm for the Nostradamus problem making approximately $$\root 3 \of {n}\cdot 2^{3n/7}$$ compression function evaluations, significantly improving over the classical bound. We also prove that quantum herding attacks cannot do better than $$2^{3n/7}$$ evaluations for random compression functions, showing that our algorithm is (essentially) optimal. We also discuss a slightly less tight bound of roughly $$2^{3n/7-s}$$ for general Nostradamus attacks against random compression functions, where $$s$$ is the maximal block length of the adversarially chosen suffix $$S$$ .
Local directional gradients extension for recognising face and facial expressions

This paper proposes new descriptors for recognising face and facial expressions. Our descriptors consist in combining local directional gradients with support vector machine (SVM) linear classification. This combination allows extracting discriminant facial expressions features for better classification accuracy with good efficiency than existing classifiers. Both descriptors are built based on the reduced texture features extracted from the face image based on magnitude and orientation maps on the horizontal and vertical coordinates. JAFFE and YALE benchmarks have been used to evaluate the accuracy and execution time of the requested face in the classification process. The experimental results are very promising and show that the proposed descriptors are effective and efficient compared to current works.
The Urgency of Prototype Curriculum Based E-Book for Mathematics Learning for Vocational School Students

The development and learning paradigm is always followed by adjustments to the implementation of learning activities. Currently, a solution is being offered to improve the quality of learning through the implementation of kurikulum prototype. This study aims to see the potential and opportunities for product development of teaching materials based on E-Books for learning mathematics. This research belongs to the mixed methods approach. The research subjects consisted of teachers, and 42 students. While the data collected in the form of qualitative and quantitative data. Qualitative data were collected with technical observations, and interviews with the instruments, namely observations and interviews. Quantitative data were collected using a non-test technique, namely a questionnaire and followed by a questionnaire as an instrument. The research data will be analyzed descriptively with interactive analysis through data collection, data reduction, data presentation, and drawing conclusions. The results showed that during the implementation of learning, teachers had basically used ICT-based media which were limited to the type of presentation slides and the internet, student responses stated that they needed ICT-based teaching materials for learning mathematics (92.9%), total 90.5% of students also agrees with the development of ICT-based teaching materials in e-book format for learning mathematics, this is certainly an initiative and desire that must be realized in order to implement innovative learning. Thus, the results of this study can be used as a basis for developing an innovation through ICT-based teaching materials in e-book format to improve the learning process in vocational high schools.
Unconditionally Secure NIZK in the Fine-Grained Setting

Non-interactive zero-knowledge (NIZK) proof systems are often constructed based on cryptographic assumptions. In this paper, we propose the first unconditionally secure NIZK system in the $$\mathsf {AC^0}$$ -fine-grained setting. More precisely, our NIZK system has perfect soundness for all adversaries and unconditional zero-knowledge for $$\mathsf {AC^0}$$ adversaries, namely, an $$\mathsf {AC^0}$$ adversary can only break the zero-knowledge property with negligible probability unconditionally. At the core of our construction is an OR-proof system for satisfiability of 1 out of polynomial many statements.
Key-Reduced Variants of 3kf9 with Beyond-Birthday-Bound Security

3kf9 is a three-key CBC-type MAC that enhances the standardized integrity algorithm f9 (3GPP-MAC). It has beyond-birthday-bound security and is expected to be a possible candidate in constrained environments when instantiated with lightweight blockciphers. Two variants 2kf9 and 1kf9 were proposed to reduce key size for efficiency, but recently, Leurent et al. (CRYPTO’18) and Shen et al. (CRYPTO’21) pointed out critical flaws on these two variants and invalidated their security proofs with birthday-bound attacks. In this work, we revisit previous constructions of key-reduced variants of 3kf9 and analyze what went wrong in security analyses. Interestingly, we find that a single doubling near the end restores the intended beyond-birthday-bound security of both 2kf9 and 1kf9. We then propose two new key-reduced variants of 3kf9, called n2kf9 and n1kf9. By leveraging previous attempts, we prove that n2kf9 is secure up to $$2^{2n/3}$$ queries, and prove that n1kf9 is secure up to $$2^{2n/3}$$ queries when the message space is prefix-free. We also provide beyond-birthday analysis of n2kf9 in the multi-user setting. Note that compared to EMAC and CBC-MAC, the additional cost to provide a higher security guarantee is expected to be minimal for n2kf9 and n1kf9. It only requires one additional blockcipher call and one doubling.
Research on academic early warning and assistance under artificial intelligence vision: current situation, trend and application

Academic early warning and help are the key measures for colleges and universities to improve the quality of talent training in the new era. Big data generated from education informatization practice can play its value under the support of artificial intelligence technology. With the background of the digital transformation of local colleges and universities, we can examine the current situation and characteristics trend of students’ academic problems through scientific data analysis methods, and identify the key tasks. Three application scenarios with comprehensive data, integrating teaching and learning behavior data, teaching goal achievement, and second classroom, supported by artificial intelligence recommendation algorithm, are launched to help accurate academic assistance, students’ self-improvement, scientific prediction and decision-making.
Can AI emulate slow &amp; fast thinking?

Artificial Intelligence (AI) attempts to emulate natural intelligence of Homo sapiens. Sometimes, the wise men have biases. This review pointed out the gaps, and the shortfalls to develop new AI. Currently, given the technology of the 5th G. m.m.w. communication broadband channel, real-time smart sensory inputs with rapid electronic processing become possible to overcome some gaps reasonably. The questions remain are whether AI can capture the chemical signals, such as hormones effects? We provide the motivation for AI developers care to overcome these shortfalls? (1) Financial Reasons: Space travel can have intelligent as well as emotion-sensitive robots to colonize the Space sites. (2) Earthbound Humanity Reasons: Aging WWII Baby Boomers about 100M and most of them about half without offspring to take care them, they will live long contribute their experience and wisdom but will need Humanoids take care of them with the understanding of their loneliness emotional needs.
Sophisticated CPBIS methods applied for FBISODATA clustering algorithm using with real time image database

Data mining is a process of mining hidden information to the previously unknown data and theoretically useful unknown information from a large amount of genuine data to be stored in a database. Image mining is a part of data mining with used as a predictive measure to identify with the age of the tiger. This research work is mainly focused on, to identify with the age of the Tiger using data mining techniques. This research work incorporates with which those domains of image processing and data mining to predict the age of the tiger using different kinds of color images are used. The fuzzy iterative self-organizing data analysis (FISODATA) clustering method requires more predefined parameters tofind the maximum number of iterations, the minimum number of points in the cluster, and smallest amount of distance with the centers of the clusters. The key undertaking of the studies of diverse colors mechanism is to decide the age of the tiger; the usage of shade action pixel primarily based on image segmentation; the usage of facts that are used in the mining techniques. However, the more matrix components to be measuring the processing time, retrieval time, accuracy, and blunders fee with the aid of using producing better performance.
JavaBIP meets VerCors: Towards the Safety of Concurrent Software Systems in Java

Abstract We present “Verified JavaBIP”, a tool set for the verification of JavaBIP models. A JavaBIP model is a Java program where classes are considered as components, their behaviour described by finite state machine and synchronization annotations. While JavaBIP guarantees execution progresses according to the indicated state machines, it does not guarantee properties of the data exchanged between components. It also does not provide verification support to check whether the behaviour of the resulting concurrent program is as (safe as) expected. This paper addresses this by extending the JavaBIP engine with run-time verification support, and by extending the program verifier VerCors to verify JavaBIP models deductively. These two techniques complement each other: feedback from run-time verification allows quicker prototyping of contracts, and deductive verification can reduce the overhead of run-time verification. We demonstrate our approach on the “Solidity Casino” case study, known from the VerifyThis Collaborative Long Term Challenge.
On the Effectiveness of ViT Features as Local Semantic Descriptors

We study the use of deep features extracted from a pre-trained Vision Transformer (ViT) as dense visual descriptors. We observe and empirically demonstrate that such features, when extracted from a self-supervised ViT model (DINO-ViT), exhibit several striking properties, including: (i) the features encode powerful, well-localized semantic information, at high spatial granularity, such as object parts; (ii) the encoded semantic information is shared across related, yet different object categories, and (iii) positional bias changes gradually throughout the layers. These properties allow us to design simple methods for a variety of applications, including co-segmentation, part co-segmentation and semantic correspondences. To distill the power of ViT features from convoluted design choices, we restrict ourselves to lightweight zero-shot methodologies (e.g., binning and clustering) applied directly to the features. Since our methods require no additional training nor data, they are readily applicable across a variety of domains. We show by extensive qualitative and quantitative evaluation that our simple methodologies achieve competitive results with recent state-of-the-art supervised methods, and outperform previous unsupervised methods by a large margin. Code is available in https://dino-vit-features.github.io/ .
Line-encoded structured light measurement method in measuring shiny and transparent objects

Abstract Structured light measurement (SLM) has been widely used in acquiring the three-dimensional (3D) shape of objects because of its high precision and high speed. However, the intensity based structured light coding strategies like sinusoidal patterns is difficult to measure transparent and shiny objects with high dynamic range surface reflectance due to inaccurate light intensity information. Divergent multi-line structured light projection and traditional gray-code method avoids this problem by extracting lines. However, traditional gray-code method requires additional many patterns to distinguish the order of lines, which reduces the measurement efficiency. To overcome defects, this paper proposes a line-encoded few-pattern SLM method in measuring shiny and transparent objects. In this method, we simultaneously extract the edge line, the center line and obtain their orders by projecting only five patterns. Many experiments were implemented to prove its accuracy and robustness. Because only five patterns are required, the proposed method greatly accelerate the process of reconstruction, which is indispensable for 3D real-time reconstruction. We conducted a dental model dynamic scanning experiment to prove it.
Performance Analysis of Deep Learning Supported Kalman Filter

Sensor fusion is critical in localization and positioning problems, it combines measurements from different sensors to improve estimation accuracy and increase system redundancy and integrity. A Kalman filter (KF) is one of the most commonly used algorithms in sensor fusion. As a model-based algorithm, a KF leverages the known models and manages to provide reasonable sensor fusion solutions. However, the KF performance is limited by noise model assumptions, tuning problems and non-linearity in the systems. Multi-sensor systems provide a large quantity of data which makes learning-based algorithms possible, and it has been proved that the learning-based algorithms can provide promising accuracy without requiring the precise knowledge of one system and only need to make a few generic assumptions on the systems. On the other hand, Learning-based algorithms require large training effort and are susceptible to the errors in data. In this paper, we hybridize model-based and learning-based algorithms. A deep neural network is integrated into the KF structure and assists the KF to learn the optimal Kalman gain from data. We test the resulting “deep learning supported KF” and compare the results with the standard KF on simulated datasets of 2D linear motion and 2D circular motion. The hybridization algorithm shows equal performance to the KF in case the KF is optimum (linear case) and improved performance on position and velocity estimation, when the KF models are known to deviate from the simulated motion (circular case).