Reviewerly/exHarmony huggingface dataset:
Efficient Strategy for Multi-UAV Path Planning in Target Coverage Problems

In recent years, multi unmanned aerial vehicles (UAVs) are used in the same system to accomplish more complex missions. In many multi-UAV system applications, the main objective is to visit some predetermined checkpoints in operational area. If the number of check points and constraints increases, finding a feasible solution takes up too much time. In this paper, a checkpoint based multi-UAV path planning problem is solved by using improved genetic algorithm. The main contributions of this paper are: (1) the introducing revisit time interval concept, (2) the investigating of the effect of objective function description, and (3) looking into an outcome of using multiple runways on optimal multi-UAV path planning. The proposed strategy-based optimization methodology is performed for checkpoint based multi-UAV path planning problems in two-dimensional (2D) environment. Performance results show that the proposed strategy provides effective and feasible paths for each UAV.
Using Scaffolding Strategy in Developing Speaking Ability for EFL University Students

This research aims at finding whether the scaffolding strategy can improve the students’ speaking ability. The problem highlighted in this research is the students' low level of speaking in English language. The scaffolding Strategy can improve Students' motivation to control English language. There are many ways that can be used to develop the speaking skill, among these ways are games, group work and many other ways.&#x0D; Through using this strategy students can be motivated to join the lesson, so they will take effective part of the lesson. Without using this strategy students are unable to achieve their aims. The teacher has to use this strategy for the benefit of himself and his students. Students of English as foreign language may face many problems that cannot be solved without the teachers help.&#x0D; There are many advantages and disadvantages of using this strategy that can help or retard the student's ability to learn how to use this language. All things in this Strategy can be very helpful for the students to control language. Every teacher has to use this strategy in his teaching as a result he can control his class and enhance his students' speaking ability.
Quantum circuits for discrete-time quantum walks with position-dependent coin operator

The aim of this paper is to build quantum circuits that implement discrete-time quantum walks having an arbitrary position-dependent coin operator. The position of the walker is encoded in base 2: with n wires, each corresponding to one qubit, we encode $$2^n$$ position states. The data necessary to define an arbitrary position-dependent coin operator is therefore exponential in n. Hence, the exponentiality will necessarily appear somewhere in our circuits. We first propose a circuit implementing the position-dependent coin operator, that is naive, in the sense that it has exponential depth and implements sequentially all appropriate position-dependent coin operators. We then propose a circuit that “transfers” all the depth into ancillae, yielding a final depth that is linear in n at the cost of an exponential number of ancillae. The main idea of this linear-depth circuit is to implement in parallel all coin operators at the different positions. Reducing the depth exponentially at the cost of having an exponential number of ancillae is a goal which has already been achieved for the problem of loading classical data on a quantum circuit (Araujo in Sci Rep 11:6329, 2021) (notice that such a circuit can be used to load the initial state of the walker). Here, we achieve this goal for the problem of applying a position-dependent coin operator in a discrete-time quantum walk. Finally, we extend the result of Welch (New J Phys 16:033040, 2014) from position-dependent unitaries which are diagonal in the position basis to position-dependent $$2\times 2$$ -block-diagonal unitaries: indeed, we show that for a position dependence of the coin operator (the block-diagonal unitary) which is smooth enough, one can find an efficient quantum-circuit implementation approximating the coin operator up to an error $$\epsilon $$ (in terms of the spectral norm), the depth and size of which scale as $$O(1/\epsilon )$$ . A typical application of the efficient implementation would be the quantum simulation of a relativistic spin-1/2 particle on a lattice, coupled to a smooth external gauge field; notice that recently, quantum spatial-search schemes have been developed which use gauge fields as the oracle, to mark the vertex to be found (Zylberman in Entropy 23:1441, 2021), (Fredon arXiv:2210.13920). A typical application of the linear-depth circuit would be when there is spatial noise on the coin operator (and hence a non-smooth dependence in the position).
<scp>Bilateral‐Weighted</scp> Online Adaptive Isolation Forest for anomaly detection in streaming data

Abstract We propose a method called Bilateral‐Weighted Online Adaptive Isolation Forest (BWOAIF) for unsupervised anomaly detection based on Isolation Forest (IF), which is applicable to streaming data and able to cope with concept drift. Similar to IF, the proposed method has only few hyperparameters whose effect on the performance are easy to interpret by human intuition and therefore easy to tune. BWOAIF ingests data and classifies it as normal or anomalous, and simultaneously adapts its classifier by removing old trees as well as by creating new ones. We show that BWOAIF adapts gradually to slow concept drifts, and, at the same time, it is able to adapt fast to sudden changes of the data distribution. Numerical results show the efficacy of the proposed algorithm and its ability to learn different classes of concept drifts, such as slow/fast concept shift, concept split, concept appearance, and concept disappearance.
Local power consumption method of distributed photovoltaic generation in rural distribution network based on blockchain

Abstract With the increasing penetration of distributed photovoltaic generation (DPVG) in the rural distribution network, some problems such as abandoning solar energy and increasing voltage fluctuation have become more and more prominent. In order to promote local or nearby power consumption of the photovoltaic (PV) generation in the distribution network, the profit strategy is given with an optimal electricity price based on blockchain technology (BCT) by considering the topology, the load fluctuation of the distribution network and the reputation value of agricultural park consumers. The electricity price in the blockchain (BC) transaction is used as an intermediate variable to establish the revenue model for the PV generation, the agricultural park consumers and the distribution network. The power trading mechanism of the PV generation is constructed in the decentralized management agency of the BC. The maximum overall comprehensive income of the PV generation and the park consumers is used as the objective function, and the Stackelberg game theory is applied to prove the existence of the optimal game strategy in the transaction. The local power consumption method of the PV generation is simulated with the optimal electricity price in the IEEE 33‐node distribution network. The problem of abandoning solar energy is effectively relieved within an appropriate voltage limitation, and the comprehensive benefit of the PV generation and the park consumers is raised in the distribution network.
Structured knowledge creation for Urdu language: A <scp>DBpedia</scp> approach

Abstract Wikipedia information is extracted by DBpedia and linked to other web resources as Linked Open Data, which is an important contribution to the field of semantics. As part of its internationalisation endeavour, DBpedia now has 20 language chapters that have been mapped to it; nonetheless, there have been very few attempts from Urdu. This article outlines the procedures and highlights the efforts put forward as the first contribution to the manual creation of Urdu mappings with DBpedia Ontology classes. Our approach led to an increase in the number of mapped infoboxes, thus enhancing the DBpedia. The mapping procedure is broken down into two parts. The infobox template is first mapped to the DBpedia ontology's relevant class, and then the attributes of the infobox are mapped to the properties of that class. In addition, alongside other mapped languages, Urdu labels are included to the description of Ontology classes. We have covered around a thousand properties and attributes of Urdu with English DBpedia Ontology on DBpedia mapping server.
DSC-MDE: Dual structural contexts for monocular depth estimation

Geometric and semantic contexts are essential to solving the ill-posed problem of monocular depth estimation (MDE). In this paper, we propose a deep MDE framework that can aggregate dual-modal structural contexts for monocular depth estimation (DSC-MDE). First, a cross-shaped context (CSC) aggregation module is developed to globally encode the geometric structures in depth maps observed under the fields of vision of robots/autonomous vehicles. Next, the CSC-encoded geometric features are further modulated with semantic context in an object-regional context (ORC) aggregation module. Finally, to train the proposed network, we present a focal ordinal loss (FOL), which pays more attention to distant samples to avoid the issue of over-relaxed constraints on these samples occurring in the ordinal regression loss (ORL). We compare the proposed model to recent methods with geometric and multi-modal contexts, and show that the proposed model obtains state-of-the-art performance on both indoor and outdoor datasets, including NYU-Depth-V2, Cityscapes and KITTI.
MMTrans: MultiModal Transformer for realistic video virtual try-on

Video virtual try-on methods aim to generate coherent, smooth, and realistic try-on videos, it matches the target clothing with the person in the video in a spatiotemporally consistent manner. Existing methods can match the human body with the clothing and then present it by the way of video, however it will cause the problem of excessive distortion of the grid and poor display effect at last. Given the problem, we found that was due to the neglect of the relationship between inputs lead to the loss of some features, while the conventional convolution operation is difficult to establish the remote information that is crucial in generating globally consistent results, restriction on clothing texture detail can lead to excessive deformation in the process of TPS fitting, make a lot of the try-on method in the final video rendering is not real. For the above problems, we reduce the excessive distortion of the garment during deformation by using a constraint function to regularize the TPS parameters; it also proposes a multimodal two-stage combinatorial Transformer: in the first stage, an interaction module is added, in which the long-distance relationship between people and clothing can be simulated, and then a better remote relationship can be obtained as well as contribute to the performance of TPS; in the second stage, an activation module is added, which can establish a global dependency, and this dependency can make the input important regions in the data are more prominent, which can provide more natural intermediate inputs for subsequent U-net networks. This paper’s method can bring better results for video virtual fitting, and experiments on the VVT dataset prove that the method outperforms previous methods in both quantitative and qualitative aspects.
Bridging Curatorial Intent and Visiting Experience: Using AR Guidance as a Storytelling Tool

Augmented Reality (AR) visits enhances the art exhibition experience by overlaying digital content. Although there has been significant interest in AR guides, few works leverage AR to bridge curatorial intent and audiences understanding. This paper focuses on integrating the curatorial intent within the AR overlays by developing the narrative layers established by the relationships between works. We develop a narrative system that identifies and links the primary art pieces of the exhibition within a digital story consistent with the curator’s perspective. The system is applied to a physical exhibition composed of seven art pieces. We evaluate the impact of AR overlays through two user experiments, conducted on art professionals and general audience, respectively. Both groups considered that the AR tour system improved interactivity, self-reported learning, and user satisfaction significantly (> 4/5). Besides, visitors found the system easy to get to do what they want to (4.7/5), and would use it for future visits (4.6/5). This study raises essential design considerations towards designing integrated AR museum guides that combine the perspective of artists and curators towards a better visiting experience.
Evaluating the Sense of Embodiment through Out-of-Body Experience and Tactile Feedback

Out-of-body experience (OBE) is generated by sensory disintegration. In virtual reality (VR), we can provide OBE to people by switching the first-person perspective (1PP) to the third-person perspective (3PP). Generally, 1PP is the choice for high body ownership and presence. Moreover, tactile feedback that is experienced from the 1PP can provide a higher immersive experience. However, whether the combination of 3PP and tactile feedback could affect the sense of embodiment in immersive environments is underexplored. Thus, we conducted a 2 × 2 (OBE: 1PP vs. 3PP × Tactile Feedback [TF]: with vs. without tactile feedback) VR study to discover the effect of OBE in the presence of TF. In our study, we examined OBE and TF through the five dimensions of the sense of embodiment: body ownership, agency, tactile sensations, location of the body, and response to external stimuli. We developed an application to replicate the rubber hand illusion (RHI) study with partial body tracking. We found significant results for both OBE and TF in different dimensions of embodiment. Specifically, we revealed that 3PP decreased the body’s sense of body ownership, agency, and location. Moreover, enabling tactile feedback induced tactile sensations and responses to external stimuli. In the remainder of this paper, we discuss our findings and limitations and provide directions for future studies on OBE in VR.
The Impacts of Lyrics Training on Improving Listening Skills for ESL Students

This study found out the impacts of the apps Lyrics Training on learning and teaching Listening skills. A few papers still researched this app and its impact on how students learn the English Language. The qualitative research happened in two weeks with the content analysis method. The researcher collected all highlighted points from the available papers, affecting the learning process. Furthermore, Lyrics Training is a beautiful app because it helps students to achieve their learning goals via music and detect new beautiful songs to add to their playlists. Therefore, it is an ideal tool for teachers to consider applying in their Listening class. After the research process, the researcher will analyze its pros/cons. She also suggested effective ways of learning the Listening skill and teaching this skill with Lyrics Training in class.
FIRE 2022 ILSUM Track: Indian Language Summarization

This abstract provides a short overview of the first edition of the shared task on Indian Language Summarization (ILSUM) organized at the 14th Forum for Information Retrieval Evaluation (FIRE 2022). A more detailed discussion is available in the track overview paper. The objective of this shared task was to create benchmark data for text summarization in Indian languages. This edition included three languages Hindi, Gujarati, and Indian English which is an officially recognized dialect of English mainly used in the Indian subcontinent. The task saw an enthusiastic response, with registrations from over 50 teams. A total of 12 teams submitted test runs across the three languages out of which 10 teams submitted working notes. Standard ROUGE metrics were used as the evaluation metric.
Classification of Waste Materials using CNN Based on Transfer Learning

Waste Management is important for humans as well as nature for healthy life and a clean environment. The major step for effective waste management is the segregation of waste according to its types. The advancement of technology such as hardware and artificial intelligence is used for the segregation of waste. There are several machine learning and deep learning algorithms available for image classification. Among them, Convolutional Neural Network is the most used one. The main objective of this work is to classify images of waste materials using CNN into seven categories (cardboard, glass, metal, organic, paper, plastic, and trash). Then, cardboard, organic, and paper class images are considered biodegradable waste, and other classes are considered non-biodegradable waste. The pre-trained CNN model such as InceptionV3, InceptionResNetV2, Xception, VGG19, MobileNet, ResNet50 and DenseNet201 have been trained and performed fine-tuning on the waste dataset. Among these models, the VGG19 model performed with less accuracy, whereas the InceptionV3 model performed with high learning accuracy. Overall, the obtained result is promising.
Causal Categorization of Mental Health Posts using Transformers

With recent developments in digitization of clinical psychology, NLP research community has revolutionized the field of mental health detection on social media. Existing research in mental health analysis revolves around the cross-sectional studies to classify users’ intent on social media. For in-depth analysis, we investigate existing classifiers to solve the problem of causal categorization which suggests the inefficiency of learning based methods due to limited training samples. To handle this challenge, we use transformer models and demonstrate the efficacy of a pre-trained transfer learning on "CAMS" dataset [4]. The experimental result improves the accuracy and depicts the importance of identifying cause-and-effect relationships in the underlying text.
NrityaManch: An Annotation and Retrieval System for Bharatanatyam Dance

This paper presents an annotation and retrieval application named NrityaManch dedicated explicitly to the Indian classical dance. We primarily choose Bharatanatyam dance for the application development. We exploit ontology technique which captures dance image's annotation details and structurally organizes the dance database. An OWL2 ontology is developed in Protégé 5.5.0 which is validated using HermiT 1.4.3.456 reasoner to maintain consistency. A user interface is provided for the manual annotation of dance images. Initially, we focus on dancer details, dance details, and elements of static dance posture like hasta mudra during the annotation. All annotation details are saved in RDF/XML file. A search window is provided, which facilitates two types of search - natural language query search and tight query search. Named Entity Recognition (NER) pipeline mechanism is utilized in this work which facilitates keyword extraction from natural language queries. A SPARQL query is automatically generated by the system which is applied to the RDF corpus in order to retrieve distinct images. The NER pipeline mechanism achieves an accuracy of 80% for our dance dataset. The system achieves an average f-score value of 0.8547 for the retrieval functionality. The proposed system intends to help dance learners to find dance resources in a dedicated place and will also help in Indian classical dance preservation.
AUTOMATIC SELECTION KERNEL WITH ENSEMBLE CONCEPT IN SUPPORT VECTOR MACHINE (SVM) FOR CLASSIFICATION OF SOYBEAN PLANT DISEASE

Soybean is one of the plants with high vegetable protein value as a substitute for animal protein. In the process of cultivating soybeans, soybeans are often exposed to diseases caused by pathogens caused by viruses, bacteria and pests. To find out the disease usually requires direct observation to the field which requires an expert, so we need a way to be able to identify the disease automatically without having to directly monitor the field with an expert. SVM method is one method that can be used to classify the types of diseases that attack soybean plants. The SVM method has a lot of Kernel functions that can be used, where the Kernel is the core of the SVM method process, there are many kernels that can be used so that if you choose the wrong Kernel will have an impact on the results obtained. To overcome this problem, this research applies 3 Ensemble Kernel concepts which are used to combine several Kernels into 1, from the tests carried out using the Ensemble concept, better results are obtained than the classical SVM method. There are 3 concepts that can be used, namely bagging, boosting and stacking, Ensemble Kernel SVM with the stacking concept and the ANOVA kernel for the testing process to get the best accuracy value of 76.62%.
User Experience and Spatial Presence in a Walk-In Augmented Reality 3D Model for Tourists

Abstract In the context of vulnerable tourist attractions, it is often the case that tourists cannot be allowed the enter all the premises of the attraction, which potentially has a negative impact on the visitors’ experience. Virtual reality technologies are available for presenting the attraction for tourists in these cases, but the use of augmented reality (AR) also seems to have a role in certain use cases. The current paper presents our first experiment of using a walk-in augmented reality model to present an attraction to visitors near the original attraction. The tourist attraction visualized using AR was Mannerheim’s Saloon Car dating back to World War II. Nine participants used a tablet-based AR application to examine a realistically sized three-dimensional (3D) model of the saloon car in the close vicinity of the actual tourist attraction and filled in a questionnaire, which probed their user experience, spatial situation model, and spatial presence when using the system. These early results suggested that viewing a 3D model of the attraction using tablet-based AR provided a positive user experience for the participants and that the tablet-based AR implementation was successful in supporting the participants’ formation of a spatial situation model of the saloon car. The participants also gave relatively high ratings for spatial presence while viewing the 3D model using tablet-based AR.
Enabling Latency-Sensitive DNN Inference via Joint Optimization of Model Surgery and Resource Allocation in Heterogeneous Edge

Nowadays, edge computing is widely adopted to resolve the emerging deep neural networks (DNNs)-driven intelligence scenarios with the requirement of low-latency and high-accuracy, which includes heterogeneous end devices and DNNs. In such scenarios, the influx of data and computation into a shared edge server incurs prohibitive latency. Thus, we exploit the advantage of Multi-exit DNNs (ME-DNNs) that tasks can exit early at appropriate depths to save inference time. However, naively using ME-DNNs in the heterogeneous edge still fails to deliver fast inference due to improper model surgery and resource allocation.