Reviewerly/exHarmony huggingface dataset:
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation

Most of the existing works on pedestrian pose estimation do not consider estimating the pose of an occluded pedestrian, as the annotations of the occluded parts are not available in relevant automotive datasets. For example, CityPersons, a well-known dataset for pedestrian detection in automotive scenes does not provide pose annotations, whereas MS-COCO, a non-automotive dataset, contains human pose estimation. In this work, we propose a multi-task framework to extract pedestrian features through detection and instance segmentation tasks performed separately on these two distributions. Thereafter, an encoder learns pose specific features using an unsupervised instance-level domain adaptation method for the pedestrian instances from both distributions. The proposed framework has improved state-of-the-art performances of pose estimation, pedestrian detection, and instance segmentation.
Multiobjective path optimization of an indoor AGV based on an improved ACO-DWA

With their intelligence, flexibility, and other characteristics, automated guided vehicles (AGVs) have been popularized and promoted in traditional industrial markets and service industry markets. Compared with traditional transportation methods, AGVs can effectively reduce costs and improve the efficiency of problem solving in various application developments, but they also lead to serious path-planning problems. Especially in large-scale and complex map environments, it is difficult for a single algorithm to plan high-quality moving paths for AGVs, and the algorithm solution efficiency is constrained. This paper focuses on the indoor AGV path-planning problem in large-scale, complex environments and proposes an efficient path-planning algorithm (IACO-DWA) that incorporates the ant colony algorithm (ACO) and dynamic window approach (DWA) to achieve multiobjective path optimization. First, inspired by the biological population level, an improved ant colony algorithm (IACO) is proposed to plan a global path for AGVs that satisfies a shorter path and fewer turns. Then, local optimization is performed between adjacent key nodes by improving and extending the evaluation function of the traditional dynamic window method (IDWA), which further improves path security and smoothness. The results of simulation experiments with two maps of different scales show that the fusion algorithm shortens the path length by 9.9 and 14.1% and reduces the number of turns by 60.0 and 54.8%, respectively, based on ensuring the smoothness and safety of the global path. The advantages of this algorithm are verified. QBot2e is selected as the experimental platform to verify the practicability of the proposed algorithm in indoor AGV path planning.
A Systematic Literature Review of Keyphrases Extraction Approaches

The keyphrases of a document are the textual units that characterize its content such as the topics it addresses, its ideas, their field, etc. Thousands of books, articles and web pages are published every day. Manually extracting keyphrases is a tedious task and takes a lot of time. Automatic keyphrases extraction is an area of text mining that aims to identify the most useful and important phrases that give meaning to the content of a document. Keyphrases can be used in many Natural Language Processing (NLP) applications, such as text summarization, text clustering and text classification. This article provides a Systematic Literature Review (SLR) to investigate, analyze, and discuss existing relevant contributions and efforts that use new concepts and tools to improve keyphrase extraction. We have studied the supervised and unsupervised approaches to extracting keyphrases published in the period 2015-2022. We have also identified the steps most commonly used by the different approaches. Additionally, we looked at the criteria that should be evaluated to improve the accuracy of keyphrases extraction. Each selected approach was evaluated for its ability to extract keyphrases. Our findings highlight the importance of keyphrase extraction, and provide researchers and practitioners with information about proposed solutions and their limitations, which contributes to extract keyphrases in a powerful and meaningful way effective.
The Need for Digital Education in the Teaching Profession: A Path Toward Using the European Digital Competence Framework in Albania

Digital competence is part of 21st Century skills that allow individual teaching professionals to engage in new and flexible ways of learning. Studies have shown that digital competence improves education and equips the teaching staff with expertise on how to use information, communication, and basic problem-solving. The need for digital competence is clearly evident in the current pandemic situation, where digital technologies have taken a more prominent role in communication and education processes. Beyond the digital competence of educators, proper school infrastructure and curricula are needed during pre-service training to help teachers achieve digital competence. To better understand this need, an online survey was developed to analyze digital education in Albania. The survey was designed to analyze the teaching competence of teachers in pre-service and in-service programs, as well as their schools’ curricula and infrastructure. Results from the questionnaire highlighted a need for the acquisition of digital knowledge for teachers according to different age groups. The results of the study found that difficulties teachers encountered in the acquisition of digital knowledge were not only due to deficiencies in teacher training but also other issues such as lack of infrastructure. The study concludes by recommending that providing digital education should be in line with European and national policy and legislation, as well as with national and international organizations. The paper reports findings assessing the level of preparedness of Albanian educators in regard to digital education and explores opportunities and identifies challenges for coping with enhancing digital development.
Formal reasoning about layered monadic interpreters

Monadic computations built by interpreting, or handling , operations of a free monad are a compelling formalism for modeling language semantics and defining the behaviors of effectful systems. The resulting layered semantics offer the promise of modular reasoning principles based on the equational theory of the underlying monads. However, there are a number of obstacles to using such layered interpreters in practice. With more layers comes more boilerplate and glue code needed to define the monads and interpreters involved. That overhead is compounded by the need to define and justify the relational reasoning principles that characterize the equivalences at each layer. This paper addresses these problems by significantly extending the capabilities of the Coq interaction trees (ITrees) library, which supports layered monadic interpreters. We characterize a rich class of interpretable monads ---obtained by applying monad transformers to ITrees---and show how to generically lift interpreters through them. We also introduce a corresponding framework for relational reasoning about "equivalence of monads up to a relation R". This collection of typeclasses, instances, new reasoning principles, and tactics greatly generalizes the existing theory of the ITree library, eliminating large amounts of unwieldy boilerplate code and dramatically simplifying proofs.
Harnessing the power of single photons

Abstract The concept of photon counting is already a century old. However, we have only managed to harness its power over the last few decades. Generating and detecting these single quanta of light energy is essential for many applications and disruptive technologies that will solve many of today's global challenges, from super‐efficient photonic quantum processors and ultrasecure communication to highly accurate sensors for biomedical imaging. Single‐photon generation and detection technologies are redesigning the map of photonics applications.
Classification of Transformed and Geometrically Distorted Images using Convolutional Neural Network

Convolutional Neural Network is a deep learning method that is used in many image-related applications, such as image recognition and classification, it has achieved great performance in these fields, but it still suffers from some shortcomings.One of these shortcomings is not being able to be invariant to the input data due to some image transformations like translation, rotation, scaling, and geometric distortions such as skewness, perspective distortion and pincushion distortion.This study presents an optimized CNN which uses the Geometric Heat Flow (GHF) to improve the performance of the CNN regarding the invariant limitation and classification accuracy.GHF is a partial differential equation that expresses how the heat would diffuse on a surface concerning time in a specific location.GHF is invariant to image transformations and geometric distortions if it was taken concerning the object's arc length which will lead to an invariant CNN.The experiments show that GHF improves the performance of the CNN, and the proposed work achieves an accuracy of 98.09% on the MNIST handwritten dataset, 92.58% on the MNIST-Fashion dataset, and 86.09% on the CIFAR-10 dataset.
High quality of an absolute phase reconstruction for coherent digital holography with an enhanced anti-speckle deep neural unwrapping network

It is always a challenge how to overcome speckle noise interference in the phase reconstruction for coherent digital holography (CDH) and its application, as this issue has not been solved well so far. In this paper, we are proposing an enhanced anti-speckle deep neural unwrapping network (E-ASDNUN) approach to achieve high quality of absolute phase reconstruction for CDH. The method designs a special network-based noise filter and embeds it into a deep neural unwrapping network to enhance anti-noise capacity in the image feature recognition and extraction process. The numerical simulation and experimental test on the phase unwrapping reconstruction and the image quality evaluation under the noise circumstances show that the E-ASDNUN approach is very effective against the speckle noise in realizing the high quality of absolute phase reconstruction. Meanwhile, it also demonstrates much better robustness than the typical U-net neural network and the traditional phase unwrapping algorithms in reconstructing high wrapping densities and high noise levels of phase images. The E-ASDNUN approach is also examined and confirmed by measuring the same phase object using a commercial white light interferometry as a reference. The result is perfectly consistent with that obtained by the E-ASDNUN approach.
A survey on human activity recognition using CNN and LSTM

Human Activity Recognition (HAR) plays a massive role inside the everyday life of people due to its capability to investigate tremendous high-degree data approximately human interest from wearable or stationary devices. An extensive quantity of studies has been completed on HAR and numerous techniques primarily based on deep gaining knowledge of and device analyzing were exploited through the research community to categorize human sports. The principle motive of this evaluation is to summarize contemporary works based on a big range of deep neural networks structure, mainly convolution neural networks (CNNs) for human hobby reputation. The reviewed systems are clustered into 4 classes relying on the use of input devices like multimodal sensing gadgets, smart phones, radar, and vision devices. This assessment describes the performances, strengths, weaknesses, and the used hyper parameters of CNN architectures for every reviewed device with an evaluate of available public statistics property in addition, a communicate with the cutting edge demanding situations to CNN based HAR systems is furnished. sooner or later, this assessment is concluded with a few functionality future guidelines that might be of outstanding assistance for the researchers who would really like to make contributions to this subject.
A Multi-level Methodology for Behavioral Comparison of Software-Intensive Systems

Software-intensive systems constantly evolve. To prevent software changes from unintentionally introducing costly system defects, it is important to understand their impact to reduce risk. However, it is in practice nearly impossible to foresee the full impact of software changes when dealing with huge industrial systems with many configurations and usage scenarios. To assist developers with change impact analysis we introduce a novel multi-level methodology for behavioral comparison of software-intensive systems. Our fully automated methodology is based on comparing state machine models of software behavior. We combine existing complementary comparison methods into a novel approach, guiding users step by step through relevant differences by gradually zooming in on more and more details. We empirically evaluate our work through a qualitative exploratory field study, showing its practical value using multiple case studies at ASML, a leading company in developing lithography systems. Our method shows great potential for preventing regressions in system behavior for software changes.
What to watch: Practical considerations and strategies for using YouTube for research

YouTube is the second-most visited webpage in the world and boasts over 2 billion users and 500 h of videos uploaded every hour. Despite this popularity, relatively few articles have discussed the practical use of searching and YouTube as a research tool and source of data. The purpose of our paper is to propose a step-by-step schematic for utilizing the YouTube platform. Our discussions include (a) when/whether to use YouTube for research; (b) selecting an appropriate research design; (c) how to search for YouTube data; (d) what data can be pulled from YouTube; and (e) the contextual limitations for interpreting YouTube data. Further, we provide practical strategies and considerations when searching, collecting, or interpreting YouTube data. These discussions are informed by our own work using the YouTube platform. Effective methods used to search for YouTube data are likely to extend beyond simply searching the platform itself; the search strategy and search results themselves should also be documented. While not exhaustive, we feel these considerations and strategies present themselves as a conceptual foothold for future research using the YouTube platform.
Translation, Adaptation, and Psychometric Testing of the Almere Technology Acceptance Questionnaire (ATAQ) Among Older Adults in China

Lack of information on technology Acceptance of Social Assistant robots (SARs) limits the application of robots in the elderly care sector. Currently, no study has not reported a robot acceptance assessment tool in China.The current study aimed to translate the Almere Technology Acceptance Questionnaire (ATAQ) from English to Mandarin Chinese, perform cross-cultural adaptations, and to evaluate its psychometric properties among elderly people in China.This study was conducted in two phases. Phase 1 comprised the context relevance evaluation, instrument translation, and cultural adaption from English to Mandarin Chinese. Phase 2 involved a quantitative cross-sectional survey of psychometric testing among 317 elderly Chinese, including reliability and evaluation of construct validity.In Phase 1, the Context Relevance Index of all items was rated "3 = relevant" or "4 = totally relevant", and the Translation Validity Index of the 100% items of the ATAQ was rated 3 or 4. The ATAQ-Chinese questionnaire comprised 30 items. In Phase 2, all items of the ATAQ-Chinese questionnaire had a CR above 3 (P<0.001). Correlation coefficients of entries in the current study ranged from 0.403 to 0.763 (P<0.001). Nine factors were extracted through Exploratory Factor Analysis, and the cumulative variance contribution rate was 77.175%. Confirmatory Factor Analysis showed that the model had a good fit (χ2/df=2.006, RMSEA=0.069, RMR=0.059, GFI=0.816, IFI=0.913, TLI=0.896, CFI=0.912). The content validity index was 0.92 indicating that all questions were relevant. The value of Cronbach's alpha coefficient showed high validity (α= 0.945, 0.664-0.891). The test-retest reliability coefficient was 0.980, indicating that the tool was reliable.The Chinese version of ATAQ has good reliability and validity, and it is an acceptable, reliable, and valid tool for determining technology acceptance of Social assistant robots in older adults.
A Novel Co-simulation Framework for Verification and Validation of GNC Algorithms for Autonomous UAV

The use of autonomous Unmanned Aerial Vehicles (UAVs) in commercial applications has the potential to disrupt several industries. To effectively cover the broad spectrum of possible applications, UAV integrators require the ability to develop drone platforms that meet the requirements specified for the missions to accomplish. This is not limited to the correct sizing of the UAV physical subsystems, but also on the Guidance, Navigation, and Control (GNC) algorithms that enable the UAV to operate autonomously in an efficient way. System simulation can provide a valuable support to both activities. In the predesign phase, it allows exploring the design space, analyzing the performance of multiple UAV variants, and selecting the most promising concepts. Once this phase is completed, the resulting dynamic and multi-physics performance model of the UAV presents a sufficient fidelity to support the continuous development of GNC algorithms. Thanks to a novel co-simulation framework proposed in this paper, the performance model can then be integrated in a co-simulation framework to include perception sensors, mission environment, and GNC algorithms. Two use cases based on the framework are presented and discussed.
360-VIRTOncology: Virtual Reality to Improve Cancer Patients' Well-Being

Cyberpsychology, Behavior, and Social NetworkingVol. 25, No. 9 CyberEurope360-VIRTOncology: Virtual Reality to Improve Cancer Patients' Well-BeingCompiled by: Maria Sansoni and Giuseppe RivaCompiled by: Maria SansoniAddress correspondence to: Maria Sansoni, Catholic University of Milan, Largo Gemelli 1, 20100 Milan, Italy E-mail Address: maria.sansoni@unicatt.itCatholic University of Milan, Milan, Italy.Search for more papers by this author and Giuseppe RivaDr. Giuseppe Riva, Humane Technology Lab, Catholic University of Milan, Largo Gemelli 1, 20100 Milan, Italy E-mail Address: giuseppe.riva@unicatt.itApplied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy.Humane Technology Lab., Catholic University of Milan, Milan, Italy.Search for more papers by this authorPublished Online:14 Sep 2022https://doi.org/10.1089/cyber.2022.29255.ceuAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byFrom Virtual Reality to Regenerative Virtual Therapy: Some Insights from a Systematic Review Exploring Inner Body Perception in Anorexia and Bulimia Nervosa30 November 2022 | Journal of Clinical Medicine, Vol. 11, No. 23 Volume 25Issue 9Sep 2022 InformationCopyright 2022, Mary Ann Liebert, Inc., publishersTo cite this article:Compiled by: Maria Sansoni and Giuseppe Riva.360-VIRTOncology: Virtual Reality to Improve Cancer Patients' Well-Being.Cyberpsychology, Behavior, and Social Networking.Sep 2022.620-622.http://doi.org/10.1089/cyber.2022.29255.ceuPublished in Volume: 25 Issue 9: September 14, 2022PDF download
ConvMLP-Mixer Based Real-Time Stereo Matching Network Towards Autonomous Driving

Stereo matching is a classical problem in computer vision. It has been widely used in many fields, especially autonomous driving in recent years. Two key aspects of speed and accuracy are both desirable but conflicting characteristics in autonomous driving. In this paper, we present CMNet, a lightweight stereo matching architecture for improving the trade-off between speed and accuracy on resource-limited devices. A novel feature extraction network consisted of a patch embedding layer and a ConvMLP-mixer is proposed. The patch embedding layer enhances the receptive field and makes the feature vectors compact. The accuracy of the disparity map is increased by mixing the spatial information in the channel dimension through the ConvMLP-mixer. The absolute difference volume is concatenated with the group-wise correlation volume to provide multi-dimensional matching cost information for the cost aggregation stage. Being evaluated on KITTI 2012 and KITTI 2015 stereo matching datasets, the inference time of CMNet on NVIDIA GTX 2080ti GPU is 8.7 ms. While realizing fast predictions beyond real-time, the results of D1-all are 3.41% on KITTI 2012 and 3.84% on KITTI 2015, achieving state-of-the-art result between speed and accuracy. Besides, the lightweight architecture of CMNet enables a fast inference time of 40.7 ms on Nvidia Jetson Nano to realize real-time applications on edge devices.
TransFusionNet: Semantic and Spatial Features Fusion Framework for Liver Tumor and Vessel Segmentation Under JetsonTX2

Liver cancer is one of the most common malignant diseases worldwide. Segmentation and reconstruction of liver tumors and vessels in CT images can provide convenience for physicians in preoperative planning and surgical intervention. In this paper, we introduced a TransFusionNet framework, which consists of a semantic feature extraction module, a local spatial feature extraction module, an edge feature extraction module, and a multi-scale feature fusion module to achieve fine-grained segmentation of liver tumors and vessels. In addition, we applied the transfer learning approach to pre-train using public datasets and then fine-tune the model to further improve the fitting effect. Furthermore, we proposed an intelligent quantization scheme to compress the model weights and achieved high performance inference on JetsonTX2. The TransFusionNet framework achieved mean IoU of 0.854 in vessel segmentation task, and achieved mean IoU of 0.927 in liver tumor segmentation task. When profiling the Computational Performance of the quantized inference, our quantized model achieved 4TFLOPs on Node with NVIDIA RTX3090 and 132GFLOPs on JetsonTX2. This unprecedented segmentation effect solves the accuracy and performance bottleneck of automated segmentation to a certain extent.
Session-based Recommendation with Transformers

Large item catalogs and constantly changing preference trends make recommendations a critically important component of every fashion e-commerce platform. However, since most users browse anonymously, historical preference data is rarely available and recommendations have to be made using only information from within the session. In the 2022 ACM RecSys challenge, Dressipi released a dataset with 1.1 million online retail sessions in the fashion domain that span an 18-month period. The goal is to predict the item purchased at the end of each session. To simulate a common production scenario all sessions are anonymous and no previous user preference information is available. In this paper, we present our approach to this challenge. We leverage the Transformer architecture with two different learning objectives inspired by the self-supervised learning techniques to improve generalization. Our team, LAYER 6, achieves strong results placing 2’nd on the final leaderboard out of over 300 teams.