Reviewerly/exHarmony huggingface dataset:
People detection with omnidirectional cameras using a spatial grid of deep learning foveatic classifiers

A novel deep-learning people detection algorithm using omnidirectional cameras is presented, which only requires point-based annotations, unlike most of the prominent works that require bounding box annotations. Thus, the effort of manually annotating the needed training databases is significantly reduced, allowing a faster system deployment. The algorithm is based on a novel deep neural network architecture that implements the concept of Grid of Spatial-Aware Classifiers, but allowing end-to-end training that improves the performance of the whole system. The designed algorithm satisfactorily handles the severe geometric distortions of the omnidirectional images, which typically degrades the performance of state-of-the-art detectors, without requiring any camera calibration. The algorithm has been evaluated in well-known omnidirectional image databases (PIROPO, BOMNI, and MW-18Mar) and compared with several works of the state of the art.
Level set framework based on local scalable Gaussian distribution and adaptive-scale operator for accurate image segmentation and correction

Active contour model is a popular image segmentation method, but its current development is still limited by the problem of uneven image intensity. This paper proposes a new active contour model based on level set framework using local scalable Gaussian distribution (LSGD) and adaptive-scale operator for accurate image segmentation and correction, and uses the split Bregman method to minimize the energy functional. We construct a fitting energy with local Gaussian distribution characteristics, and segment the image with the maximum posterior probability. Then we use an adaptive-scale operator to adjust the scale of the LSGD according to the degree of the intensity inhomogeneity. In addition, the bias field information is added to the model to achieve better robustness to unevenness and correction effects. Moreover, the energy functional is minimized by the split Bregman method to improve the efficiency of image segmentation. In order to meet the needs of more diverse image segmentation, we extend the two-phase energy functional to the multi-scale and the vector-valued energy functionals. To evaluate the effectiveness of the proposed methods, we select the composite images as well as brain MRI, melanoma and Berkeley nature images. We select the quantitative indexes to evaluate the image segmentation effect, including JS, Dice, CV, the Precision and Recall, correlation index, SSIM and MSE index. We compare our model with the classic traditional image segmentation models and U-net machine learning model. Experimental results show that the models can perform appropriate segmentation and bias field estimation, and the quantitative index values are better than other segmentation methods.
Detecting Duplicate Questions in Stack Overflow via Semantic and Relevance Approaches

Stack Overflow is a popular online Q&amp;A website related to programming. Although Stack Overflow has detailed questioning guidance, duplicate questions still appear frequently, and a large number of duplicate questions make the quality of the community degraded. To solve this problem, Stack Overflow allows users with high reputations to manually mark duplicate questions. However, this method is inefficient and causes many duplicate questions to remain undiscovered. Therefore, this paper proposes a duplicate questions detection model based on semantic and relevance. The model employs Siamese BiLSTM to encode question pairs and captures the semantic interaction information of title and body through soft align attention and inference composition. The soft term match captures the relevance information in the title. We evaluate the effectiveness of the model in six question groups on Stack Overflow. Compared with the latest deep learning model, the F1-Score and ACC of our model increased by 9.401% and 8.901%, respectively. Experimental results show that our model outperforms the baselines and achieves competitive performance.
Investigation of Soils and Pine Needles Using WDXRF and TXRF Techniques for Assessment of the Environmental Pollution of Shelekhov District, Eastern Siberia, by the Aluminum Industry and Heat Power Engineering

X-ray fluorescence analysis was applied to assess the ecological state of the area potentially polluted by emissions of the aluminum industry and heat power engineering. Soil and pine needle samples were collected in areas with industrial activity and analyzed using wavelength-dispersive X-ray fluorescence (WDXRF) and total-reflection X-ray fluorescence (TXRF) techniques. Both techniques were validated using the matrix-matched certified reference materials. Different sample preparation procedures, such as fusion and pressing for WDXRF and acid digestion and suspensions for TXRF as well as quantification approaches (the external calibration for WDXRF and the internal standard for TXRF) were applied according to the features of the analyzed samples. The rock-forming oxides (Na2O, MgO, Al2O3, SiO2, P2O5, K2O, CaO, TiO2, MnO, and Fe2O3) and trace elements (V, Cr, Ni, Cu, Zn, Rb, Sr, Y, Ba, and Pb) were quantified in soils, as well as several elements (Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Br, Rb, Sr, Pb, and Ba) were quantified in pine needles. Comparing the results of soils and pine needles analysis with the regional background values indicated a significant contamination pollution level of the studied area.
AirCamRTM: Enhancing Vehicle Detection for Efficient Aerial Camera-based Road Traffic Monitoring

Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities. Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to provide flexible and infrastructure-free traffic monitoring. However, real-time traffic monitoring from UAV imagery poses several challenges, due to the large image sizes and presence of non-relevant targets. In this paper, we propose the AirCam-RTM framework that combines road segmentation and vehicle detection to focus only on relevant vehicles, which as a result, improves the monitoring performance by ~2 × and provides ~ 18% accuracy improvement. Furthermore, through a real experimental setup we qualitatively evaluate the performance of the proposed approach, and also demonstrate how it can be used for real-time traffic monitoring using UAVs.
Efficiency Monitoring of Engineering System Designer Work Based on Multi-System User Behavior Analysis with AI/ML Algorithms

Efficiency monitoring is one of the important tasks in monitoring of employees activities, the solution of which is constantly in the focus of researchers and is still not supported by automated methods and universal tools. This task is especially relevant if the field of activity is narrowly specific, and solving such a task of efficiency monitoring is not a trivial accumulation of statistical data, but requires a unique method. However, potentially the method of evaluating the work efficiency of this specific field is not commonly used or adaptable in the analysis of a wide range of activities. The work of an engineering system designer is an example of such specific activities. This paper offers a method for construction of a unified model of behavior of users of specialized IT systems in the engineering design industry, which offers to evaluate the efficiency of work based on the development of a user profile that collects information about the designer's activities, sequence and times. The method defines the input data, the result output data, AI/ML algorithms for output data acquisition and is supported with the prototype system for the visual representation of the expected result, which is based on the input / output data regularities.
SILP-autoencoder for face de-occlusion

Recognizing faces with partial occlusion is a challenging problem in many real-world applications. Although various methods have been proposed to deal with the facial image de-occlusion tasks, most of them only concern the local features of occluded images, obviously ignoring the global facial expressions and structural prior information. In this paper, we propose a novel end-to-end SILP-Autoencoder to effectively restore partial occluded faces. To improve the recovery quality and occlusion removal robustness, our framework mainly consists of two components, Laplacian prior subnetwork, and left-and-right symmetric match module (LR-match module), which preserve the global facial expression features and fully make use of the symmetrical characteristics of facial regions and structures respectively. Based on the above characteristics, a composite loss function is designed to achieve end-to-end training of the entire network. Extensive experiments on the face expression datasets with various shaded areas suggest that our approach achieves superior performance against the state-of-the-art methods. In particular, our method is more useful for facial detail recovery and distortion expression suppression.
3DRefTransformer: Fine-Grained Object Identification in Real-World Scenes Using Natural Language

In this paper, we study fine-grained 3D object identification in real-world scenes described by a textual query. The task aims to discriminatively understand an instance of a particular 3D object described by natural language utterances among other instances of 3D objects of the same class appearing in a visual scene. We introduce the 3DRefTransformer net, a transformer-based neural network that identifies 3D objects described by linguistic utterances in real-world scenes. The network’s input is 3D object segmented point cloud images representing a real-world scene and a language utterance that refers to one of the scene objects. The goal is to identify the referred object. Compared to the state-of-the-art models that are mostly based on graph convolutions and LSTMs, our 3DRefTrans-former net offers two key advantages. First, it is an end-to-end transformer model that operates both on language and 3D visual objects. Second, it has a natural ability to ground textual terms in the utterance to the learning representation of 3D objects in the scene. We further incorporate object pairwise spatial relation loss and contrastive learning during model training. We show in our experiments that our model improves the performance upon the current SOTA significantly on Referit3D Nr3D and Sr3D datasets. Code and Models will be made publicly available at https://vision-cair.github.io/3dreftransformer/.
Tuning Hyperparameters of Machine Learning Methods for Afan Oromo Hate Speech Text Detection for Social Media

With the rapidly growing penetration of social media networks in linguistically diverse and multicultural developing nations like Ethiopia, the conversations of online users have increasingly become more casual and multilingual. The emergency of hate speech text system. To this end, various automated hate speech detection and classification systems have been developed for resource-rich languages such as English and French even though online users are using many other languages on different social media platforms. Afan Oromo is one natural language used by social media users to express feelings, emotions and share messages. Hence, there is an urgent need for the development of an intelligent system that can automatically detect and classify hate speech, especially for resource-scarce indigenous Ethiopian languages like Afan Oromo. This work is about the identification of hate speech text from comments and posts generated in resource scary poor language Afan Oromo. We prepared first hate speech text detection dataset of Afan Oromo that containing comments and posts from social media. Then, n-gram and TF-IDF feature selection approaches were employed to select features. After the important feature selected Natural language processing tasks applied on the dataset. We applied six machine learning classifiers from default and tuned parameters to detect hate speech text posts and comments. The experiment show that Support Vector Machine outperform 92&#x0025; values of F-measure than classifiers Afan Oromo hate speech text detection dataset. This Afan Oromo hate speech text dataset publicly available on https://www.naolinfo.info/for further research.
Co-Net: A Collaborative Region-Contour-Driven Network for Fine-to-Finer Medical Image Segmentation

In this paper, a fine-to-finer segmentation task is investigated driven by region and contour features collaboratively on Glomerular Electron-Dense Deposits (GEDD) in view of the complementary nature of these two types of features. To this end, a novel network (Co-Net) is presented to dynamically use fine saliency segmentation to guide finer segmentation on boundaries. The whole architecture contains double mutually boosted decoders sharing one common encoder. Specifically, a new structure named Global-guided Interaction Module (GIM) is designed to effectively control the information flow and reduce redundancy in the cross-level feature fusion process. At the same time, the global features are used in it to make the features of each layer gain access to richer context, and a fine segmentation map is obtained initially; Discontinuous Boundary Supervision (DBS) strategy is applied to pay more attention to discontinuity positions and modifying segmentation errors on boundaries. At last, Selective Kernel (SK) is used for dynamical aggregation of the region and contour features to obtain a finer segmentation. Our proposed approach is evaluated on an independent GEDD dataset labeled by pathologists and also on open polyp datasets to test the generalization. Ablation studies show the effectiveness of different modules. On all datasets, our proposal achieves high segmentation accuracy and surpasses previous methods.
MovingFashion: a Benchmark for the Video-to-Shop Challenge

Retrieving clothes that are worn in social media videos (Instagram, TikTok) is the latest frontier of e-fashion, referred to as "video-to-shop" in the computer vision literature. In this paper, we present MovingFashion, the first publicly available dataset to cope with this challenge. MovingFashion is composed of 14855 social videos, each one of them associated with e-commerce "shop" images where the corresponding clothing items are clearly portrayed. In addition, we present a novel baseline for this scenario, dubbed SEAM Match-RCNN. The model is trained by image-tovideo domain adaptation, allowing the use of video sequences where only their association with a shop image is given, eliminating the need for millions of annotated bounding boxes. SEAM Match-RCNN builds an embedding, where an attention-based weighted sum of few frames (10) of a social video is enough to individuate the correct product within the first 5 retrieved items in a 14K+ shop element gallery with an accuracy of 80%. This provides the best performance on MovingFashion, comparing exhaustively against the related state-of-the-art approaches and alternative baselines <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A data structure for substring-substring LCS length queries

The longest common subsequence (LCS) length of two strings is used as one of the most fundamental metrics measuring the similarity between the strings. To find out the local structures common to the strings under this similarity metric, we need a fast calculation of the LCS length of any pair of substrings of the two strings. For supporting such queries, it makes sense to preprocess the two strings in a quadratic time, because it takes about the same amount of time to compute the LCS length of the entire strings from scratch. We propose a quadratic-time constructible data structure that supports sublinear-time queries of the LCS length for any pair of substrings. The query time is O(llog1+ϵ⁡l), where ϵ is a positive constant arbitrarily small and l is the sum of the substring lengths.
Register variation in spoken and written language use across technology-mediated and non-technology-mediated learning environments

In the realm of language proficiency assessments, the domain description inference and the extrapolation inference are key components of a validity argument. Biber et al.’s description of the lexicogrammatical features of the spoken and written registers in the T2K-SWAL corpus has served as support for the TOEFL iBT test’s domain description and extrapolation inferences. In the time since the T2K-SWAL corpus was collected, however, university learning environments have increasingly become technology-mediated. Accordingly, any description of the linguistic features of university language should account for the language produced in technology-mediated learning environments (TMLEs) in addition to non-technology-mediated learning environments (non-TMLEs). Kyle et al. recently began to address this issue by collecting a corpus of TMLE language use, which they then compared to language use in non-TMLEs using multidimensional analysis (MDA). The results indicated both similarities and substantive differences across the learning environments, but the study did not investigate the effects of particular registers on these results. In this study, we build on previous research by investigating lexicogrammatical features of specific spoken and written registers across technology-mediated and non-technology-mediated learning environments.
Deploying Blockchain in Education: Security, Challenges, and Solutions

The recent inventions and developments in the existing technologies and the accessibility of high-speed connections are highly involved in each and every point of our day-to-day lives. This advancement brought about numerous challenges, especially the ones associated with security. Blockchain technology, which is no doubt, is amongst the most powerful and ambitious developments of the last two decades, seeks attentiveness for its future aspects to deliver safety, security, and integrity in all the sectors involving shipping, medicine, supply chain management, to name a few. The education field, like all other fields, is in need to deploy the advantages and conveniences that blockchain technology provides. All the academic organizations are now eagerly anticipating to deploy this application to advance the schooling and studying undertakings and advance association among the collaborators like the learners, educators, and guardians, along with providing security measures. It can be used in e- transcripts, certification, and electronic degrees, cloud storage, and individuality regulation. This paper discusses blockchain technology and its uses which can be utilized by the education sector for its advancement.
Enhanced Hybrid Encryption Through Slicing and Merging of Data With Randomization Of Algorithms

nowadays, data stored in mobile phones become often vulnerable that it is stolen by hackers. More than 50&#x0025; of mobile users do not encrypt their mobile phones which might result in stealing data when mobile is hacked or stolen. Android encryption is used in android mobiles. The encryption algorithm used is 128 Advanced Encryption Standard (AES) with cipher-block chaining (CBC) and SHA256.In this paper, the data in mobile is encrypted using multiple algorithms(AES, Salsa20, fernet). This method is called hybrid encryption. The data is split and encrypted with multiple algorithms. The selected file to be encrypted is divided into three segments and encrypted with AES, Salsa20, and Fernet algorithms. Then the private keys are encrypted with the RSA (Rivest-Shamir-Adleman) algorithm. The RSA cipher file is stored in phone storage. For decryption, the RSA cipher file is obtained and the responding keys are obtained. With the keys, the AES, Salsa20, Fernet algorithms decrypt the responding encrypted segments. The decrypted segments are combined and the original file is obtained. Then the file is stored in the device.
Developing 21st Century Competencies among Youth through an Online Learning Program: BE a Global Citizen

Background: Within the current labor market, continuously changing and accelerating since the COVID-19 crisis (World Economic Forum, 2021), the educational system must offer a pedagogical alternative to face and anticipate these 21st century changes and demands (OECD, 2018). Based on the four-dimensional education framework, from the Center for Curriculum Redesign (CCR), the online learning literature, and students’ perspectives, the Beyond Education (BE) organization has proposed a series of interactive online pedagogical programs aiming to develop 21st-century competencies in school-age students. Methods: The aim of this paper is to propose both quantitative and qualitative data analyses of the first pilot program: “BE a Global Citizen” through a semi-experimental study, using a mixed-method approach, analyzing the pre- and post-tests results of 26 students aged 16 to 18 years old. Results: Analyses presented evidence of the program’s efficacy at developing 21st century competencies in students, particularly those competencies targeted by the program, which were significantly increased. Conclusions: Although these are promising results, a replicability study needs to be done on a larger sample in order to generalize the results.
Fast Semantic Matching via Flexible Contextualized Interaction

Deep pre-trained language models (e.g., BERT) lead to remarkable headway in many Natural Language Processing tasks. Their superior capacity in perceiving textual data is also witnessed in semantic matching tasks (e.g., question answering, web search). Particularly for matching a pair of query and text candidate, the current state-of-the-arts usually rely on the semantic representations produced by BERT, and compute relevance scores with various interaction (i.e., matching) methods. However, they may 1) miss fine-grained phrase-level interaction between the input query and candidate context or 2) lack a thoughtful consideration of both effectiveness and efficiency. Motivated by this, we propose \hyttInteractor, a BERT-based semantic matching model with a flexible contextualized interaction paradigm. It is capable of capturing fine-grained phrase-level information in the interaction, and thus is more effective to be applied for semantic matching tasks. Moreover, we further facilitate \hyttInteractor with a novel partial attention scheme, which significantly reduces the computational cost while maintaining the high effectiveness. We conduct comprehensive experimental evaluations on three datasets. The results show that \hyttInteractor achieves superior effectiveness and efficiency for semantic matching.
From Reality to Virtuality: Re-discussing Cities with the Concept of the Metaverse

The increase in the transaction volume of cryptocurrencies, the widespread use of blockchain technology, and the increase in the number of augmented reality devices (virtual, augmented, and mixed reality) have led to the formation of a suitable environment for Metaverse. Metaverse is a virtual or augmented reality created beyond the physical world via an internet connection with augmented reality devices, game consoles, computers, tablets, or mobile phones. This universe may be beyond the real world, or it is created by the virtualization of real-world objects to a certain extent. While Metaverse has the opportunity to be applied comfortably in all areas of daily life, it can also be designed as an urban policy design tool. In the study, the conceptual framework of the metaverse will be presented, the metaverse investments of global companies will be determined, the concept of metaverse will be examined in the context of virtual reality relationship, the reflections of metaverse applications on the city will be examined, and finally, the effects and role of the metaverse in urban policy production will be discussed and conclusions and evaluations will be given.