Reviewerly/exHarmony huggingface dataset:
Research on Prediction of Physical Fitness Test Results in Colleges and Universities Based on Deep Learning

All-round development strategy of quality education makes primary and secondary school students not only pursue the improvement of achievement but also carry out physical exercise. Physical training is the material basis for students to study other disciplines, and the core is to improve students’ own physical quality and increase their physique. Having a strong body helps students have certain physical strength to study in other courses. In recent years, in the background of the scientific era, college students in China obviously have some problems, such as insufficient awareness of physical exercise and serious decline in physical fitness. Nowadays, teenagers are addicted to games and go out to become members of the low-headed people. Nowadays, it is very unhealthy for teenagers to go out with their mobile phones as “low-headed people.” In order to avoid college students getting rid of this living condition, colleges and universities carry out physical fitness tests every year to promote contemporary college students to strengthen exercise. College students, as the main force in the future construction of the motherland, should not only master professional knowledge but also improve their physical fitness. Good health is the greatest capital in one’s life. Every year, some students fail to pass the physical fitness test in universities. It stands to reason that college students are in the age of high youth, and physical fitness test should be a piece of cake for them. In the face of the inconsistency between the predicted results and the actual results, this paper analyzes this. Based on the above situation, With the aim of improving students’ training efficiency and physical performance, the physical performance prediction model of deep learning is designed and analyzed to predict the performance, analyze the influencing factors of the model and how to reduce the influencing components of the factors, and analyze and compare the performance of various prediction models to find out the best model, so as to make the predicted value closer to the true value.
Managed Evolution of Automotive Software Product Line Architectures: A Systematic Literature Study

The rapidly growing number of software-based features in the automotive domain as well as the special requirements in this domain ask for dedicated engineering approaches, models, and processes. Nowadays, software development in the automotive sector is generally developed as product line development, in which major parts of the software are kept adaptable in order to enable reusability of the software in different vehicle variants. In addition, reuse also plays an important role in the development of new vehicle generations in order to reduce development costs. Today, a high number of methods and techniques exist to support the product line driven development of software in the automotive sector. However, these approaches generally consider only partial aspects of development. In this paper, we present an in-depth literature study based on a conceptual model of artifacts and activities for the managed evolution of automotive software product line architectures. We are interested in the coverage of the particular aspects of the conceptual model and, thus, the fields covered in current research and research gaps, respectively. Furthermore, we aim to identify the methods and techniques used to implement automotive software product lines in general, and their usage scope in particular. As a result, this in-depth review reveals that none of the studies represent a holistic approach for the managed evolution of automotive software product lines. In addition, approaches from agile software development are of growing interest in this field.
Physical Adversarial Attack Scheme on Object Detectors using 3D Adversarial Object

Adversarial attacks are being frequently used these days to exploit different machine learning models including the deep neural networks (DNN) either during the training or testing stage. DNN under such attacks make the false predictions. Digital adversarial attacks are not applicable in physical world. Adversarial attack on object detection is more difficult as compared to the adversarial attack on image classification. This paper presents a physical adversarial attack on object detection using 3D adversarial objects. The proposed methodology overcome the constraint of 2D adversarial patches as they only work for certain viewpoints only. We have mapped an adversarial texture onto a mesh to create the 3D adversarial object. These objects are of various shapes and sizes. Unlike adversarial patch attacks, these adversarial objects are movable from one place to another. Moreover, application of 2D patch is limited to confined viewpoints. Experimentation results show that our 3D adversarial objects are free from such constraints and perform a successful attack on object detection. We used the ShapeNet dataset for different vehicle models. 3D objects are created using Blender 2.93 [1]. Different HDR images are incorporated to create the virtual physical environment. Moreover, we targeted the FasterRCNN and YOLO pre-trained models on the COCO dataset as our target DNN. Experimental results demonstrate that our proposed model successfully fooled these object detectors.
Securing Smart City Services in Cyber-Physical Systems Using the Computation Annealed Selection Process

Cyber-physical systems in a smart city environment offer secure computations in addition to robust resources and secure information exchanges. From the security aspect, the selection of legitimate computing resources is considered the most trustworthy measure for preserving user and data privacy. However, the outlier information results in false computations and time-consuming privacy measures for smart city users. This article introduces the Computation Annealed Selection Process (CASP) for combating outlier information in cyber-physical system networks. This process is assimilated with different infrastructure units in the smart city in an adaptable fashion. The adaptive measure administers information and user privacy through mutual time-key-based authentication. Information privacy is endured until the smart city provider provides a valid service interval. W2The service interval is evaluated for its consistency in maintaining the privacy and is validated using decision-tree learning. The decision tree performs interval classification and key assignment recommendations. Based on the decisions, the privacy is either prolonged or withdrawn for the information exchanged between the users and service providers. This process is repeated until the end of the service interval, identifying the outlier information in legitimate intervals. The proposed process improves detection, legitimacy rate, interval time, and interval rate by 7.57%, 10.4%, 13.15%, and 7.59%, respectively.
Hybrid semiparametric Bayesian networks

Abstract This paper presents a new class of Bayesian networks called hybrid semiparametric Bayesian networks, which can model hybrid data (discrete and continuous data) by mixing parametric and nonparametric estimation models. The parametric estimation models can represent a conditional linear Gaussian relationship between variables, while the nonparametric estimation model can represent other types of relationships, such as non-Gaussian and nonlinear relationships. This new class of Bayesian networks generalizes the conditional linear Gaussian Bayesian networks, including them as a special case. In addition, we describe a learning procedure for the structure and the parameters of our proposed type of Bayesian network. This learning procedure finds the best combination of parametric and nonparametric models automatically from data. This requires the definition of a cross-validated score. We also detail how new data can be sampled from a hybrid semiparametric Bayesian network, which in turn can be useful to solve other related tasks, such as inference. Furthermore, we intuitively relate our proposal with adaptive kernel density estimation models. The experimental results show that hybrid semiparametric Bayesian networks are a valuable contribution when dealing with data that do not meet the parametric assumptions that are expected for other models, such as conditional linear Gaussian Bayesian networks. We include experiments with synthetic data and real-world data from the UCI repository which demonstrate the good performance and the ability to extract useful information about the relationship between the variables in the model.
Application of Intelligent Technology in Facade Style Recognition of Harbin Modern Architecture

The judgment of facade styles is an important part of the sustainable utilization and restorative process of historical architectures. Contemporary Harbin needs the help of modern architectural facade forms in the planning of the famous historic city, especially with the facade renovation of old architectures with non-cultural heritage. This paper discusses the possibility of applying advanced image recognition algorithms to the classification of the modern Harbin architectural facade styles and argues that the keys to the classification and positioning of the styles are the forms, the details, and the decorative patterns of the architectural facades, together with the deformation and the quantitative variation factors of the facade decoration symbols. Based on the conventional classification method, the facade styles of Harbin modern architecture were divided into 12 categories after data analysis. To better capture the overall structure information and the style features of the local components in the architectural images, the group convolution and the dilated convolution were added into the ResNet model, and then, the improved channel attention mechanism was introduced to construct a novel CA-MSResNet model. The CA-MSResNet model could more accurately identify the morphological elements and the style categories of the architectures, and the average accuracy reached 87.5%. These techniques, with their promising results, are expected to be further applied in the future research on the sustainable utilization and renovation of Harbin modern architecture.
On the Design of deformTable: Attending to Temporality and Materiality for Supporting Everyday Interactions with a Shape-Changing Artifact

HCI research on shape change has highlighted the importance of materiality and temporality in design practice. However, little research has addressed the challenges of tailoring these qualities for everyday interactions with a shape-changing artifact. In this paper, we report from a designer-researcher perspective into the crafting of deformTable: a shape-changing form that responds to the weight of objects placed on its surface. We crafted 5 deformTables over 18 months with special attention given to the physical, temporal, and computational aspects. Specifically, we describe how we focused on speed adjustments and response times in the process to frame the temporal expressions of computing; we also discuss how the material and tools helped shape our decisions in the context of the materiality of computing. This study contributes an RtD case of designing a particular shape-changing artifact through the lens of temporality and materiality to support long-term engagements and everyday interactions.
A fuzzy set based approach for effective feature selection

High dimensionality of input dataset is an important issue in machine learning. Though there are a few effective ways of dimensionality reduction, none of them, other than feature selection, preserve the original features of the input dataset. Feature selection assesses relevance of features along with their potential redundancy for dimensionality reduction. This paper proposes a first of its kind fuzzy graph based technique for measuring feature relevance and redundancy. The proposed technique addresses the uncertainty in interpretation of feature relevance and redundancy by defining a fuzzy set based formulation of feature relevance and redundancy. Superior performance of the proposed algorithm is illustrated by simulation experiments with 20 benchmark datasets. The proposed algorithm has demonstrated an average feature reduction of 86% along with a 10% improvement in average classification accuracy compared to the state-of-the-art algorithms.
Hate Speech Detection Using Machine Learning In Bengali Languages

Hate speech is a common problem in the current time of social media and the internet as it is very easy to be in touch with everything through the internet and social media. Hate speech detection research is not very rare but in terms of Bengali language there are very few works related to hate speech in Bengali language. The proposed research experiment has developed a machine learning based project to detect hate speech from Bengali language data or comments, posts in social media that are in Bengali language. This research work has used 3006 pure Bengali data from social media pages (such as Facebook, YouTube) groups, comment sections of news portals. Further, this research work has categorized them in 0 for non-Hate-Speech and 1 for Hate-Speech to classify the data between non-abusive and abusive data. This research work has used several algorithms to find the best possible result in order to determine whether the sentence is abusive or non-abusive such as Logistic Regression, Naive Bayes, Random Forest, Support Vector Machine, K Nearest Neighbor Classifier. From these algorithms, the best result for detecting non-abusive data is the Random Forest [RF] algorithm, which is 67%.
SKETCHING SOMA BITS

To support sketching in soma design processes, we built the Soma Bits. These simple technology pieces enable one bit actuation like vibration, heat or shape-changing behaviors. After using them for three years, we ask how well they support soma design processes and what designs they spur? Through analyzing nine soma design projects, we note how they contributes to forming subtle, intimate, body awareness designs, as well as fast, uncomfortable, sensory misaligned, somaesthetic designs or skill training. More importantly, experiencing actuation in the early sketching phases helps the design team to articulate joint somatic understanding and direction. During past design sessions, we noticed that the Soma Bits lack support for the overall orchestration: looping, tweaking, or arranging sequences of interactions, and coupling sensing to actuation. Hence, we present a novel Soma Bits toolkit concept for sketching, composing, documenting, and sharing somatic experience.
Distribution regularized self-supervised learning for domain adaptation of semantic segmentation

This paper proposes a novel pixel-level distribution regularization scheme (DRSL) for self-supervised domain adaptation of semantic segmentation. In a typical setting, the classification loss forces the semantic segmentation model to greedily learn the representations that capture inter-class variations in order to determine the decision (class) boundary. Due to the domain shift, this decision boundary is unaligned in the target domain, resulting in noisy pseudo labels adversely affecting self-supervised domain adaptation. To overcome this limitation, along with capturing inter-class variation, we capture pixel-level intra-class variations through class-aware multi-modal distribution learning (MMDL). Thus, the information necessary for capturing the intra-class variations is explicitly disentangled from the information necessary for inter-class discrimination. Features captured thus are much more informative, resulting in pseudo-labels with low noise. This disentanglement allows us to perform separate alignments in discriminative space and multi-modal distribution space, using cross-entropy based self-learning for the former. For later, we propose a novel stochastic mode alignment method, by explicitly decreasing the distance between the target and source pixels that map to the same mode. The distance metric learning loss, computed over pseudo-labels and backpropagated from multi-modal modeling head, acts as the regularizer over the base network shared with the segmentation head. The results from comprehensive experiments on synthetic to real domain adaptation setups, i.e., GTA-V/SYNTHIA to Cityscapes, show that DRSL outperforms many existing approaches (a minimum margin of 2.3% and 2.5% in mIoU for SYNTHIA to Cityscapes).
TxtAlign: Efficient Near-Duplicate Text Alignment Search via Bottom-k Sketches for Plagiarism Detection

In this paper, we study the near-duplicate text alignment search problem, which, given a collection of source (data) documents and a suspicious (query) document, finds all the near-duplicate passage pairs between the suspicious document and every source document. It finds applications in plagiarism detection. Specifically, the first two steps in plagiarism detection are source retrieval and text alignment. Source retrieval finds candidate source documents in a corpus that share content with the suspicious document while text alignment finds all the similar passage pairs between the suspicious document and every candidate source document. This problem is computation-intensive, especially for long documents. This is because there are O(n2m2) passage pairs between a single source document with n words and a suspicious document with m words, not to mention the large number of source documents in a corpus. Due to the high computation cost, existing solutions primarily rely on heuristic rules, such as the "seeding-extension-filtering" pipeline, and involve many hard-to-tune hyper-parameters. To address these issues, a recent work ALLIGN leverages the min-wise hash sketch for the text alignment problem. However, ALLIGN only works for two documents and leaves the source retrieval problem unattended. In this paper, we propose to leverage the bottom-k sketch (a.k.a. conditional random sampling) to estimate the similarity of two passages. We observe that many nearby passages in a document would share the same bottom-k sketch. Thus we propose to group all the passages in a document by their sketches. We prove that all the O(n2) passages can be partitioned into O(nk) groups in a document with n words and develop an algorithm to generate these groups in O(nlogn+nk) time. Then, to address the source retrieval problem, we only need to find groups of passages with "similar" bottom-k sketches. Every passage pair in two groups with "similar" sketches are near-duplicates. Experimental results on real-world datasets show that our techniques are highly efficient.
Deep learning based Answering Questions using T5 and Structured Question Generation System’

Since there is an agile change in all the domains, it is imperative for students to keep themselves updated to gain expertise in their field of study. Although there is easy access to resources from the web, learning only becomes complete after a thorough system of assessment to identify gaps in the learning process if any. Automatic question generation techniques have been introduced to reduce the time associated with the manual construction of questions. Researchers have come up with stateof-art models to generate these questions. Models like BERT and GPT gained popularity among the masses due to their deduction of complex sentences. These have however been replaced with advanced models such as Transformers which are much more versatile. This paper has fine-tuned one such transformer and proposed a custom model to generate questions. Our system mainly focuses on two types of questions, namely multiple-choice, and long answer types. The generator model is primarily concerned with producing questions from selected phrases and generating semantically motivated phrase-level distractors as MCQ response choices. In contrast, the evaluator model is concerned with identifying the most relevant question-answer combinations. This research work has gained the ability to acquire 76% syntactic accuracy and 84% fluency from the question-answer pairs evaluated using our approach. Along with this, the proposed result has achieved a final accuracy of 82% from the evaluator's bert-base-cased model.
Credit Card Fraud Detection with Automated Machine Learning Systems

The steady increase at the turnover of online trading during the last decade and the increasing use of credit cards has subsequently made credit card frauds more prevalent. Machine Learning (ML) models are among the most prominent techniques in detecting illicit transactions. In this paper, we apply the Just-Add-Data (JAD), a system that automates the selection of Machine Learning algorithms, the tuning of their hyper-parameter values, and the estimation of performance in detecting fraudulent transactions using a highly unbalanced dataset, swiftly providing prediction model for credit card fraud detection. The training of the model does not require the user setting up any of the methods’ (hyper)parameters. In addition, it is trivial to retrain the model with the arrival of new data, to visualize, interpret, and share the results at all management levels within a credit card organization, as well as to apply the model. The model selected by JAD identifies 32 out of a total of 39 fraudulent transactions of the test sample, with all missed fraudulent transactions being small transactions below 50€. The comparison with other methods on the same dataset reveals that all the above come with a high forecasting performance that matches the existing literature.
Designing and implementing a data model for describing environmental monitoring and research sites

With new technological advancements and increasing demands of open data in environmental sciences, the requirements for data are increasing in a variety of ways. Having machine and human readable documentation about environmental research and monitoring sites available online is one of them. The Dynamic Ecological Information Management System - Site and Dataset Registry (DEIMS-SDR, https://www.deims.org/) is a research and monitoring site registry that allows the description of in-situ observation or experimental sites, generating persistent, unique and resolvable identifiers for each site. The aim of DEIMS-SDR is to collect site information in a catalogue describing a wide range of sites across the globe, providing information including each site's location, ecosystems, facilities, measured parameters and research themes and enabling that standardised information to be openly available. This article describes the outcomes of the revision of its data model, the conceptual considerations behind it and how it is implemented. These conceptual considerations also encompass the definition of what we call the "onion model of site data interoperability" - a fundamental concept for the design of site data models against the backdrop of data interoperability. Furthermore, we illustrate the capabilities of the revised data model and provide an overview of common data formats for the description of sites, current initiatives driving the harmonisation of descriptions and the outlook of future developments.