Reviewerly/exHarmony huggingface dataset:
The Right Mix of Visual and Mechanical Constraints to Guide the Relative Pose between Rigid Objects

Restoring and/or obtaining the correct relative pose between rigid objects is a common precision task in many sectors, from industry to healthcare, as during the reduction of long-bones fractured. Augmented Reality (AR) can provide useful indications, showing how one bone should be positioned with respect to another, for example. However, moving a rigid body by manually managing its six degrees of freedom (DOF) until the desired pose is obtained can be difficult. At first, because AR visualization is commonly not able to provide an accurate depth perception. Second, because it is very challenging to control and handle all the six DOFs simultaneously. In this work, we evaluated whether the addition of adaptable and easily adjustable mechanical constraints, each constraining a single degree of freedom, allow increasing the feasibility, accuracy, and efficiency of an AR guidance. This preliminary study shows that handling simultaneously all the 6 DOFs of a rigid object is hardly feasible under hand-free AR guidance, and that we can therefore insert simple mechanical constraints to facilitate the achievement of the desired relative pose between rigid objects.
Neural keypoint detection for visual gestures on micro-controllers

The human pose estimation, particularly focused on the hands, is an important topic for the metaverse’s researchers. Deep learning has increased the accuracy of keypoints detection applied to deformable objects, with respect to hand-crafted methods. Human hand pose recognition on color images has encouraged new application cases. This paper is focused on the visual hand keypoints estimation estimation in real time deployable on memory and computationally constrained embedded devices. In particular, implementing such technology on microcontrollers is one of the most challenging tasks since the model must be tiny enough to be deployable in less than one megabyte memory. while ensuring adequate accuracy. In this paper, the approach was based on image regression to achieve a proper key points approximation accuracy within the required memory and computation assets of the micro-controller. The quality of projection, complexity and reliability of the proposed solution were validated through comparative analysis of several hyper-parameters combinations, trained ad-hoc and quality measurements. As result, a modified memory optimized version of MobileNetV2 has been developed which required 0.73 MBytes of Flash memory and 0.385 MBytes of RAM memory. The percentage of detected joints achieved was equal to 77.7%. The inference time was, for example, 398 ms at 480MHz on STM32H7 featuring an ARM Cortex M7 instruction set.
A comparative study for the estimation of Global solar radiation using different input variable selection techniques

Better utilization of solar energy applications is possible if solar radiation prediction is made more accurate. Many prediction models were developed from time to time. In this study, an easy and convenient demonstration has been presented to reduce the model complexity with high accuracy in the predicted values. PCA, ANN, ANFIS, and other techniques were discussed in this paper to pre-process the input data. How conveniently do these techniques apply to row data sets to reduce the model complexity? A comparison shows how the model performance degrades due to the use of the large variables data set as well as the removal of key elements. So, this study shows the necessity and importance of pre-processing data and identification of the most significant variables.
FPGA Prototyping of Web Service Using REST and SOAP Packages

This Communication reports on FPGA prototyping of an embedded web service that sends XML messages under two different packages, namely Simple Object Access Protocol (SOAP) and Representational State Transfer (REST). The request and response messages are communicated through a 100 Mbps local area network between a Spartan-3E FPGA board and washing machine simulator. The performances of REST-based and SOAP-based web services implemented on reconfigurable hardware are then compared. In general, the former performs better than the latter in terms of FPGA resource utilization (~12% less), message length (~57% shorter), and processing time (~4.5 μs faster). This work confirms the superiority of REST over SOAP for data transmission using reconfigurable computing, which paves the way for adoption of these low-cost systems for web services of consumer electronics such as home appliances.
Human-Object Interaction Prediction with Natural Language Supervision

Although the dataset for the HOI task already contains a rich set of Human-Object Interaction types, it is impractical to label and learns all (object-interaction) combinations since the same objects can have different categories of interactions with humans. When some uncommon interaction combinations occur in real application scenarios, it is difficult for existing models to make correct predictions. To address these issues, we propose a novel Transformer-based HOI prediction model. The model converts the triad labels (human-interaction-object) of HOI tasks into natural language descriptions of images and uses the converted description sentences as new image labels to predict their interactions in the space of joint natural language and HOI interaction features. This approach transforms the image to triplet mapping problem into a mapping problem from image to natural language, so it can deal with uncommon HOI interaction combinations. In addition, we use a new image Precise Relative Position Embedding method for enhancing the distance perception between image instances and enhancing the instance relevance detection in the joint space. We can also apply our model to zero-sample learning experiments since it can identify new interaction combinations. Extensive experiments on the datasets SWIG-HOI and HICO-DET show that our model is noticeably improved compared to previous methods.
AMV-TSN: Temporal Segment Networks Based on Appearance and Motion-variation for Videos

With the growing demand for video content analysis, action recognition has attracted lots of interest. Similar actions in videos differ in speed, direction, acceleration and spatial appearance. However, existing methods do not fully explore the appearance and motion information which is vital for action recognition. Therefore, we propose a novel temporal segment network based on appearance and motion-variation information (AMV-TSN). Our contribution is two-fold. First, a novel method is proposed to extract the appearance and motion-variation information of videos. Second, The AMV is design to obtain more valuable information of actions. Experiments demonstrate that the temporal segment network based on more effective AMV achieves better performance on action recognition. We carry out experiments on two popular datasets: UCF101 and HMDB51. Experimental results indicate that AMV contains more distinctive motion-variation features and our model outperforms previous works.
LEAN: Local-Enhanced Attention Network for Bad Weather Image Restoration

Bad weather image restoration is an important pre-processing task for many vision applications. Seeking a remarkable performance, existing works select to place their focus on one specific degradation, lacking the ability to cope with various weather conditions simultaneously. In this paper, we propose a local-enhanced attention network (LEAN) for restoring images with multiple weather degradations. First, we initiatively bring in cross-level transformer blocks to the encoder-decoder structure for a more effective utilization of the multi-scale features. Second, we design a local-enhanced self-attention (LESA) mechanism using a residual convolutional projection to reserve richer original information for subsequent feature reconstruction. Third, we bond smooth-L1 loss, frequency loss and perceptual loss together to accelerate the training. Experiment results show that LEAN has surpassed all the existing peering works in PSNR, increasing 2.06% on OutdoorRain-Test1, 3.04% on Snow100K-L and 3.36% on Raindrop-TestA, respectively and simultaneously.
Efficient Loop Abort Fault Attacks on Supersingular Isogeny based Key Exchange (SIKE)

Post-quantum secure public key algorithm Super-singular Isogeny based Key Exchange (SIKE) has emerged as a viable candidate for post-quantum secure key encapsulation mechanism. SIKE is based on isogeny property of elliptic curves and its security depends upon the intractability of computing the isogenous path from the source and image curve. In this paper, we focus on the vulnerability of SIKE against fault attacks, specifically against loop abort fault attacks. The fault attacks proposed in this paper can be applied to both naive and optimized implementations of large degree isogeny computation. The attack on naive implementation is based on creating loop abort faults during scalar multiplication and isogeny computation. The effectiveness of such loop abort faults is twofold: it can transform the post-quantum hardness of SIKE to a post-quantum vulnerable ECDLP (Elliptic Curve Discrete Log), while in the other case the adversary can retrieve the secret key of SIKE protocol with little computational effort. The attack on optimized implementation of SIKE takes advantage of the publicly available computation strategy of isogeny computation. Therefore, it can recover the private key of SIKE with only a few fault injections.
Performance Analysis of Federated Learning in wireless networks

With the proliferation of connected devices and the increase in the use of Machine Learning(ML), more confidential data is being generated. Traditional ML, whereby data is sent to a server for training and processing using models, is becoming less suitable due to privacy concerns. Thus, distributed approaches such as Federated Learning(FL) are becoming more popular. In the latter approach, the model is sent to the clients, where it is trained using the client’s data. The updated model is sent to a server to be aggregated. FL is expected to be used extensively in wireless networks. Therefore, researchers are interested in optimizing Federated Learning for wireless networks. This paper aims to study the performance of FL in terms of accuracy and amount of data exchanged in a wireless network considering the impact of delay using different datasets. The accuracy of the FL model was found to be reliable when benchmarked to the centralized approach(less than 0.1 difference in accuracy). The data transfer size with FL was also significantly smaller than in the centralized approach for all the tested datasets.
Generating Questions for Abstractive Answers with a Multi-Channel Fusion Encoder-Decoder Model

The conventional seq2seq-based question generation usually treats the answer as a span within the document. But answers cannot always be literally found in documents, especially for those generated in an abstractive manner. In order to address this issue, we propose a Multi-Channel Fusion Encoder-Decoder model (MCFED) to generate questions corresponding to abstractive answers. The model uses two encoders, one for the document and the other for the abstractive answer. Additionally, we explore three channels to fuse abstractive answers into the encoder-decoder model: (1) answer matching at the word embedding layer, capturing the partial match between the abstractive answer and the document in either a hard or soft manner; (2) attentive fusion at the encoder layer, which learns answer-aware document representation; (3) answer-guided generation at the decoder layer to directly fuse the answer information into the decoder. Experiments and analyses demonstrate that the proposed MCFED model achieves significant improvements over strong baselines on three QA datasets (i.e., SQuAD, NarrativeQA and MS-MARCO).
Metaverse in Investment Using Sentiment Analysis and Machine Learning

At the end of 2019, individuals' outdoor activities were restricted due to the emergence of COVID-19. As a result of this phenomenon, interest in online activities and interaction in the metaverse environment has increased. Online games have exploded in popularity with the young generation in Metaverse where they can earn money through the platforms. Thus, it is desirable to investigate emerging technology and analyse how to invest using techniques, such as sentiment analysis and machine learning (ML), to predict crypto trends. This study analysed time series data for crypto price and text, where information like news, articles, and feedback from social media can use the input to generate the sentiment score to understand the crypto trends. FinBERT is a sentiment model that was used for this study to generate the result. The AI investing framework is built to incorporate both sentiment analysis technique and predictive model for this chapter, to address the research questions and enable one to make more informed decisions.
Reviewing the Components of Evaluation Methods

The present research work consists of a revision of the main components of the evaluation methods, from the educational, technological, and scientific perspectives. It highlights the interrelation of mathematics, the media, and the social sciences in the generation of new educational paradigms. The advantage of diachronic and synchronic storytelling is also disclosed to confront historical and current realities in secular European institutions, which have an international projection and are aimed at education. Finally, the contributions of the formal, natural, and social sciences are investigated in the generation of evaluation methods, techniques, and instruments that range from usability engineering through user experience to the expansion of communicability.
Evolution of the Sciences, Informatics, Human Integration, and Scientific Education

This chapter presents a novel quadrangular and bidirectional interrelation in the field of science and the modernization of education: ESIHISE (evolution of the sciences, informatics, human integration, and scientific education). To this evolutionary interrelation, the authors add a diachronic and synchronic vision, always placing the human being as a user of new technologies at the centre of all these interrelationships. In addition, they describe the origin of these interrelationships in the sciences, their evolution or revolution, over time, until currently known, the tactics used for the loss of human capacities (natural and/or acquired) in the face of the infodemic of intelligent networks, as well as the generation of divergent parallel lines, between scientific theory and the reality of research and teaching environments. Finally, cases of analysis compiled over three decades are presented through the use of storytelling to avoid the phenomenon of “fleeting kites,” which never provide practical and real educational solutions in normal and abnormal teaching situations.
Three-Dimensional Images, Computer Animation, and Communicability

The author analyze the importance of the evolution of the notion of perspective and communicability between engineers and artists. In addition, some digital and interactive practical cases are investigated in the 3D reconstruction of innovative and old inventions and/or mechanisms, based on stored designs, on paper. There is a review of digitized personal designs, with a high degree of creativity and originality, between Western and Eastern culture. Simultaneously and independently of the STEAM knowledge (science, technology, engineering, art, and math) of the potential users of interactive systems, the main advantages of including computer animations for educational purposes are presented. Finally, it is verified how the contents related to human ingenuity and that belong to the universal cultural heritage serve to promote the latest advances in new technologies among the various generations of users that range from hypertext to the metaverse.
On product decomposition

Given a finite set W in k¯n where k¯ is the algebraic closure of a field k one would like to determine if W can be decomposed as ∏i=1nVi where Vi⊂k¯ under a linear transformation, that is, W→λ∏i=1nVi where λ∈Gln(k¯). We assume that W is presented as W=Z(F), the zero set of a polynomial system F in n variables over k. We study algebraic characterization of such product decomposition. For decomposition into component sets of the same cardinality we obtain a stronger characterization and show that the decomposition in this case is essentially unique (up to permutation and scalar multiplication of coordinates). We investigate computational problems that arise from the decomposition problem.
Bayesian classification learning framework based on bias&amp;ndash;variance trade-off

朴素贝叶斯由于其简单、高效和有效性成为十大数据挖掘算法之一. 然而它要求的属性条件独立假设在实际应用中很难成立. 为了削弱其属性条件独立假设, 学者们提出了结构扩展、属性选择、属性加权、实例选择、实例加权五类改进方法. 现有改进方法虽然在一定程度上降低了模型的偏差, 但同时也提高了模型的方差, 因而限制了模型的泛化性能. 偏差-方差权衡是机器学习的核心原则之一, 该原则要求模型具有较低偏差的同时, 方差也要尽量低. 如何在贝叶斯分类学习中引入偏差-方差权衡, 同时获得较低的偏差和方差, 从而进一步提升模型的泛化性能, 是本文关注的重点. 为此, 本文首先理论分析了在贝叶斯分类学习中做偏差-方差权衡的可行性, 探讨了保证可行性的关键因素; 然后通过构建回归任务来学习贝叶斯分类模型的后验概率损失, 调控关键因素的变化; 最终提出了一种基于偏差-方差权衡的贝叶斯分类学习框架, 并在提出的学习框架下重新实现了朴素贝叶斯及其各类改进模型. 在大量经典的UCI标准数据集上的实验结果表明, 现有的各类最先进的贝叶斯分类模型在本文所提学习框架下的分类性能显著优于其原始性能.
408P Machine learning for glioblastoma screening from histopathology whole slide imaging

Glioblastoma (GBM) is one of the most common malignant primary brain tumours, which accounts for 60-70% of all glioma. Conventional diagnosis and post-operation treatment plan for glioblastoma is mainly based on the identification of tumor cells on haematoxylin and eosin stained (H&E) histopathological slides by both experienced medical technologist and pathologist. The recent development of digital whole slide scanners allows the histopathological image analysis feasible. This study aimed to build an image feature based model using histopathology whole slide images to differentiate patients with GBM from healthy control (HC).
Selection and study of fuzzy semantics in machine translation

In daily life, English is increasingly used in various scenarios, and the technology of translation using machines and others is gradually maturing, but there are still cases of inaccurate translation. To enhance translation’s accuracy, this study optimizes the method of fuzzy semantic selection, and then optimizes the method of domain analysis combined with neural networks to improve the accuracy of machine translation in different domains. The accuracy of the optimized neural network tends to be stable when the number of iterations is 15, the accuracy is 0.96, the accuracy of the traditional neural network is 0.91, and RNN is 0.82. Compared with the benchmark system, the bilingual mapping model has increased by 0.67% in the news field and 0.56% in the education field. The precision, recall and F value of machine translation are 93%, 86% and 0.8 respectively. The comprehensive experimental results show that the selection method based on fuzzy semantics, combined with the domain analysis method of network neural, can remarkably enhance the accuracy.
Specific emitter individual identification based on the characteristics of synchronization signal rising edges and deep neural network

Specific emitter identification technology has always been a hot issue for the vital research of radio-related departments in various countries. However, due to the sharp increase in the types of radiation sources and the complexity of electromagnetic space, identifying individual radiation sources has become more challenging. In order to provide a convenient and effective radiation source identification method, this paper proposes an emitter individual identification research method based on the characteristics of synchronization signal rising edges. With the help of artificial intelligence technology, the method proposed in this paper has achieved a very high individual identification rate of radiation sources by using the characteristics of synchronization signal rising edges and deep neural networks.
Clinical application effect of traditional Chinese medicine nursing program for vertigo (essential hypertension)

<strong>Objective:</strong> Taking patients with vertigo as an example, to explore the effect of applying traditional Chinese medicine nursing methods on the patients. <strong>Methods:</strong> Taking the patients with vertigo in our hospital as an example (from June 2021 to June 2022), a total of 80 patients were selected. With different nursing methods as the main method, one group of 40 people used routine nursing (control group), and the other group of 40 people used traditional Chinese medicine nursing (experimental group), to compare and analyze the nursing effect under different nursing methods. <strong>Results:</strong> After nursing intervention, the scores of TCM symptom scores such as palpitations, fatigue, dizziness and headache in the experimental group were all lower than those in the control group, and there was a big difference between the two groups, P&lt;0.05. At the same time, after nursing, the patients in the experimental group had higher nursing satisfaction and lower incidence of adverse reactions, and the diastolic and systolic blood pressure of the patients in the experimental group were lower than those in the control group. When the data were compared with the data of the control group, the difference was statistically significant, P&lt;0.05. <strong>Conclusion:</strong> The application of traditional Chinese medicine nursing method in patients with vertigo has positive significance in improving the blood pressure level and reducing the incidence of adverse reactions in patients, and the effect is outstanding.
Example-based makeup transfer

As one of the significant applications of image transformation, digital makeup combines efficiency and entertainment, providing inspiration for the development of computer vision in the fields of fashion, film, and games. However, the traditional makeup transformation method is time-consuming and requires a pair of “before” and “after” makeup images as examples. Also, how to separate the information from the illumination and color in the images is another issue. Therefore, in this paper, we propose an example-based makeup transfer algorithm for image composition and re-composition by using facial landmark, face warping in adjusted MLS, fast guided filter, and alpha-blending methods. Experiments demonstrate that our method has higher speed in image decomposition. More importantly, we find fitting parameters that have a better impact on color transfer in image composition.
Prediction of breast cancer using regression model

Breast cancer is the most common malignant tumor nowadays, which is even more common than lung cancer among human being. Fine Needle Aspiration (FNA) is the most common way to detect whether the breast mass is malignant or not. Although the rate of survive after the malignant tumor spreads is pretty low, if the tumor can be noticed at early stage, patients still hold a great chance of survive. With a low death rate in early stage comparing to other cancers, a convenient and efficient way to help diagnosing whether the breast mass is benign or malignant is crucial. However, many existing models on predicting breast cancer includes variables of the patients themselves, like weight changes and lifestyle, which requires long-term tracking survey, and that will cost a lot of precious time during the early stage of the tumor. Thus, a model which only consider the numeric variable from the FNA would save a lot of time and increase the chance of living. In this study a model is created based on the FNA data of the breast tumor patients in Wisconsin in 1995. A set of variable filtrations are employed to get the best variable combination such as AIC, ridge regression, variance inflation factor (VIF), LASSO, and cross validation. After the variable filtration a model based on General Linear Model (GLM) is created with an accuracy of 0.88.