Reviewerly/exHarmony huggingface dataset:
Patents on Inventions Involving AI in the Life Sciences and Healthcare

In recent years, the relevance of artificial intelligence (AI) and its potential applications in various technical areas of the life sciences and healthcare have increased. The accumulation of big data, the development of algorithms, and improvements in computer processing capacity have made possible the integration of AI in the life sciences and healthcare. Some examples of AI applications in these fields are drug and molecular design, the prediction of disease risk, diagnosis of diseases, detection of cancerous cells and tumors, genomics, the monitoring of therapeutic progress, the adjustment of therapeutic treatment, and improvements in manufacturing processes. Such applications change the mode of work in hospitals and for doctors and give rise to interdisciplinary products for which a company seeks a monopoly under patent law. A patented technical monopoly may facilitate a company a dominant market position and a return on investment. However, a patent monopoly is subject to legal regulations that define the preconditions for a patent grant as well as exclusion criteria. These may vary in the particular country for which a patent is sought. This chapter is based on the European Patent Convention (EPC) and the case law of the Boards of Appeal of the European Patent Office (EPO). The examples of granted or refused patents presented here may be assessed differently by other jurisdictions [e.g., the United States Patent and Trademark Office (USPTO), Japan Patent Office (JPO), China National Intellectual Property Administration (CNIPA)].
Evaluation Metrics for Hierarchical Multi-label Text Classification: Combining Structural and Semantic Features

For hierarchical multi-label classification tasks, performance measures play a critical role in model selection and evaluation, which are complicated by the rich hierarchical structure. Several evaluation measures have been proposed without however taking both semantic similarity and hierarchical structure into account. Thus, this study reviews the use of hierarchical measures previously mentioned in academia and introduces a new metric that considers both hierarchical structure features and category semantic features. We demonstrate the efficacy of this metric in terms of consistency and discriminancy.
Video Moving Objects Detection by Minimizing the Partial Sum of Singular Values of the Matrix

As an important part of computer image processing technology, moving object detection has been applied more and more widely in real life, whether it is security monitoring in public places, artificial intelligence behavior recognition, or even military fields. Robust Principal Component Analysis (RPCA), also known as Low Rank and Sparse Decomposition (LRSD), is widely used in moving target detection. In a low-rank matrix decomposition problem, we not only know that the underlying structure is low-rank, but also know the condition of the exact rank of the matrix data. In the rank constraint problem, the prior information of the target rank can be fully utilized in the expression of the objective function, which can solve the problem better. The innovation of this paper is to use this information to improve the robustness of object detection in various complex scenarios. Finally, the effectiveness of the algorithm is verified by experimental results.
Behavioral Sequence Classification with Constraints

The algorithms for sequence classification usually include three steps: first, discovering the supporting features from the sequences; next, using these features to build the classifier; and last, inputting the new sequences into the classifier to get predictable results. The interesting Behavioral Constraint Miner (iBCM), as one of the constraint-based algorithms, can deliver predefined informative templates that indicate item positions, binary relations, and absence information for different labels of the sequences. Furthermore, when combined with window- based approaches, iBCM can shorten the long sequences used to capture concept drift. Based on this, we propose a novel behavioral analysis algorithm named BSClass to firstly analyze the individual performances of the old templates and make a few modifications to their original designs. Using a flexible way to search for the best templates and window combinations, we improved the calculation for obtaining frequent itemsets in the test. As evidenced by 10-fold cross-validation, these improvements can significantly enhance the accuracy of the challenging classification task and also reduce the size of the features.
Identity Dispositions, Development and Economic Progress in Africa

The world we live in today is characterised by technology, and the twenty-first century in particular can be adequately and justifiably called the digital era. The current era has seen an upsurge in the use of technology in all spheres of life, and this has revolutionised the way we do things. From communication to politics, to morality and religion, technology has become a central determinant factor. However, inasmuch as technology has become a characteristic feature of the twenty-first century, Africa still lags behind in terms of the development of uniquely African technology that the world can adopt and use. In this chapter, I examine the role of identity dispositions in the development of technology and economic development. I argue that the more members of a given country have a proclivity towards scientific/secular reasoning, the more economically developed it is. I also show that the main reason why Africa is lagging behind in terms of technological advancement is that it is stuck in the pre-industrialisation era where religious/traditional identity dispositions are given a dominant role. Although the preservation of such values in essential to maintaining a people’s identity, I argue that such an environment has a tendency of promoting ignorance. An environment that works on the conviction of absolutes and does not pay heed to evidence, stifles creativity and subsequently innovation. I propose a culture where traditional values and norms are considered important, insofar as these do not impede the development of new ideas and solutions that promote economic emancipation.
Ranking of attributes—comparative study based on data from stylometric domain

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors.
A Query Intention Classification Method of e-commerce Platform Based on Support Vector Machine

With the development of social informatization, various resource information has increased exponentially, forming a large amount of data. How to use the existing user related information on the network to quickly help users find the information they need is an important problem that major e-commerce platforms need to face. The e-commerce platform can preliminarily understand the user's query intention according to the user's query log. Classify according to the query intention, so as to improve the efficiency and accuracy of user query. This paper proposes a classification method of log query intention based on support vector machine (SVM). This method clusters, vectorizes and classifies the query topics hidden in the log by constructing a topic oriented framework. The experimental results show that the classification effect is remarkable.
Decentralized GAN Training Through Diffusion Learning

Most available studies on distributed GAN architectures focus on implementations with a fusion center. In this work, we propose a fully decentralized scheme by employing a diffusion strategy to train a network of GANs. We interpret the training procedure as a team competition problem and use the paradigm of competing adaptive networks to solve it. We explain that the local discriminators and generators will cluster around their respective centroids. We present simulation results to illustrate that the proposed strategy allows local agents to match the performance of the centralized GAN. More importantly, we also illustrate that local GANs are able to generate different types of images from a dataset, even when they are locally trained with a subset that does not contain all image types.
Towards Synthesizing Realistic Data for Efficient On-Device Image Deblurring

Existing works on image deblurring operate either with an assumption of white Gaussian noise or ignore the noise term completely in formulating the blur generation process. However, real world images may additionally be corrupted with noise, compression and scaling artifacts, detail loss, etc. The state of the art image deblurring methods do not work well with real images due to this issue. In this work, we propose a blur generation formula that considers composite nature of real world artifacts, enabling deep learning methods to learn an end-to-end mapping between real world blurred and realistic sharp images. We propose an extremely light-weight multiscale-multiresidue architecture, designed for efficient execution on embedded devices (90ms/MP on smartphone). We show the efficacy of our method by comprehensive comparative study against existing methods, on real world blur images as well as popular public deblurring data. Quantitative comparison of our work on public benchmark datasets shows significant improvement (+2.19 dB PSNR) over the state of the art while being almost 100 times faster.
Automated Late Fusion of Low Level Descriptors for Feature Extraction and Texture Classification Using Data Augmentation

Feature extraction is an important task for texture image classification. Many descriptors have been proposed in the literature in order to describe textured images locally as well as globally. Researchers’ interpretations differ on the effectiveness of these descriptors depending on the field of application, but no one can deny their complementarity. However, fusing different descriptors is not always easy, notably because of their different types (local vs. global, dense vs. sparse $$\ldots $$ ) and the heterogeneity of the generated features. In this work, we propose to use genetic programming to generate and fuse two different texture classifiers based respectively on HOG and uniform LBP descriptors. Indeed, the proposed method includes a late fusion and data augmentation process in order to combine the classifier’s results while using small set of training data. The suggested method benefits from the different information captured by both descriptors while being robust to rotation changes. The performance of the proposed method has been validated on four challenging datasets including different variations. Results show that the proposed method significantly outperforms other low-level methods as well as GP-based methods intended for texture description and classification.
Research and Practice on Effective Instructional Design of Computer Foundation Course of University

With the development of the reform of computer general education courses, teaching of the computer foundation course of university is faced with a series of challenges, the prominent problem is how to effectively achieve the teaching goal and how to effectively develop students' ability under the circumstance of limited class hours, which is faced with the transformation of course concept, the reconstruction of content system and the upgrading of teaching objectives. The author thinks that the important way to solve the problems is to carry out research and practice base on effective instructional design so as to improve the effectiveness of teaching. First of all, this paper analyzes the new situation and changes of reform of the computer foundation course of university, clarifies the challenges; Secondly, it studies the connotation and characteristics of effective instructional design in the theoretical level; Thirdly, it gives the instructional design plan in the design level; Fourth, a teaching case of the course is shared in practical level; Finally, the paper carries on reflections about instructional design of the computer foundation of university according to teaching practice. The author hopes it will be helpful to solve the prominent problems in the teaching of the computer foundation course of university and provide valuable reference for the effective instructional design.
MRI Super-Resolution using Implicit Neural Representation with Frequency Domain Enhancement

High resolution (HR) Magnetic Resonance Imaging (MRI) is a popular diagnostic tool, which provides detail structural information and rich textures, benefiting accurate diagnosis and disease detection. However, obtaining HR MRI remains a challenge due to longer scan time and lower peak signal-to-noise ratio (PSNR). Recently, Single Image Super-Resolution (SISR) has generated interest, which shows promising ability for recovering an HR image only relies on a Low Resolution (LR) image. MR images have some characteristics different with natural images: derived from frequency domain, simpler textures and structural information. However, Most of previous methods treat MR images as same as natural images, they only apply SR methods on natural images to MR images and fail to preserve low-frequency information and capture high-frequency details. In this paper, we mimic the process of an MRI machine produces an MRI in practice and propose an Implicit Neural Representation based module, which enable reconstruct high frequency contents effectively while preserving low frequency contents unchanged. Moreover, vanilla L1 loss cannot reflect the differences for each frequency, to address this problem, we design a frequency loss to disentangle each frequency and calculate the differences respectively. Finally, to further capture high frequency contents, we propose High-Frequency Pixel Loss, which can decouples the HF contents from pixel domain and emphasize the HF differences between SR and HR images. Extensive experiments show the effectiveness of our proposed method in terms of visual quality and PSNR score, which produces sharper edges and clearer details compared to previous works.
Emergency Remote Teaching of Technical Drawing During the COVID-19 Pandemic

Hybrid teaching, which combines face-to-face and online teaching, was widely adopted in higher education during the Covid-19 pandemic, especially in courses where implementation, experimentation, and practice are core activities. This paper presents observations of teaching effectiveness of a hybrid teaching framework designed within emergency remote teaching (ERT) in a technical drawing course during the pandemic. The objectives of the study are to analyse the teaching effectiveness of the designed framework and to understand its benefits and challenges for improvement of ERT of practice-based courses. Teaching effectiveness is analysed based on students' feedback, students' participation, analysis of students' grades, and instructors' observations. The findings revealed that students' adaption and participation was high and there was a positive correlation between in-class practice and individual learning processes. Most students stated that some of the online teaching activities (e.g. feedback, class notes, answer keys) can be integrated in face-to-face teaching in the future.
Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption

Machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts. We address the challenging domain generalization (DG) problem, where a model trained on a set of source domains is expected to generalize well in unseen domains without any exposure to their data. The main challenge of DG is that the features learned from the source domains are not necessarily present in the unseen target domains, leading to performance deterioration. We assume that learning a richer set of features is crucial to improve the transfer to a wider set of unknown domains. For this reason, we propose COLUMBUS, a method that enforces new feature discovery via a targeted corruption of the most relevant input and multilevel representations of the data. We conduct an extensive empirical evaluation to demonstrate the effectiveness of the proposed approach which achieves new state-of-the-art results by outperforming 18 DG algorithms on multiple DG benchmark datasets in the DomainBed framework.
Trajectory Planning of Semi-Trailer Truck Vehicle Based on Algebraic General Trajectory Formula

The present group previously derived an algebraic general trajectory formula (GTF) for semi-trailer trucks based on six parameters relating to the truck size and truck turning situation. The present study proposes a trajectory planning method based on the GTF which ensures that several arbitrarily-chosen points on the truck pass sequentially through a set of user-defined target points along the required path. An illustrative example is provided to demonstrate the effectiveness of the proposed method. The results confirm that the trajectory planning method provides an effective, accurate, and simple methodology for planning the motion path of semi-trailer trucks.
Multimodal Generation of Novel Action Appearances for Synthetic-to-Real Recognition of Activities of Daily Living

Domain shifts, such as appearance changes, are a key challenge in real-world applications of activity recognition models, which range from assistive robotics and smart homes to driver observation in intelligent vehicles. For example, while simulations are an excellent way of economical data collection, a Synthetic→Real domain shift leads to > 60% drop in accuracy when recognizing Activities of Daily Living (ADLs). We tackle this challenge and introduce an activity domain generation framework which creates novel ADL appearances (novel domains) from different existing activity modalities (source domains) inferred from video training data. Our frame-work computes human poses, heatmaps of body joints, and optical flow maps and uses them alongside the original RGB videos to learn the essence of source domains in order to generate completely new ADL domains. The model is optimized by maximizing the distance between the existing source appearances and the generated novel appearances while ensuring that the semantics of an activity is preserved through an additional classification loss. While source data multimodality is an important concept in this design, our setup does not rely on multi-sensor setups, (i.e., all source modalities are inferred from a single video only.) The newly created activity domains are then integrated in the training of the ADL classification networks, resulting in models far less susceptible to changes in data distributions. Extensive experiments on the Synthetic→Real benchmark Sims4Action demonstrate the potential of the domain generation paradigm for cross-domain ADL recognition, setting new state-of-the-art results. Our code is publicly available at https://github.com/Zrrr1997/syn2real_DG.
A Comparative Study on Single Image De-Raining Using Convolutional Neural Network

Most of the algorithms for computer vision require a clear image for its processing. For finding the proper solution to the visual degradation of the image due to streaks caused by rain, an effective de-raining algorithm needs to be developed. De-raining is the task of removing streaks of rain and their effect from the distorted image that contains rain. The huge success of methods based on learning and that too CNN-based methods in different domains have prompted its use in de-raining. For effectively obtaining the automatic feature information, deep CNN methods are used. The efficiency of various methods of de-raining is applied and tested on datasets such as Rain1200, Rain100H, Rain100L, Rain1400, and many more that are discussed and analyzed here. The quantitative metrics for the comparison are PSNR and SSIM.
Few-shot Text Classification with Saliency-equivalent Concatenation

In few-shot text classification, the lack of significant features limits models from generalizing to data not included in the training set. Data augmentation is a solution to the classification tasks; however, the standard augmentation methods in natural language processing are not feasible in few-shot learning. In this study, we explore data augmentation in few-shot text classification. We propose saliency-equivalent concatenation (SEC) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our code is available at https://github.com/IKMLab/SEC.. The core concept of SEC is to append additional key information to an input sentence to help a model understand the sentence easier. In the proposed method, we first leverage a pre-trained language model to generate several novel sentences for each sample in datasets. Then we leave the most relevant one and concatenate it with the original sentence as additional information for each sample. Our experiments on the two few-shot text classification tasks verified that the proposed method can boost the performance of meta-learning models and outperform the previous unsupervised data augmentation methods.
Multi Visual Modality Fall Detection Dataset

Falls are one of the leading causes of injury-related deaths among the elderly worldwide.Effective detection of falls can reduce the risk of complications and injuries.Fall detection can be performed using wearable devices or ambient sensors; these methods may struggle with user compliance issues or false alarms.Video cameras provide a passive alternative; however, regular red, green, and blue (RGB) cameras are impacted by changing lighting conditions and privacy concerns.From a machine learning perspective, developing an effective fall detection system is challenging because of the rarity and variability of falls.Many existing fall detection datasets lack important real-world considerations, such as varied lighting, continuous activities of daily living (ADLs), and camera placement.The lack of these considerations makes it difficult to develop predictive models that can operate effectively in the real world.To address these limitations, we introduce a novel multi-modality dataset (MUVIM) that contains four visual modalities: infra-red, depth, RGB and thermal cameras.These modalities offer benefits such as obfuscated facial features and improved performance in low-light conditions.We formulated fall detection as an anomaly detection problem, in which a customized spatio-temporal convolutional autoencoder was trained only on ADLs so that a fall would increase the reconstruction error.Our results showed that infra-red cameras provided the highest level of performance (AUC ROC=0.94),followed by thermal (AUC ROC=0.87),depth (AUC ROC=0.86) and RGB (AUC ROC=0.83).This research provides a unique opportunity to analyze the utility of camera modalities in detecting falls in a home setting while balancing performance, passiveness, and privacy.