Reviewerly/exHarmony huggingface dataset:
Recognizing activities of the elderly using wearable sensors: a comparison of ensemble algorithms based on boosting

Purpose In human action recognition based on wearable sensors, most previous studies have focused on a single type of sensor and single classifier. This study aims to use a wearable sensor based on flexible sensors and a tri-axial accelerometer to collect action data of elderly people. It uses a statistical modeling approach based on the ensemble algorithm to classify actions and verify its validity. Design/methodology/approach Nine types of daily actions were collected by the wearable sensor device from a group of elderly volunteers, and the time-domain features of the action sequences were extracted. The dimensionality of the feature vectors was reduced by linear discriminant analysis. An ensemble learning method based on XGBoost was used to build a model of elderly action recognition. Its performance was compared with the action recognition rate of other algorithms based on the Boosting algorithm, and with the accuracy of single classifier models. Findings The effectiveness of the method was validated by three experiments. The results show that XGBoost is able to classify nine daily actions of the elderly and achieve an average recognition rate of 94.8 per cent, which is superior to single classifiers and to other ensemble algorithms. Practical implications The research could have important implications for health care, including the treatment and rehabilitation of the elderly, and the prevention of falls. Originality/value Instead of using a single type of sensor, this research used a wearable sensor to obtain daily action data of the elderly. The results show that, by using the appropriate method, the device can obtain detailed data of joint action at a low cost. Comparing differences in performance, it was concluded that XGBoost is the most suitable algorithm for building a model of elderly action recognition. This method, together with a wearable sensor, can provide key data and accurate feedback information to monitor the elderly in their rehabilitation activities.
Application of electronic management system in governmental institutions

Organizations often face some challenges, such as the need to keep pace with technological developments to achieve high performance and manage their business efficiently free from time-consuming procedures. This study aims to evaluate the applicability of e-management in government institutions, namely civil registry administrations, to identify the main obstacles, challenges, and benefits of this process. This research was conducted using a descriptive and survey method by distributing a questionnaire to collect data from the research community to determine the degree, to which e-management can be applied in civil registry offices in Libya. The research community consists of employees in many civil registry offices. Significant results have been obtained, most notably the identification of the most important obstacles to the implementation of e-management in the civil registry offices, both administratively and technically. The results also illustrate the insufficiency of the advantages of the e-management application.
Blockchain Technology for Data Management in Industry 4.0

Nowadays data plays a vital role in our lives so, it should not go under negative influence other than computer connoisseur else it can lead to detrimental consequences.
Depth and thermal information fusion for head tracking using particle filter in a fall detection context

The security of elderly people living alone is a major issue. A system that detects anomalies can be useful for both individual and retirement homes. In this paper, we present an adaptive human tracking method built on particle filter, using depth and thermal information based on the velocity and t he position of the head. The main contribution of this paper is the fusion of information to improve tracking. For each frame, there is a new combination of coefficients for each particle based on an adaptive weighting. Results show that the tracking method can deal with the cases of fast motion (fall), partial occultation and scale variation. To assess the impact of fusion on the tracking process, the robustness and accuracy of the method are tested on a variety of challenging scenarios with or without depth-thermal fusion.
Feature Statistics Guided Efficient Filter Pruning

Building compact convolutional neural networks (CNNs) with reliable performance is a critical but challenging task, especially when deploying them in real-world applications. As a common approach to reduce the size of CNNs, pruning methods delete part of the CNN filters according to some metrics such as l1-norm. However, previous methods hardly leverage the information variance in a single feature map and the similarity characteristics among feature maps. In this paper, we propose a novel filter pruning method, which incorporates two kinds of feature map selections: diversity-aware selection (DFS) and similarity-aware selection (SFS). DFS aims to discover features with low information diversity while SFS removes features that have high similarities with others. We conduct extensive empirical experiments with various CNN architectures on publicly available datasets. The experimental results demonstrate that our model obtains up to 91.6% parameter decrease and 83.7% FLOPs reduction with almost no accuracy loss.
Structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications

Deep neural networks (DNNs) are widely considered as a key technology for perception in high and full driving automation. However, their safety assessment remains challenging, as they exhibit specific insufficiencies: black-box nature, simple performance issues, incorrect internal logic, and instability. These are not sufficiently considered in existing standards on safety argumentation. In this paper, we systematically establish and break down safety requirements to argue the sufficient absence of risk arising from such insufficiencies. We furthermore argue why diverse evidence is highly relevant for a safety argument involving DNNs, and classify available sources of evidence. Together, this yields a generic approach and template to thoroughly respect DNN specifics within a safety argumentation structure. Its applicability is shown by providing examples of methods and measures following an example use case based on pedestrian detection.
Less Is More: Pay Less Attention in Vision Transformers

Transformers have become one of the dominant architectures in deep learning, particularly as a powerful alternative to convolutional neural networks (CNNs) in computer vision. However, Transformer training and inference in previous works can be prohibitively expensive due to the quadratic complexity of self-attention over a long sequence of representations, especially for high-resolution dense prediction tasks. To this end, we present a novel Less attention vIsion Transformer (LIT), building upon the fact that the early self-attention layers in Transformers still focus on local patterns and bring minor benefits in recent hierarchical vision Transformers. Specifically, we propose a hierarchical Transformer where we use pure multi-layer perceptrons (MLPs) to encode rich local patterns in the early stages while applying self-attention modules to capture longer dependencies in deeper layers. Moreover, we further propose a learned deformable token merging module to adaptively fuse informative patches in a non-uniform manner. The proposed LIT achieves promising performance on image recognition tasks, including image classification, object detection and instance segmentation, serving as a strong backbone for many vision tasks. Code is available at https://github.com/zip-group/LIT.
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination

We propose an unsupervised method for learning a generic and efficient shape encoding network for different shape analysis tasks. Our key idea is to jointly encode and learn shape and point features from unlabeled 3D point clouds. For this purpose, we adapt HRNet to octree-based convolutional neural networks for jointly encoding shape and point features with fused multiresolution subnetworks and design a simple-yet-efficient Multiresolution Instance Discrimination (MID) loss for jointly learning the shape and point features. Our network takes a 3D point cloud as input and output both shape and point features. After training, Our network is concatenated with simple task-specific back-ends and fine-tuned for different shape analysis tasks. We evaluate the efficacy and generality of our method with a set of shape analysis tasks, including shape classification, semantic shape segmentation, as well as shape registration tasks. With simple back-ends, our network demonstrates the best performance among all unsupervised methods and achieves competitive performance to supervised methods. For fine-grained shape segmentation on the PartNet dataset, our method even surpasses existing supervised methods by a large margin.
Video Relation Detection via Tracklet based Visual Transformer

Video Visual Relation Detection (VidVRD), has received significant attention of our community over recent years. In this paper, we apply the state-of-the-art video object tracklet detection pipeline MEGA[7] and deepSORT [27] to generate tracklet proposals. Then we perform VidVRD in a tracklet-based manner without any pre-cutting operations. Specifically, we design a tracklet-based visual Transformer. It contains a temporal-aware decoder which performs feature interactions between the tracklets and learnable predicate query embeddings, and finally predicts the relations. Experimental results strongly demonstrate the superiority of our method, which outperforms other methods by a large margin on the Video Relation Understanding (VRU) Grand Challenge in ACM Multimedia 2021. Codes are released at https://github.com/Dawn-LX/VidVRD-tracklets.
More Efficient Algorithms for the NTRU Key Generation Using the Field Norm

NTRU lattices [13] are a class of polynomial rings which allow for compact and efficient representations of the lattice basis, thereby offering very good performance characteristics for the asymmetric algorithms that use them. Signature algorithms based on NTRU lattices have fast signature generation and verification, and relatively small signatures, public keys and private keys. A few lattice-based cryptographic schemes entail, generally during the key generation, solving the NTRU equation: $$\begin{aligned} f G - g F = q \mod x^n + 1 \end{aligned}$$ Here f and g are fixed, the goal is to compute solutions F and G to the equation, and all the polynomials are in $${\mathbb {Z}}[x]/(x^n + 1)$$ . The existing methods for solving this equation are quite cumbersome: their time and space complexities are at least cubic and quadratic in the dimension n, and for typical parameters they therefore require several megabytes of RAM and take more than a second on a typical laptop, precluding onboard key generation in embedded systems such as smart cards. In this work, we present two new algorithms for solving the NTRU equation. Both algorithms make a repeated use of the field norm in tower of fields; it allows them to be faster and more compact than existing algorithms by factors $${\tilde{O}}(n)$$ . For lattice-based schemes considered in practice, this reduces both the computation time and RAM usage by factors at least 100, making key pair generation within range of smart card abilities.
Pathology Image Exchange: The Dutch Digital Pathology Platform for Exchange of Whole-Slide Images for Efficient Teleconsultation, Telerevision, and Virtual Expert Panels

Among the many uses of digital pathology, remote consultation, remote revision, and virtual slide panels may be the most important ones. This requires basic slide scanner infrastructure in participating laboratories to produce whole-slide images. More importantly, a software platform is needed for exchange of these images and functionality to support the processes around discussing and reporting on these images without breaching patient privacy. This poses high demands on the setup of such a platform, given the inherent complexity of the handling of digital pathology images. In this article, we describe the setup and validation of the Pathology Image Exchange project, which aimed to create a vendor-independent platform for exchange of whole-slide images between Dutch pathology laboratories to facilitate efficient teleconsultation, telerevision, and virtual slide panels. Pathology Image Exchange was released in April 2018 after technical validation, and a first successful validation in real life has been performed for hematopathology cases.
Com-educational Platforms: Creativity and Community for Learning

Education is at a time of redefinition and transformation, in line with an era characterized by considerable technological development and profound social changes. One would expect it to be accompanied by a media context in which narrative models are transformed by the impact of digitalization, affecting student-teacher interactions. However, it has been observed that the media usage of an entire generation emphasizes the gap between formal education and young people's everyday digital life. Within this framework, and at the international level, a series of innovative pedagogical proposals have emerged, which approach education from the field of communication: Minecraft Education, NFB Education, Educ'Arte, Scratch and 7 de Cinema. We have called them com-educational platforms, because of their investment in the education-communication vector, based on an educommunicative idea. The proposed study implements a multimodal discourse analysis (MDA) to gain an in-depth knowledge of its characteristics. Beyond their individual idiosyncrasies, our analysis reveals a common central feature: the placement of community-creativity combination as the core phenomenon for learning.
Certified Robustness to Adversarial Word Substitutions

State-of-the-art NLP models can often be fooled by adversaries that apply seemingly innocuous label-preserving transformations (e.g., paraphrasing) to input text. The number of possible transformations scales exponentially with text length, so data augmentation cannot cover all transformations of an input. This paper considers one exponentially large family of label-preserving transformations, in which every word in the input can be replaced with a similar word. We train the first models that are provably robust to all word substitutions in this family. Our training procedure uses Interval Bound Propagation (IBP) to minimize an upper bound on the worst-case loss that any combination of word substitutions can induce. To evaluate models' robustness to these transformations, we measure accuracy on adversarially chosen word substitutions applied to test examples. Our IBP-trained models attain $75\%$ adversarial accuracy on both sentiment analysis on IMDB and natural language inference on SNLI. In comparison, on IMDB, models trained normally and ones trained with data augmentation achieve adversarial accuracy of only $8\%$ and $35\%$, respectively.
A Method for Generating Synthetic Electronic Medical Record Text

Machine learning (ML) and Natural Language Processing (NLP) have achieved remarkable success in many fields and have brought new opportunities and high expectation in the analyses of medical data, of which the most common type is the massive free-text electronic medical records (EMR). However, the free EMR texts are lacking consistent standards, rich of private information, and limited in availability. Also, it is often hard to have a balanced number of samples for the types of diseases under study. These problems hinder the development of ML and NLP methods for EMR data analysis. To tackle these problems, we developed a model called Medical Text Generative Adversarial Network or mtGAN, to generate synthetic EMR text. It is based on the GAN framework and is trained by the REINFORCE algorithm. It takes disease tags as inputs and generates synthetic texts as EMRs for the corresponding diseases. We evaluate the model from micro-level, macro-level and application-level on a Chinese EMR text dataset. The results show that the method has a good capacity to fit real data and can generate realistic and diverse EMR samples. This provides a novel way to avoid potential leakage of patient privacy while still supply sufficient well-controlled cohort data for developing downstream ML and NLP methods.
Distribution and existing forms of trace elements from Maliping Pb-Zn deposit in northeastern Yunnan, China: A LA-ICPMS study

The Sichuan-Yunnan-Guizhou (SYG) Pb-Zn metallogenic province, located in southwestern margin of the Yangtze Block, is an important part of the large-scale low-temperature metallogenic domain in southwestern China. The Maliping Pb-Zn deposit, situated in the central part of Zhaotong-Qujing metallogenic belt, was found in northeastern Yunnan Province recently. The orebody is hosted in Late Cambrian Yuhucun Formation, occurring as stratabound, lense and venis. The mineral assemblage of the Maliping deposit is relatively simple. The main sulfide minerals are sphalerite and galena with minor pyrite. Gangue minerals include mainly dolomite, calcite, quartz and barite. LA-ICPMS spots and mapping analysis for the different sulfides from Maliping Pb-Zn deposit, and the distribution and existing forms of germanium, cadmium, indium and other trace elements were investigated. The results show that different sulfides are characterized by different contents of trace elements. Mn, Cu, Sn, Cd, In and Ge are mainly enriched in sphalerite, while galena from this deposit is enrichment of Ag, Sb and Se, and pyrite is characterized by enrichment of As, Co and Ni. Comparing with the content of dispersed elements in different sulfides, the results indicate that sphalerite is the primary carrier mineral of Ge, In and Cd. Cd, Ge, In, Mn, As, Sb and Ag occur as isomorphous substitution in the sphalerite, and Cu mostly exists in sphalerite as isomorphism but part of Cu occurs as micro-inclusions (chalcopyrite) in sphalerite. Considered the distinct positive relationship between Cu and Ge, the results imply that the substitution mechanism of Ge and Cu is possibly 3Zn2+↔Ge4++2Cu+. Additionally, sphalerite from Maliping Pb-Zn deposit is characterized by enrichment of Cd, Ge and depleted in Mn, Fe, Co and Sn which coincides with the feature of MVT Pb-Zn deposit and differs from the sedimentary-exhalative deposit and magmatic-hydrothermal deposit. On account of the geological features, other geochemical researches and its ore-forming temperature belonging to low temperature, it is suggested that the Maliping deposit belongs to an MVT Pb-Zn deposit. Notably, we imply that ore-forming fluid extracted indium of magmatic and volcaniclastic rocks from the metamorphic basement, resulting in the enrichment of indium in sphalerite from the deposit.
Quantum walks: The mean first detected transition time

The authors study the time it takes for a quantum particle to reach a target state, using a stroboscopic measurement protocol. In certain cases, the mean time for detection can diverge, even for small systems, and the paper uncovers an Einstein-like relation that connects strong fluctuations in the system with the mean hitting time.
Optical color image encryption based on chaotic fingerprint phase mask in various domains and comparative analysis

Random phase masks serve as secret keys and play a vital role in double random phase encoding architecture. In this paper, we propose a new, to the best of our knowledge, method to generate the random phase masks using the chaotic Henon map and fingerprint. We then extend the generated chaotic fingerprint phase masks to the Fourier transform domain, fractional Fourier transform domain, Fresnel transform domain, and Gyrator transform domain to encrypt color images. In these four color image encryption schemes, the fingerprint and chaotic parameters serve as secret keys directly, and the chaotic fingerprint phase masks are just used as interim variables and functions. If the sender and receiver share the fingerprint, only the chaotic parameters are needed to transmit over the network. Thus, the management and transmission of the secret keys in these four encryption schemes are convenient. In addition, the fingerprint keys which are strongly linked with the sender or receiver can enhance the security of these four encryption schemes greatly. Extensive numerical simulations have been carried out to verify the feasibility, security, and robustness of these four color image encryption schemes.
A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

The evolvement of the fuzzy system has shown influential and successful in many universal approximation capabilities and applications. This paper proposes a hybrid Neuro-Fuzzy and Feature Reduction (NF-FR) model for data analysis. This proposed NF-FR model uses a feature-based class belongingness fuzzification process for all the patterns. During the fuzzification process, all the features are expanded based on the number of classes available in the dataset. It helps to deal with the uncertainty issues and assists the Artificial Neural Network- (ANN-) based model to achieve better performance. However, the complexity of the problem increases due to this expansion of input features in the fuzzification process. These expanded features may not always contribute significantly to the model. To overcome this problem, feature reduction (FR) is used to filter out the insignificant features, resulting the network less computational cost. These reduced significant features are used in the ANN-based model to classify the data. The effectiveness of this proposed model is tested and validated with ten benchmark datasets (both balanced and unbalanced) to demonstrate the performance of the proposed NF-FR model. The performance comparison of the NF-FR model with other counterparts has been carried out based on various performance measures such as classification accuracy, root means square error, precision, recall, and f-measure for quantitative analysis of the results. The obtained simulated results have been tested using the Friedman, Holm, and ANOVA tests under the null hypothesis for statistical validity and correctness proof of the results. The result analysis and statistical analysis show that the NF-FR model has achieved a considerable improvement in accuracy and is found to be efficient in eliminating redundant and noisy information.
Isochronous Gaussian Sampling: From Inception to Implementation

Gaussian sampling over the integers is a crucial tool in lattice-based cryptography, but has proven over the recent years to be surprisingly challenging to perform in a generic, efficient and provable secure manner. In this work, we present a modular framework for generating discrete Gaussians with arbitrary center and standard deviation. Our framework is extremely simple, and it is precisely this simplicity that allowed us to make it easy to implement, provably secure, portable, efficient, and provably resistant against timing attacks. Our sampler is a good candidate for any trapdoor sampling and it is actually the one that has been recently implemented in the Falcon signature scheme. Our second contribution aims at systematizing the detection of implementation errors in Gaussian samplers. We provide a statistical testing suite for discrete Gaussians called SAGA (Statistically Acceptable GAussian). In a nutshell, our two contributions take a step towards trustable and robust Gaussian sampling real-world implementations.