Reviewerly/exHarmony huggingface dataset:
Activities Recognition, Anomaly Detection and Next Activity Prediction Based on Neural Networks in Smart Homes

In this paper, we propose a unified deep learning model for monitoring elderly in execution of daily life activities such as eating, sleeping or taking medication. The proposed approach consists of three stages which are activity recognition, anomaly detection and next activity prediction. Such a system can provide useful information for the elderly, caregivers and medical teams to identify activities and generate preventive and corrective measures. In literature, these stages are discussed separately, however, in our approach, we make use of each stage to progress into the next stage. At first, activity recognition based on different extracted features is performed using a deep neural network (DNN), then an overcomplete-deep autoencoder (OCD-AE) is employed to separate the normal from anomalous activities. Finally, a cleaned sequence of consecutive activities is constructed and used by a long short-term memory (LSTM) algorithm to predict the next activity. Since the last two stages depend on the activity recognition stage, we propose to increase its accuracy by exploiting different extracted features. The performance of the proposed unified approach has been evaluated on real smart home datasets to demonstrate its ability to recognize activities, detect anomalies and predict the next activity.
Impact of IoT Adoption on Agility and Flexibility of Healthcare Organization

This study addresses one of the most critical advents and highly sought after the technological breakthrough of today’s service sector. Internet of things has been finding relevance in today’s service sector as a significant impetus to superior service delivery and advanced service proposition to customers. Healthcare sector is also no exception. This study taking data from the Indian healthcare sector attempts to check the relationship between IoT adoption and proposed service delivery gains experienced by healthcare organizations (if any). The study based on the theoretical premises of cybernetic control theory and technology adoption model by Davis, Hypothesized that IoT adoption must positively influence flexibility and agility and in turn, flexibility positively influence readiness. The empirical evidence supports these hypotheses, and all the findings validate the propositions that healthcare organizations and the players and actors involved in healthcare consider IoT adoption as pivotal. Because the survey outcomes establish path analysis linkages through Structural equation modeling (SEM). SEM results highlight the significant positive impact of IoT adoption on flexibility and agility and in turn, even stronger association and effects of flexibility on readiness in the services offered by healthcare organizations. This study outcomes are very vital for hospital managers and upcoming healthcare practitioners as it establishes empirical evidence supporting IoT adoption as a helpful step and prominent success factor for better flexible patient care delivery and agility.
Spatial Channel Attention for Deep Convolutional Neural Networks

Recently, the attention mechanism combining spatial and channel information has been widely used in various deep convolutional neural networks (CNNs), proving its great potential in improving model performance. However, this usually uses 2D global pooling operations to compress spatial information or scaling methods to reduce the computational overhead in channel attention. These methods will result in severe information loss. Therefore, we propose a Spatial channel attention mechanism that captures cross-dimensional interaction, which does not involve dimensionality reduction and brings significant performance improvement with negligible computational overhead. The proposed attention mechanism can be seamlessly integrated into any convolutional neural network since it is a lightweight general module. Our method achieves a performance improvement of 2.08% on ResNet and 1.02% on MobileNetV2 in top-one error rate on the ImageNet dataset.
Construction of a Nondegenerate 2D Chaotic Map with Application to Irreversible Parallel Key Expansion Algorithm

The key expansion algorithm is an important part of a general iterated block cipher, because its security determines the security level of the encryption scheme. Thus, generating secure round keys with statistical independence and sensitivity is desirable. An irreversible parallel key expansion algorithm is proposed based on a chaotic map in this paper. First, an enhanced nondegenerate 2D exponential chaotic map (2D-ECM) with ergodicity is constructed, with analysis results demonstrating that it has better dynamical characteristics. Then, an irreversible key expansion algorithm is designed based on the 2D-ECM, which has high sensitivity to initial key with independence between round keys. Experimental results and performance analysis demonstrate that the algorithm is feasible and can resist the side channel power attack.
Un-mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning

The recently advanced unsupervised learning approaches use the siamese-like framework to compare two "views" from the same image for learning representations. Making the two views distinctive is a core to guarantee that unsupervised methods can learn meaningful information. However, such frameworks are sometimes fragile on overfitting if the augmentations used for generating two views are not strong enough, causing the over-confident issue on the training data. This drawback hinders the model from learning subtle variance and fine-grained information. To address this, in this work we aim to involve the soft distance concept on label space in the contrastive-based unsupervised learning task and let the model be aware of the soft degree of similarity between positive or negative pairs through mixing the input data space, to further work collaboratively for the input and loss spaces. Despite its conceptual simplicity, we show empirically that with the solution -- Unsupervised image mixtures (Un-Mix), we can learn subtler, more robust and generalized representations from the transformed input and corresponding new label space. Extensive experiments are conducted on CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet and standard ImageNet-1K with popular unsupervised methods SimCLR, BYOL, MoCo V1&amp;V2, SwAV, etc. Our proposed image mixture and label assignment strategy can obtain consistent improvement by 1~3% following exactly the same hyperparameters and training procedures of the base methods. Code is publicly available at https://github.com/szq0214/Un-Mix.
Development of a Real-Time Wearable Fall Detection System in the Context of Internet of Things

Fall detection is of increasing significance in terms of the health monitoring of the elderly and disabled people, as falls may lead to physical injuries or even mental trauma. The existing fall detection methods have achieved impressive performance, but limitations present in operability, interface with public healthcare systems, and other technical issues such as high-power consumption, cost, and reliability. In this article, we present a wearable fall detection system, which is based on a novel multilevel threshold algorithm. The algorithm combines micro-electro-mechanical-systems (MEMS) with narrow band Internet of Things (NB-IoT). The system also includes a user interface for healthcare professionals, developed based on the cloud technology and server-client architecture. For the verification of the algorithm, we recruited 20 volunteers to perform the activities of daily living and falls. The experimental result showed that the proposed algorithm can achieve an accuracy of 94.88%, a sensitivity of 95.25%, and a specificity of 94.5%, suggesting the effectiveness of our system.
Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study

Heart failure (HF) is a common disease and a major public health problem. HF mortality prediction is critical for developing individualized prevention and treatment plans. However, due to their lack of interpretability, most HF mortality prediction models have not yet reached clinical practice.We aimed to develop an interpretable model to predict the mortality risk for patients with HF in intensive care units (ICUs) and used the SHapley Additive exPlanation (SHAP) method to explain the extreme gradient boosting (XGBoost) model and explore prognostic factors for HF.In this retrospective cohort study, we achieved model development and performance comparison on the eICU Collaborative Research Database (eICU-CRD). We extracted data during the first 24 hours of each ICU admission, and the data set was randomly divided, with 70% used for model training and 30% used for model validation. The prediction performance of the XGBoost model was compared with three other machine learning models by the area under the curve. We used the SHAP method to explain the XGBoost model.A total of 2798 eligible patients with HF were included in the final cohort for this study. The observed in-hospital mortality of patients with HF was 9.97%. Comparatively, the XGBoost model had the highest predictive performance among four models with an area under the curve (AUC) of 0.824 (95% CI 0.7766-0.8708), whereas support vector machine had the poorest generalization ability (AUC=0.701, 95% CI 0.6433-0.7582). The decision curve showed that the net benefit of the XGBoost model surpassed those of other machine learning models at 10%~28% threshold probabilities. The SHAP method reveals the top 20 predictors of HF according to the importance ranking, and the average of the blood urea nitrogen was recognized as the most important predictor variable.The interpretable predictive model helps physicians more accurately predict the mortality risk in ICU patients with HF, and therefore, provides better treatment plans and optimal resource allocation for their patients. In addition, the interpretable framework can increase the transparency of the model and facilitate understanding the reliability of the predictive model for the physicians.
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation

Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is still constrained by several factors, such as <i>(i)</i> low-quality region proposals for novel classes and <i>(ii)</i> negligence of the inter-class correlation among different classes. Such limitations hinder the generalization of base-class knowledge for the detection of novel-class objects. In this work, we design Meta-DETR, which <i>(i)</i> is the first image-level few-shot detector, and <i>(ii)</i> introduces a novel inter-class correlational meta-learning strategy to capture and leverage the correlation among different classes for robust and accurate few-shot object detection. Meta-DETR works entirely at image level without any region proposals, which circumvents the constraint of inaccurate proposals in prevalent few-shot detection frameworks. In addition, the introduced correlational meta-learning enables Meta-DETR to simultaneously attend to multiple support classes within a single feedforward, which allows to capture the inter-class correlation among different classes, thus significantly reducing the misclassification over similar classes and enhancing knowledge generalization to novel classes. Experiments over multiple few-shot object detection benchmarks show that the proposed Meta-DETR outperforms state-of-the-art methods by large margins. The implementation codes are available at <uri>https://github.com/ZhangGongjie/Meta-DETR</uri>.
Micromasers as quantum batteries

Abstract We show that a micromaser is an excellent model of quantum battery. A highly excited, pure, and effectively steady state of the cavity mode, charged by coherent qubits, can be achieved, also in the ultrastrong coupling regime of field-matter interaction. Stability of these appealing features against loss of coherence of the qubits and the effect of counter-rotating terms in the interaction Hamiltonian are also discussed.
Revisiting Shift Cipher Technique for Amplified Data Security

Ever since manual work is overtaken by technology and the rapid advancement in the technologies for performing all kinds of work online has created new possibilities for the organizations and institutions of all types. But this has also created opportunities for attackers and opponents by reducing the powers of existing controls over data sharing. All private, public and any other sectors are using the internet for sharing their data. Transmission of unencrypted data over the internet is not secure as it poses many privacy concerns as they can be easily hacked and misused by any unintended person. So, everyone is concerned about safe and secure ways of data transmission in order to avoid leak of private data, as hackers always try to chase the transmitted data and to recover it and therefore various different techniques are developed in order to make data transmission more secure. Encryption is essential to protect and prevent such lapses in the transmission of sensitive information over the internet and any other networks. In this paper, the author has worked on a better version of Caesar cipher and invented a method in which modular arithmetic is used to convert plaintext into ciphertext in order to amplify and to bolster up the security of the sensitive data or information, the author composed the decryption method in such a way that it is no way related to encryption by involving the divisibility tests and arithmetic modulo.
A Multifaceted benchmarking of synthetic electronic health record generation models

Abstract Synthetic health data have the potential to mitigate privacy concerns in supporting biomedical research and healthcare applications. Modern approaches for data generation continue to evolve and demonstrate remarkable potential. Yet there is a lack of a systematic assessment framework to benchmark methods as they emerge and determine which methods are most appropriate for which use cases. In this work, we introduce a systematic benchmarking framework to appraise key characteristics with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records data from two large academic medical centers with respect to several use cases. The results illustrate that there is a utility-privacy tradeoff for sharing synthetic health data and further indicate that no method is unequivocally the best on all criteria in each use case, which makes it evident why synthetic data generation methods need to be assessed in context.
HairCLIP: Design Your Hair by Text and Reference Image

Hair editing is an interesting and challenging problem in computer vision and graphics. Many existing methods require well-drawn sketches or masks as conditional inputs for editing, however these interactions are neither straight-forward nor efficient. In order to free users from the tedious interaction process, this paper proposes a new hair editing interaction mode, which enables manipulating hair attributes individually or jointly based on the texts or reference images provided by users. For this purpose, we encode the image and text conditions in a shared embedding space and propose a unified hair editing framework by leveraging the powerful image text representation capability of the Contrastive Language-Image Pre-Training (CLIP) model. With the carefully designed network structures and loss functions, our framework can perform high-quality hair editing in a disentangled manner. Extensive experiments demonstrate the superiority of our approach in terms of manipulation accuracy, visual realism of editing results, and irrelevant attribute preservation.
A Real-Time High-Quality Complete System for Depth Image-Based Rendering on FPGA

Depth image-based rendering (DIBR) techniques have recently drawn more attention in various 3D applications. In this paper, a real-time high-quality DIBR system that consists of disparity estimation and view synthesis is proposed. For disparity estimation, a local approach that focuses on depth discontinuities and disparity smoothness is presented to improve the disparity accuracy. For view synthesis, a method that contains view interpolation and extrapolation is proposed to render high-quality virtual views. Moreover, the system is designed with an optimized parallelism scheme to achieve a high throughput, and can be scaled up easily. It is implemented on an Altera Stratix IV FPGA at a processing speed of 45 frames per second for 1080p resolution. Evaluated on selected image sets of the Middlebury benchmark, the average error rate of the disparity maps is 6.02%; the average peak signal to noise ratio and structural similarity values of the virtual views are 30.07 dB and 0.9303, respectively. The experimental results indicate that the proposed DIBR system has the top-performing processing speed and its accuracy performance is among the best of state-of-the-art hardware implementations.
Unlabeled Short Text Similarity With LSTM Encoder

Short texts play an important role in our daily communication. It has been applied in many fields. In this paper, we propose a novel short text similarity measurement algorithm-based long short-term memory (LSTM) encoder. It contains preprocessing, training, and evaluating stages. Our preprocessing algorithm can avoid gradient vanishing problems in the process of backward propagation faster after normalization. The training stage fully leverages the inception module to extract the features of different dimensions and improves the LSTM network to process the relationships of word sequences. The evaluating stage employs cosine distance to calculate the semantic similarity of two short texts. We do experiments on two short text dataset of different lengths and analyze the experiment result. The experiment result shows that our algorithm can fully employ semantic information and sequence information of short texts and have a higher accuracy and recall compared to other short text similarity measurement algorithms.
Anchor Link Prediction across Attributed Networks via Network Embedding

Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation, user profiling, etc. Previous studies mainly use hand-crafted structure features, which, if not carefully designed, may fail to reflect the intrinsic structure regularities. Moreover, most of the methods neglect the attribute information of social networks. In this paper, we propose a novel semi-supervised network-embedding model to address the problem. In the model, each node of the multiple networks is represented by a vector for anchor link prediction, which is learnt with awareness of observed anchor links as semi-supervised information, and topology structure and attributes as input. Experimental results on the real-world data sets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.
Dependency Management in Large-Scale Agile: A Case Study of DevOps Teams

Managing dependencies between teams and within teams is critical when running large-scale agile projects. In large-scale software development, work is carried out simultaneously by many developers and development teams. Results are delivered frequently and iteratively, which requires management of dependencies on both the project and team level. This study explores coordination mechanisms in agile DevOps teams in a large-scale project and how the mechanisms address different types of dependencies. We conducted a case study where we observed 38 scheduled meetings and interviewed members of five DevOps teams and two teams supporting the DevOps teams. By using a dependency taxonomy, we identified 20 coordination mechanisms (eleven synchronization activities and nine synchronization artifacts). Eight of these mechanisms seem essential for coordination in large-scale projects because they addressed more than four types of dependencies. The main implication is that project management needs to combine many practices handling all the dependencies in large-scale projects.
Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square

In data mining, there are several algorithms that are often used in grouping data, including K-Means. However, this method still has several disadvantages, including the problem of the level of accuracy of the methods used to measure the similarities between the objects being compared. To overcome this problem, in this study a comparison was made between three methods (euclidean distance, manhattan distance, and minkowski distance) to determine the status of disparity in Teacher's needs in Tegal City. The results showed that of the three methods compared had a good level of accuracy, which is 84.47% (for euclidean distance), 83.85% (for manhattan distance), and 83.85% (for minkowski distance). In addition, this study also informs that there are still 6 (six) schools with conditions that are very poorly available for teachers (in the category of HIGH disparity labels) and need to get more attention, which is SMP Atmaja Wacana, SMKN 3 Tegal, SMAS Muhammadiyah, SMAS Pancasakti Tegal, SMKS Muhammadiyah 1 Kota Tegal, and SMP IC Bias Assalam.
Aspect extraction on user textual reviews using multi-channel convolutional neural network

Aspect extraction is a subtask of sentiment analysis that deals with identifying opinion targets in an opinionated text. Existing approaches to aspect extraction typically rely on using handcrafted features, linear and integrated network architectures. Although these methods can achieve good performances, they are time-consuming and often very complicated. In real-life systems, a simple model with competitive results is generally more effective and preferable over complicated models. In this paper, we present a multichannel convolutional neural network for aspect extraction. The model consists of a deep convolutional neural network with two input channels: a word embedding channel which aims to encode semantic information of the words and a part of speech (POS) tag embedding channel to facilitate the sequential tagging process. To get the vector representation of words, we initialized the word embedding channel and the POS channel using pretrained word2vec and one-hot-vector of POS tags, respectively. Both the word embedding and the POS embedding vectors were fed into the convolutional layer and concatenated to a one-dimensional vector, which is finally pooled and processed using a Softmax function for sequence labeling. We finally conducted a series of experiments using four different datasets. The results indicated better performance compared to the baseline models.