Reviewerly/exHarmony huggingface dataset:
Smart home modification design strategies for ageing in place: a systematic review

Abstract This research explores current strategies and approaches directed to integrate innovative technologies in the home modification process to support independent living and ageing in place. The systematic review considered studies conducted from the perspective of architecture, smart technology, and gerontology. Scientific databases of related disciplines (e.g. Scopus, Web of Science, Engineer village, Google Scholar, Crossref) were searched and supplemented by hand search method. Thirty-three out of 2594 articles were analysed from three perspectives: the framework of the smart home environment for ageing in place, the smart home modification process, and problems and countermeasures of independent living. The result shows that both home modification and smart technologies can support older adults’ independent living, especially with fall prevention and indoor accessibility. Technologies deployed in older adults’ homes are transiting from manual assistive technology to more intelligent devices, and the notion of the robotic home has emerged. According to existing practices, universal design is an extensively adopted strategy for smart home design and modification. However, in most cases, universal design is used as a retrofitting guideline for general home settings rather than specifically for smart homes. The fundamental requirements in smart home modification phases are customisation, minimum life interference, and extensible technologies to cope with the ageing process.
Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy/interpretability tradeoff, which means that their complexity leads to high performance levels but lacks interpretability. This work aims at addressing this tradeoff by proposing a technique for feature selection embedded in deep neural networks that uses a feature selection (FS) layer trained with the rest of the network to evaluate the input features’ importance. The importance values are used to determine which will be considered for deployment of a PHM model. For comparison with other techniques, this paper introduces a new metric called ranking quality score (RQS), that measures how performance evolves while following the corresponding ranking. The proposed framework is exemplified with three case studies involving health state diagnostics and prognostics and remaining useful life prediction. Results show that the proposed technique achieves higher RQS than the compared techniques, while maintaining the same performance level when compared to the same model but without an FS layer.
Practical Guide to Natural Language Processing for Radiology

Natural language processing (NLP) is the subset of artificial intelligence focused on the computer interpretation of human language. It is an invaluable tool in the analysis, aggregation, and simplification of free text. It has already demonstrated significant potential in the analysis of radiology reports. There are abundant open-source libraries and tools available that facilitate its application to the benefit of radiology. Radiologists who understand its limitations and potential will be better positioned to evaluate NLP models, understand how they can improve clinical workflow, and facilitate research endeavors involving large amounts of human language. The advent of increasingly affordable and powerful computer processing, the large quantities of medical and radiologic data, and advances in machine learning algorithms have contributed to the large potential of NLP. In turn, radiology has significant potential to benefit from the ability of NLP to convert relatively standardized radiology reports to machine-readable data. NLP benefits from standardized reporting, but because of its ability to interpret free text by using context clues, NLP does not necessarily depend on it. An overview and practical approach to NLP is featured, with specific emphasis on its applications to radiology. A brief history of NLP, the strengths and challenges inherent to its use, and freely available resources and tools are covered to guide further exploration and study within the field. Particular attention is devoted to the recent development of the Word2Vec and BERT (Bidirectional Encoder Representations from Transformers) language models, which have exponentially increased the power and utility of NLP for a variety of applications. Online supplemental material is available for this article. ©RSNA, 2021
Improving Agile Software Development using User-Centered Design and Lean Startup

Agile methods have limitations concerning problem understanding and solution finding, which can cause organizations to push misguided products and accrue waste. Some authors suggest combining agile methods with discovery-oriented approaches to overcome this, with notable candidates being User-Centered Design (UCD) and Lean Startup, a combination of which there is yet not a demonstrated, comprehensive study on how it works. To characterize a development approach combination of Agile Software Development, UCD, and Lean Startup; exposing how the three approaches can be intertwined in a single development process and how they affect development. We conducted a case study with two industry software development teams that use this combined approach, investigating them through interviews, observation, focus groups, and a workshop during a nine-month period in which they were stationed in a custom-built development lab. The teams are made up of user advocates, business advocates, and solution builders; while their development approach emphasizes experimentation by making heavy use of build-measure-learn cycles. The combined approach promotes a problem-oriented mindset, encouraging team members to work together and engage with the entire development process, actively discovering stakeholders needs and how to fulfill them. Each of its approaches provide a unique contribution to the development process: UCD fosters empathy with stakeholders and enables teams to better understand the problem they are tasked with solving; Lean Startup introduces experimentation as the guiding force of development; and Extreme Programming (the teams’ agile method) provides support to experimentation and achieving technical excellence. The combined approach pushes teams to think critically throughout the development effort. Our practical example provides insight on its essence and might inspire industry practitioners to seek a similar development approach based on the same precepts.
Heterogeneous Relational Complement for Vehicle Re-identification

The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning viewpoint-invariant representations. In this paper, we propose to solve this problem from two aspects: constructing robust feature representations and proposing camera-sensitive evaluations. We first propose a novel Heterogeneous Relational Complement Network (HRCN) by incorporating region-specific features and cross-level features as complements for the original high-level output. Considering the distributional differences and semantic misalignment, we propose graph-based relation modules to embed these heterogeneous features into one unified high-dimensional space. On the other hand, considering the deficiencies of cross-camera evaluations in existing measures (i.e., CMC and AP), we then propose a Cross-camera Generalization Measure (CGM) to improve the evaluations by introducing position-sensitivity and cross-camera generalization penalties. We further construct a new benchmark of existing models with our proposed CGM and experimental results reveal that our proposed HRCN model achieves new state-of-the-art in VeRi-776, VehicleID, and VERI-Wild.
Energetics of a Single Qubit Gate

Qubits are physical, a quantum gate thus not only acts on the information carried by the qubit but also on its energy. What is then the corresponding flow of energy between the qubit and the controller that implements the gate? Here we exploit a superconducting platform to answer this question in the case of a quantum gate realized by a resonant drive field. During the gate, the superconducting qubit becomes entangled with the microwave drive pulse so that there is a quantum superposition between energy flows. We measure the energy change in the drive field conditioned on the outcome of a projective qubit measurement. We demonstrate that the drive's energy change associated with the measurement backaction can exceed by far the energy that can be extracted by the qubit. This can be understood by considering the qubit as a weak measurement apparatus of the driving field.
A Robust Access Control Protocol for the Smart Grid Systems

Lightweight cryptography (LWC)-based authenticated encryption with associative data (AEAD) cryptographic primitives require fewer computational and energy resources than conventional cryptographic primitives as a single operation of an AEAD scheme provides confidentiality, integrity, and authenticity of data. This feature of AEAD schemes helps design an access control (AC) protocol to be leveraged for enhancing the security of the resource-constrained Internet of Things (IoT)-enabled smart grid (SG) system with low computational overhead and fewer cryptographic operations. This article presents a novel and robust AC protocol, called RACP-SG, which aims to enhance the security of resource-constrained IoT-enabled SG systems. RACP-SG employs an LWC-based AEAD scheme, ASCON and the hash function, ASCON-hash, along with elliptic curve cryptography to accomplish the AC phase. Besides, RACP-SG enables a smart meter (SM) and a service provider (SEP) to mutually authenticate each other and establish a session key (SK) while communicating across the public communication channel. By using the SK, the SM can securely transfer the gathered data to the SEP. We verify the security of the SK using the widely accepted random oracle model. Moreover, we conduct Scyther-based and informal security analyses to demonstrate that RACP-SG is protected against various covert security risks, such as replay, impersonation, and desynchronization attacks. Besides, we present a comparative study to illustrate that RACP-SG renders superior security features while reducing energy, storage, communication, and computational overheads compared to the state of the art.
ChipQA: No-Reference Video Quality Prediction via Space-Time Chips

We propose a new model for no-reference video quality assessment (VQA). Our approach uses a new idea of highly-localized space-time (ST) slices called Space-Time Chips (ST Chips). ST Chips are localized cuts of video data along directions that \textit{implicitly} capture motion. We use perceptually-motivated bandpass and normalization models to first process the video data, and then select oriented ST Chips based on how closely they fit parametric models of natural video statistics. We show that the parameters that describe these statistics can be used to reliably predict the quality of videos, without the need for a reference video. The proposed method implicitly models ST video naturalness, and deviations from naturalness. We train and test our model on several large VQA databases, and show that our model achieves state-of-the-art performance at reduced cost, without requiring motion computation.
Joint extraction of entities and relations via an entity correlated attention neural model

Named entity recognition and relation extraction are crucial tasks in natural language processing. As the traditional pipelined manners may suffer from the error propagation issue and ignore underlying interactions, joint extraction of entities and relations has become the dominant trend. However, the performance of existing joint extraction models needs improvement. This paper presents a two-stage tagging scheme that separately labels candidate head entities and multiple tail entities in specific relations. Next, it proposes a novel lightweight joint extraction neural model based on the entity-first labeling strategy. In the proposed model, the BiLSTM-based encoder combines the hidden state and global context features and feeds them as input for the next two entity labeling tasks. Further, with the input of the mixed context representation, the candidate-head-entity recognition module is adopted to identify the candidate head entity, while the multiple-tail-entities recognition module is equipped with an entity-correlated attention mechanism to identify the corresponding tail entity under a specific head entity. Comprehensive experiments on two widely used English datasets and one self-constructed Chinese dataset were performed. The experimental results showed that the proposed model outperformed the baseline approaches in the relation extraction task and achieved a competitive entity recognition effect via a lightweight architecture.
DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning

Face deepfake detection has seen impressive results recently. Nearly all existing deep learning techniques for face deepfake detection are fully supervised and require labels during training. In this paper, we design a novel deepfake detection method via unsupervised contrastive learning. We first generate two different transformed versions of an image and feed them into two sequential sub-networks, i.e., an encoder and a projection head. The unsupervised training is achieved by maximizing the correspondence degree of the outputs of the projection head. To evaluate the detection performance of our unsupervised method, we further use the unsupervised features to train an efficient linear classification network. Extensive experiments show that our unsupervised learning method enables comparable detection performance to state-of-the-art supervised techniques, in both the intra- and inter-dataset settings. We also conduct ablation studies for our method.
Human Activity Recognition Using K-Nearest Neighbor Machine Learning Algorithm

Smart factory in the era of Industry 4.0 requires humans to have continuous communication capabilities among each other’s and with the existing smart assets in order to integrate their activities into a cyber-physical system (CPS) within the smart factory. Machine learning (ML) algorithms can help precisely recognize the human activities, provided that well-designed and trained ML algorithms for high performance recognition are developed. This paper presents a k-nearest neighbor (KNN) algorithm for classification of human activities, namely Laying, Downstairs walking, Sitting, Upstairs walking, Standing, and Walking. This algorithm is trained and the algorithm’s parameters are precisely tuned of for high accuracy achievement. Experimentally, a normalized confusion matrix, a classification report of human activities, receiver operating characteristic (ROC) curves, and precision-recall curves are used to analyze the performance of the KNN algorithm. The results show that the KNN algorithm provides a high performance in the classification of human activities. The weighted average precision, recall, F1-score, and the area under the micro-average precision-recall curve for the KNN are 90.96%, 90.46%, 90.37%, and 96.5%, respectively, while the area under the ROC curve is 100%.
The State of Serverless Applications: Collection, Characterization, and Community Consensus

Over the last five years, all major cloud platform providers have increased their serverless offerings. Many early adopters report significant benefits for serverless-based over traditional applications, and many companies are considering moving to serverless themselves. However, currently there exist only few, scattered, and sometimes even conflicting reports on when serverless applications are well suited and what the best practices for their implementation are. We address this problem in the present study about the state of serverless applications. We collect descriptions of 89 serverless applications from open-source projects, academic literature, industrial literature, and domain-specific feedback. We analyze 16 characteristics that describe why and when successful adopters are using serverless applications, and how they are building them. We further compare the results of our characterization study to 10 existing, mostly industrial, studies and datasets; this allows us to identify points of consensus across multiple studies, investigate points of disagreement, and overall confirm the validity of our results. The results of this study can help managers to decide if they should adopt serverless technology, engineers to learn about current practices of building serverless applications, and researchers and platform providers to better understand the current landscape of serverless applications.
Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction

We investigate whether model extraction can be used to ‘steal’ the weights of sequential recommender systems, and the potential threats posed to victims of such attacks. This type of risk has attracted attention in image and text classification, but to our knowledge not in recommender systems. We argue that sequential recommender systems are subject to unique vulnerabilities due to the specific autoregressive regimes used to train them. Unlike many existing recommender attackers, which assume the dataset used to train the victim model is exposed to attackers, we consider a data-free setting, where training data are not accessible. Under this setting, we propose an API-based model extraction method via limited-budget synthetic data generation and knowledge distillation. We investigate state-of-the-art models for sequential recommendation and show their vulnerability under model extraction and downstream attacks.
End-to-End Image Steganography Using Deep Convolutional Autoencoders

Image steganography is used to hide a secret image inside a cover image in plain sight.Traditionally, the secret data is converted into binary bits and the cover image is manipulated statistically to embed the secret binary bits.Overloading the cover image may lead to distortions and the secret information may become visible.Hence the hiding capacity of the traditional methods are limited.In this paper, a lightweight yet simple deep convolutional autoencoder architecture is proposed to embed a secret image inside a cover image as well as to extract the embedded secret image from the stego image.The proposed method is evaluated using three datasets -COCO, CelebA and ImageNet.Peak Signal-to-Noise Ratio, hiding capacity and imperceptibility results on the test set are used to measure the performance.The proposed method has been evaluated using various images including Lena, airplane, baboon and peppers and compared against other traditional image steganography methods.The experimental results have demonstrated that the proposed method has higher hiding capacity, security and robustness, and imperceptibility performances than other deep learning image steganography methods.
A New Adversarial Embedding Method for Enhancing Image Steganography

Image steganography aims to embed secret messages into cover images in an imperceptible manner. While steganalysis tries to identify stegos from covers, which is a special binary classification problem. Recently, some literatures show that the adversarial embedding can mislead the advanced steganalyzers based on convolutional neural network (CNN), and thus enhance the steganography security. Since adding perturbations to stegos may lead to messages extraction failure due to properties of syndrome-trellis codes (STC), the existing adversarial examples are derived from covers or their enhanced versions, while those stegos are not fully utilized. In this paper, we propose a new adversarial embedding scheme for image steganography. Unlike those related works, we first combine multiple gradients of cover and generated stegos to determine the directions of cost modifications. Next, instead of adjusting all or a random part of embedding costs in existing works, we carefully select the candidate costs according to the amplitudes of cover gradients and their costs. Extensive experimental results demonstrate that by adjusting a tiny part of embedding costs (less than 5% in most cases), the proposed method can significantly improve the security of five modern steganographic methods evaluated on both re-trained CNN-based and traditional steganalyzers, and achieve much better security performances compared with related methods. In addition, the security performances evaluated on different image database show that the generalization of the proposed method is good.
ExpressEar

Continuous and unobtrusive monitoring of facial expressions holds tremendous potential to enable compelling applications in a multitude of domains ranging from healthcare and education to interactive systems. Traditional, vision-based facial expression recognition (FER) methods, however, are vulnerable to external factors like occlusion and lighting, while also raising privacy concerns coupled with the impractical requirement of positioning the camera in front of the user at all times. To bridge this gap, we propose ExpressEar, a novel FER system that repurposes commercial earables augmented with inertial sensors to capture fine-grained facial muscle movements. Following the Facial Action Coding System (FACS), which encodes every possible expression in terms of constituent facial movements called Action Units (AUs), ExpressEar identifies facial expressions at the atomic level. We conducted a user study (N=12) to evaluate the performance of our approach and found that ExpressEar can detect and distinguish between 32 Facial AUs (including 2 variants of asymmetric AUs), with an average accuracy of 89.9% for any given user. We further quantify the performance across different mobile scenarios in presence of additional face-related activities. Our results demonstrate ExpressEar's applicability in the real world and open up research opportunities to advance its practical adoption.