Generic Techniques Research Articles

Backdoor Attacks against Voice Recognition Systems: A Survey

Voice Recognition Systems (VRSs) employ deep learning for speech recognition and speaker recognition. They have been widely deployed in various real-world applications, from intelligent voice assistance to telephony surveillance and biometric authentication. However, prior research has revealed the vulnerability of VRSs to backdoor attacks, which pose a significant threat to the security and privacy of VRSs. Unfortunately, existing literature lacks a thorough review on this topic. This paper fills this research gap by conducting a comprehensive survey on backdoor attacks against VRSs. We first present an overview of VRSs and backdoor attacks, elucidating their basic knowledge. Then we propose a set of evaluation criteria to assess the performance of backdoor attack methods. Next, we present a comprehensive taxonomy of backdoor attacks against VRSs from different perspectives and analyze the characteristic of different categories. After that, we comprehensively review existing attack methods and analyze their pros and cons based on the proposed criteria. Furthermore, we review classic backdoor defense methods and generic audio defense techniques. Then we discuss the feasibility of deploying them on VRSs. Finally, we figure out several open issues and further suggest future research directions to motivate the research of VRSs security.

Potential for Harm in the Treatment of Pediatric Obsessive-Compulsive Disorder: Pitfalls and Best Practices.

Pediatric obsessive-compulsive disorder (OCD) can be debilitating and chronic unless treated early with efficacious intervention. The past several decades of intervention research have identified cognitive-behavioral therapy (CBT) with exposure and response/ritual prevention (ERP) as the first-line, evidence-based psychological intervention for pediatric OCD. Yet, many youths with OCD unfortunately remain inadequately treated. In well-meaning but misguided efforts to treat this complex disorder, clinicians holding misconceptions about ERP may fail to apply evidence-based treatments, misapply generic CBT techniques and ERP principles, or turn to non-evidence-based interventions. Potentially harmful treatments may worsen symptoms, while ineffective treatments can waste resources, impede patient access to efficacious treatment, and weaken public confidence in psychotherapy. The overarching goals of this review paper are to describe potentially harmful and ineffective practices in the treatment of pediatric OCD and to offer recommendations aligned with evidence-based practice. First, we dispel common misconceptions about ERP that may underlie its underuse among clinicians. We then describe potentially harmful and ineffective interventions for pediatric OCD, starting with misapplication of generic CBT techniques and ERP principles. We also identify non-evidence-based treatments for pediatric OCD that have limited conceptual or empirical foundations. Finally, we conclude with recommendations for clinicians who treat pediatric OCD, intervention researchers, training programs across mental health-related disciplines, and policymakers.

One-dimensional N-layer thermal modelling for effective machine learning training data generation for nondestructive testing of composite parts with infrared thermography

The analytical solution of the one-dimensional N-layer thermal model (NLM) was successfully employed to generate training data for a machine learning (ML) based procedure for the nondestructive inspection of carbon fiber reinforced composite parts with infrared thermography (IRT). The main objective was to identify a reliable correlation between the experimental data of a pulsed IRT experiment and the NLM prediction, thereby enabling the use of simulated data for ML training. This paper focuses on the initial stages of this process, in more detail on the analytical modelling and experimental data preprocessing, such as normalization, correction of experimental shortcomings and feature selection for machine learning. For pulse phase thermography (PPT) simulated and experimentally derived phase data was compared directly in the frequency domain. Therefore, the features for training and validation of ML were defined from those phase spectra in frequency domain. The suitability of these features for automated and reliable defect depth and/or defect material detection was investigated in both simulated and measured IRT test data. As a basis for feature selection, we used referenced and normalized phase-frequency curves as a function of defect depth. A correlation was identified between the results of the experimental and the simulated feature sets, both qualitatively and quantitatively. To demonstrate the practical applicability of this method, two different, generic ML techniques, multilayer perceptron and random forest regression, were tested as examples. The investigation was performed on plates made of multidirectional carbon fiber reinforced polymer (CFRP) with artificial defects made from three different materials.

A new framework for improving semantic segmentation in aerial imagery

High spatial resolution (HSR) remote sensing imagery presents a rich tapestry of foreground-background intricacies, rendering semantic segmentation in aerial contexts a formidable and vital undertaking. At its core, this challenge revolves around two pivotal questions: 1) Mitigating Background Interference and Enhancing Foreground Clarity. 2) Accurate Segmentation in Dense Small Object Cluster. Conventional semantic segmentation methods primarily cater to the segmentation of large-scale objects in natural scenes, yet they often falter when confronted with aerial imagery’s characteristic traits such as vast background areas, diminutive foreground objects, and densely clustered targets. In response, we propose a novel semantic segmentation framework tailored to overcome these obstacles. To address the first challenge, we leverage PointFlow modules in tandem with the Foreground-Scene (F-S) module. PointFlow modules act as a barrier against extraneous background information, while the F-S module fosters a symbiotic relationship between the scene and foreground, enhancing clarity. For the second challenge, we adopt a dual-branch structure termed disentangled learning, comprising Foreground Precedence Estimation and Small Object Edge Alignment (SOEA). Our foreground saliency guided loss optimally directs the training process by prioritizing foreground examples and challenging background instances. Extensive experimentation on the iSAID and Vaihingen datasets validates the efficacy of our approach. Not only does our method surpass prevailing generic semantic segmentation techniques, but it also outperforms state-of-the-art remote sensing segmentation methods.

LibFewShot: A Comprehensive Library for Few-Shot Learning.

Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or "tricks", such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover, different works may employ different software platforms, backbone architectures and input image sizes, making fair comparisons difficult and practitioners struggle with reproducibility. To address these situations, we propose a comprehensive library for few-shot learning (LibFewShot) by re-implementing eighteen state-of-the-art few-shot learning methods in a unified framework with the same single codebase in PyTorch. Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmarks with various backbone architectures to evaluate common pitfalls and effects of different training tricks. In addition, with respect to the recent doubts on the necessity of meta- or episodic-training mechanism, our evaluation results confirm that such a mechanism is still necessary especially when combined with pre-training. We hope our work can not only lower the barriers for beginners to enter the area of few-shot learning but also elucidate the effects of nontrivial tricks to facilitate intrinsic research on few-shot learning. The source code is available from https://github.com/RL-VIG/LibFewShot.

Development and application of feature engineered geological layers for ranking magmatic, volcanogenic, and orogenic system components in Archean greenstone belts

Geologically representative feature engineering is a crucial component in geoscientific applications of machine learning. Many commonly applied feature engineering techniques used to produce input variables for machine learning apply geological knowledge to generic data science techniques, which can lead to ambiguity, geological oversimplification, and/or compounding subjective bias. Workflows that utilize minimally processed input variables attempt to overcome these issues, but often lead to convoluted and uninterpretable results. To address these challenges, new and enhanced feature engineering methods were developed by combining geological knowledge, understanding of data limitations, and a variety of data science techniques. These include non-Euclidean fluid pre-deformation path distance, rheological and chemical contrast, geologically constrained interpolation of characteristic host rock geochemistry, interpolation of mobile element gain/loss, assemblages, magnetic intensity, structural complexity, host rock physical properties. These methods were applied to compiled open-source and new field observations from Archean greenstone terranes in the Abitibi and western Wabigoon sub-provinces of the Superior Province near Timmins and Dryden, Ontario, respectively. Resulting feature maps represent conceptually significant components in magmatic, volcanogenic, and orogenic mineral systems. A comparison of ranked feature importance from random forests to conceptual mineral system models show that the feature maps adequately represent system components, with a few exceptions attributed to biased training data or limited constraint data. The study also highlights the shared importance of several highly ranked features for the three mineral systems, indicating that spatially related mineral systems exploit the same features when available. Comparing feature importance when classifying orogenic Au mineralization in Timmins and Dryden provides insights into the possible cause of contrasting endowment being related to fluid source. The study demonstrates that integrative studies leveraging multi-disciplinary data and methodology have the potential to advance geological understanding, maximize data utility, and generate robust exploration targets.

Research skills modules for further education college based top-up degrees

This case study investigated to what degree the research skills needed for college based top-up degrees differ from each other. The four academic areas studied ranged from natural and formal science subjects to social science and visual arts. They were explored by means of a thematic analysis of both their individual module handbooks on research skills and interviews with lecturers on these modules. The results were compared against the relevant subject benchmark statements, Biglan’s framework of classifying academic fields and related research. The interviews suggest that the research skills development needs of students at top up degree level can be met through a dedicated module in an effective way. Both the handbooks and the interviews intimated that there is a certain amount of overlap when it came to secondary research, which may be effectively met by a suitable librarian. Furthermore it was found that only two dimensions of Biglan’s framework are necessary to explain the needs of detailed ethics consideration for student projects and the existence of primary research tools and techniques peculiar to a certain field. Further work could include the definition of more generic primary research tools and techniques for academic fields where a paradigm consensus is relatively weak.

A Systematic Review of UAV Applications for Mapping Neglected and Underutilised Crop Species’ Spatial Distribution and Health

Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data for crop analysis at the plot level in small, fragmented croplands where NUS are often grown. The objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluating the progress, opportunities, challenges, and associated research gaps. This study systematically reviewed 171 peer-reviewed articles from Google Scholar, Scopus, and Web of Science using the PRISMA approach. The findings of this study showed that the United States (n = 18) and China (n = 17) were the primary study locations, with some contributions from the Global South, including southern Africa. The observed NUS crop attributes included crop yield, growth, leaf area index (LAI), above-ground biomass (AGB), and chlorophyll content. Only 29% of studies explored stomatal conductance and the spatial distribution of NUS. Twenty-one studies employed satellite-borne sensors, while only eighteen utilised UAV-borne sensors in conjunction with machine learning (ML), multivariate, and generic GIS classification techniques for mapping the spatial extent and health of NUS. The use of UAVs in mapping NUS is progressing slowly, particularly in the Global South, due to exorbitant purchasing and operational costs, as well as restrictive regulations. Subsequently, research efforts must be directed toward combining ML techniques and UAV-acquired data to monitor NUS’ spatial distribution and health to provide necessary information for optimising food production in smallholder croplands in the Global South.

Smart Polymers for Soft Materials: From Solution Processing to Organic Solids.

Polymeric materials are ubiquitous in our everyday life, where they find a broad range of uses-spanning across common household items to advanced materials for modern technologies. In the context of the latter, so called "smart polymers" have received a lot of attention. These systems are soluble in water below their lower critical solution temperature Tℓ and often exhibit counterintuitive solvation behavior in mixed solvents. A polymer is known as smart-responsive when a slight change in external stimuli can significantly change its structure, functionm and stability. The interplay of different interactions, especially hydrogen bonds, can also be used for the design of lightweight high-performance organic solids with tunable properties. Here, a general scheme for establishing a structure-property relationship is a challenge using the conventional simulation techniques and also in standard experiments. From the theoretical side, a broad range of all-atom, multiscale, generic, and analytical techniques have been developed linking monomer level interaction details with macroscopic material properties. In this review, we briefly summarize the recent developments in the field of smart polymers, together with complementary experiments. For this purpose, we will specifically discuss the following: (1) the solution processing of responsive polymers and (2) their use in organic solids, with a goal to provide a microscopic understanding that may be used as a guiding tool for future experiments and/or simulations regarding designing advanced functional materials.

The development of a road network flood risk detection model using optimised ensemble learning

Floods are natural phenomena that invade different parts of the globe annually, leaving severe adverse effects on the natural landscape, particularly in areas where humans practice large-scale urban development activities. Inadequate corporate strategic stormwater management or maintenance plans make the issue even worse. Therefore, efforts have been consolidated to confront flood hazards by incorporating precautious and proactive strategies toward better forecasting and preventing flood disasters or even mitigating the damage if it occurs. Further, there is inadequacy in incorporating computational intelligence techniques to detect flood susceptibility in road networks. This paper proposes a data-driven flood risk areas detection model incorporating various ensemble and generic machine learning techniques as well as autoML tools. A systematic and iterative approach is followed to examine different solutions to tackle the data balancing problem, including oversampling and undersampling techniques. Also, hyperparameter optimisation is undertaken using both grid search and genetic algorithms, and the models’ performance using each designated strategy is reported and assessed. The empirical results from the experiments indicate that the optimised ensembled Extremely Randomized Trees classifier proves an ability to perform well on the given dataset with an averaged ROC AUC value of 90% and outperforms several state-of-the-art generic, ensembled, and autoML classifiers in all evaluation measures. The proposed framework could be possibly generalised to other road networks in similar topographical and geological regional areas.

Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better

Deep learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval, and more. However, with the progressive improvements in deep learning models, their number of parameters, latency, and resources required to train, among others, have all increased significantly. Consequently, it has become important to pay attention to these footprint metrics of a model as well, not just its quality. We present and motivate the problem of efficiency in deep learning, followed by a thorough survey of the five core areas of model efficiency (spanning modeling techniques, infrastructure, and hardware) and the seminal work there. We also present an experiment-based guide along with code for practitioners to optimize their model training and deployment. We believe this is the first comprehensive survey in the efficient deep learning space that covers the landscape of model efficiency from modeling techniques to hardware support. It is our hope that this survey would provide readers with the mental model and the necessary understanding of the field to apply generic efficiency techniques to immediately get significant improvements, and also equip them with ideas for further research and experimentation to achieve additional gains.

Optimisation of Thermal and Geometric Parameters of Cylindrical Fins during Natural Convection

The efficient use of energy is a crucial goal related to the world economy, and efforts are aimed at reducing energy demand for the same quality and quantity of products and services. The drive to improve energy efficiency in the context of technological development, as well as the intensification of industrial and other processes, imposes a constant demand for more efficient heat exchange devices and systems. As devices in which the heat transfer process is crucial, heat exchangers play an essential role in many engineering and other applications. There are several generic techniques for improving the performance of thermal devices, which ultimately manifest through an increase in the heat transfer coefficient as the primary efficiency indicator. These methods include initiating or intensifying the turbulent flow of working fluids, increasing the heat exchange surface by applying specific shapes and additional elements, and using working fluids with improved thermophysical characteristics. This work presents a model for optimising a heat-exchanging surface with cylindrical fins based on the assumption that the material has a constant volume. In doing so, the maximum heat flux at the optimal diameter of the cylindrical fins made of different materials is compared. This research was conducted through a combination of three methods, namely, analytical, numerical (based on the computational fluid dynamic (CFD) technique), and experimental support, for the verification of the obtained results. The optimisation model is based on the analytical and numerical simulation of the heat flux through the fins to obtain the relevant thermophysical parameters of the investigated fin profiles. The optimisation of cylindrical fin profiles is performed for three different fin materials (steel, aluminium, and copper) based on the constant heat transfer coefficient and for different fin materials based on variable heat flux. The heat flux change along the fin was evaluated using analytical and numerical methods. The analysis showed that, due to the trade-off between the convective heat transfer surface area for each of the fin materials, an optimal fin diameter could be identified. Furthermore, the temperature along the cylindrical fin was evaluated through analytical, numerical, and experimental methods. The numerical simulation of the fin model was performed using CFD simulations, and the practical experimental research was carried out on a physical installation using a TESTO 875-2 thermal camera to obtain a clearer picture of the temperature profile. The results include heat flux through cylindrical fins as a function of the fin diameter, the material used, and the convective heat transfer coefficient. Moreover, a comparison of analytical and numerical solutions for the heat flux of cylindrical fins is presented. The temperature profile as a function of the fin element height x, obtained using three methods, has been used as a possible comparison between the methods. The optimisation of the fins has implications on the heat transfer efficiency, as well as on the material used to build the thermal devices and other heat-exchanging equipment.

Cryptanalysis of Strong Physically Unclonable Functions

Physically unclonable functions (PUFs) are being proposed as a low-cost alternative to permanently store secret keys or provide device authentication without requiring nonvolatile memory, large e-fuses, or other dedicated processing steps. In the literature, PUFs are split into two main categories. The so-called strong PUFs are mainly used for authentication purposes; hence, also called authentication PUFs. They promise to be lightweight by avoiding extensive digital post-processing and cryptography. The so-called weak PUFs, also called key generation PUFs, can only provide authentication when combined with a cryptographic authentication protocol. Over the years, multiple research results have demonstrated that Strong PUFs can be modeled and attacked by machine learning (ML) techniques. Hence, the general assumption is that the security of a strong PUF is solely dependent on its security against ML attacks. The goal of this article is to debunk this myth, by analyzing and breaking three recently published Strong PUFs (Suresh et al., VLSI Circuits 2020; Liu et al., ISSCC 2021; and Jeloka et al., VLSI Circuits 2017). The attacks presented in this article have practical complexities and use generic symmetric key cryptanalysis techniques.

Efficient privacy-preserving aggregation for demand side management of residential loads

An effective tool used in practice to maintain network balance is residential demand side management (DMS). However, privacy concerns related to the processing of user personal consumption data often result in a slow wide-scale adoption and acceptance. Various general purpose cryptographic techniques are available, such as homomorphic encryption and secure multi-party computation (MPC), to address these privacy-concerns. Unfortunately, these come at a significant computation price which makes them not applicable in many practical scenarios. In this work, a privacy-preserving aggregation algorithm, based on additive random shares and a combination of well-established symmetric and asymmetric key cryptography methods is proposed and compared with additive homomorphic encryption (AHE) techniques and state-of-the-art MPC protocols. The results show that generic techniques such as homomorphic encryption come with significant computational and communication cost, especially at the users side. On the other hand, MPC approaches provide better performance and resilience to dropouts for very large networks but entail a complex communication among users. In this context, the proposed additive random shares algorithm was the most balanced choice for DSM, with a good performance, simpler information flow and the possibility of adding redundant intermediary parties for enhanced resilience.

Solution sampling with random table constraints

Constraint programming provides generic techniques to efficiently solve combinatorial problems. In this paper, we tackle the natural question of using constraint solvers to sample combinatorial problems in a generic way. We propose an algorithm, inspired from Meel’s ApproxMC algorithm on SAT, to add hashing constraints to a CP model in order to split the search space into small cells. By uniformly sampling the solutions in one cell, we can generate random solutions without revamping the model of the problem. We ensure the randomness by introducing a new family of hashing constraints: randomly generated tables, which keeps the cost of the hashing process tractable. We implemented this solving method using the constraint solver Choco-solver. The quality of the randomness and the running time of our approach are experimentally compared to a random branching strategy. We show that our approach improves the randomness while being in the same order of magnitude in terms of running time. We also use our algorithm with an other, more powerful, set of hashing constraints: linear modular equalities. We experimentally show that the resulting sampling is uniform, at the cost of a longer running time.

Auto-encoded Latent Representations of White Matter Streamlines for Quantitative Distance Analysis

Parcellation of whole brain tractograms is a critical step to study brain white matter structures and connectivity patterns. The existing methods based on supervised classification of streamlines into predefined streamline bundle types are not designed to explore sub-bundle structures, and methods with manually designed features are expensive to compute streamline-wise similarities. To resolve these issues, we propose a novel atlas-free method that learns a latent space using a deep recurrent auto-encoder trained in an unsupervised manner. The method efficiently embeds any length of streamlines to fixed-size feature vectors, named streamline embedding, for tractogram parcellation using non-parametric clustering in the latent space. The method was evaluated on the ISMRM 2015 tractography challenge dataset with discrimination of major bundles using clustering algorithms and streamline querying based on similarity, as well as real tractograms of 102 subjects Human Connectome Project. The learnt latent streamline and bundle representations open the possibility of quantitative studies of arbitrary granularity of sub-bundle structures using generic data mining techniques.

Regularization on Augmented Data to Diversify Sparse Representation for Robust Image Classification.

Image classification is a fundamental component in modern computer vision systems, where sparse representation-based classification has drawn a lot of attention due to its robustness. However, on the optimization of sparse learning systems, regularization and data augmentation are both powerful, but currently isolated. We believe that regularization and data augmentation can cooperate to generate a breakthrough in robust image classification. In this article, we propose a novel framework, regularization on augmented data (READ), which creates diversification in the data using the generic augmentation techniques to implement robust sparse representation-based image classification. When the training data are augmented, READ applies a distinct regularizer, l1 or l2 , in particular, on the augmented training data apart from the original data, so that regularization and data augmentation are utilized and enhanced synchronously. We introduce an elaborate theoretical analysis on how to optimize the sparse representation by both l1 -norm and l2 -norm with the generic data augmentation and demonstrate its performance in extensive experiments. The results obtained on several facial and object datasets show that READ outperforms many state-of-the-art methods when using deep features.

Workplace violence against emergency health care workers: What Strategies do Workers use?

BackgroundWorkplace violence by patients and bystanders against health care workers, is a major problem, for workers, organizations, patients, and society. It is estimated to affect up to 95% of health care workers. Emergency health care workers experience very high levels of workplace violence, with one study finding that paramedics had nearly triple the odds of experiencing physical and verbal violence.Many interventions have been developed, ranging from zero-tolerance approaches to engaging with the violent perpetrator. Unfortunately, as a recent Cochrane review showed, there is no evidence that any of these interventions work in reducing or minimizing violence.To design better interventions to prevent and minimize workplace violence, more information is needed on those strategies emergency health care workers currently use to prevent or minimize violence.The objective of the study was to identify and discuss strategies used by prehospital emergency health care workers, in response to violence and aggression from patients and bystanders. Mapping the strategies used and their perceived usefulness will inform the development of tailored interventions to reduce the risk of serious harm to health care workers.In this study the following research questions were addressed: (1) What strategies do prehospital emergency health care workers utilize against workplace violence from patients or bystanders? (2) What is their experience with these strategies?MethodsFive focus groups with paramedics and dispatchers were held at different urban and rural locations in Canada. The focus group responses were transcribed verbatim and analyzed using thematic analysis.ResultsIt became apparent that emergency healthcare workers use a variety of strategies when dealing with violent patients or bystanders. Most strategies, other than generic de-escalation techniques, reflect a reliance on the systems the workers work with and within.ConclusionThe study results support the move away from focusing on the individual worker, who is the victim, to a systems-based approach to help reduce and minimize violence against health care workers. For this to be effective, system-based strategies need to be implemented and supported in healthcare organizations and legitimized through professional bodies, unions, public policies, and regulations.

Type 2 Diabetes Mellitus in Latinx Populations in the United States: A Culturally Relevant Literature Review.

Type 2 diabetes mellitus (T2DM) affects a large number of the American population. When compared to their representation in the general American population, a disproportionate number of Latinx individuals are affected. Within the Latinx American population, T2DM prevalence rates vary among individuals based on their country of origin. Deaths from T2DM among Latinx American population are also more compared to other ethnicities. This disparity underlines the importance of understanding the cultural considerations of T2DM disease presentation and management in Latinx communities, including risk factors, socioeconomic variables, and other social determinants of health such as access to care. There are various modifiable and non-modifiable risk factors for the development of T2DM, regardless of race. Staple foods in the diet of Latinx American communities, such as tortillas, rice, and beans, can cause spikes in blood sugar levels and can lead to obesity, which predisposes patients to develop T2DM. Latinx American populations suffer from lower access to healthcare than the general population due to many reasons, including language proficiency, immigration status, socioeconomic status, and level of acculturation. This study utilized the format of a commentary, while incorporating elements of a scoping review for data collection, to further explore these disparities and their impact on these populations. Understanding the cultural beliefs of Latinx individuals and how these beliefs contribute to the perceived development of T2DM is essential to properly treat these unique populations. Despite high rates of T2DM affecting Latinx individuals, non-adherence to prescribed diabetes medications is high among these populations. Interventions in the form of culturally tailored preventative education, in addition to active T2DM management, are necessary to combat the toll of this disease on Latinx Americans. Generic interventional techniques and methods should be replaced entirely by those that acknowledge, highlight, and utilize the sociocultural characteristics of Latinx Americans.

Optimal Symmetric Ratcheting for Secure Communication

AbstractTo mitigate state exposure threats to long-lived instant messaging sessions, ratcheting was introduced, which is used in practice in protocols like Signal. However, existing ratcheting protocols generally come with a high cost. Recently, Caforio et al. proposed pragmatic constructions, which compose a weakly secure ‘light’ protocol and a strongly secure ‘heavy’ protocol, in order to achieve so-called ratcheting on-demand. The light protocol they proposed has still a high complexity.In this paper, we propose the lightest possible protocol we could imagine, which essentially encrypts and then hashes the secret key. We prove it secure in the standard model by introducing a new security notion, which relates symmetric encryption with key updates by hashing. Our protocol composes well with the generic transformation techniques by Caforio et al. to offer high security and performance at the same time. In a second step, we propose another protocol based on a newly defined integrated primitive, extending standard one-time authenticated encryption with an additional output block used as a secret key for the next message. We instantiate this primitive firstly from any authenticated encryption with associated data, and then we propose an efficient instantiation using advanced encryption standard (AES) encryption to update the key and AES-Galois/Counter mode of operation to encrypt and decrypt messages.