Embedding Changes Research Articles

Reinforcement learning with time intervals for temporal knowledge graph reasoning

Temporal reasoning methods have been successful in temporal knowledge graphs (TKGs) and are widely employed in many downstream application areas. Most existing TKG reasoning models transform time intervals into continuous time snapshots, with each snapshot representing a subgraph of the TKG. Although such processing can produce satisfactory outcomes, it ignores the integrity of a time interval and increases the amount of data. Meanwhile, many previous works focuses on the logic of sequentially occurring facts, disregarding the complex temporal logics of various time intervals. Consequently, we propose a Reinforcement Learning-based Model for Temporal Knowledge Graph Reasoning with Time Intervals (RTTI), which focuses on time-aware multi-hop reasoning arising from complex time intervals. In RTTI, we construct the time learning part to obtain the time embedding of the current path. It simulates the temporal logic with relation historical encoding and compute the time interval between two facts through the temporal logic feature matrix. Furthermore, we propose a new method for representing time intervals that breaks the original time interval embedding method, and express the time interval using median and embedding changes of two timestamps. We evaluate RTTI on four public TKGs for the link prediction task, and experimental results indicate that our approach can still perform well on more complicated tasks. Meanwhile, our method can search for more interpretable paths in the broader space and improve the reasoning ability in sparse spaces.

To Fit or Not to Fit? Service designers' double-edged dilemma

In recent years, service design has become increasingly important in driving organisational change. One contributing reason is the growing recognition that employee experience significantly impacts both customer satisfaction and the organisation’s bottom line. Service designers are at the forefront of this change, responsible for designing service offerings and implementing and embedding change within organisations. However, this evolution towards becoming catalysts for change comes with challenges, particularly for service designers tasked with changing organisational culture while trying to fit into it.

Cost polarization by dequantizing for JPEG steganography

In this article, we study a recently proposed method for improving empirical security of steganography in JPEG images in which the sender starts with an additive embedding scheme with symmetrical costs of ± 1 changes and then decreases the cost of one of these changes based on an image obtained by applying a deblocking (JPEG dequantization) algorithm to the cover JPEG. This approach provides rather significant gains in security at negligible embedding complexity overhead for a wide range of quality factors and across various embedding schemes. Challenging the original explanation of the inventors of this idea, which is based on interpreting the dequantized image as an estimate of the precover (uncompressed) image, we provide alternative arguments. The key observation and the main reason why this approach works is how the polarizations of individual DCT coefficients work together. By using a MiPOD model of content complexity of the uncompressed cover image, we show that the cost polarization technique decreases the chances of “bad” combinations of embedding changes that would likely be introduced by the original scheme with symmetric costs. This statement is quantified by computing the likelihood of the stego image w.r.t. the multivariate Gaussian precover distribution in DCT domain. Furthermore, it is shown that the cost polarization decreases spatial discontinuities between blocks (blockiness) in the stego image and enforces desirable correlations of embedding changes across blocks. To further prove the point, it is shown that in a source that adheres to the precover model, a simple Wiener filter can serve equally well as a deep-learning based deblocker

Side-Informed Steganography for JPEG Images by Modeling Decompressed Images

Side-informed steganography has always been among the most secure approaches in the field. However, a majority of existing methods for JPEG images use the side information, here the rounding error, in a heuristic way. For the first time, we show that the usefulness of the rounding error comes from its covariance with the embedding changes. Unfortunately, this covariance between continuous and discrete variables is not analytically available. An estimate of the covariance is proposed, which allows to model steganography as a change in the variance of DCT coefficients. Since steganalysis today is best performed in the spatial domain, we derive a likelihood ratio test to preserve a model of a decompressed JPEG image. The proposed method then bounds the power of this test by minimizing the Kullback-Leibler divergence between the cover and stego distributions. We experimentally demonstrate in two popular datasets that it achieves state-of-the-art performance against deep learning detectors. Moreover, by considering a different pixel variance estimator for images compressed with Quality Factor 100, even greater improvements are obtained.

Randomized experimental testing of new survey approaches to improve abortion reporting in the United States.

Abortions are substantially underreported in surveys due to social stigma, compromising the study of abortion, pregnancy, fertility, and related demographic and health outcomes. In this study, we evaluated six methodological approaches identified through formative mixed-methods research to improve the measurement of abortion in surveys. These approaches included altering the placement of abortion items in the survey, the order of pregnancy outcome questions, the level of detail, the introduction to the abortion question, and the context of the abortion question, and using graduated sensitivity. We embedded a preregistered randomized experiment in a newly designed online survey about sexual and reproductive health behaviors (N=6536). We randomized respondents to experimental arms in a fully crossed factorial design; we estimated an average treatment effect using standardized estimators from logistic regression models, adjusted for demographic covariates associated with reporting. None of the experimental arms significantly improved abortion reporting compared to the control condition. More work is needed to improve reporting of abortion in future surveys, particularly as abortion access becomes increasingly restricted in the United States. Despite this study's null results, it provides a promising path for future efforts to improve abortion measurement. It is proof of concept for testing new approaches in a less expensive, faster, and more flexible format than embedding changes in existing national fertility surveys.

A realist review of interventions to dismantle mental health and substance use related structural stigma in healthcare settings

Efforts to address mental illness stigma within the healthcare sector have not produced sufficient or sustained changes to delivery, quality, or models of care. One such reason is that many anti-stigma efforts tend to be targeted at interpersonal aspects of stigma, seeking to reduce prejudicial attitudes and improve discriminatory behaviors. Shifting attention to structural aspects of stigma may have unique potential to advance stigma reduction into the future. In this study, the authors conducted a realist case study of interventions to address structural stigma in healthcare organizations. Utilizing a realist multiple explanatory case study approach, authors reviewed data from 62 cases. After developing their initial program theory, 6 exemplar cases were analyzed for possible context-mechanism-outcome configurations. Results suggest that effective interventions required organizational readiness to disrupt existing power asymmetries, shared governance infrastructures, and an alignment of values despite historical mistrust between disparate partners. Mechanisms for change involved proactive management of resistance, disruptive innovation, co-designing processes, and embedding structural change into existing policy. Outcomes included sustainability, reduced coercion, and improved trust. Findings suggest that interventions to address structural stigma can produce sustained policy and practice change if organizations embrace power sharing and trust building while embedding change within policies and governance structures.

Working across boundaries in regional place-based food systems: Triggering transformation in a time of crisis

Drawing on research exploring food system change in a region in the North of England during the Covid-19 health pandemic, in this paper we explore how a “managerial” approach to food system governance and provisioning embedded in national food systems and international supply chains was suddenly supplanted by a more “open” approach. By enabling understanding of how food system change unfolded as a diverse range of hidden practices unexpectedly became visible, it contributes to food systems thinking through the development of a regional place-based perspective. We use this approach to understand the outcome of these processes and to investigate how embedding change across administrative and geographical boundaries can enable food system transformation to improve food security for future generations.

Non-additive image steganographic framework based on variational inference in Markov Random Fields

The majority of image steganography methods lie within the additive scheme that assumes the pixel modifications are independent. Whereas, adapting the embedding modifications based on the mutual embedding impact of neighboring pixels reduce changes in the higher-order statistics of the cover imposed by embedding. However, non-additive schemes are more challenging due to the lack of practical embedding codes that minimize an arbitrary distortion function. In this paper, we propose a general non-additive image steganographic framework based on Markov Random Fields (MRF) which generalizes a few existing research in many aspects and offers an elegant way to model the mutual dependencies between neighborhood modifications in terms of pairwise cliques. The proposed model satisfies spatial coherence by penalizing the desynchronized embedding changes, which orients the changes towards the direction of the majority of changes in the neighbors. Mean Field (MF) inference is used to iteratively estimate the marginal probability at each pixel based on its neighbors so that the original interactions are information projected to the resulting marginals. MF replaces the values of variables with expectations so the result is compatible with practical embedding methods.The proposed framework can be applied to any additive scheme; the initial cost assignment is done by an additive method and MRF modeling defines a non-additive distortion related to statistical detectability that encourages the adjacent changes to synchronize. We study our framework in both symmetric and asymmetric schemes. In the symmetric scheme, a parallel MF is used as an adaptive filter to encourage synchronized embedding changes. The framework can upgrade to an asymmetric scheme that reduces the detectability by conditioning embedding based on the message using clamping exerted in a structural MF. Therefore, our framework is compatible with and generalizes existing non-additive methods, and as shown in experimental results, outperforms the state-of-the-art in both schemes.

DEDGCN: Dual Evolving Dynamic Graph Convolutional Network

With the wide application of graph data in many fields, the research of graph representation learning technology has become the focus of scholars’ attention. Especially, dynamic graph representation learning is an important part of solving the problem of change graph in reality. On the one hand, most dynamic graph representation methods focus either on graph structure changes or node embedding changes, ignoring the internal relationship. On the other hand, most dynamic graph neural networks require learn node embeddings from specific tasks, resulting in poor universality of node embeddings and cannot be used in unsupervised tasks. Hence, Dual Evolving Dynamic Graph Convolutional Network (DEDGCN) was proposed to solve the above problems. DEDGCN uses the recurrent neural network to push the evolvement of GCN and nodes, from which it can extract the structural features of dynamic graph and learns the stability features of nodes, respectively, forming an adaptive dynamic graph convolution network. DEDGCN can be classified as unsupervised graph convolutional network. Thus, it is capable of training the unlabeled dynamic graph, it has more extensive application scenarios, and the calculated node embedding has strong generality. We evaluate our proposed method on experimental data in three tasks which are node classification, edge classification, and link prediction. In the classification task, facing the graph with large scale, complex connection relationship, and uncertain change rule, the F1 value of node classification task obtained by DEDGCN reaches 77%, and the F1 value of edge classification task reaches more than 90%. The results show that DEDGCN is effective in capturing graph features, and the effect of DEDGCN is much higher than other baseline methods, which proves the importance of capturing node stability features in dynamic graph representation learning. At the same time, the ability of DEDGCN in unsupervised tasks is further verified by using clustering and anomaly detection tasks, which proves that DEDGCN learning network embedding is widely used.

Multi Images Steganography using Neural Network

Abstract: Currently, the most successful approach to steganography in empirical objects, such as digital media, is to embed the payload while minimizing a suitably defined distortion function. The design of the distortion is essentially the only task left to the stegnographer since efficient practical codes exist that embed near the payload-distortion bound. The ractitioner goal is to design the distortion to obtain a scheme with a high empirical statistical detectability. In this paper, we propose a universal distortion design called universal wavelet relative distortion (UNIWARD) that can be applied for embedding in an arbitrary domain. The embedding distortion is computed as a sum of relative changes of coefficients in a directional filter bank decomposition of the cover image. The directionality forces the embedding changes to such parts of the cover object that are difficult to model in multiple directions, such as textures or noisy regions, while avoiding smooth regions or clean edges. We demonstrate experimentally using rich models as well as targeted attacks that steganographic methods built using UNIWARD match or outperform the current state of the art in the spatial domain, JPEG domain, and sideinformed JPEG domain.

Survey on deep learning applications in digital image security

In the digital era, sharing pictures on social media has become a common privacy issue. To prevent private images from being eavesdropped on and destroyed, developing secure and efficient image steganography, image cryptography, and image authentication has been difficult. Deep learning provides a solution for digital image security. First, we make an overall conclusion on deep learning applications in image steganography to generate five aspects: the cover image, stego-image, embedding change probabilities, coverless steganography, and steganalysis. Second, we also combine and compare deep learning methods used in six aspects: image cryptography from image compression, image resolution improvement, image object detection and classification, key generation, end-to-end image encryption, and image cryptoanalysis. Third, we collect deep learning methods in image authentication from five perspectives: image forgery detection, watermarked image generation, image watermark extraction and detection, image watermarking attack, and image watermark removal. Finally, we summarize future research directions of deep learning utilization in image steganography, image cryptography, and image authentication.

Social Work Practice with Adults under the Rising Second Wave of Covid-19 in England: Frontline Experiences and the Use of Professional Judgement

The impacts on adult social work in England of the Covid-19 pandemic were sudden and are proving long-standing. In England, many social workers moved to home working and virtual contact with colleagues, managers, staff from other agencies and service users. A first national lockdown was followed by a lessening of restrictions, but a second wave started at the end of Summer 2020 and restrictions were re-introduced. This study draws on telephone interviews with a sample of twenty-two social workers working with adults in a wide range of roles and settings in ten local authorities and two National Health Service Hospital Trusts, interviewed August–October 2020. Following transcription, interview data were analysed thematically. Findings are reported under three emerging themes: using professional judgement, new and emerging case work and embedding change. These are compared with findings from studies of practice in children’s services and of surveys of social workers during the Covid-19 pandemic. Implications for practice, service users and research are explored.

Embedding changes in legislation for organ and tissue donation across England and Jersey.

Cathy Miller, Louise Hubner (louise.hubner@nhsbt.nhs.uk), Sonya Paterson, Bobbee Cotter, Phil Walton, Helen Bentley, and Claire Roberts, of NHS Blood and Transplant.

Experience of implementing and delivering group consultations in UK general practice: a qualitative study.

BackgroundGroup consultations are a relatively new concept in UK primary care and are a suggested solution to current workload pressures in general practice. Little is known about the experience of implementing and delivering this approach from staff and organisational perspectives.AimTo explore the experience of implementing and delivering group consultations in general practice.Design and settingQualitative telephone interview study.MethodTopic guides explored the perspectives and experiences of general practice staff on the implementation and delivery of group consultations. Data analysis adopted principles of the Framework Method underpinned by Normalisation Process Theory.ResultsInterviews were conducted with 8 GPs, 8 practice nurses, 1 nurse associate, 1 practice pharmacist, 1 deputy practice manager, and 1 healthcare assistant. Four themes were identified: sense making of group consultations; the work associated with initiating group consultations; the experiences of operationalising group consultations; and sustaining change. Group consultations made sense to participants as a mechanism to reduce burden on primary care, enhance multidisciplinary working, and provide patient-centred care. Implementation required strong leadership from a ‘champion’, and a facilitator had a pivotal role in operationalising the approach. The associated workload was often underestimated. Barriers to embedding change included achieving whole practice buy-in, competing practice priorities, and system-level flexibility.ConclusionGeneral practice clinicians enjoyed group consultations, yet significant work is required to initiate and sustain the approach. An implementation plan considering leadership, roles and responsibilities, and wider organisational support is required at the outset. Further research or evaluation is needed to measure process outcomes.

Adaptive Audio Steganography Based on Improved Syndrome-Trellis Codes

Syndrome-Trellis Code (STC) is a near-optimal convolutional method for adaptive steganography. Hitherto, the existing adaptive steganography commonly depends on the carefully designed distortion cost function, which controls the embedding position of the message in the cover signal. From another point of view, we implement adaptive steganography by improving the STC coding process (named Adaptive-STC). The parity-check matrix is the key to the encoding and extraction process of STC. In this work, we prove that the average embedding change probability of corresponding elements can be changed by adjusting its submatrix. Following that, we specially design an adaptive parity-check matrix to replace the designed distortion cost to restrict the embedding position. The generation of the adaptive parity-check matrix can be formulated as a multi-constrained integer programming problem in which the width of the submatrix is allocated at a fixed height. To solve this particular problem, we propose a targeted intelligent optimization algorithm (named GOAS) that can adaptively generate the parity-check matrix according to different audio cover. The experimental results show that the proposed method outperforms the state-of-the-art adaptive steganography with reduced embedding changes and improved audio quality while ensuring the ability against steganalysis.

Guiding principles for quality, ethical standards and ongoing learning in implementation research: multicountrylearnings from participatory action research to strengthen health systems.

Global health gains can be achieved through strengthening health systems to identify and address implementation challenges in low- and middle-income countries. Participatory research, that promotes joint problem and solution finding between communities and different health systems actors, supports policy implementation analysis at all levels. Within the neglected tropical disease programmes in Liberia and Nigeria, we applied participatory action research (PAR) to address programmatic and health system bottlenecks with health systems strengthening embedded. This paper shares learning from 20 interviews with co-researchers, from national and sub-national levels and academic researchers who worked collaboratively to understand challenges, co-create solutions and advocate for policy change. Through analysis and reflections of existing PAR principles, we inductively identified five additional guiding principles for quality, ethical standards and ongoing learning within PAR projects that aim to strengthen health systems. (1) Recognize communities as units of identity and define stakeholder participation to ensure equitable engagement of all actors; (2) enable flexible action planning that builds on existing structures whilst providing opportunities for embedding change; (3) address health systems and research power differentials that can impede co-production of knowledge and solution development; (4) embed relational practices that lead to new political forms of participation and inquiry within health systems and (5) develop structures for ongoing learning at multiple levels of the health system. PAR can strengthen health systems by connecting and co-creating potentially sustainable solutions to implementation challenges. Additional research to explore how these five additional principles can support the attainment of quality and ethical standards within implementation research using a PAR framework for health systems strengthening is needed.

The state of business sustainability reporting in sub-Saharan Africa: an agenda for policy and practice

PurposeDeveloping countries experience their own social, political and environmental issues, but surprisingly limited papers have examined sustainability reporting in these regions, notably in sub-Saharan Africa. To fill this gap and understand the state of sustainability reporting in sub-Saharan Africa, this paper aims to investigate the current state of reporting, identifies the major motivations and barriers for reporting and suggests an agenda of future issues that need to be considered by firms, policymakers and academics.Design/methodology/approachThis paper includes analysis of reporting practices in 48 sub-Saharan African countries using the lens of New Institutional Economics. It comprises three phases of data collection and analysis: presentation of overall reporting data collected and provided by Global Reporting Initiative (GRI). analysis of stand-alone sustainability reports using qualitative data analysis and interviews with key report producers.FindingsThe analysis identifies key issues that companies in selected sub-Saharan African countries are grappling within their contexts. There are significant barriers to reporting but institutional mechanisms, such as voluntary reporting frameworks, provide an important bridge between embedding informal norms and changes to regulatory requirements. These are important for the development of better governance and accountability mechanisms.Research limitations/implicationsFindings have important implications for policymakers and institutions such as GRI in terms of regulation, outreach and localised training. More broadly, global bodies such as GRI and IIRC in a developing country context may require more local knowledge and support. Limitations include limited data availability, particularly for interviews, which means that these results are preliminary and provide a basis for further work.Practical implicationsThe findings of this paper contribute to the knowledge of sustainability reporting in this region, and provide some policy implications for firms, governments and regulators.Originality/valueThis paper is one of only a handful looking at the emerging phenomenon of sustainability reporting in sub-Saharan African countries.

Minimizing Embedding Impact for H.264 Steganography by Progressive Trellis Coding

This paper proposes a novel coding strategy to achieve distortion minimization for H.264 steganography with quantized discrete cosine transform (QDCT) coefficients. Currently, with the help of syndrome-trellis codes (STCs), state-of-the-art image steganography embeds messages while minimizing a heuristically defined distortion function. However, this concept cannot be directly ported to steganography using compressed video as the cover media. According to the intra prediction principle, an H.264 QDCT coefficient block is predicted and coded based on previously encoded blocks, so even a slight embedding change will set off a chain reaction in the remaining cover blocks. Considering the cover block dependency, we make necessary changes to the standard trellis coding structure so as to be applicable for the joint compression embedding scenario. During the coding/embedding procedure, we maintain multiple contexts corresponding to possible optimal routes, and retrace each route periodically to determine how each cover block should be modified. After each modification, the remaining cover blocks, as well as their embedding costs, are re-evaluated, and each context is updated to reflect the embedding effect. In this way, the global optimality can be approached progressively in a block-by-block manner, so our proposed method is named progressive trellis coding (PTC). Extensive experiments have been conducted, and corresponding results show that the adoption of PTC brings about a significant gain in embedding performance.

Translational simulation for rapid transformation of health services, using the example of the COVID-19 pandemic preparation

Healthcare simulation has significant potential for helping health services to deal with the COVID-19 pandemic. Rapid changes to care pathways and processes needed for protection of staff and patients may be facilitated by a translational simulation approach—diagnosing changes needed, developing and testing new processes and then embedding new systems and teamwork through training. However, there are also practical constraints on running in situ simulations during a pandemic—the need for physical distancing, rigorous infection control for manikins and training equipment and awareness of heightened anxiety among simulation participants. We describe our institution’s simulation strategy for COVID-19 preparation and reflect on the lessons learned—for simulation programs and for health services seeking to utilise translational simulation during and beyond the COVID-19 pandemic. We offer practical suggestions for a translational simulation strategy and simulation delivery within pandemic constraints. We also suggest simulation programs develop robust strategies, governance and relationships for managing change within institutions—balancing clinician engagement, systems engineering expertise and the power of translational simulation for diagnosing, testing and embedding changes.

Color image steganalysis based on embedding change probabilities in differential channels

It is a potential threat to persons and companies to reveal private or company-sensitive data through the Internet of Things by the color image steganography. The existing rich model features for color image steganalysis fail to utilize the fact that the content-adaptive steganography changes the pixels in complex textured regions with higher possibility. Therefore, this article proposes a variant of spatial rich model feature based on the embedding change probabilities in differential channels. The proposed feature is extracted from the residuals in the differential channels to reduce the image content information and enhance the stego signals significantly. Then, the embedding change probability of each element in the differential channels is added to the corresponding co-occurrence matrix bin to emphasize the interference of the residuals in textured regions to the improved co-occurrence matrix feature. The experimental results show that the proposed feature can significantly improve the detection performances for the WOW and S-UNIWARD steganography, especially when the payload size is small. For example, when the payload size is 0.05 bpp, the detection errors can be reduced respectively by 5.20% and 4.90% for WOW and S-UNIWARD by concatenating the proposed feature to the color rich model feature CRMQ1.