Reviewerly/exHarmony huggingface dataset:
Edge Computing-Enabled Crowd Density Estimation based on Lightweight Convolutional Neural Network

In public areas, crowd stampedes and incidents generate huge negative impacts on public security. Accurate and efficient crowd density estimation is critical to monitor crowd status for developing evacuation strategies. The existing crowd density estimation methods are established based on complex deep-learning algorithms which are usually more accurate, but, on the other side, they require much more computational resources. Consequently, cloud-computing is the only option for deploying crowd density estimation algorithms, which needs tremendous resources for real-time video data transmission and the malfunction and delay of internet service may cause wrong and delayed estimation results. Edge computing is a novel concept of accomplishing computing tasks only relying on the computational resources of edge devices. Estimating crowd density on edge devices rather than conveying images to cloud server for further analysis has several advantages, including 1) reducing network bandwidth pressure from remote transmission; 2) avoiding risks of leaking privacy on images; and 3) improving computation efficient. This study designs an edge computing-enabled crowd density estimation model based on the residual bottleneck block and dilated convolution. The experiments are designed and conducted on public crowd data sets to verify accuracy, storage cost and computation efficiency of our model. According to the experimental results, the proposed model achieves a considerable improvement in operational efficiency, while keep the accuracy at the same level with the complex deep-learning algorithms. Furthermore, the proposed model is implemented on a real edge device to detect real-world crowd density in a Beijing subway station.
Apologize or justify? Examining the impact of data breach response actions on stock value of affected companies?

As cybersecurity incidents increase and become more potent, affected companies must react. This is especially relevant for data breaches, where companies are legally required to notify those affected. Hence, it can be assumed that data breaches invariably cause damage through disappointed customers and dissatisfied investors. By applying response actions of a response strategy in a data breach announcement, a company can mitigate these consequences. However, the literature reveals an inconsistent landscape regarding the effect of response actions on customers or investors. Using an event study, we show that the response actions impact a company's stock value and that this impact does not necessarily match the impact on the customer. We code the real-world response actions to data breaches, examining their impact on investor behavior using stock value. The results show that data breaches involving customer data have a negative impact on stock value. In addition, an apology for a data breach has been found to have harmful effects on investor behavior. Whereas justifying the data breach has no or a positive impact. These results add to the investor-focused literature on data breaches, showing that an impact on stock value can emerge from response actions. The literature on data breaches can be informed by the fact that not just is the customer perspective important, but also reactions from other affected groups should be included in formulating a response strategy. The paper also provides practical implications, showing how companies can improve their response strategies based on the results.
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer

While the field of style transfer (ST) has been growing rapidly, it has been hampered by a lack of standardized practices for automatic evaluation.In this paper, we evaluate leading ST automatic metrics on the oft-researched task of formality style transfer.Unlike previous evaluations, which focus solely on English, we expand our focus to Brazilian-Portuguese, French, and Italian, making this work the first multilingual evaluation of metrics in ST.We outline best practices for automatic evaluation in (formality) style transfer and identify several models that correlate well with human judgments and are robust across languages.We hope that this work will help accelerate development in ST, where human evaluation is often challenging to collect.
Better pseudo-label: Joint domain-aware label and dual-classifier for semi-supervised domain generalization

With the goal of directly generalizing trained model to unseen target domains, domain generalization (DG), a newly proposed learning paradigm, has attracted considerable attention. Previous DG models usually require a sufficient quantity of annotated samples from observed source domains during training. In this paper, we relax this requirement about full annotation and investigate semi-supervised domain generalization (SSDG) where only one source domain is fully annotated along with the other domains totally unlabeled in the training process. With the challenges of tackling the domain gap between observed source domains and predicting unseen target domains, we propose a novel deep framework via joint domain-aware labels and dual-classifier to produce high-quality pseudo-labels. Concretely, to predict accurate pseudo-labels under domain shift, a domain-aware pseudo-labeling module is developed. Also, considering inconsistent goals between generalization and pseudo-labeling: former prevents overfitting on all source domains while latter might overfit the unlabeled source domains for high accuracy, we employ a dual-classifier to independently perform pseudo-labeling and domain generalization in the training process. When accurate pseudo-labels are generated for unlabeled source domains, the domain mixup operation is applied to augment new domains between labeled and unlabeled domains, which is beneficial for boosting the generalization capability of the model. Extensive results on publicly available DG benchmark datasets show the efficacy of our proposed SSDG method.
Region-based adaptive single image dehazing, detail enhancement and pre-processing using auto-colour transfer method

A region-based adaptive single image dehazing and detail enhancement for hazy environments are proposed in this paper. In most of the existing methods, the dark regions of a hazy image become over-dark after dehazing. Hence, there is a need to enhance those regions for better visibility. Also, in the existing methods, the entire image is considered at a time for dehazing and the variations in local region characteristics are not considered limiting the performance of these algorithms. The haze effect of an image varies according to the depth or the object distance, i.e., the more the object distance, the more the effect of haze. Hence, there is a need to adaptively dehaze an image according to haze levels that are affecting various regions of the hazy image for better dehazing performance. To overcome the above drawbacks, in this paper, the auto-colour transfer method is proposed to enhance the dark regions of the hazy image as part of pre-processing. Also, an adaptive single image dehazing method is proposed, which classifies various regions of input hazy image as less affected, moderately affected, and more affected by haze; and subsequently dehazes adaptively according to the haze affected regions. The pre-processed hazy image is passed to three dehazing modules (less affected, moderately affected, and more affected by haze). In each block, the pre-processed hazy image is decomposed into base and detail layers by choosing different scale factors w.r.t different modules. Then in these modules, the image dehazing and detail enhancement are performed for base and detail layers, respectively. Finally, after image dehazing and detail enhancement, the recovered images of three blocks are combined based on the respective regions to obtain the final dehazed output. Quantitative and Qualitative image assessment metrics are evaluated for the proposed method using several datasets, which show better results when compared with the existing methods.
JetStream: Graph Analytics on Streaming Data with Event-Driven Hardware Accelerator

Graph Processing is at the core of many critical emerging workloads operating on unstructured data, including social network analysis, bioinformatics, and many others. Many applications operate on graphs that are constantly changing, i.e., new nodes and edges are added or removed over time. In this paper, we present JetStream, a hardware accelerator for evaluating queries over streaming graphs and capable of handling additions, deletions, and updates of edges. JetStream extends a recently proposed event-based accelerator for graph workloads to support streaming updates. It handles both accumulative and monotonic graph algorithms via an event-driven computation model that limits accesses to a smaller subset of the graph vertices, efficiently reuses the prior query results to eliminate redundancy, and optimizes the memory access pattern for enhanced memory bandwidth utilization. To the best of our knowledge, JetStream is the first graph accelerator that supports streaming graphs, reducing the computation time by 90% compared with cold-start computation using an existing accelerator. In addition, JetStream achieves about 18 × speedup over KickStarter and GraphBolt software frameworks at the large baseline batch sizes that these systems use with significantly higher speedup at smaller batch sizes.
Malaria Parasite Detection Using a Quantum-Convolutional Network

Malaria is a severe illness triggered by parasites that spreads via mosquito bites. In underdeveloped nations, malaria is one of the top causes of mortality, and it is mainly diagnosed through microscopy. Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups. Therefore, in this study, we present a new idea based on the ensemble quantum-classical framework for malaria classification. The methods comprise three core steps: localization, segmentation, and classification. In the first core step, an improved FRCNN model is proposed for the localization of the infected malaria cells. Then, the RGB localized images were converted into YCbCr channels to normalize the image intensity values. Subsequently, the actual lesion region was segmented using a histogram-based color thresholding approach. The segmented images were employed for classification in two different ways. In the first method, a CNN model is developed by the selection of optimum layers after extensive experimentation, and the final computed feature vector is passed to the softmax layer for classification of the infection/non-infection of the microscopic malaria images. Second, a quantum-convolutional model is employed for informative feature extraction from microscopic malaria images, and the extracted feature vectors are supplied to the softmax layer for classification. Finally, classification results were analyzed from two different models and concluded that the quantum-convolutional model achieved maximum accuracy as compared to CNN. The proposed models attain a precision rate greater than 90%, thereby proving that these models performed better than the existing models.
A New Hyperchaotic Image Encryption Algorithm Based on Stochastic Signals

Image encryption is often used to protect private images during transmission on a public channel. A high dimensional chaotic map has a greater secret key space, better ergodicity and dynamic property than a low-dimensional chaotic system. A seven-dimensional (7D) hyperchaotic map is used to produce chaotic sequences. Given secret keys and SHA-512 function are employed to generate initial values for iteration. Many stochastic signals are injected into one of the variables during iteration to transfer trajectory and increase dynamic behavior of a chaotic system. Three matrices are constructed with generated chaotic sequences. Permutation is performed on the basis of a control matrix. It keeps the pixel far from its neighboring pixels. Cycle shift is executed during bit-level permutation. Characteristic values of scrambling image are calculated and temporary values are achieved successfully. Two dynamic values are also applied during the diffusion process. Experimental results display the effects of the proposed algorithm. Security analysis reveals that the proposed method has some special advantages.
A Transformer based Approach for Image Manipulation Chain Detection

Image manipulation chain detection aims to identify the existence of involved operations and also their orders, playing an important role in multimedia forensics and image analysis. However,all the existing algorithms model the manipulation chain detection as a classification problem, and can only detect chains containing up to two operations. Due to the exponentially increased solution space and the complex interactions among operations, how to reveal a long chain from a processed image remains a long-standing problem in the multimedia forensic community. To address this challenge, in this paper, we propose a new direction for manipulation chain detection. Different from previous works, we treat the manipulation chain detection as a machine translation problem rather than a classification one, where we model the chains as the sentences of a target language, and each word serves as one possible image operation. Specifically, we first transform the manipulated image into a deep feature space, and further model the traces left by the manipulation chain as a sentence of a latent source language. Then, we propose to detect the manipulation chain through learning the mapping from the source language to the target one under a machine translation framework. Our method can detect manipulation chains consisting of up to five operations, and we obtain promising results on both the short-chain detection and the long-chain detection.
DI-AA: An interpretable white-box attack for fooling deep neural networks

White-box adversarial example (AE) attacks on deep neural networks (DNNs) have a more powerful destructive capacity than black-box attacks using AE strategies. However, few studies have been conducted on the generation of low-perturbation adversarial examples from the interpretability perspective. Specifically, adversaries who conducted attacks lacked interpretation from the point of view of DNNs, and the perturbation was not further considered. To address these, we propose an interpretable white-box AE attack approach, DI-AA, which not only explores the application of the interpretable method of deep Taylor decomposition in selecting the most contributing features but also adopts the Lagrangian relaxation optimization of the logit output and Lp norm to make the perturbation more unnoticeable. We compare DI-AA with eight baseline attacks on four representative datasets. Experimental results reveal that our approach can (1) attack nonrobust models with low perturbation, where the perturbation is closer to or lower than that of the state-of-the-art white-box AE attacks; (2) evade the detection of the adversarial-training robust models with the highest success rate; (3) be flexible in the degree of AE generation saturation. Additionally, the AE generated by DI-AA can reduce the accuracy of the robust black-box models by 16–31 % in the black-box manner.
A two-phase quasi-affine transformation evolution with feedback for parameter identification of photovoltaic models

Metaheuristic algorithm is a prestigious technique for solving optimization problems. QUATRE is a simple but powerful algorithm. However, QUATRE also shows premature convergence and is easily trapped in local optima for complex optimization problems. This work presents a novel algorithm named two-phase QUasi-Affine Transformation Evolution with feedback (tfQUATRE). The proposed tfQUATRE is an enhanced quasi-affine transformation evolution algorithm. In tfQUATRE, a two-phase approach is introduced to improve the exploration and exploitation abilities by adjusting the search tendency at different phases. Moreover, the historical population is employed for the feedback approach to guide the search towards promising areas to maintain population diversity, which boosts the exploration ability. The comprehensive performance of tfQUATRE is evaluated in the simulations. First, the performance of tfQUATRE is evaluated under the CEC2017 test suite. The simulations prove that tfQUATRE is superior to 12 state-of-the-art algorithms. In addition, tfQUATRE is applied to extract the parameters of photovoltaic (PV) systems in real application. The experimental results confirm that the proposed tfQUATRE is more competitive than 17 recent counterparts.
Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models

Providing a metric of uncertainty alongside a state estimate is often crucial when tracking a dynamical system. Classic state estimators, such as the Kalman filter (KF), provide a time-dependent uncertainty measure from knowledge of the underlying statistics; however, deep learning based tracking systems struggle to reliably characterize uncertainty. In this paper, we investigate the ability of KalmanNet, a recently proposed; hybrid; model-based; deep state tracking algorithm, to estimate an uncertainty measure. By exploiting the interpretable nature of KalmanNet, we show that the error covariance matrix can be computed based on its internal features, as an uncertainty measure. We demonstrate that when the system dynamics are known, KalmanNet—which learns its mapping from data without access to the statistics—provides uncertainty similar to that provided by the KF; and while in the presence of evolution model-mismatch, KalmanNet provides a more accurate error estimation.
On revisiting energy and performance in microservices applications: A cloud elasticity-driven approach

Monolithic applications are a subject that includes several knowledge areas. Sometimes it can be a challenge to optimize CPU or IO requirements because it is not trivial to recognize the problem itself and improve it. There are many approaches to resolve this situation, where a trending one is the microservices. As a variant of the service-oriented architecture, microservices is a technique that arranges an application as a collection of loosely coupled services. This decomposition enables better software management in cloud-based environments since we can replicate each part individually using cloud elasticity to avoid execution bottlenecks. Also, since elasticity mitigates resource overprovisioning, it favors better energy consumption: the cloud owner can redistribute finite available resources among different tenants, and users can pay less to use the infrastructure. However, elasticity tuning is not trivial and depends on several factors, such as user experience, application architecture, and parameter modeling. Today, we observe a lack of initiatives in the literature that address both performance and energy perspectives to support the execution of microservices applications in the cloud. Concerning this context, this article introduces Elergy as a lightweight proactive elasticity model that provides resource reorganization for a cloud-based microservices application. Its differential approach appears in improving energy consumption by periodically handling the most appropriate amount of resources to execute an application while maintaining or yet improving the performance of CPU-bound applications. Elergy performs these functions proactively, in such a way of preventing future problems related to either resource under- or overprovisioning. The results showed energy consumption reduction and a competitive cost (application time x consumed resources) when comparing Elergy with a non-elastic scenario. Elergy obtained savings from 1.93% to 27.92% for energy consumption.
Cross-Modal Recipe Embeddings by Disentangling Recipe Contents and Dish Styles

Nowadays, cooking recipe sharing sites on the Web are widely used, and play a major role in everyday home cooking. Since cooking recipes consist of dish photos and recipe texts, cross-modal recipe search is being actively explored. To enable cross-modal search, both food image features and cooking text recipe features are embedded into the same shared space in general. However, in most of the existing studies, a one-to-one correspondence between a recipe text and a dish image in the embedding space is assumed, although an unlimited number of photos with different serving styles and different plates can be associated with the same recipe. In this paper, we propose a RDE-GAN (Recipe Disentangled Embedding GAN) which separates food image information into a recipe image feature and a non-recipe shape feature. In addition, we generate a food image by integrating both the recipe embedding and a shape feature. Since the proposed embedding is free from serving and plate styles which are unrelated to cooking recipes, the experimental results showed that it outperformed the existing methods on cross-modal recipe search. We also confirmed that only either shape or recipe elements can be changed at the time of food image generation.
The Meaning of “Participation” in <scp>Co‐Design</scp> with Children and Youth: Relationships, Roles, and Interactions

Abstract The paper examines the concept of participation in co‐design practices with children and youth. Rooted in Participatory Design and Participatory Action Research frameworks, the paper draws from multi‐disciplinary literature to survey existing definitions of the relationships, roles, and types of human interactions in participatory co‐design. The paper advocates for the active role of children and youth in the co‐design process and presents models of youth participation. The paper highlights the importance of understanding and clearly communicating various degrees of participation, with the ultimate goal of empowering youth and involving them in brainstorming, planning, decision‐making, and interpretation stages of the design process. We introduce the concept of conscious co‐design and the need to reflect on the design process at a meta level in Participatory Design and Participatory Action Research .
Complementary Factorization towards Outfit Compatibility Modeling

Recently, outfit compatibility modeling, which aims to evaluate the compatibility of a given outfit that comprises a set of fashion items, has gained growing research attention. Although existing studies have achieved prominent progress, most of them overlook the essential global outfit representation learning, and the hidden complementary factors behind the outfit compatibility uncovering. Towards this end, we propose an Outfit Compatibility Modeling scheme via Complementary Factorization, termed as OCM-CF. In particular, OCM-CF consists of two key components: context-aware outfit representation modeling and hidden complementary factors modeling. The former works on adaptively learning the global outfit representation with graph convolutional networks and the multi-head attention mechanism, where the item context is fully explored. The latter targets at uncovering the latent complementary factors with multiple parallel networks, each of which corresponds to a factor-oriented context-aware outfit representation modeling. In this part, a new orthogonality-based complementarity regularization is proposed to encourage the learned factors to complement each other and better characterize the outfit compatibility. Finally, the outfit compatibility is obtained by summing all the hidden complementary factor-oriented outfit compatibility scores, each of which is derived from the corresponding outfit representation. Extensive experiments on two real-world datasets demonstrate the superiority of our OCM-CF over the state-of-the-art methods.