Reviewerly/exHarmony huggingface dataset:
A semi-symmetric domain adaptation network based on multi-level adversarial features for meningioma segmentation

Early diagnosis of meningiomas has led to many lives saved. And the MRI is the commonly used mean for meningiomas, which can locate the early lesions for excision to prevent growth or malignant. Due to the increasing pressure on the physicians, Computer Aided Diagnostics (CAD) systems have been widely noted to improve work efficiency. However, the available methods are mostly based on a large number of labeled samples and lack of generalization to multiple situations. Once the data source changes, the model may fail. Meanwhile the variety of imaging devices used for meningioma MRI can lead to dramatic shifts in data distribution across data sources. Considering to this the paper proposed a plug-and-play model to achieve unsupervised domain adaptation. We design a semi-symmetric structure based on partial parameters sharing for the different situations, especially for the various devices. And a novel multi-level adversarial features method is proposed to retain segmentation performance during domain adaptation. What is more, we conducted a large meningiomas dataset about different devices to verify our method’s performance and effectiveness.
MINet: Multilevel Inheritance Network-Based Aerial Scene Classification

Scene classification of aerial images is the basis of automatic recognition of complex scenes, and it is also a challenging computer vision task. In recent years, with the rapid development of deep learning, the semantic feature extraction method based on a convolutional neural network (CNN) has made great progress. Moreover, a recent study indicates that combining the semantic information of deep-layer features with the detailed texture information of shallow-layer features in CNN can further improve the performance of classification. In this letter, an end-to-end multilevel feature-based network named multilevel inheritance network (MINet) is proposed for aerial scene classification. First, the feature extraction module based on the feature pyramid network (FPN) is used to get multilevel feature maps. In the process of merging shallow features, high-level semantics of deep-layer are inherited. Then, an attention mechanism is added after the multilevel features to reduce the interference of redundant information and noise. Finally, we use a feature fusion module to automatically learn the weight of each feature layer and make a comprehensive decision. The effectiveness of the proposed method is verified in AID, WHU-RS19 and NWPU-RESISC45 datasets. Results show that the proposed method achieves competitive classification accuracy.
Large-scale Fake Click Detection for E-commerce Recommendation Systems

With the development of e-commerce platforms, e-commerce recommendation systems are playing an increasingly important role for the purpose of product recommendation. As a new attack model against e-commerce recommendation systems, the "Ride Item's Coattails" attack creates fake click information to establish the deceptive correlation between popular products and low-quality products in order to mislead the recommendation system of e-commerce platform to boost the sales of low-quality products. This attack is characterized by high concealment and strong destructiveness, which can cause great damage to e-commerce recommendation systems, and adversely affect the usability of the e-commerce platform and users' shopping experience. It is therefore of great practical significance to study how to quickly and effectively identify the false click information and the corresponding "Ride Item's Coattails" attack to better safeguard e-commerce recommendation systems. At present, there is no previously reported relevant research work conducted specifically for addressing the detection of the "Ride Item's Coattails" attack. In this work, we carried out pioneering work in analyzing and summarizing the characteristics of the false click information produced by attackers on the target products in the "Ride Item's Coattails" attack and designed a set of attack detection techniques suitable for e-commerce recommendation systems. Experimental results on real e-commerce datasets show that our proposed techniques can quickly and effectively detect the large-scale fake click information as well as the associated "Ride Item's Coattails" attack in e-commerce recommendation systems.
Turkish Sentiment Analysis on Social Media

The number of social media users has increased significantly as internet access and internet usage has grown all over the world. As it is the case in any other field, raw data collected from social media platforms are now being transformed into information. Millions of pieces of content are posted every day on platforms such as Twitter, Facebook, and Instagram. Moreover, the ability to extract meaningful information from such content has become an important field of research. This study reports a system for sentiment analysis, based on the data made available on Facebook, Twitter, Instagram, YouTube along with RSS data. Logistic Regression, Random Forest and deep learning algorithm such as Long Short-Term Memory (LSTM) are used to develop the classification model used in the system built as part of this study. Dataset is a new dataset that included data collected from Twitter, used was created and labeled by us. It was found that LSTM model provided the highest accuracy among the models generated using the training dataset. The final version of the model was tested on five different social media platforms and results are communicated.
A Simple Baseline for Fast and Accurate Depth Estimation on Mobile Devices

In this paper, we propose a simple but effective encoder-decoder based network for fast and accurate depth estimation on mobile devices. Unlike other depth estimation methods using heavy context modeling modules, the encoder with a fast downsampling strategy is employed to obtain sufficient receptive field and contexts at a faster rate. To obtain dense prediction, a light decoder is adopted to recover back to the original resolution. Additionally, to improve the representative ability of the light network, we introduce a teacher-student strategy. It relies on a distillation process ensuring that the student (the proposed light network) learns from the teacher. The proposed method achieves a good trade-off between latency and accuracy. We evaluated the proposed algorithm on the MAI 2021 Monocular Depth Estimation Challenge and achieved a score of 129.41, ranked the first place, which wins the second by a large margin (129.41 v.s. 14.51). More specifically, the proposed method achieves a si-RMSE score of 0.28 with 97 ms on the Raspberry Pi 4.
Driver’s Face Detection in Poor Illumination for ADAS Applications

The detection of the driver's face is an important part of the active safety system for advanced driver assistance systems (ADAS) and this task becomes challenging and complex when normal visual cameras are used in poor illumination. This paper incorporates the adaptive attenuation quantification retinex (AAQR) method to improve the details of low and poorly illuminated images and videos with the Viola-Jones face detection algorithm. This paper also makes a comparative study of the implementation of the proposed solution and analyzes its performance on three different datasets: EBDD, 3MDAD IR2, and our self-generated validation dataset. The results have shown that when AAQR is combined with Viola-Jones, the face detection has been improved in the range of 11.18% to 24.21% for different illumination categories of EBDD, 16.71% for 3MDAD IR2 (night), and 46.79% for validation datasets.
Explaining the performance of multilabel classification methods with data set properties

Abstract Meta learning generalizes the empirical experience with different learning tasks and holds promise for providing important empirical insight into the behavior of machine learning algorithms. In this paper, we present a comprehensive meta-learning study of data sets and methods for multilabel classification (MLC). MLC is a practically relevant machine learning task where each example is labeled with multiple labels simultaneously. Here, we analyze 40 MLC data sets by using 50 meta features describing different properties of the data. The main findings of this study are as follows. First, the most prominent meta features that describe the space of MLC data sets are the ones assessing different aspects of the label space. Second, the meta models show that the most important meta features describe the label space, and, the meta features describing the relationships among the labels tend to occur a bit more often than the meta features describing the distributions between and within the individual labels. Third, the optimization of the hyperparameters can improve the predictive performance, however, quite often the extent of the improvements does not always justify the resource utilization.
How experts explain motion planner output: a preliminary user-study to inform the design of explainable planners

Motion planning is a hard problem that can often overwhelm both users and designers: due to the difficulty in understanding the optimality of a solution, or reasons for a planner to fail to find any solution. Inspired by recent work in machine learning and task planning, in this paper we are guided by a vision of developing motion planners that can provide reasons for their output—thus potentially contributing to better user interfaces, debugging tools, and algorithm trustworthiness. Towards this end, we propose a preliminary taxonomy and a set of important considerations for the design of explainable motion planners, based on the analysis of a comprehensive user study of motion planning experts. We identify the kinds of things that need to be explained by motion planners ("explanation objects"), types of explanation, and several procedures required to arrive at explanations. We also elaborate on a set of qualifications and design considerations that should be taken into account when designing explainable methods. These insights contribute to bringing the vision of explainable motion planners closer to reality, and can serve as a resource for researchers and developers interested in designing such technology.
Towards a Conceptual Modelling of Ontologies

Ontologies are considered as a cornerstone for knowledge-based systems and semantic web for reusing and sharing the knowledge by explicit specification of shared conceptualizations. For representing and organizing knowledge, ontology needs a conceptual model to express the real-world objects. The conceptual model explores a series of entities, relationships and attributes to integrate and correlate the knowledge of domain-related data. It allows the layout of architectures for the usage of contents and the ontology provides an initial conceptual level for the knowledge organization. In this paper, the authors have presented a different conceptual model for the management and representation of specific domain knowledge.
Testing the randomness of shares in color visual cryptography

Abstract The concept of black-and-white visual cryptography with two truly random shares, previously applied to color images, was improved by mixing the contents of the segments of each coding image and by randomly changing a specified number of black pixels into color ones. This was done in such a way that the changes of the contents of the decoded image were as small as possible. These modifications made the numbers of color pixels in the shares close to balanced, which potentially made it possible for the shares to be truly random. The true randomness was understood as that the data pass the suitably designed randomness tests. The randomness of the shares was tested with the NIST randomness tests. Part of the tests passed successfully, while some failed. The target of coding a color image in truly random shares was approached, but not yet reached. In visual cryptography, the decoding with the unarmed human eye is of primary importance, but besides this, simple numerical processing of the decoded image makes it possible to greatly improve the quality of the reconstructed image, so that it becomes close to that of the dithered original image.
A novel blockchain protocol for selecting microservices providers and auditing contracts

Software architectures based on containers and microservices are often used to develop and manage large-scale distributed applications. Still, large vertical deployments spanning over multiple cloud and edge infrastructures are cumbersome to negotiate for, as each infrastructure provider is usually unique concerning prices, management strategies and Quality-of-Service (QoS) levels. In this scenario, Service Level Agreement (SLA) contracts are primarily crafted through pre-established templates and clients must trust providers to manage provisioned resources. The present paper proposes Dawn, a novel blockchain protocol for selecting microservice providers and auditing contracts. The protocol exploits the distributed and verifiable storage of a blockchain, as well as its decentralized consensus to enable contracts establishments in unreliable environments. Besides providing a formal definition of the protocol, this work discusses the possible threats to the correct operation of the network, originated by tenants and providers. We show that Dawn is secure under the evaluated terms, that it can efficiently help the contract establishment process as well as it guarantees a functional systematic way of auditing through monitoring. Finally, we studied both best and worst case scenarios regarding the number of issued messages, stored data volume and network traffic to execute Dawn with different numbers of clients and providers.
Circular Species

Science education is paramount as humanity works to mitigate the major socio-economic, technological, and environmental impact of the Anthropocene and build a sustainable future. For children to engage with and one day contribute to finding sustainable solutions, they must learn not just science concepts, but also how to think critically about the role of the human in the systems we exist in.
Methods of Implementation, Maturity Models and Definition of Roles in DevOps Frameworks: A Systematic Mapping

The software development industry has been evolving and has produced new development standards and models for providing services. Agile methodologies have reached their plateau with DevOps, which has increased software quality and hastened its delivery. However, a gap in terms of its formalization and unresolved issues concerning its adoption have become relevant. This study seeks, by using a Systematic Mapping, to elucidate existing gaps in this area such as methods for implementing DevOps, the use of maturity models and defining the roles of those who participate in this process. This Systematic Method was applied to five databases (ACM Digital Library, IEEE Xplore, Science Direct, Springer Link and Scopus), during which automatic searches are conducted with a view to answering the research questions. 32 studies returned answers to these. 11 maturity models were found; however, only two of them had been validated by companies, one of which only applied to IBM software.
Bidirectional recurrent gamma belief network for HRRP target recognition

To characterize the bidirectional temporal dependence across the range cell of high resolution range profile (HRRP), we propose a bidirectional deep Poisson gamma dynamical system (bi-DPGDS) to extract features for target recognition. The proposed bi-DPGDS is a bidirectional dynamical deep probabilistic generative model, which builds temporal deep structure with a hierarchy of gamma distributions. For scalable training and fast out-of-sample prediction, we generalize bi-DPGDS to bidirectional recurrent gamma belief network (bi-rGBN) by incorporating a bidirectional recurrent variational inference network, which incorporates the temporal correlations from both directions into latent representation, and introduce a hybrid Bayesian inference scheme combining stochastic gradient Markov chain Monte Carlo (SG-MCMC) and amoritized variational inference. Moreover, we further propose an attention bi-rGBN (attn-bi-rGBN) for supervised learning. Experimental results on measured HRRP data demonstrate the effectiveness and efficiency of our model in classification and generalization, and its robustness to HRRP shift and data size.
Approximation algorithms for fuzzy C-means problem based on seeding method

As a kind of important soft clustering model, the fuzzy C-means method is widely applied in many fields. In this method, instead of the strict distributive ability in the classical k-means method, all the sample points are endowed with degrees of membership to each center to depict the fuzzy clustering. In this paper, we show that the fuzzy C-means++ algorithm, which introduces the k-means++ algorithm as a seeding strategy, gives a solution for which the approximation guarantee is O(k2ln⁡k). A novel seeding algorithm is then designed based on the contribution of the fuzzy potential function, which improves the approximation ratio to O(kln⁡k). Preliminary numerical experiments are proposed to support the theoretical results of this paper.
A Secure and Decentralized Reconfiguration Protocol For Sharding Blockchains

Most present reconfiguration methods in sharding blockchains rely on a secure randomness, whose generation might be complicated. Besides, a reference committee is usually in charge of the reconfiguration, making the process not decentralized. To address the above issues, this paper proposes a secure and decentralized shard reconfiguration protocol, which allows each shard to complete the selection and confirmation of its own shard members in turn. The PoW mining puzzle is calculated using the public key hash value in the member list confirmed by the last shard. Through the mining and shard member list commitment process, each shard can update its members safely and efficiently once in a while. Furthermore, it is proved that our protocol satisfies the safety, consistency, liveness, and decentralization properties. The honest member proportion in each confirmed shard member list is guaranteed to exceed a certain safety threshold, and all honest nodes have an identical view on the list. The reconfiguration is ensured to make progress, and each node has the same right to participate in the process. Our secure and decentralized shard reconfiguration protocol could be applied to all committee-based sharding blockchains.
Algorithm-hardware Co-design for Deformable Convolution

FPGAs provide a flexible and efficient platform to accelerate rapidly-changing algorithms for computer vision. The majority of existing work focuses on accelerating image classification, while other fundamental vision problems, including object detection and instance segmentation, have not been adequately addressed. Compared with image classification, detection problems are more sensitive to the spatial variance of objects, and therefore, require specialized convolutions to aggregate spatial information. To address this, recent work proposes dynamic deformable convolution to augment regular convolutions. Regular convolutions process a fixed grid of pixels across all the spatial locations in an image, while dynamic deformable convolutions may access arbitrary pixels. The access pattern of deformable convolutions is input-dependent and varies per spatial location. These properties lead to inefficient memory accesses of inputs with existing hardware. In this work, we first investigate the overhead of the deformable convolution on embedded FPGA SoCs, and then show the accuracy-latency tradeoffs for a set of algorithm modifications, including full versus depthwise, fixed-shape, and limited-range. These modifications benefit the efficiency of the embedded accelerator in general. We build an efficient object detection network with modified deformable convolutions and quantize the network using state-of-the-art quantization methods. Experiments show that our co-design optimization for the deformable convolution achieves significant hardware speedup with little accuracy compromised.
Adaptive Visualizations for Enhanced Data Understanding and Interpretation

In a data driven economy where data volume and dimensions are explosively increasing, businesses rely on business intelligence and analytics (BI&A) platforms for analysing their data and coming to beneficial decisions. With the ever-growing generation of data, the process of data analysis is becoming more complicated for the business users, as the exploration of more demanding use cases increases. While the existing BI&A platforms provide myriads of data visualizations that support data exploration, none of those account for the user’s individual differences, needs or requirements, and thus may hinder the user’s understanding of visual data and consequently their decision-making processes. This work embarks on an interdisciplinary endeavour to introduce a human-centred adaptive data visualizations framework in the context of business, as the core of an adaptive data analytics platform, that aims to enhance the business user’s decision making by increasing her understanding of data. The framework is built using a multi-dimensional human-centred user model that goes beyond traditional user characteristics and accounts for cognitive factors, domain expertise and experience and factors related to the business context i.e., data, visualizations and tasks; a data visualization engine that will recommend to the unique-user the best-fit data visualizations based on the abovementioned user model; and an intelligent data analytics component that enhances the efficiency and effectiveness of the data exploration process by leveraging user interactions during the explorations to further inform the user model on the user’s expertise and experience.
Knowledge Graph for Power Grid Dispatching of Digital Homes based on Graph Convolutional Network

With the wide deployment of numerous digital media equipment into home and life, power grid dispatching (PGD) systems, as the energy source of digital media equipment, generate a large amount of data. Those data are stored in large volumes, various types, separate features, and lacking unified data specifications, making it harder to organize and learn. In view of the above challenges, inspired by the knowledge graph models which hold strong power for knowledge representing and reasoning, this paper constructs a knowledge graph model for power grid dispatching regulations with graph convolutional network (KGPGD). Specifically, in the context of the massive growth of grid dispatching data, knowledge extraction methods are used to obtain the entity words and relation words in the specification from the smart grid dispatching control system. The dataset of technical specifications of the smart grid dispatching control system is the real-world provided by the Northeast Branch of State Grid Corporation of China. And then a domain knowledge graph is automatically constructed to study domain-oriented big data of PGD. At the same time, the graph convolutional neural network model is utilized to learn the power grid dispatching knowledge graph features. KGPGD provides a reference basis and decision support for the formulation of the technical specifications of the power grid dispatching system of digital homes.