Reviewerly/exHarmony huggingface dataset:
Tripartite Feature Enhanced Pyramid Network for Dense Prediction

Learning pyramidal feature representations is important for many dense prediction tasks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., object detection, semantic segmentation) that demand multi-scale visual understanding. Feature Pyramid Network (FPN) is a well-known architecture for multi-scale feature learning, however, intrinsic weaknesses in feature extraction and fusion impede the production of informative features. This work addresses the weaknesses of FPN through a novel tripartite feature enhanced pyramid network (TFPN), with three distinct and effective designs. First, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature reference module</i> with lateral connections to adaptively extract bottom-up features with richer details for feature pyramid construction. Second, we design a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature calibration module</i> between adjacent layers that calibrates the upsampled features to be spatially aligned, allowing for feature fusion with accurate correspondences. Third, we introduce a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature feedback module</i> in FPN, which creates a communication channel from the feature pyramid back to the bottom-up backbone and doubles the encoding capacity, enabling the entire architecture to generate incrementally more powerful representations. The TFPN is extensively evaluated over four popular dense prediction tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., object detection, instance segmentation, panoptic segmentation, and semantic segmentation. The results demonstrate that TFPN consistently and significantly outperforms the vanilla FPN. Our code is available at https://github.com/jamesliang819.
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

Large-scale pre-trained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks.This paper presents mPLUG, a new vision-language foundation model for both cross-modal understanding and generation.Most existing pre-trained models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in crossmodal alignment.To address both problems, mPLUG introduces an effective and efficient vision-language architecture with novel crossmodal skip-connections.mPLUG is pre-trained end-to-end on largescale image-text pairs with both discriminative and generative objectives.It achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering.mPLUG also demonstrates strong zero-shot transferability on visionlanguage and video-language tasks.The code and pre-trained models are available at https://github.com/alibaba/AliceMind.
A context sensitive multilevel thresholding using swarm based algorithms

In this paper, a comprehensive energy function is used to formulate the three most popular objective functions: Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.
Anthropomorphic Features for On-Line Signatures

Many features have been proposed in on-line signature verification. Generally, these features rely on the position of the on-line signature samples and their dynamic properties, as recorded by a tablet. This paper proposes a novel feature space to describe efficiently on-line signatures. Since producing a signature requires a skeletal arm system and its associated muscles, the new feature space is based on characterizing the movement of the shoulder, the elbow and the wrist joints when signing. As this motion is not directly obtained from a digital tablet, the new features are calculated by means of a virtual skeletal arm (VSA) model, which simulates the architecture of a real arm and forearm. Specifically, the VSA motion is described by its 3D joint position and its joint angles. These anthropomorphic features are worked out from both pen position and orientation through the VSA forward and direct kinematic model. The anthropomorphic features' robustness is proved by achieving state-of-the-art performance with several verifiers and multiple benchmarks on third party signature databases, which were collected with different devices and in different languages and scripts.
Identification of the anomaly component using BEMD combined with PCA from element concentrations in the Tengchong tin belt, SW China

Concentration of elements or element groups in a geological body is the result of multiple stages of rock-forming and ore-forming geological processes. An ore-forming element group can be identified by PCA (principal component analysis) and be separated into two components using BEMD (bi-dimensional empirical mode decomposition): (1) a high background component which represents the ore-forming background developed in rocks through various geological processes favorable for mineralization (i.e. magmatism, sedimentation and/or metamorphism); (2) the anomaly component which reflects the ore-forming anomaly that is overprinted on the high background component developed during mineralization. Anomaly components are used to identify ore-finding targets more effectively than ore-forming element groups. Three steps of data analytical procedures are described in this paper; firstly, the application of PCA to establish the ore-forming element group; secondly, using BEMD on the ore-forming element group to identify the anomaly components created by different types of mineralization processes; and finally, identifying ore-finding targets based on the anomaly components. This method is applied to the Tengchong tin-polymetallic belt to delineate ore-finding targets, where four targets for Sn (W) and three targets for Pb-Zn-Ag-Fe polymetallic mineralization are identified and defined as new areas for further prospecting. It is shown that BEMD combined with PCA can be applied not only in extracting the anomaly component for delineating the ore-finding target, but also in extracting the residual component for identifying its high background zone favorable for mineralization from its ore-forming element group.
A Regularization Framework for Learning Over Multitask Graphs

This letter proposes a general regularization framework for inference over multitask networks. The optimization approach relies on minimizing a global cost consisting of the aggregate sum of individual costs regularized by a term that allows to incorporate global information about the graph structure and the individual parameter vectors into the solution of the inference problem. An adaptive strategy, which responds to streaming data and employs stochastic approximations in place of actual gradient vectors, is devised and studied. Methods allowing the distributed implementation of the regularization step are also discussed. This letter shows how to blend real-time adaptation with graph filtering and a generalized regularization framework to result in a graph diffusion strategy for distributed learning over multitask networks.
Learners’ Experience Towards E-Assessment Tools: A Comparative Study on Virtual Reality and Moodle Quiz

Although the integration of virtual world in teaching and learning has been investigated, there is a scarcity of research considering its influence on assessing learners’ understanding and comparing it with traditional e-assessment tools such as that in Moodle quiz. In this research, a virtual reality (VR) game-based e-assessment application was proposed first. Subsequently, the study aims at (1) understanding whether the type of e-assessment method can affect students’ performance, and (2) investigating the difference in learners’ perceptions based on the type of e-assessment technology. A combination of quasi-pre-test and post-test experimental and survey research design methods were adopted. Overall, 32 undergraduate students were assigned to either control (N=17) or experimental (N=15) group. The key findings are (1) no statistical differences in students’ performance were found for both groups, and (2) significant differences between learners who completed the VR game-based e-assessment and those on the control group were found on perceived playfulness and ease of use. The conclusion drawn from the research outcomes is that a VR game-based e-assessment application is a successful approach to enhance learners’ engagement in evaluation sessions, although students may face a lack of experience in its use.
Robust Decision Trees Against Adversarial Examples.

Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust against adversarial examples is still limited. In this paper, we show that tree based models are also vulnerable to adversarial examples and develop a novel algorithm to learn robust trees. At its core, our method aims to optimize the performance under the worst-case perturbation of input features, which leads to a max-min saddle point problem. Incorporating this saddle point objective into the decision tree building procedure is non-trivial due to the discrete nature of trees --- a naive approach to finding the best split according to this saddle point objective will take exponential time. To make our approach practical and scalable, we propose efficient tree building algorithms by approximating the inner minimizer in this saddle point problem, and present efficient implementations for classical information gain based trees as well as state-of-the-art tree boosting models such as XGBoost. Experimental results on real world datasets demonstrate that the proposed algorithms can substantially improve the robustness of tree-based models against adversarial examples.
iSheets: A Spreadsheet-Based Machine Learning Development Platform for Data-Driven Process Analytics

In the era of big data, the quality of services any organization provides largely depends on the quality of their data-driven processes. In this context, the goal of process data science, is to enable innovative forms of information processing that enable enhanced insight and decision making. For example, consider the data-driven and knowledge-intensive processes in Australian government’s office of the e-Safety commissioner, where the goal is to empowering all citizens to have safer, more positive experiences online. An example process, is to analyze the large amount of data generated every second on social networks to understand patterns of suicidal thoughts, online bullying and criminal/exterimist behaviour. Current processes leverage machine learning systems, e.g., to perform automatic mental-health-disorders detection from social networks. This approach is challenging for knowledge workers (end-user analysts) who have little knowledge of computer science to use machine learning solutions in their data-driven processes. In this paper, we present a novel platform, namely iSheets, that makes it easy for knowledge workers of all skill levels to use machine learning technology, the way people use spreadsheet. We present and develop a Machine Learning (ML) as a service framework and a spreadsheet-based ML development platform to enable knowledge workers in data-driven processes engage with ML tasks and uncover hidden insights through learning in an easy way.
A fuzzy rule-based fog–cloud computing for solar panel disturbance investigation

The electrical energy produced by solar panel depends on the light intensity falling on the panel, but this process is prone to disturbances from external factors. Unfortunately, models of online solar panel disturbance diagnosis have not been widely developed. This research proposes a model of fog computing using fuzzy rule-based algorithm is capable of automatic monitoring and diagnosing factors affecting solar panel efficiency. Data from physical parameter of sensors are acquired by the System on Chip (SoC) Wi-Fi microcontroller and sent to the fog server via a Wi-Fi gateway. The fuzzy rule-based algorithm consists of investigation rules showing the relationships among efficiency, light intensity, output electrical power, temperature, and humidity. Output of fog network computing is sent to the cloud server and serves as information for users of this investigation system. The fog network system is able to improve cloud performance, in terms of the transmission time has increased performance from 246.1 to 27.9 ms. In general, this system is able to improve relative efficiency of solar panel by 2.1%, compared to solar panels not equipped with this instrument. In order to obtain accurate investigation results, detailed conditions of all possible events in the field are required.
Optimising Clifford Circuits with Quantomatic

We present a system of equations between Clifford circuits, all derivable in the ZX-calculus, and formalised as rewrite rules in the Quantomatic proof assistant.By combining these rules with some non-trivial simplification procedures defined in the Quantomatic tactic language, we demonstrate the use of Quantomatic as a circuit optimisation tool.We prove that the system always reduces Clifford circuits of one or two qubits to their minimal form, and give numerical results demonstrating its performance on larger Clifford circuits.
Mining significant trend sequences in dynamic attributed graphs

Discovering patterns in graphs has many applications such as social network, biological and chemistry data analysis. Although many algorithms were proposed to identify interesting patterns in graphs, most of them consider simple types of graphs such as static graphs or graphs having a single attribute per vertex. Recently, studies have considered discovering frequent patterns in dynamic attributed graphs. These graphs can represent not only relationships between entities but also how they evolve over time, and describe entities with multiple attributes. Algorithms for mining frequent patterns in dynamic attributed graphs select patterns based on their occurrence frequency. But a major drawback of this approach is that many frequent patterns may contain entities that are weakly correlated. Thus, numerous frequent but spurious patterns may be shown to the user. To allows discovering strongly correlated patterns in dynamic attributed graphs, this paper proposes a novel significance measure named Sequence Virtual Growth Rate. It allows evaluating if a pattern represents entities that are correlated in terms of their proximity in a graph over time. Based on this measure a novel type of graph patterns is defined called Significant Trend Sequence. To efficiently mine these patterns, two algorithms named TSeqMinerdfs−bfs and TSeqMinerdfs−dfs are proposed. They rely on a novel upper bound and pruning strategy to reduce the search space. Experimental results show that the proposed algorithms are efficient and can identify interesting patterns in real-world social network and flight data.
Facial Attribute Recognition With Feature Decoupling and Graph Convolutional Networks

The objective of facial attribute recognition is to predict a set of labels for each face image.Similar to general multi-label image classification task, facial attribute recognition suffers from several typical problems: discriminative feature learning, handling the correlations among attributes, and imbalanced training data.In this paper, we propose a unified facial attribute recognition solution via feature decoupling and graph convolutional networks (GCN): 1) We utilize the orthonormal regularizer to constrain that each dimension of the general facial representation represents a certain visual pattern.Meanwhile, a learnable matrix is unveiled, which can convert general facial feature into attribute-specific representation through modeling the importance of different visual patterns.2) To handle the correlations between facial attributes, we build a GCN to capture the label dependencies and map the nodes in the proposed GCN to a set of inter-dependent attribute classifiers.Besides, we normalize the weights in all classifiers to alleviate the influence of data imbalance.We have conducted extensive experiments on two benchmarks, and both the qualitative and quantitative evaluation results have demonstrated the effectiveness of the proposed method.
Overview of Long Short-Term Memory Neural Networks

Long Short-term Memory was designed to avoid vanishing and exploding gradient problems in recurrent neural networks. Over the last twenty years, various modifications of an original LSTM cell were proposed. This chapter gives an overview of basic LSTM cell structures and demonstrates forward and backward propagation within the most widely used configuration called traditional LSTM cell. Besides, LSTM neural network configurations are described.
Identity at the margins

This paper examines refugees' experiences with and perspectives on the digital identity systems used by humanitarian organizations to collect, manage, and share their personal data. Through a qualitative study with 198 refugees in Lebanon, Jordan, and Uganda, we show how existing humanitarian identity systems present numerous challenges for refugees. For example, we find that refugees have little to no knowledge of the institutional systems and processes through which their personal data is managed and used. In addition, refugees are typically not able to exercise agency with regard to data that is collected about them (e.g., given choices about the data collected). At the same time, we show how refugees make active efforts to negotiate the various identities available to them, consciously weighing the benefits and constraints associated with different statuses to maximize their access to services, eligibility for employment, and spatial mobility. We use Bardzell's lens of feminist interaction design [8] to make sense of these findings and suggest a path forward that engages refugees in the design of improved identity management systems.
An Incremental Iterated Response Model of Pragmatics

Recent Iterated Response (IR) models of pragmatics conceptualize language use as a recursive process in which agents reason about each other to increase communicative efficiency. These models are generally defined over complete utterances. However, there is substantial evidence that pragmatic reasoning takes place incrementally during production and comprehension. We address this with an incremental IR model. We compare the incremental and global versions using computational simulations, and we assess the incremental model against existing experimental data and in the TUNA corpus for referring expression generation, showing that the model can capture phenomena out of reach of global versions.
Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks

Human-robot interaction benefits greatly from multimodal sensor inputs as they enable increased robustness and generalization accuracy.Despite this observation, few HRI methods are capable of efficiently performing inference for multimodal systems.In this work, we introduce a reformulation of Interaction Primitives which allows for learning from demonstration of interaction tasks, while also gracefully handling nonlinearities inherent to multimodal inference in such scenarios.We also empirically show that our method results in more accurate, more robust, and faster inference than standard Interaction Primitives and other common methods in challenging HRI scenarios.
Linked Data in the European Data Portal: A Comprehensive Platform for Applying DCAT-AP

Abstract The European Data Portal (EDP) is a central access point for metadata of Open Data published by public authorities in Europe and acquires data from more than 70 national data providers. The platform is a starting point in adopting the Linked Data specification DCAT-AP, aiming to increase interoperability and accessibility of Open Data. In this paper, we present the design of the central data management components of the platform, responsible for metadata storage, data harvesting and quality assessment. The core component is based on CKAN, which is extended by the support for native Linked Data replication to a triplestore to ensure legacy compatibility and the support for DCAT-AP. Regular data harvesting and the creation of detailed quality reports are performed by custom components adressing the requirements of DCAT-AP. The EDP is well on track to become the core platform for European Open Data and fostered the acceptance of DCAT-AP. Our platform is available here: https://www.europeandataportal.eu .
Complexity at Mesoscales: A Common Challenge in Developing Artificial Intelligence

Exploring the physical mechanisms of complex systems and making effective use of them are the keys to dealing with the complexity of the world. The emergence of big data and the enhancement of computing power, in conjunction with the improvement of optimization algorithms, are leading to the development of artificial intelligence (AI) driven by deep learning. However, deep learning fails to reveal the underlying logic and physical connotations of the problems being solved. Mesoscience provides a concept to understand the mechanism of the spatiotemporal multiscale structure of complex systems, and its capability for analyzing complex problems has been validated in different fields. This paper proposes a research paradigm for AI, which introduces the analytical principles of mesoscience into the design of deep learning models. This is done to address the fundamental problem of deep learning models detaching the physical prototype from the problem being solved; the purpose is to promote the sustainable development of AI.
Multiple-image encryption and authentication based on optical interference by sparsification and space multiplexing

The silhouette problem is the main drawback of the interference-based encryption (IBE) scheme, while the capacity of IBE is always low. In this paper, we utilize sparsification and space multiplexing to process the Fresnel spectrums of multiple original images into one spectrum, and then decompose the synthetized spectrum into two phase-only masks using IBE. Due to the sparsification of the spectrums, the decrypted images can’t be observed by naked eyes with total silhouette-removal, but can be nonlinear correlated with original images. Moreover, the capacity of this proposal is also significantly improved. The feasibility and effectiveness of this proposal have been demonstrated by numerical simulation.