Reviewerly/exHarmony huggingface dataset:
A Novel Multiface Recognition Method With Short Training Time and Lightweight Based on ABASNet and H-Softmax

In order to solve the problem of low face recognition accuracy of traditional algorithms and excessive long training time in deep learning methods, a novel lightweight and short training time multiface recognition method is proposed in this paper. Firstly, an extraction model of facial feature vectors is established based on local binary mode and principal component analysis. Secondly, the beetle antennae search algorithm (BAS) is optimized using adaptive factors, and the ABAS algorithm is proposed. This paper uses the ABAS algorithm to optimize the initial threshold of the neural network and proposes the ABASNet method. Thirdly, ABASNet method is combined with the face features extracted by the face feature vector extraction model to be used for multi-face classification tasks. Finally, H-softmax (hierarchical softmax) is used to replace softmax in neural networks. By reducing the amount of calculation in the process of multi-category face classification, the training time of the ABASNet network is reduced. Through ablation experiments and comparative experiments with various methods such as IKDA + PNN algorithm and PCANet algorithm, to verify the accuracy, robustness and short training time of the ABASNet method. Among them, the maximum accuracy of the method in the ORL face dataset, ExtYaleB face dataset and FERET face dataset is 99.35%, 99.54% and 99.18% respectively. In addition, actual test results indicate that the training time and recognition time of the method in this paper are 21 seconds and 0.06 seconds respectively, which has demonstrated the lightweight and real-time performance of the proposed method. At the same time, more test results show that the proposed ABASNet method can also be implemented in embedded devices and other classification tasks.
Multi-Objective Blended Ensemble For Highly Imbalanced Sequence Aware Tweet Engagement Prediction

In this paper we provide a description of the methods we used as team BanaNeverAlone for the ACM RecSys Challenge 2020, organized by Twitter. The challenge addresses the problem of user engagement prediction: the goal is to predict the probability of a user engagement (Like, Reply, Retweet or Retweet with comment), based on a series of past interactions on the Twitter platform. Our proposed solution relies on several features that we extracted from the original dataset, as well as on consolidated models, such as gradient boosting for decision trees and neural networks. The ensemble model, built using blending, and a multi-objective optimization allowed our team to rank in position 4.
Towards Population-Based Structural Health Monitoring, Part VI: Structures as Geometry

One of the requirements of the population-based approach to Structural Health Monitoring (SHM) proposed in the earlier papers in this sequence, is that structures can be represented by points in an abstract space. Furthermore, these spaces should be metric spaces in a loose sense; i.e. there should be some measure of distance applicable to pairs of points; similar structures should then be ‘close’ in the metric. This geometrical structure is not enough for the framing of problems in data-based SHM, as it leaves undefined the notion of feature spaces. Interpreting the feature values on a structure-by-structure basis as a type of field over the space of structures, it seems sensible to borrow an idea from modern theoretical physics, and define feature assignments as sections in a vector bundle over the structure space. With this idea in place, one can interpret the effect of environmental and operational variations as gauge degrees of freedom, as in modern gauge field theories. One can then regard data normalisation procedures like cointegration as gauge-fixing operations. This paper will discuss the various geometrical structures required for an abstract theory of feature spaces in SHM, and will draw analogies with how these structures have shown their power in modern physics.
Strong tracking extended particle filter for manoeuvring target tracking

To improve the stability and accuracy of manoeuvring target tracking in three-dimensional space based on the angle of arrival (AOA) and its rate of change observations, this study presents a new observation fusion method by fusing the received signal strength (RSS) with AOA and the rate of change of AOA. To enhance the adaptive ability of traditional strong tracking extended particle filter (TSTEPF) against model mismatch, this study re-determines the position of the fading factor in the strong tracking extended Kalman filter based on the orthogonal principle and gives the calculating method. And by combining the method with the particle filter, a new strong tracking extended particle filter (STEPF) algorithm is proposed. Simulation results show that after fusing RSS into the observation model, the tracking speed and precision are both improved, especially precision, as the position root-mean-square error has a 58% decline on average. And it is found that STEPF proposed in this study has a more stable adaptive ability than TSTEPF, and is superior in terms of position, velocity, and acceleration estimation accuracy.
Self-Supervised Deep TripleNet for Video Object Segmentation

Most of previous video object segmentation methods require a large amount of pixel-level annotated video data to construct a robust model. It is quite expensive to label segmentation mask in video. In this paper, we propose a self-supervised triplenet for video object segmentation, which only leverages nearly unlimited unlabeled video data in training phase. Our method consists of two modules, i.e., the temporal motion module and appearance matching module. The temporal motion module is trained based on the pixel correspondence between two video frames in a self-supervised manner, which models the motion patterns between two video frames and propagates the labels from one frame to another. Meanwhile, the appearance matching module encodes the reference frame and its corresponding mask, and generates the segmentation mask of the same object in target frame. The appearance matching module can adjust and refine the outout of temporal motion module, and avoid error accumulation by matching the reference appearance. In order to train the appearance matching module in self-supervised manner, we propose two mask generation strategies: foreground region mask generation and random color region mask generation. Extensive experiments conducted on four challenging video object segmentation datasets, i.e., DAVIS-2017, Youtube-VOS, DAVIS- 2016 and SegTrack v2, demonstrate that the proposed method performs favorable against the state-of-the-art self-supervised methods, and performs even competitively with fully-supervised methods. We also show our self-supervised approach has actually superior generalizability to the majority of supervised methods.
Differential privacy protection over deep learning: An investigation of its impacted factors

Deep learning (DL) has been widely applied to achieve promising results in many fields, but it still exists various privacy concerns and issues. Applying differential privacy (DP) to DL models is an effective way to ensure privacy-preserving training and classification. In this paper, we revisit the DP stochastic gradient descent (DP-SGD) method, which has been used by several algorithms and systems and achieved good privacy protection. However, several factors, such as the sequence of adding noise, the models used etc., may impact its performance with various degrees. We empirically show that adding noise first and clipping second will not only significantly achieve high accuracy, but also accelerate convergence. Rigorous experiments have been conducted on three different datasets to train two popular DL models, Convolutional Neural Network (CNN) and Long and Short-Term Memory (LSTM). For the CNN, the accuracy rate can be increased by 3%, 8% and 10% on average for the respective datasets, and the loss value is reduced by 18%, 14% and 22% on average. For the LSTM, the accuracy rate can be increased by 18%, 13% and 12% on average, and the loss value can be reduced by 55%, 25% and 23% on average. Meanwhile, we have compared the performance of our proposed method with a state-of-the-art SGD-based technique. The results show that under the premise of a reasonable clipping threshold, the proposed method not only has better performance, but also achieve ideal privacy protection effects. The proposed alternative can be applied to many existing privacy preserving solutions.
SAEA: Self-Attentive Heterogeneous Sequence Learning Model for Entity Alignment

We consider the problem of entity alignment in knowledge graphs. Previous works mainly focus on two aspects: One is to improve the TransE-based models which mostly only consider triple-level structural information i.e. relation triples or to make use of graph convolutional networks holding the assumption that equivalent entities are usually neighbored by some other equivalent entities. The other is to incorporate external features, such as attributes types, attribute values, entity names and descriptions to enhance the original relational model. However, the long-term structural dependencies between entities have not been exploited well enough and sometimes external resources are incomplete and unavailable. These will impair the accuracy and robustness of combinational models that use relations and other types of information, especially when iteration is performed. To better explore structural information between entities, we novelly propose a Self-Attentive heterogeneous sequence learning model for Entity Alignment (SAEA) that allows us to capture long-term structural dependencies within entities. Furthermore, considering low-degree entities and relations appear much less in sequences prodeced by traditional random walk methods, we design a degree-aware random walk to generate heterogeneous sequential data for self-attentive learning. To evaluate our proposed model, we conduct extensive experiments on real-world datasets. The experimental results show that our method outperforms various state-of-the-art entity alignment models using relation triples only.
Software architecture and task plan co-adaptation for mobile service robots

Self-adaptive systems increasingly need to reason about and adapt both structural and behavioral system aspects, such as in mobile service robots, which must reason about missions that they need to achieve and the architecture of the software executing them. Deciding how to best adapt these systems to run time changes is challenging because it entails considering mutual dependencies between the software architecture that the system is running and the outcome of plans for completing tasks, while also considering multiple trade-offs and uncertainties. Considering all these aspects in planning for adaptation often yields large solution spaces which cannot be adequately explored at run time. We address this challenge by proposing a planning approach able to consider the impact of mutual dependencies between software architecture and task planning on the satisfaction of mission goals. The approach is able to reason quantitatively about the outcome of adaptation decisions handling both the reconfiguration of the system's architecture and adaptation of task plans under uncertainty and in a rich trade-off space. Our results show: (i) feasibility of run-time decision-making for self-adaptation in an otherwise intractable solution space by dividing-and-conquering adaptation into architecture reconfiguration and task planning sub-problems, and (ii) improved quality of adaptation decisions with respect to decision making that does not consider dependencies between architecture and task planning.
Multi-Scale Structured Dictionary Learning for 3-D Point Cloud Attribute Compression

Existing 3-D point cloud attribute compression methods are rigid for complex signals defined in spatially irregular domain, as they solely depend on geometric information or adopt pre-defined analytic bases for transform. In this article, we propose a multi-scale structured dictionary learning algorithm for 3-D point cloud attribute compression. The proposed multi-scale dictionary leverages hierarchical sparsity within signals by adapting atoms in an arborescent structure. It supports hierarchical sparse transform for encoding voxelized point cloud attributes with progressive refinement of high-frequency details from coarser to finer scales. To represent spatial blocks with varying amounts of occupied voxels, a non-uniform binary weight matrix is incorporated in dictionary learning to characterize the dimensional irregularity of signals. To guarantee optimal approximation with improved efficiency, alternating optimizations including Alternating Direction Method of Multipliers and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gauss-Seidel</i> iterations are adopted for the weighted mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2}/\ell _{1}$ </tex-math></inline-formula> -minimization. Furthermore, we develop a lossy attribute compression framework by integrating sparse transform based prediction with dedicated quantization and entropy coding routines. Experimental results demonstrate that the proposed framework outperforms the state-of-the-art transform-based coding methods and is competitive with the most recent MPEG PCC test models in point cloud attribute compression.
Covariance Attention for Semantic Segmentation

The dependency between global and local information can provide important contextual cues for semantic segmentation. Existing attention methods capture this dependency by calculating the pixel wise correlation between the learnt feature maps, which is of high space and time complexity. In this article, a new attention module, covariance attention, is presented, and which is interesting in the following aspects: 1) covariance matrix is used as a new attention module to model the global and local dependency for the feature maps and the local-global dependency is formulated as a simple matrix projection process; 2) since covariance matrix can encode the joint distribution information for the heterogeneous yet complementary statistics, the hand-engineered features are combined with the learnt features effectively using covariance matrix to boost the segmentation performance; 3) a covariance attention mechanism based semantic segmentation framework, CANet, is proposed and very competitive performance has been obtained. Comparisons with the state-of-the-art methods reveal the superiority of the proposed method.
Analysis of the Persuasiveness of Argumentation in Popular Science Texts

The paper discusses the methods of modeling and assessing the quality of the argumentation used in popular science texts, as well as the software supporting them. First, the authors study the aspects of argumentation persuasiveness, i.e. the validity of conclusions presented in articles. Argumentation modeling is performed using the argumentation ontology based on the AIF format (Argument Interchange Format), which was adopted by the international community as a standard notation for describing arguments and argumentation schemes. The authors have supplemented this ontology with the facilities necessary for modeling and analyzing the quality of argumentation in popular science discourse. In particular, we have introduced into the ontology facilities allowing us to assign the estimates of persuasiveness (degree of the truth) to the arguments and statements and to model the target audience. Thanks to these facilities, it has become possible to analyze the persuasiveness of the argumentation regarding different target audiences. To solve this problem, the authors propose a model and an algorithm for calculating the persuasiveness of arguments allowing taking into account conflicts between the arguments. The paper also provides an example of constructing a network of arguments and calculating the degree of their persuasiveness using the software system developed.
ImRec: Learning Reciprocal Preferences Using Images

Reciprocal Recommender Systems are recommender systems for social platforms that connect people to people. They are commonly used in online dating, social networks and recruitment services. The main difference between these and conventional user-item recommenders that might be found on, for example, a shopping service, is that they must consider the interests of both parties. In this study, we present a novel method of making reciprocal recommendations based on image data. Given a user's history of positive and negative preference expressions on other users images, we train a siamese network to identify images that fit a user's personal preferences. We provide an algorithm to interpret those individual preference indicators into a single reciprocal preference relation. Our evaluation was performed on a large real-world dataset provided by a popular online dating service. Based on this, our service significantly improves on previous state-of-the-art content-based solutions, and also out-performs collaborative filtering solutions in cold-start situations. The success of this model provides empirical evidence for the high importance of images in online dating.
Behavior-Driven Development

Software development practices which enhance software quality and help teams better develop collaboratively have received attention by the academic community. Among these techniques is Behavior-Driven Development (BDD), a development method which proposes software to be developed focusing primarily on its expected behavior. In this context, this paper investigates how BDD impacts agile software development teams. In order to achieve this, we have conducted a case study on a mobile application development environment which develops software using agile. In total, 42 interviews were performed. Our results indicate that BDD can have positive impacts, increasing collaboration among team members, and negative impacts, like difficulties in writing unit tests. We concluded that BDD has more positive than negative outcomes.
Methodological aspects for cognitive architectures construction: a study and proposal

Abstract In the field of Artificial Intelligence (AI), efforts to achieve human-like behavior have taken very different paths through time. Cognitive Architectures (CAs) differentiate from traditional AI approaches, due to their intention to model cognitive and behavioral processes by understanding the brain’s structure and their functionalities in a natural way. However, the development of distinct CAs has not been easy, mainly because there is no consensus on the theoretical basis, assumptions or even purposes for their creation nor how well they reflect human function. In consequence, there is limited information about the methodological aspects to construct this type of models. To address this issue, some initial statements are established to contextualize about the origins and directions of cognitive architectures and their development, which help to outline perspectives, approaches and objectives of this work, supported by a brief study of methodological strategies and historical aspects taken by some of the most relevant architectures to propose a methodology which covers general perspectives for the construction of CAs. This proposal is intended to be flexible, focused on use-case tasks, but also directed by theoretic paradigms or manifestos. A case study between cognitive functions is then detailed, using visual perception and working memory to exemplify the proposal’s assumptions, postulates and binding tools, from their meta-architectural conceptions to validation. Finally, the discussion addresses the challenges found at this stage of development and future work directions.
Topic recommendation system using personalized fuzzy logic interest set

First, the recommendation system and its advantages are introduced in detail, and based on the characteristics of the intelligent topic logical interest set resource and user behavior in the existing intelligent topic logical interest set resource platform, a personalized fuzzy logic model of the intelligent topic logical interest set resource is established and adapted to it. The personalized fuzzy logic user personalized fuzzy logic interest model of personalized fuzzy logic is designed, and the user personalized fuzzy logic interest transfer method is designed to simulate the user learning process. Secondly, on the basis of the established model, according to the idea of collaborative filtering, the personalized fuzzy logic user’s personalized fuzzy logic interest value and the user’s rating of resources are respectively predicted, and the two prediction results are combined to recommend resources to the user. Finally, the ontology is applied to user interest description, and a method based on personalized fuzzy logic user rough interest vector and nearest neighbor concept aggregation is proposed to find fine-grained user interest and recommend interest resources. Experimental tests show that this method can better describe the composition and development of user interests, making the recommendation effect of interest resources for specific users more accurate and reliable. The problem of collaborative recommendation in personalized fuzzy logic systems is further studied, the basic principles and typical technologies of collaborative recommendation are analyzed, and the collaborative recommendation method based on users with similar interests and the collaborative recommendation method based on weighted association rules are proposed.
Zero-watermarking method for resisting rotation attacks in 3D models

It is challenging to relocate the embedded positions of the watermark when 3D models are subjected to rotation attacks, because the vertex data of 3D models has no implicit order. This paper proposes a zero-watermarking scheme for 3D models, using the Beamlet transform method. First, the area of the 1-ring neighbourhood of each vertex is calculated. Then, the 1-ring neighbourhoods of the vertices with moderate areas are projected onto the tangent planes; the X-axis of each tangent plane corresponds to the mesh line with the maximum curvature in the 1-ring neighbourhood. The Beamlet dictionary is constructed by recording the line segments on both sides of the X-axis; thus, a zero-watermarking image which protects the copyright of the model is produced. Simulations and experiments show that the proposed watermarking scheme exhibits a robust resistance to common signal-processing attacks, especially rotation attacks, and can protect the copyrights of 3D models reliably without distorting the content in any manner.
Intelligent models for movement detection and physical evolution of patients with hip surgery

Abstract This paper develops computational models to monitor patients with hip replacement surgery. The Kinect camera (Xbox One) is used to capture the movements of patients who are performing rehabilitation exercises with both lower limbs, specifically, ‘side step’ and ‘knee lift’ with each leg. The information is measured at 25 body points with their respective coordinates. Features selection algorithms are applied to the 75 attributes of the initial and final position vector of each rehab exercise. Different classification techniques have been tested and Bayesian networks, supervised classifier system and genetic algorithm with neural network have been selected and jointly applied to identify the correct and incorrect movements during the execution of the rehabilitation exercises. Besides, prediction models of the evolution of a patient are developed based on the average values of some motion related variables (opening leg angle, head movement, hip movement and execution speed). These models can help to fasten the recovery of these patients.