Reviewerly/exHarmony huggingface dataset:
Unseen Face Presentation Attack Detection Using Sparse Multiple Kernel Fisher Null-Space

We address the face presentation attack detection problem in the challenging conditions of an unseen attack scenario where the system is exposed to novel presentation attacks that were not available in the training stage. To this aim, we pose the unseen face presentation attack detection (PAD) problem as the one-class kernel Fisher null-space regression and present a new face PAD approach that only uses bona fide (genuine) samples for training. Drawing on the proposed kernel Fisher null-space face PAD method and motivated by the availability of multiple information sources, next, we propose a multiple kernel fusion anomaly detection approach to combine the complementary information provided by different views of the problem for improved detection performance. And the last but not the least, we introduce a sparse variant of our multiple kernel Fisher null-space face PAD approach to improve inference speed at the operational phase without compromising much on the detection performance. The results of an experimental evaluation on the OULU-NPU, Replay-Mobile, Replay-Attack and MSU-MFSD datasets illustrate that the proposed method outperforms other methods operating in an unseen attack detection scenario while achieving very competitive performance to multi-class methods (that benefit from presentation attack data for training) despite using only bona fide samples in the training stage.
What factors influence the lifetime of pull requests?

Summary When external contributors want to collaborate with an open‐source project, they fork the repository, make changes, and send a pull request to the core team. However, the lifetime of a pull request, defined by the time interval between its opening and its closing, has a high variation, potentially affecting the contributor engagement. In this context, understanding the root causes of pull request lifetime is important to both the external contributors and the core team. The former can adopt strategies that increase the chances of fast review, while the latter can establish priorities in the reviewing process, alleviating the pending tasks and improving the software quality. In this work, we mined association rules from 97,463 pull requests from 30 projects in order to find characteristics that have affected the pull requests lifetime. In addition, we present a qualitative analysis, helping to understand the patterns discovered from the association rules. The results indicate that: (i) contributions with shorter lifetimes tend to be accepted; (ii) structural characteristics, such as number of commits, changed files, and lines of code, have influence, in an isolated or combined way, on the pull request lifetime; (iii) the files changed and the directories to which they belong can be robust predictors for pull request lifetime; (iv) the profile of external contributors and their social relationships have influence on lifetime; and (v) the number of comments in a pull request, as well as the developer responsible for the review, are important predictors for its lifetime.
Learning and Updating Node Embedding on Dynamic Heterogeneous Information Network

Heterogeneous information networks consist of multiple types of edges and nodes, which have a strong ability to represent the rich semantics underpinning network structures. Recently, the dynamics of networks has been studied in many tasks such as social media analysis and recommender systems. However, existing methods mainly focus on the static networks or dynamic homogeneous networks, which are incapable or inefficient in modeling dynamic heterogeneous information networks. In this paper, we propose a method named Dynamic Heterogeneous Information Network Embedding (DyHINE), which can update embeddings when the network evolves. The method contains two key designs: (1) A dynamic time-series embedding module which employs a hierarchical attention mechanism to aggregate neighbor features and temporal random walks to capture dynamic interactions; (2) An online real-time updating module which efficiently updates the computed embeddings via a dynamic operator. Experiments on three real-world datasets demonstrate the effectiveness of our model compared with state-of-the-art methods on the task of temporal link prediction.
A Fast Path Planning Algorithm Fusing PRM and P-Bi-RRT

The path planned by the rapidly expanding random tree (RRT) algorithm is tortuous and the planning speed of RRT algorithm is slow. The path planning failure rate of the probabilistic roadmap (PRM) algorithm is high in complex scenarios. To solve these problems, this paper proposes a fusion algorithm fusing PRM and probability-based bidirectional RRT (P-Bi-RRT), which divides the planning area into two areas on average, and uses the PRM algorithm with faster planning speed for path pre-planning in each area, and then selects a node in each of the two areas to form a pair of optimal matching-points. The optimal matching-points is used to connect the two regional paths with the P-Bi-RRT algorithm or method of barrier-free direct connection. Finally, the planned path is optimized twice, the redundant nodes are trimmed and the path length is shortened. After a large number of simulation experiments, it is shown that the time of path planning is reduced by about 71% to 80%, the number of nodes is reduced by 70% to 80%, and the path length is shortened by 20% to 30% compared with the RRT algorithm.
Sledge: Towards Efficient Live Migration of Docker Containers

Modern large-scale cloud platforms require live migration technique on Docker containers with stateful workload to support load balancing, host maintenance, and Quality of Service (QoS) improvement. Efficient and scalable Docker live migration is expected to guarantee the component-integrity (image, runtime, and management context) with negligible downtime. In this paper, we present a highly efficient live migration system called Sledge, which ensures the component-integrity by integrating both images and management context during runtime migration. The key insight is that the layered image can be leveraged to reduce the migration overhead, and appropriately selective migration of management context will effectively improve QoS with negligible downtime. To achieve good scalability, a lightweight container registry mechanism for end-to-end image migration is designed to avoid the redundant layers transmission. In addition, a dynamic context loading scheme is proposed to precisely load the management context into the running daemon, which can significantly reduce downtime. Experiments show that, compared with the state-of-the-art, Sledge reduces 57% of total migration time, 55% of image migration time, and 70% downtime.
Optical image encryption using different twiddle factors in the butterfly algorithm of fast Fourier transform

We studied the flow of data in the paths of a fast Fourier transform (FFT) and inverse FFT in the butterfly algorithm in order to establish a relationship between the forward and backward data. We show that an FFT image can be encrypted by modifying the data of a path of FFT and decrypted by restoring the data during the inverse FFT. For encryption, the data in a path of FFT are shuffled by enforcing right bit shifts on the bit representation of the data index. Moreover, the phases of the data are changed by multiples of a fixed fraction of 2π; the data index is used as the multiplication constant, and the divisor for the fraction is used as an encryption key. The path number and number of right bit shifts are two other encryption keys. The three integers constitute a set of encryption keys for two-dimensional images. Four different images were encrypted with two sets of encryption keys. The decryptions were performed with partially correct keys in order to test the robustness of our method against brute attacks. The correlation between the original and decrypted images was evaluated using the correlation coefficient.
Picking BERT’s Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis

As the name implies, contextualized representations of language are typically motivated by their ability to encode context.Which aspects of context are captured by such representations?We introduce an approach to address this question using Representational Similarity Analysis (RSA).As case studies, we investigate the degree to which a verb embedding encodes the verb's subject, a pronoun embedding encodes the pronoun's antecedent, and a full-sentence representation encodes the sentence's head word (as determined by a dependency parse).In all cases, we show that BERT's contextualized embeddings reflect the linguistic dependency being studied, and that BERT encodes these dependencies to a greater degree than it encodes less linguistically-salient controls.These results demonstrate the ability of our approach to adjudicate between hypotheses about which aspects of context are encoded in representations of language.
An Efficient Algorithm for Maneuvering Target Tracking [Tips &amp;amp; Tricks]

Maneuvering target tracking is an important technology in engineering applications [1]-[3]. The traditional methodologies for designing it can be divided into two categories: modelbased and model-free algorithms. Almost all tracking algorithms are model based. The main idea behind modelbased tracking algorithms is to choose a representation that fits the actual state trajectories of the target movement and then to estimate the state based on the noisy observations recorded by sensors. The Kalman filter and its extensions are the most popular methods to estimate the state of a system. However, the stability and convergence rate of these algorithms depend directly on the accurate initial state estimation, unknown parameters, and covariance matrices of the process and measurement noise.
An Augmented Reality Platform for training in the industrial context

Augmented Reality (AR) is considered within the Internet of Things (IIoT) goals as a training tool because it provides virtual experiences that are impossible in a real environment, giving to the employee safety on equipment operation and low-cost of training. For this reason, this research presents an AR system that allows users to train in the calibration of HART instrumentation, using the Unity 3D platform and Meta 2 glasses. This application offers a platform that has advantages for users since it can train in the operation of expensive industrial equipment in an error-prone system. The validation of the system was carried out through an evaluation module, which allows the user to determine the level of training and autonomously build their own learning, obtaining positive responses that show that around 82% of the population evaluated.
Adversarial and Auxiliary Features-Aware BERT for Sarcasm Detection

Sarcasm is a way to express a negative viewpoint using positive or intensified positive words in social media. This intentional ambiguity makes sarcasm detection, a key task of sentiment analysis. Sarcasm detection is modeled as a binary classification problem wherein both feature-rich traditional models and deep learning models have been successfully built to detect sarcastic comments. To help deep learning models generalize better huge labeled data collection is required, which can be laborious and time-consuming. An adversarial process which is carried out in the embedding space provides an alternative mechanism to generate real-world like examples. However, these examples are not real sentences, but act as a regularization method and can make neural networks more robust. In this paper, we present a novel model, Adversarial and Auxiliary Features-Aware BERT (AAFAB), which utilizes contextual word embedding (BERT) for encoding the semantic meaning of the sentence and then combine it with high quality manually extracted auxiliary features for sarcasm detection. Further, adversarial training is performed by adding perturbations to the input word embedding which enables AAFAB to generalize parameters in a better way. The experiment results show that the inclusion of auxiliary features and adversarial training enhances the performance of AAFAB, and it performs better than various baseline traditional models and deep learning models.
Retrieval-enhanced adversarial training with dynamic memory-augmented attention for image paragraph captioning

Existing image paragraph captioning methods generate long paragraph captions solely from input images, relying on insufficient information. In this paper, we propose a retrieval-enhanced adversarial training with dynamic memory-augmented attention for image paragraph captioning (RAMP), which makes full use of the R-best retrieved candidate captions to enhance the image paragraph captioning via adversarial training. Concretely, RAMP treats the retrieved captions as reference captions to augment the discriminator during adversarial training, encouraging the image captioning model (generator) to incorporate informative content in retrieved captions into the generated caption. In addition, a retrieval-enhanced dynamic memory-augmented attention network is devised to keep track of the coverage information and attention history along with the update-chain of the decoder state, and therefore avoiding generating repetitive or incomplete image descriptions. Finally, a copying mechanism is applied to select words from the retrieved candidate captions, which are then put into the proper positions of the target caption so as to improve the fluency and informativeness of the generated caption. Extensive experiments on a benchmark dataset (i.e., Stanford) demonstrate that the proposed RAMP model significantly outperforms the state-of-the-art methods across multiple evaluation metrics. For reproducibility, we submit the code and data at https://github.com/anonymous-caption/RAMP.
Study of Process-Focused Assessment Using an Algorithm for Facial Expression Recognition Based on a Deep Neural Network Model

This study proposes an approach for process-focused assessment (PFA) utilizing the concept of deep neural networks with a sequence of facial images. Recently, process-based assessment has received significant attention compared to result-based assessment in the field of education. Continuously evaluating and quantifying student engagement, as well as understanding and interacting with teachers in study activities are considered important factors. However, to achieve PFA, from the technical and systematic perspectives, the real-time monitoring of the learning process of students is desired, which requires time consumption and extremely high attention to each student. This study proposes an approach to develop an efficient method for evaluating the process of learning and studying students in real time using facial images. We developed a method for PFA by learning facial expressions using a deep neural network model. The model learns and classifies facial expressions into three categories: easy, neutral, and difficult. Because the demand for online learning is increasing, PFA is required to achieve efficient, convenient, and confident assessment. This study chiefly considers a sequence of 2D image data of students solving some exam problems. The experimental results demonstrate that the proposed approach is feasible and can be applied to PFA in classrooms.
Recognition of basketball players’ action detection based on visual image and Harris corner extraction algorithm

At present, there are efficiency problems in related algorithms for athlete detection and recognition. Based on this, this study analyzes the characteristics of athletes’ sports process. In this study, the Otsu method was used to perform grayscale feature processing. At the same time, based on the Harris corner extraction algorithm, this study proposes that the multi-target tracking combined with the corner feature of the target can be used to track different parts of the athlete as different target areas. In addition, this study uses a sequential algorithm to perform connected component labeling. Finally, in order to test the performance and recognition efficiency of the proposed algorithm, the performance of the algorithm is explored through experimental analysis. The research shows that the algorithm has good performance and has certain practical effects, and it has certain reference significance for subsequent related research.
Quantum Graphs as Quantum Relations

Abstract The “noncommutative graphs” which arise in quantum error correction are a special case of the quantum relations introduced in Weaver (Quantum relations. Mem Am Math Soc 215(v–vi):81–140, 2012). We use this perspective to interpret the Knill–Laflamme error-correction conditions (Knill and Laflamme in Theory of quantum error-correcting codes. Phys Rev A 55:900-911, 1997) in terms of graph-theoretic independence, to give intrinsic characterizations of Stahlke’s noncommutative graph homomorphisms (Stahlke in Quantum zero-error source-channel coding and non-commutative graph theory. IEEE Trans Inf Theory 62:554–577, 2016) and Duan, Severini, and Winter’s noncommutative bipartite graphs (Duan et al., op. cit. in Zero-error communication via quantum channels, noncommutative graphs, and a quantum Lovász number. IEEE Trans Inf Theory 59:1164–1174, 2013), and to realize the noncommutative confusability graph associated to a quantum channel (Duan et al., op. cit. in Zero-error communication via quantum channels, noncommutative graphs, and a quantum Lovász number. IEEE Trans Inf Theory 59:1164–1174, 2013) as the pullback of a diagonal relation. Our framework includes as special cases not only purely classical and purely quantum information theory, but also the “mixed” setting which arises in quantum systems obeying superselection rules. Thus we are able to define noncommutative confusability graphs, give error correction conditions, and so on, for such systems. This could have practical value, as superselection constraints on information encoding can be physically realistic.
Robust Driver Head Pose Estimation in Naturalistic Conditions from Point-Cloud Data

Head pose estimation has been a key task in computer vision since a broad range of applications often requires accurate information about the orientation of the head. Achieving this goal with regular RGB cameras faces challenges in automotive applications due to occlusions, extreme head poses and sudden changes in illumination. Most of these challenges can be attenuated with algorithms relying on depth cameras. This paper proposes a novel point-cloud based deep learning approach to estimate the driver's head pose from depth camera data, addressing these challenges. The proposed algorithm is inspired by the PointNet++ framework, where points are sampled and grouped before extracting discriminative features. We demonstrate the effectiveness of our algorithm by evaluating our approach on a naturalistic driving database consisting of 22 drivers, where the benchmark for the orientation of the driver's head is obtained with the Fi-Cap device. The experimental evaluation demonstrates that our proposed approach relying on point-cloud data achieves predictions that are almost always more reliable than state-of-the-art head pose estimation methods based on regular cameras. Furthermore, our approach provides predictions even for extreme rotations, which is not the case for the baseline methods. To the best of our knowledge, this is the first study to propose head pose estimation using deep learning on point-cloud data.
End-to-End Computer Vision Framework

Computer Vision is a cross research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. In addition to research on techniques and algorithms that enhance the capabilities of a Computer Vision system, research on the architecture and design of such systems is also important. In this paper we present an End-to-End Computer Vision Framework open-source solution, based on Python programming language, that aims to support researchers in this field. The main focus of the framework is the configurability and scalability of the system in the continuous need to add new Computer Vision algorithms or machine learning models for a day-to-day research activity.
AgentDSM-Eval: A framework for the evaluation of domain-specific modeling languages for multi-agent systems

Software development required for constructing multi-agent systems (MAS) usually becomes challenging and time-consuming due to the properties of autonomy, distributedness, and openness of these systems in addition to the complicated nature of internal agent behaviors and agent interactions. To facilitate MAS development, the researchers propose various domain-specific modeling languages (DSMLs) by enriching MAS metamodels with a defined syntax and semantics. Although the descriptions of these languages are given in the related studies with the examples of their use, unfortunately, many are not evaluated in terms of either the usability (being hard to learn, understand and use) or the quality of the generated artifacts. Hence, in this paper, we introduce an evaluation framework, called AgentDSM-Eval, with its supporting tool which can be used to evaluate MAS DSMLs systematically according to various quantitative and qualitative aspects of agent software development. The empirical evaluation, presented by the AgentDSM-Eval framework, was successfully applied for one of the well-known MAS DSMLs. The assessment showed that both MAS domain coverage of DSMLs and the agent developers’ adoption of modeling elements can be determined with this framework. Moreover, the tool's quantitative results can assess MAS DSML's performance on the development time and throughput. AgentDSM-Eval also enables the qualitative assessment of MAS DSML features according to novel quality characteristics and measures, which it defines specifically for the MAS domain.
Bicomplex-valued twin-hyperbolic Hopfield neural networks

A complex-valued multistate Hopfield neural network (CMHNN) is a multistate model of a Hopfield neural network and has been applied as a multistate neural associative memory. CMHNNs require many resources for weight parameters. To reduce the number of weight parameters, twin-multistate activation functions were introduced. A quaternion-valued twin-multistate Hopfield neural network (QTMHNN) is the first model to employ a twin-multistate activation function. A bicomplex-valued twin-multistate Hopfield neural network (BTMHNN) was also introduced. Weak noise tolerance is a disadvantage of QTMHNNs and BTMHNNs. To improve the noise tolerance, a BTMHNN can be modified to a bicomplex-valued twin–hyperbolic Hopfield neural network (BTHHNN). A BTMHNN is defined by the decomposition of a bicomplex number to a pair of complex numbers, whereas a BTHHNN is defined by decomposition of a bicomplex number to a pair of hyperbolic numbers. Computer simulations have improved the noise tolerance of BTHHNNs.