Reviewerly/exHarmony huggingface dataset:
SAN: Selective Alignment Network for Cross-Domain Pedestrian Detection

Cross-domain pedestrian detection, which has been attracting much attention, assumes that the training and test images are drawn from different data distributions. Existing methods focus on aligning the descriptions of whole candidate instances between source and target domains. Since there exists a giant visual difference among the candidate instances, aligning whole candidate instances between two domains cannot overcome the inter-instance difference. Compared with aligning the whole candidate instances, we consider that aligning each type of instances separately is a more reasonable manner. Therefore, we propose a novel Selective Alignment Network for cross-domain pedestrian detection, which consists of three components: a Base Detector, an Image-Level Adaptation Network, and an Instance-Level Adaptation Network. The Image-Level Adaptation Network and Instance-Level Adaptation Network can be regarded as the global-level and local-level alignments, respectively. Similar to the Faster R-CNN, the Base Detector, which is composed of a Feature module, an RPN module and a Detection module, is used to infer a robust pedestrian detector with the annotated source data. Once obtaining the image description extracted by the Feature module, the Image-Level Adaptation Network is proposed to align the image description with an adversarial domain classifier. Given the candidate proposals generated by the RPN module, the Instance-Level Adaptation Network firstly clusters the source candidate proposals into several groups according to their visual features, and thus generates the pseudo label for each candidate proposal. After generating the pseudo labels, we align the source and target domains by maximizing and minimizing the discrepancy between the prediction of two classifiers iteratively. Extensive evaluations on several benchmarks demonstrate the effectiveness of the proposed approach for cross-domain pedestrian detection.
Crowd Scene Analysis Encounters High Density and Scale Variation

Crowd scene analysis receives growing attention due to its wide applications. Grasping the accurate crowd location is important for identifying high-risk regions. In this article, we propose a Compressed Sensing based Output Encoding (CSOE) scheme, which casts detecting pixel coordinates of small objects into a task of signal regression in encoding signal space. To prevent gradient vanishing, we derive our own sparse reconstruction backpropagation rule that is adaptive to distinct implementations of sparse reconstruction and makes the whole model end-to-end trainable. With the support of CSOE and the backpropagation rule, the proposed method shows more robustness to deep model training error, which is especially harmful to crowd counting and localization. The proposed method achieves state-of-the-art performance across four mainstream datasets, especially achieves excellent results in highly crowded scenes. A series of analysis and experiments support our claim that regression in CSOE space is better than traditionally detecting coordinates of small objects in pixel space for highly crowded scenes.
Ethnic Diversity and Inequality in Sub-Saharan Africa: Do Institutions Reduce the Noise?

Studies on the causes of income differences between the rich and the poor have received an extensive attention in the inequality empirics. While ethnic diversity has also been identified as one of the fundamental causes of income inequality, the role of institutions as a mediating factor in the ethnicity-inequality nexus has not received the scholarly attention it deserves. To this end, this study complements the existing literature by investigating the extent to which institutional framework corrects the noisy influence originating from the nexus between "ethnic diversity" and inequality in 26 sub-Saharan African countries for the period 1996–2015. The empirical evidence is based on pooled OLS, fixed effects and system GMM estimators. The main findings reveal that the mediating influences of institutional settings are defective, thus making it extremely difficult to modulate the noisy impacts of ethno-linguistic and religious heterogeneity on inequality. In addition, the negative influences orchestrated by ethno-linguistic and religious diversities on inequality fail to attenuate the impact of income disparity even when interacted with institutions. On the policy front, institutional reforms tailored toward economic, political and institutional governances should be targeted.
Dynamically Feasible Task Space Planning for Underactuated Aerial Manipulators

In this work, we address the problem of planning dynamically feasible trajectories for underactuated aerial manipulators to achieve a desired trajectory for the end effector. We consider a quadrotor equipped with an arbitrary n-joint articulated arm. We show that the combined underactuated system is differentially flat, however the flat outputs do not correspond directly to the motion of the end effector. We therefore develop a method which determines the family of flat output trajectories which will exactly produce any desired task trajectory, even in the case of dynamic maneuvers. We also give criteria on the manipulator geometry which will ensure certain important stability properties, informing hardware design. The entire approach is demonstrated in simulation for systems of varying geometry and number of joints. The simultaneous resolution of the kinematic and dynamic constraints allows these tasks to be performed dynamically without sacrificing accuracy.
HHARNet: Taking inspiration from Inception and Dense Networks for Human Activity Recognition using Inertial Sensors

Human Activity Recognition (HAR) is an important area of research in the light of enormous applications that it provides, such as health monitoring, sports, entertainment, efficient human-computer interface, child care, education, and many more. The use of Computer Vision for Human Activity Recognition has many limitations. The use of inertial sensors which include an accelerometer and gyroscopic sensors for HAR is becoming the norm these days considering their benefits over traditional Computer Vision techniques. In this paper, we have proposed a l-dimensional Convolutions Neural Network which is inspired by two state-of-the-art architectures proposed for image classifications; namely Inception Net and Dense Net. We have evaluated its performance on two different publicly available datasets for HAR. Precision, Recall, Fl-measure, and accuracies are reported.
Sequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods

Hierarchy and leadership interactions commonly occur in animal groups, crowds of people, and in vehicle motions. Such interactions are often affected by one or more individuals who possess key domain information (e.g., final destination, environmental constraints, and best routes) or pertinent traits (e.g., better navigation, sensing, and decision making capabilities) compared with the rest of the group. This article presents a framework for the automatic identification of group structure and leadership from noisy sensory observations of tracked groups. Accordingly, a new leader-follower model is developed, which assumes the dynamics of the group to be a multivariate Ornstein-Uhlenbeck process with the designated leader(s) drifting to the destination and followers reverting to the leaders' state. Sequential Monte Carlo approaches, and specifically the sequential Markov chain Monte Carlo approach, are adopted to infer, probabilistically, the evolving leadership structure. A Rao-Blackwellisation scheme is employed such that the kinematic state of the objects in the group is inferred in closed form by Kalman filtering. Experiments show that the proposed techniques can successfully determine the leadership structures in challenging scenarios with a corresponding enhancement in tracking accuracy through direct consideration of the leadership interactions of the group.
The Economics of Cybercrime

Under what conditions is the Internet more likely to be used maliciously for criminal activity? This study examines the conditions under which the Internet is associated with cybercriminal offenses. Using comprehensive state-level data in the United States during 2004–2010, our findings show that there is no clear empirical evidence that the Internet penetration rate is related to the number of Internet crime perpetrators; however, cybercriminal activities are contingent upon socioeconomic factors and connection speed. Specifically, a higher income, more education, a lower poverty rate, and a higher inequality are likely to make the Internet penetration be more positively related with cybercrime perpetrators, which are indeed different from the conditions of terrestrial crime in the real world. In addition, as opposed to narrowband, the broadband connections are significantly and positively associated with the number of Internet crime perpetrators, and it amplifies the aforementioned moderating effects of socioeconomic status on Internet crime offenses. Taken together, cybercrime requires more than just a skilled perpetrator, and it requires an infrastructure to facilitate profiteering from the act.
Software Defects Prediction using Machine Learning Algorithms

Software development and the maintenance life cycle are lengthy processes. However, the possibility of having defects in the software can be high. Software reliability and performance are essential measures of software success, which affects user satisfaction and software cost. Predicting software defects using machine learning (ML) algorithms is one approach in this direction. Implementing this approach in the earlier stages of the software development improves software performance quality and reduces software maintenance cost. Different models and techniques have been implemented in many studies to predict software defects. This investigation implements ML algorithms, such as artificial neural networks (ANNs), random forest (RF), random tree (RT), decision table (DT), linear regression (LR), gaussian processes (GP), SMOreg, and M5P. A new software defect prediction model for software future defect prediction is proposed. The defect prediction is based on historical data. The results showed that a combination of ML algorithms could be used effectively to predict software defects. The SMOreg classifier scored the best performance results, where the ANN classifier scores the worst results.
Sub-quantum Fisher information

The Quantum Fisher Information (QFI) plays a crucial role in quantum information theory and in many practical applications such as quantum metrology. However, computing the QFI is generally a computationally demanding task. In this work we analyze a lower bound on the QFI which we call the sub-Quantum Fisher Information (sub-QFI). The bound can be efficiently estimated on a quantum computer for an $n$-qubit state using $2n$ qubits. The sub-QFI is based on the super-fidelity, an upper bound on Uhlmann's fidelity. We analyze the sub-QFI in the context of unitary families, where we derive several crucial properties including its geometrical interpretation. In particular, we prove that the QFI and the sub-QFI are maximized for the same optimal state, which implies that the sub-QFI is faithful to the QFI in the sense that both quantities share the same global extrema. Based on this faithfulness, the sub-QFI acts as an efficiently computable surrogate for the QFI for quantum sensing and quantum metrology applications. Finally, we provide additional meaning to the sub-QFI as a measure of coherence, asymmetry, and purity loss.
An Adaptive Multi-Population Optimization Algorithm for Global Continuous Optimization

Nowadays, there are various optimization problems that exact mathematical methods are not applicable. Metaheuristics are considered as efficient approaches for finding the solutions. Yet there are many real-world problems that consist of different properties. For instance, financial portfolio optimization may contain many dimensions for different sets of assets, which suggests the need of a more adaptive metaheuristic method for tackling such problems. However, few existing metaheuristics can achieve robust performance across these variable problems even though they may obtain impressive results in specific benchmark problems. In this paper, a metaheuristic named the Adaptive Multi-Population Optimization (AMPO) is proposed for continuous optimization. The algorithm hybridizes yet modifies several useful operations like mutation and memory retention from evolutionary algorithms and swarm intelligence (SI) techniques in a multi-population manner. Furthermore, the diverse control on multiple populations, solution cloning and reset operation are designed. Compared with other metaheuristics, the AMPO can attain an adaptive balance between the capabilities of exploration and exploitation for various optimization problems. To demonstrate its effectiveness, the AMPO is evaluated on 28 well-known benchmark functions. Also, the parameter sensitivity analysis and search behavior study are conducted. Finally, the AMPO is validated on its applicability through a portfolio optimization problem as a challenging example of real-world applications. The benchmark results show that the AMPO achieves a better performance than those of nine state-of-the-art metaheuristics including the IEEE CEC winning algorithms, recent SI and multi-population/hybrid metaheuristics. Besides, the AMPO can consistently produce a good performance in portfolio optimization.
Fighting fire with fire: A spatial–frequency ensemble relation network with generative adversarial learning for adversarial image classification

Adversarial images generated by generative adversarial networks are not close to any existing benign images, and contain nonrobust features that have been identified as critical to the robustness of a machine learning model. Since adversarial images have an underlying distribution that differs from normal images, these kinds of images can offer valuable features for training a robust model. To deal with these special features, we focus on a novel machine learning task of adversarial images classification, where adversarial images can be used to investigate the problem of classifying adversarial images themselves. In the setting of this novel task, adversarial images are the ONLY kind of data used in training and testing, rather than not just a set of testing images as usual. To this end, we propose a novel spatial–frequency ensemble relation network with generative adversarial learning. First, we present a spatial–frequency ensemble representation learning to extract the feature of training images. Second, we design a meta-learning-based relation model to gain the relationship between images. Third, to achieve a robust model, we utilize generative adversarial learning and transform the relationship into a Jacobian matrix. Finally, we design a discriminator model that determines whether an adversarial image is from the matching category or not. Experimental results demonstrate that our approach achieves significantly higher performance compared with other state-of-the-arts.
One-step multi-view subspace clustering with incomplete views

Multi-view clustering has gained soar attention in the field of data mining in recent years. Most multi-view clustering methods are considered to be complete for each view. However, in many applications, some views may contain missing instances, resulting in the incomplete multi-view learning problem. Incomplete views cannot be directly dealt with traditional multi-view clustering methods. In this paper, we propose a One-step multi-view subspace clustering with incomplete views (OMVSC-IV) method based on low-rank matrix factorization to overcome this problem. Specifically, the proposed algorithm uses low-rank matrix factorization to learn a consensus representation matrix, and then combined with the objective function of non-negative embedding and spectral embedding subspace clustering proposed in this paper. The whole process is established into the same objective function for joint optimization and requires no post-processing (e.g., K-means), thus avoiding the defect of being sensitive to initial values. Furthermore, the accuracy, parameter, sensitivity and convergence of the algorithm are studied by experiments. Experimental results on nine data sets show that the proposed algorithm performs superior to some of the latest algorithms.
Handling of large and heavy objects using a single mobile manipulator in combination with a roller board

This paper presents a method for autonomous loading, transportation, and unloading of large objects using a nonholonomic mobile manipulator. Here, the size of the transported object is considerably larger than the size of the mobile platform, which is made possible through the use of a roller board. In this way, the mobile manipulator can handle objects that exceed the manipulator’s payload. The robot can load and unload the object onto its platform using the differential kinematics of the system for a null space motion to maintain the object’s position in space. In order to localise the object, we apply 3D-perception using a depth-camera. While transporting the object to its destination, the robot is considered a tractor-trailer-wheeled system and can navigate using SLAM. Kinematic modelling and practical evaluation prove that the system can potentially take over arduous transportation tasks.
A State of the Art Survey and Research Directions on Blockchain Based Electronic Voting System

Elections by way of e-voting systems shall be accurate, correct, secure, and convenient. However, their implementation could be constrained by possible e-voting issues. Electronic voting has evolved as a replacement for paper ballots in order to eliminate redundancies and discrepancies. Over the last two decades, the experience demonstrates that security and privacy vulnerabilities have continued to function over time. Blockchain-based e-voting systems could reduce voter fraud and increase voter access. Blockchain was primarily used to develop e-voting applications owing to the end-to-end authentication benefits. Blockchain is a reasonably attractive alter-native for conventional e-voting systems with functionality such as distributed, non-repudiation, and security. This article highlights some Blockchain-based e-voting systems implementations analyses of current e-voting structures, the decentralized voting mechanisms possible centered on Blockchain, and the approach’s potential benefits and challenges.
Learning Mealy Machines with One Timer

We present Mealy machines with a single timer (MM1Ts), a class of models that is both sufficiently expressive to describe the real-time behavior of many realistic applications, and can be learned efficiently. We show how learning algorithms for MM1Ts can be obtained via a reduction to the problem of learning Mealy machines. We describe an implementation of an MM1T learner on top of LearnLib, and compare its performance with recent algorithms proposed by Aichernig et al. and An et al. on several realistic benchmarks.
Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition

Deep-Learning-based video recognition has shown promising improvements along with the development of large-scale datasets and spatiotemporal network architectures. In image recognition, learning spatially invariant features is a key factor in improving recognition performance and robustness. Data augmentation based on visual inductive priors, such as cropping, flipping, rotating, or photometric jittering, is a representative approach to achieve these features. Recent state-of-the-art recognition solutions have relied on modern data augmentation strategies that exploit a mixture of augmentation operations. In this study, we extend these strategies to the temporal dimension for videos to learn temporally invariant or temporally localizable features to cover temporal perturbations or complex actions in videos. Based on our novel temporal data augmentation algorithms, video recognition performances are improved using only a limited amount of training data compared to the spatial-only data augmentation algorithms, including the 1st Visual Inductive Priors (VIPriors) for data-efficient action recognition challenge. Furthermore, learned features are temporally localizable that cannot be achieved using spatial augmentation algorithms. Our source code is available at https://github.com/taeoh-kim/temporal_data_augmentation .
Variational point-obstacle avoidance on Riemannian manifolds

In this paper, we study variational point-obstacle avoidance problems on complete Riemannian manifolds. The problem consists of minimizing an energy functional depending on the velocity, covariant acceleration and a repulsive potential function used to avoid an static obstacle given by a point in the manifold, among a set of admissible curves. We derive the dynamical equations for stationary paths of the variational problem, in particular on compact connected Lie groups and Riemannian symmetric spaces. Numerical examples are presented to illustrate the proposed method.
Design of a Secure Blockchain-Based Smart IoV Architecture

Blockchain is developing rapidly in various domains for its security. Nowadays, one of the most crucial fundamental concerns is internet security. Blockchain is a novel solution to enhance the security of network applications. However, there are no precise frameworks to secure the Internet of Vehicle (IoV) using Blockchain technology. In this paper, a blockchain-based smart internet of vehicle (BSIoV) framework has been proposed due to the cooperative, collaborative, transparent, and secure characteristics of Blockchain. The main contribution of the proposed work is to connect vehicle-related authorities together to fix a secure and transparent vehicle-to-everything (V2X) communication through the peer-to-peer network connection and provide secure services to the intelligent transport systems. A key management strategy has been included to identify a vehicle in this proposed system. The proposed framework can also provide a significant solution for the data security and safety of the connected vehicles in blockchain network.
Influence, infrastructure, and recentering cybercrime policing: evaluating emerging approaches to online law enforcement through a market for cybercrime services

(2022). Influence, infrastructure, and recentering cybercrime policing: evaluating emerging approaches to online law enforcement through a market for cybercrime services. Policing and Society: Vol. 32, No. 1, pp. 103-124.
The utility of augmented reality in lateral skull base surgery: A preliminary report

To discuss the utility of augmented reality in lateral skull base surgery.Those undergoing lateral skull base surgery at our institution.Cerebellopontine angle tumor resection using an augmented reality interface.Ease of use, utility of, and future directions of augmented reality in lateral skull base surgery.Anecdotally we have found an augmented reality interface helpful in simulating cerebellopontine angle tumor resection as well as assisting in planning the incision and craniotomy.Augmented reality has the potential to be a useful adjunct in lateral skull base surgery, but more study is needed with large series.