Reviewerly/exHarmony huggingface dataset:
Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving objects in the image space and the minimum circumscribed rectangle (BLOB) of moving objects in the geographic space to calculate the projected size of moving objects in the image space, by which we can set an adaptive threshold for each moving object to precisely remove the noise interference during moving object detection. Further, we propose a moving object detection algorithm called GMM_BLOB (GMM denotes Gaussian mixture model) to achieve high-precision detection and noise removal of moving objects. The case-study results show the following: (1) Compared with the existing object detection algorithm, the median error (MD) of the MOD-AT algorithm is reduced by 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, indicating that the accuracy of the MOD-AT algorithm is higher in single-frame detection; (2) in terms of overall accuracy, the performance and time efficiency of the MOD-AT algorithm is improved by 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.
Potential of Machine Learning Based Support Vector Regression for Solar Radiation Prediction

Abstract Measurements of the solar radiation quantities profoundly affect on the energy output ratios. A decline in solar radiation measurements in many countries, which is due to reasons high cost, difficulty of measurement that necessitated developing different methods to estimate the proportion of solar radiation. Many empirical models have been developed using special variables and coefficients, such as Angstrom and Prescott models. The development of machine-learning algorithms makes these algorithms as a possible application instead of the empirical models to decrease the error rate and obtaining better results. In this paper, radial basis function is applied as the kernel function of support vector regression (SVR) method to calculate the amount of monthly average daily of the global solar radiation in four sites in Egypt. Five variables used as input (sunshine duration, air temperature, relative humidity, solar declination angle and extraterrestrial solar radiation). The experimental results have a good estimation in all locations according to root mean square error, however, this study proved that SVR models can be as an efficient machine-learning technique with a higher accuracy.
Sequential Dependency Enhanced Graph Neural Networks for Session-based Recommendations

Session-based recommendations (SBR) play an important role in many real-world applications, such as e-commerce and media streaming. To perform accurate session-based recommendations, it is crucial to capture both sequential dependencies over a sequence of adjacent items and complex item transitions over a set of items within sessions. Note that item transitions are not necessarily dependent on sequential dependencies, e.g., the transition from one item to the other distant item in a session is often not sequential. However, almost all the existing session-based recommender systems (SBRS) fail to consider both kinds of information, which leads to their limited performance improvement. Aiming at this deficiency, we propose a novel sequential dependency enhanced graph neural network (SDE-GNN) to capture both sequential dependencies and item transition relations over items within sessions for more accurate next-item recommendations. Specifically, we first devise a sequential dependency learning module to capture the sequential dependencies over a sequence of adjacent items in each session. Then, we propose an item transition learning module to capture complex transitions between items. In the module, a novel residual gate and a specialized attention mechanism are integrated into gate-GNN to build an attention augmented GNN, called AU-GNN. Finally, we devise a gated fusion component to combine the learned sequential dependencies and item transitions together in preparation for the subsequent next-item recommendations. Exhaustive experiments on two public real-world data sets demonstrate the superiority of SDE-GNN over the state-of-the-art methods.
DTaxa: An actor–critic for automatic taxonomy induction

Automatic taxonomy induction is a challenging task in the field of natural language understanding (NLU) and information retrieval (IR) because it requires machine learning and understanding the is-a relation (i.e. hypernym relation) between term pairs. Therefore, the deep taxa (DTaxa) based on the actor–critic algorithm framework is designed to deal with the aforementioned problems in this paper. The agent in the DTaxa regards the taxonomy induction process as the sequential decision steps so that the agent can take the operation of a term as an action via the policy network to jointly optimize hypernym detection and hypernym organization. Meanwhile, the DTaxa obtains a stable performance from the experiences buffered in the memory. In order to verify the effectiveness of the DTaxa for the automatic taxonomy induction, two experiments are performed on the all bottomed-out full subtrees extracted from WordNet 3.0 and the English environment and science taxonomies in the SemEval-2016 task 13, respectively. The proposed method outperforms these existing methods and achieves state-of-the-art among most metrics.
From Swarms to Hyperswarms: A New Methodology for Amplifying Group Intelligence

Swarm Intelligence (SI) is a biological phenomenon that enables social species to reach group decisions by converging as real-time systems. Studied in fish schools, bird flocks, and bee swarms, SI has been shown to greatly amplify group intelligence. Artificial Swarm Intelligence (ASI) is recently developed method that enables networked human groups, moderated by real-time swarming algorithms to achieve similar benefits. While ASI can significantly boost the accuracy of group forecasts, estimation, and decisions, the process can be brittle if a large majority supports an inaccurate view, even if their average confidence is low. Thus, a major focus of ASI research has been to increase resilience to low-confidence majorities. This paper introduces a new structure called a hyperswarm that enables a confident minority to more easily sway an unsure majority, enabling convergence on solutions that would otherwise be inaccessible. The approach involves dividing a population P into a set of overlapping subgroups (H1, H2 … HP) such that each member is only exposed to the real-time behaviors of their subgroup. And because each subgroup overlaps multiple other subgroups, interactions can quickly propagate across the full population. In this paper we simulate hyperswarm dynamics, showing that a confident minority can sway a less confident majority in real time, even when more than 65% of the participants initially harbor the majority view, so long as the minority has higher confidence. In addition, we derive theoretical guidelines to predict hyperswarm success based on population size, subgroup size, and confidence differential.
Gaussian quantum metrology in a dissipative environment

Quantum metrology pursues high-precision measurements of physical quantities by using quantum resources. However, the decoherence generally hinders its performance. Previous work found that the metrological error tends to diverge in the long-encoding-time regime due to the Born-Markovian approximate decoherence, which is called the no-go theorem of noisy quantum metrology. Here we propose a Gaussian quantum metrology scheme using bimodal quantized optical fields as the quantum probe. It achieves the precision of a sub-Heisenberg limit in the ideal case. However, the Markovian decoherence causes the metrological error contributed by the center-of-mass mode of the probe to be divergent. A mechanism to remove this ostensible no-go theorem is found in the non-Markovian dynamics. Our result gives an efficient way to realize high-precision quantum metrology in practical continuous-variable systems.
DeFFusion: CNN-based Continuous Authentication Using Deep Feature Fusion

Smartphones have become crucial and important in our daily life, but the security and privacy issues have been major concerns of smartphone users. In this article, we present DeFFusion, a CNN-based continuous authentication system using Deep Feature Fusion for smartphone users by leveraging the accelerometer and gyroscope ubiquitously built into smartphones. With the collected data, DeFFusion first converts the time domain data into frequency domain data using the fast Fourier transform and then inputs both of them into a designed CNN, respectively. With the CNN-extracted features, DeFFusion conducts the feature selection utilizing factor analysis and exploits balanced feature concatenation to fuse these deep features. Based on the one-class SVM classifier, DeFFusion authenticates current users as a legitimate user or an impostor. We evaluate the authentication performance of DeFFusion in terms of impact of training data size and time window size, accuracy comparison on different features over different classifiers and on different classifiers with the same CNN-extracted features, accuracy on unseen users, time efficiency, and comparison with representative authentication methods. The experimental results demonstrate that DeFFusion has the best accuracy by achieving the mean equal error rate of 1.00% in a 5-second time window size.
Matches Made in Heaven: Toolkit and Large-Scale Datasets for Supervised Query Reformulation

Researchers have already shown that it is possible to improve retrieval effectiveness through the systematic reformulation of users' queries. Traditionally, most query reformulation techniques relied on unsupervised approaches such as query expansion through pseudo-relevance feedback. More recently and with the increasing effectiveness of neural sequence-to-sequence architectures, the problem of query reformulation has been studied as a supervised query translation problem, which learns to rewrite a query into a more effective alternative. While quite effective in practice, such supervised query reformulation methods require a large number of training instances. In this paper, we present three large-scale query reformulation datasets, namely Diamond, Platinum and Gold datasets, based on the queries in the MS MARCO dataset. The Diamond dataset consists of over 188,000 query pairs where the original source query is matched with an alternative query that has a perfect retrieval effectiveness (an average precision of 1). To the best of our knowledge, this is the first set of datasets for supervised query reformulation that offers perfect query reformulations for a large number of queries. The implementation of our fully automated tool, which is based on a transformer architecture, and our three datasets are made publicly available. We also establish a neural query reformulation baseline performance on our datasets by reporting the performance of strong neural query reformulation baselines. It is our belief that our datasets will significantly impact the development of supervised query reformulation methods in the future.
VQA as a factoid question answering problem: A novel approach for knowledge-aware and explainable visual question answering

With recent advancements in machine perception and scene understanding, Visual Question Answering (VQA) has garnered much attraction from researchers in the direction of training neural models for jointly analyzing, grounding and reasoning over the multi-modal space of image visual context and natural language in order to answer natural language questions pertaining to the image contents. However, though recent works have achieved significant improvement over state-of-art models for answering questions that are answerable by solely referring to the visual context of the image, such models are often limited, being incapable of tackling questions involving external world knowledge beyond the visible contents. Though recently, research has been driven towards tackling external knowledge based VQA as well, there is significant room for improvement as limited studies exist in this area. Inspired by the aforementioned challenges involved, this paper is aimed at answering free form and open ended natural language questions, not limited to visual context of an image, but external world knowledge as well. With this motive, inspired by human cognitive abilities of comprehending and reasoning answers when given a set of facts, this paper proposes a novel model architecture to model VQA as a factoid question answering problem, leveraging state-of-the-art deep learning techniques for reasoning and inferring answers to free form questions, in an attempt of improving the state-of-art in open ended visual question answering.
ECQV-IBI: Identity-based identification with implicit certification

Identity-based identification (IBI) schemes allow a prover to provide entity identification, based on its unique identity. This paper provides the first non-trivial IBI scheme with implicit certification by using the Elliptic Curve Qu Vanstone (ECQV) implicit certification scheme. In contrast to the conventional identity-based schemes, the implicit certificate based approach is resistant against key escrow since the trusted authority only a part of the secret key, which is used as input by the user to construct its own user secret key. We show our scheme is able to achieve Trust Level 3 according to Girault’s definitions, while requiring less resources when compared to certificateless identification. A corresponding formal security model is defined, showing the resistance of our proposed scheme against impersonation attacks. Compared to other Schnorr-based IBI schemes, our proposed IBI scheme with implicit certification outperforms in storage, computation and communication efficiency and thus offers a viable solution to be applied in an internet of things (IoT) context.
Dissecting Social Engineering Attacks Through the Lenses of Cognition

In this paper we present, showcase, and analize a novel framework to dissect Social Engineering (SE) attacks. The framework is based on extant theories in the cognitive sciences, and is meant as an instrument for researchers and practitioners alike to structure and analyze SE attacks of varying sophistication, isolating specific features and their effects at the cognitive level, and providing a common structure for comparisons across different attacks. We showcase the framework against attacks reproduced in the academic literature as well as against real (highly-targeted) SE attacks reported in the wild, isolating and relating effects and techniques adopted by the attackers to the target’s cognitive process. We discuss implications for research and practice of the proposed framework.
Towards a Policy Management Framework for Managing Interaction Behaviors in IoT Collectives

This paper proposes a policy management framework which we call the SANIJO framework. This framework comprises three different types of policy rules that are applicable to smart devices for managing their multiuser–multidevice interactions in IoT collectives, from a socio-ethical perspective. We developed a policy language to help regulate and manage the interaction behaviors of smart internet-connected devices that are being deployed at an increasing rate around the world. The policy rules are classified into Authorization, Obligation, and Prohibition rules and are prototyped in the SANIJO system. We implemented our framework as a collection of mobile apps (running on smartphones) and a robot app (running on the robot). We then illustrate its operation based on an aged care center scenario.
Robustness of Speech Spoofing Detectors Against Adversarial Post-Processing of Voice Conversion

With the development of speech synthesis and voice conversion techniques, the quality of artificially generated speech has been significantly improved and detecting such spoofing speech becomes crucial to practical applications, such as automatic speaker verification (ASV). State-of-the-art neural-network-based spoofing detection models can distinguish most artificial utterances from natural ones effectively in the latest ASVspoof 2019 evaluation. Motivated by recent progresses of adversarial example generation, this paper studies the robustness of neural-network-based speech spoofing detectors against adversarial attacks. To this end, an adversarial post-processing network (APN) is proposed which generates adversarial examples against a white-box anti-spoofing model by post-processing the speech waveforms produced by a baseline voice conversion system. Experimental results demonstrate the adversarial ability of our proposed APNs against the white-box anti-spoofing models which were used as the adversarial targets of APNs at the training stage. For example, the equal error rate (EER) of a fused detection model based on light convolution neural networks (LCNNs) increased from 0.278% to 12.743% under the white-box condition without degrading the subjective quality of converted speech. Furthermore, the trained APNs can also perform against the detectors with either unseen structures or unseen features by raising their EERs in our experiments. All these results indicate the threat of adversarial speech generation to the performance of state-of-the-art spoofing detection models.
A Unified Approach for Tracking UAVs in Infrared

With complex camera and object movement, the tracked object often suffers camera motion, out of view, dramatic scale variation, etc., which severely influence tracking performance. Due to the fast speed and tiny size of unmanned aerial vehicles(UAV), it is crucial to design a robust framework for tracking UAVs. This paper carefully designs a unified framework, including a local tracker, camera motion estimation module, bounding box refinement module, re-detection module and model updater. The camera motion estimation module achieves motion compensation for the local tracker. Then, the bounding box refinement module aims to measure an accurate bounding box. If the target is missing, we switch to the re-detection module to re-localize the target when it reappears. We also adopt a model updater to control the updating process and filter out unreliable samples. Numerous experimental results on 9 visual/thermal datasets show the effectiveness and generalization of our framework.
Correlation Based Breast Cancer Detection using Machine Learning

Breast cancer is mostly identified in women and the statistics confirms that the mortality rate of women's due to breast cancer tremendously increasing. Even though the cancer may lead to death, but early prediction may result as a curable disease. Early prediction of breast cancer may lead to survival of patients. During prediction of any disease there is always some missing of accuracy and wrong prediction occurs. But the accurate classification can prevent the patient from unnecessary deaths and illness. This paper mainly focuses on the Machine learning (ML) algorithm. This algorithm is mainly used for classification of the dataset and patterns of various modelling. The primary data set of breast cancer is procured out of Wisconsin Breast Cancer Dataset (WBCD), which is one dimensional implemented for classification purpose of different algorithm.
MPDS-RCA: Multi-level privacy-preserving data sharing for resisting collusion attacks based on an integration of CP-ABE and LDP

In the ciphertext-policy attribute-based encryption (CP-ABE), once malicious users have gained access to the sharing data, they are able to obtain real private data, leading to serious privacy leakage issues. Thus, if the data user does not access the original private data, but instead accesses the perturbed data, while guaranteeing certain statistical characteristics, this would greatly improve the privacy of the CP-ABE technology as well as enhance its security. Motivated by this, an integration of basic CP-ABE and local differential privacy (LDP) based on randomized response for achieving multi-level privacy-preserving data sharing (MPDS), which has a relatively lower complexity and higher data utility, is constructed to provide double privacy protection for data owners at the source. To prevent different trusted data users from colluding and gaining more privacy beyond their trust levels, a random perturbation strategy is elaborately designed for resisting collusion attacks (RCA) while guaranteeing that the output of RCA perturbation strategy is the same as that of the original perturbation, which has been proved from the theoretical level. Finally, the proposed MPDS-RCA scheme is simulated and verified on both synthetic and real data sets, which indicates that the proposed scheme outperforms the existing MPPDS scheme in terms of the average error rate, the KL-divergence and the JS-divergence while reducing the computational cost by approximately 50%.
Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition

Skeleton data carries valuable motion information and is widely explored in human action recognition. However, not only the motion information but also the interaction with the environment provides discriminative cues to recognize the action of persons. In this paper, we propose a joint learning framework for mutually assisted “interacted object localization” and “human action recognition” based on skeleton data. The two tasks are serialized together and collaborate to promote each other, where preliminary action type derived from skeleton alone helps improve interacted object localization, which in turn provides valuable cues for the final human action recognition. Besides, we explore the temporal consistency of interacted object as constraint to better localize the interacted object with the absence of ground-truth labels. Extensive experiments on the datasets of SYSU-3D, NTU60 RGB+D, Northwestern-UCLA and UAV-Human show that our method achieves the best or competitive performance with the state-of-the-art methods for human action recognition. Visualization results show that our method can also provide reasonable interacted object localization results.