Reviewerly/exHarmony huggingface dataset:
An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment

Falls are an important healthcare problem for vulnerable persons like seniors. Response to potential emergencies can be fastened timely detection and classification of falls. This paper addresses the detection of human falls using relevant pixel-based features reflecting variations in body shape. Specifically, the human body is divided into five partitions that correspond to five partial occupancy areas. For each frame, area ratios are calculated and used as input data for fall detection and classification. First, the detection of falls is addressed from a statistical point of view as an anomaly detection problem. Towards this end, an integrated approach merging a detection step with a classification step is proposed for enabling efficient human fall detection in a home environment. In this regard, an effective fall detection approach using generalized likelihood ratio (GLR) scheme is designed. However, a GLR scheme cannot discriminate between true falls and like-fall events, such as lying down. To mitigate this limitation, the support vector machine algorithm has been successfully applied on features of the detected fall to recognize the type of fall. Tests on two publicly available datasets show the effectiveness of the proposed approach to appropriately detecting and identifying falls. Compared with the neural network, k -nearest neighbor, decision tree and naïve Bayes procedures, the two steps approach achieved better detection performance.
HeavyKeeper: An Accurate Algorithm for Finding Top-$k$ Elephant Flows

Finding top-k elephant flows is a critical task in network traffic measurement, with many applications in congestion control, anomaly detection and traffic engineering. As the line rates keep increasing in today's networks, designing accurate and fast algorithms for online identification of elephant flows becomes more and more challenging. The prior algorithms are seriously limited in achieving accuracy under the constraints of heavy traffic and small on-chip memory in use. We observe that the basic strategies adopted by these algorithms either require significant space overhead to measure the sizes of all flows or incur significant inaccuracy when deciding which flows to keep track of. In this paper, we adopt a new strategy, called count-with-exponential-decay, to achieve space-accuracy balance by actively removing small flows through decaying, while minimizing the impact on large flows, so as to achieve high precision in finding top-k elephant flows. Moreover, the proposed algorithm called HeavyKeeper incurs small, constant processing overhead per packet and thus supports high line rates. Experimental results show that HeavyKeeper algorithm achieves 99.99% precision with a small memory size, and reduces the error by around 3 orders of magnitude on average compared to the state-of-the-art.
Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder

In this paper, we present a novel deep learning based method for video anomaly detection and localization. The key idea of our approach is that the latent space representations of normal samples are trained to accord with a specific prior distribution by the proposed deep neural network - Multivariate Gaussian Fully Convolution Adversarial Autoencoder (MGFC-AAE), while the latent representations of anomalies do not. In order to extract deep features from input samples as latent representations, a convolutional neural network (CNN) is employed for the encoder of the deep network. Based on the probability that the test sample is associated with the prior distribution, an energy-based method is applied to obtain its anomaly score. A two-stream framework is utilized to integrate the appearance and motion cues to achieve more comprehensive detection results, taking the gradient and optical flow patches as inputs for each stream. Besides, a multi-scale patch structure is put forward to handle the perspective of some video scenes. Experiments are conducted on three public datasets, results verify that our framework can accurately detect and locate abnormal objects in various video scenes, achieving competitive performance when compared with other state-of-the-art works.
DroneRF dataset: A dataset of drones for RF-based detection, classification and identification

Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put, have become increasingly valuable commodities. This article describes the DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone's communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. An example of how this dataset can be interpreted and used can be found in the related research article “RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database” (Al-Sa'd et al., 2019).
Towards Corner Case Detection for Autonomous Driving

The progress in autonomous driving is also due to the increased availability of vast amounts of training data for the underlying machine learning approaches. Machine learning systems are generally known to lack robustness, e.g., if the training data did rarely or not at all cover critical situations. The challenging task of corner case detection in video, which is also somehow related to unusual event or anomaly detection, aims at detecting these unusual situations, which could become critical, and to communicate this to the autonomous driving system (online use case). Such a system, however, could be also used in offline mode to screen vast amounts of data and select only the relevant situations for storing and (re)training machine learning algorithms. So far, the approaches for corner case detection have been limited to videos recorded from a fixed camera, mostly for security surveillance. In this paper, we provide a formal definition of a corner case and propose a system framework for both the online and the offline use case that can handle video signals from front cameras of a naturally moving vehicle and can output a corner case score.
Joey NMT: A Minimalist NMT Toolkit for Novices

We present Joey NMT, a minimalist neural machine translation toolkit based on PyTorch that is specifically designed for novices.Joey NMT provides many popular NMT features in a small and simple code base, so that novices can easily and quickly learn to use it and adapt it to their needs.Despite its focus on simplicity, Joey NMT supports classic architectures (RNNs, transformers), fast beam search, weight tying, and more, and achieves performance comparable to more complex toolkits on standard benchmarks.We evaluate the accessibility of our toolkit in a user study where novices with general knowledge about Pytorch and NMT and experts work through a self-contained Joey NMT tutorial, showing that novices perform almost as well as experts in a subsequent code quiz.Joey NMT is available at https://github.com/joeynmt/joeynmt.
MRFN: Multi-Receptive-Field Network for Fast and Accurate Single Image Super-Resolution

Recently, convolutional neural network (CNN) based models have shown great potential in the task of single image superresolution (SISR). However, many state-of-the-art SISR solutions are reproducing some tricks proven effective in other vision tasks, such as pursuing a deeper model. In this paper, we propose a new solution (named as Multi-Receptive-Field Network - MRFN), which outperforms existing SISR solutions in three different aspects. First, from receptive field: a novel multi-receptive-field (MRF) module is proposed to extract and fuse features in different receptive fields from local to global. Integrating these hierarchical features can generate better mappings on recovering high-fidelity details at different scales. Second, from network architectures: both dense skip connections and deep supervision are utilized to combine features from the current MRF module and preceding ones for training more representative features. Moreover, a deconvolution layer is embedded at the end of the network to avoid artificial priors induced by numerical data pre-processing (e.g., bicubic stretching), and speed up the restoration process. Finally, from error modeling: different from L1 and L2 loss functions, we proposed a novel two-parameter training loss called Weighted Huber loss function which can adaptively adjust the value of back-propagated derivative according to the residual value, thus fit the reconstruction error more effectively. Extensive qualitative and quantitative evaluation results on benchmark datasets demonstrate that our proposed MRFN can achieve more accurate recovering results than most state-of-the-art methods with significantly less complexity.
An enhanced quantum key distribution protocol for security authentication

Quantum Cryptography is revolutionary discovery in the field of network security. Quantum cryptography promises to provide sophisticated functionality for security issues but it also leads to unbelievable increment in computational parallelism which is helpful for potential cryptanalytic attacks. Some of the associated properties of Quantum Key Distribution protocol that provide security that is deficient for the shared key to be transmitted securely. The identity verification process attempts to maximize success in interpreting the EPR protocol for distribution of Quantum.
Learning Part-based Convolutional Features for Person Re-Identification

Part-level features offer fine granularity for pedestrian image description. In this article, we generally aim to learn discriminative part-informed feature for person re-identification. Our contribution is two-fold. First, we introduce a general part-level feature learning method, named Part-based Convolutional Baseline (PCB). Given an image input, it outputs a convolutional descriptor consisting of several part-level features. PCB is general in that it is able to accommodate several part partitioning strategies, including pose estimation, human parsing and uniform part partitioning. In experiment, we show that the learned descriptor has a significantly higher discriminative ability than the global descriptor. Second, based on PCB, we propose refined part pooling (RPP), which allows the parts to be more precisely located. Our idea is that pixels within a well-located part should be similar to each other while being dissimilar with pixels from other parts. We call it within-part consistency. When a pixel-wise feature vector in a part is more similar to some other part, it is then an outlier, indicating inappropriate partitioning. RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency. RPP requires no part labels and is trained in a weakly supervised manner. Experiment confirms that RPP allows PCB to gain another round of performance boost. For instance, on the Market-1501 dataset, we achieve (77.4+4.2) percent mAP and (92.3+1.5) percent rank-1 accuracy, a competitive performance with the state of the art.
Aspect-based sentiment analysis with gated alternate neural network

Aspect-based sentiment analysis (ABSA) is a type of fine-grained sentiment analysis. Previous work in ABSA is mostly based on recurrent neural networks (RNNs). However, RNNs employed in ABSA have some weaknesses, such as lacking position invariance and lacking sensitivity to local key patterns. Meanwhile, a convolutional neural network (CNN) addresses the limitations in RNN, but itself is weak at capturing long-distance dependency and modeling sequence information. Moreover, the attention mechanism employed in ABSA may introduce some noise that is detrimental to capturing important sentiment expressions. In this paper, we assume that a sentence consists of some sentiment clues, and a sentence clue consists of multiple words. Based on this, we propose a novel neural network structure, named the Gated Alternate Neural Network (GANN), to address the limitations mentioned above. In GANN, a specially designed module, named the Gate Truncation RNN (GTR), is used to learn informative aspect-dependent sentiment clue representations. In these representations, the relative distance between each context word and aspect target, the sequence information, and semantic dependency within a sentiment clue are concurrently encoded. To filter out noise, a gating mechanism is designed to control information flow to obtain more precise representations. Convolution and pooling mechanisms are employed to capture key local sentiment clue features and acquire the position invariance of features. To verify the effect and generalization of GANN, we conducted abundant experiments on four Chinese and three English datasets. The experimental results show that GANN achieves state-of-the-art results and indicate that our proposed model is language-independent.
Development and applications of an intelligent crow search algorithm based on opposition based learning

Metaheuristics are proven beneficial tools for solving complex, hard optimization problems. Recently, a plethora of work has been reported on bio inspired optimization algorithms. These algorithms are mimicry of behavior of animals, plants and processes into mathematical paradigms. With these developments, a new entrant in this group is Crow Search Algorithm (CSA). CSA is based on the strategic behavior of crows while searching food, thievery and chasing behavior. This algorithm sometimes suffers with local minima stagnation and unbalance exploration and exploitation phases. To overcome this problem, a cosine function is proposed first, to accelerate the exploration and retard the exploitation process with due course of the iterative process. Secondly the opposition based learning concept is incorporated for enhancing the exploration virtue of CSA. The evolved variant with the inculcation of these two concepts is named as Intelligent Crow Search Algorithm (ICSA). The algorithm is benchmarked on two benchmark function sets, one is the set of 23 standard test functions and another is set of latest benchmark function CEC-2017. Further, the applicability of this variant is tested over structural design problem, frequency wave synthesis problem and Model Order Reduction (MOR). Results reveal that ICSA exhibits competitive performance on benchmarks and real applications when compared with some contemporary optimizers.
Adversarial attacks on an oblivious recommender

Can machine learning models be easily fooled? Despite the recent surge of interest in learned adversarial attacks in other domains, in the context of recommendation systems this question has mainly been answered using hand-engineered fake user profiles. This paper attempts to reduce this gap. We provide a formulation for learning to attack a recommender as a repeated general-sum game between two players, i.e., an adversary and a recommender oblivious to the adversary's existence. We consider the challenging case of poisoning attacks, which focus on the training phase of the recommender model. We generate adversarial user profiles targeting subsets of users or items, or generally the top-K recommendation quality. Moreover, we ensure that the adversarial user profiles remain unnoticeable by preserving proximity of the real user rating/interaction distribution to the adversarial fake user distribution. To cope with the challenge of the adversary not having access to the gradient of the recommender's objective with respect to the fake user profiles, we provide a non-trivial algorithm building upon zero-order optimization techniques. We offer a wide range of experiments, instantiating the proposed method for the case of the classic popular approach of a low-rank recommender, and illustrating the extent of the recommender's vulnerability to a variety of adversarial intents. These results can serve as a motivating point for more research into recommender defense strategies against machine learned attacks.
A novel modified bat algorithm hybridizing by differential evolution algorithm

The bat algorithm (BA) is one of the metaheuristic algorithms that are used to solve optimization problems. The differential evolution (DE) algorithm is also applied to optimization problems and has successful exploitation ability. In this study, an advanced modified BA (MBA) algorithm was initially proposed by making some modifications to improve the exploration and exploitation abilities of the BA. A hybrid system (MBADE), involving the use of the MBA in conjunction with the DE, was then suggested in order to further improve the exploitation potential and provide superior performance in various test problem clusters. The proposed hybrid system uses a common population, and the algorithm to be applied to the individual is selected on the basis of a probability value, which is calculated in accordance with the performance of the algorithms; thus, the probability of applying a successful algorithm is increased. The performance of the proposed method was tested on functions that have frequently been studied, such as classical benchmark functions, small-scale CEC 2005 benchmark functions, large-scale CEC 2010 benchmark functions, and CEC 2011 real-world problems. The obtained results were compared with the results obtained from the standard BA and other findings in the literature and interpreted by means of statistical tests. The developed hybrid system showed superior performance to the standard BA in all test problem sets and produced more acceptable results when compared to the published data for the existing algorithms. In addition, the contribution of the MBA and DE algorithms to the hybrid system was examined.
A fad or the future? Examining the effectiveness of virtual reality advertising in the hotel industry

New virtual reality technology presents hotels with wide-ranging marketing, training, and customer service opportunities. The purpose of this study was to explore both the immediate and delayed effects of hotel VR commercials as compared to traditional video commercials. Based on the perceptual load theory and elaboration likelihood model, this study designed and conducted a 2 × 2 laboratory experiment with two stages of data collection. The study demonstrated that VR commercials produced better immediate effects than the traditional commercials, especially for participants with high elaboration likelihood levels. However, the findings also revealed significant decreases in purchase intention among participants watching VR commercials and a significant improvement in attitudes among participants watching traditional commercials. Both theoretical and practical implications were discussed to leverage the power of VR in marketing.
One-step Kernel Multi-view Subspace Clustering

Multi-view subspace clustering is essential to many scientific problems. However, most existing methods suffer from three aspects of issues. First, these methods usually adopt a two-step framework, lacking the ability to achieve an optimal common affinity matrix across multiple views. Second, these methods are intended to solve the clustering problem in linear subspaces but may fail in practice as most real-world data sets may exhibit non-linear structures. Third, most existing subspace-based methods force the negative elements in the coefficient matrix to be positive, which may damage the inherent correlation among the data. To address above issues, we propose a novel approach termed One-step Kernel Multi-view Subspace Clustering (OKMSC). The common affinity matrix is learned from all views under one-step framework, which integrates the nonnegative and discriminative property of affinity matrix into the computation. Further, a kernelized model is designed to address the nonlinear multi-view clustering problem. And an iterative optimization method is designed to solve the objective function in this model. Extensive experiments have validated the superiority of the proposed method over several state-of-art clustering methods.
Switchable Whitening for Deep Representation Learning

Normalization methods are essential components in convolutional neural networks (CNNs). They either standardize or whiten data using statistics estimated in predefined sets of pixels. Unlike existing works that design normalization techniques for specific tasks, we propose Switchable Whitening (SW), which provides a general form unifying different whitening methods as well as standardization methods. SW learns to switch among these operations in an end-to-end manner. It has several advantages. First, SW adaptively selects appropriate whitening or standardization statistics for different tasks (see Fig.1), making it well suited for a wide range of tasks without manual design. Second, by integrating benefits of different normalizers, SW shows consistent improvements over its counterparts in various challenging benchmarks. Third, SW serves as a useful tool for understanding the characteristics of whitening and standardization techniques. We show that SW outperforms other alternatives on image classification (CIFAR-10/100, ImageNet), semantic segmentation (ADE20K, Cityscapes), domain adaptation (GTA5, Cityscapes), and image style transfer (COCO). For example, without bells and whistles, we achieve state-of-the-art performance with 45.33% mIoU on the ADE20K dataset.
Inequality and gender inclusion: Minimum ICT policy thresholds for promoting female employment in Sub-Saharan Africa

The study assesses how ICT modulates the effect of inequality on female economic participation in a panel of 42 countries in sub-Saharan Africa over the period 2004–2014. Three inequality indicators are used, namely: the Gini coefficient, the Atkinson index and the Palma ratio. The adopted ICT indicators are mobile phone penetration, internet penetration and fixed broadband subscriptions. Three gender economic inclusion indicators are also used for the analysis, namely: female labour force participation, female unemployment and female employment. The Generalised Method of Moments is employed as empirical strategy. The findings show that enhancing ICT beyond certain thresholds is necessary for ICT to mitigate inequality in order to enhance gender economic participation. First, for female labour force participation, a minimum threshold of 165.714 mobile phone penetration per 100 people is required for the Palma ratio. Second, minimum ICT thresholds for the reduction of female unemployment are: (i) 87.783, 107.486 and 152.500 mobile phone penetration per 100 people for respectively, the Gini coefficient, the Atkinson index and the Palma ratio; (ii) 39.618 internet penetration per 100 people for the Atkinson index and (iii) 4.500 fixed broadband subscritptions for the Palma ratio. Third, the corresponding ICT thresholds for the promotion of female employment are: (i) 120.369 and 85.533 mobile phone penetration per 100 people for respectively, the Gini coefficient and the Atkinson index and (ii) 30.005 internet penetration per 100 people for the Gini coefficient. The established thresholds make economic sense and can be feasibly implemented by policy makers in order to induce favourable effects on gender economic inclusion dynamics.