Reviewerly/exHarmony huggingface dataset:
Predicting future dynamics from short-term time series using an Anticipated Learning Machine

Predicting time series has significant practical applications over different disciplines. Here, we propose an Anticipated Learning Machine (ALM) to achieve precise future-state predictions based on short-term but high-dimensional data. From non-linear dynamical systems theory, we show that ALM can transform recent correlation/spatial information of high-dimensional variables into future dynamical/temporal information of any target variable, thereby overcoming the small-sample problem and achieving multistep-ahead predictions. Since the training samples generated from high-dimensional data also include information of the unknown future values of the target variable, it is called anticipated learning. Extensive experiments on real-world data demonstrate significantly superior performances of ALM over all of the existing 12 methods. In contrast to traditional statistics-based machine learning, ALM is based on non-linear dynamics, thus opening a new way for dynamics-based machine learning.
Metric Learning from Imbalanced Data with Generalization Guarantees

Since many machine learning algorithms require a distance metric to capture dis/similarities between data points, metric learning has received much attention during the past decade. Surprisingly, very few methods have focused on learning a metric in an imbalanced scenario where the number of positive examples is much smaller than the negatives, and even fewer derived theoretical guarantees in this setting. Here, we address this difficult task and design a new Mahalanobis metric learning algorithm (IML) which deals with class imbalance. We further prove a generalization bound involving the proportion of positive examples using the uniform stability framework. The empirical study performed on a wide range of datasets shows the efficiency of IML.
Understanding and Predicting the Memorability of Outdoor Natural Scenes

Memorability measures how easily an image is to be memorized after glancing, which may contribute to designing magazine covers, tourism publicity materials, and so forth. Recent works have shed light on the visual features that make generic images, object images or face photographs memorable. However, these methods are not able to effectively predict the memorability of outdoor natural scene images. To overcome this shortcoming of previous works, in this paper, we provide an attempt to answer: "what exactly makes outdoor natural scenes memorable". To this end, we first establish a large-scale outdoor natural scene image memorability (LNSIM) database, containing 2,632 outdoor natural scene images with their ground truth memorability scores and the multi-label scene category annotations. Then, similar to previous works, we mine our database to investigate how low-, middle- and high-level handcrafted features affect the memorability of outdoor natural scenes. In particular, we find that the high-level feature of scene category is rather correlated with outdoor natural scene memorability, and the deep features learnt by deep neural network (DNN) are also effective in predicting the memorability scores. Moreover, combining the deep features with the category feature can further boost the performance of memorability prediction. Therefore, we propose an end-to-end DNN based outdoor natural scene memorability (DeepNSM) predictor, which takes advantage of the learned category-related features. Then, the experimental results validate the effectiveness of our DeepNSM model, exceeding the state-of-the-art methods. Finally, we try to understand the reason of the good performance for our DeepNSM model, and also study the cases that our DeepNSM model succeeds or fails to accurately predict the memorability of outdoor natural scenes. Code: github.com/JiaxinLu-home/Natural-Scene-Memorability-Dataset.
Wireless Transmitter Identification Based on Device Imperfections

Mining useful patterns from databases is an important research topic. The research in utility mining mostly focuses on discovering patterns of high value in large databases, and analyzing the important factors in a data mining process. This idea is applied to the wireless device identification in this paper. Radio Frequency Fingerprint (RFF) reflects differences between transmitter hardware components. It contains rich non-linear characteristics of the internal components of the transmitter. Small differences and inaccuracies in the manufacturing process determine the unique characteristic contained in the transmitted signal. The device can be identified by the signal transmitted by the wireless device. In this paper, the generation mechanism of RFF is analyzed and two pattern mining algorithms are used to extract useful information from wireless signals for device identification. Then, a real communication transmitter link is established to study the effect of different components of a transmitter. The signals are acquired from the transmitters with different components replaced, including the amplifier, the bandpass filter, and the local oscillator. Finally, the influence of different components and pattern mining methods are evaluated.
Emojis in public health and how they might be used for hand hygiene and infection prevention and control

Abstract Emojis are frequently used picture characters known as possible surrogates for non-verbal aspects of behavior. Considering the ability of emojis to enhance and facilitate communication, there has been a growing interest in studying their effects in scientific and health-related topics over the past few years. Infection prevention and control (IPC) is a field of medicine that is directly associated with specific behaviors. These include hand hygiene, which is the cornerstone of the prevention of healthcare-associated infections, and essential in stemming the spread of antimicrobial resistance. This paper aims to provide an overview of how emojis have been used in the medical and public health literature and proposes their possible use in IPC and hand hygiene to put forth a vision for the future research.
Attacker Behaviour Forecasting Using Methods of Intelligent Data Analysis: A Comparative Review and Prospects

Early detection of the security incidents and correct forecasting of the attack development is the basis for the efficient and timely response to cyber threats. The development of the attack depends on future steps available to the attackers, their goals, and their motivation—that is, the attacker “profile” that defines the malefactor behaviour in the system. Usually, the “attacker profile” is a set of attacker’s attributes—both inner such as motives and skills, and external such as existing financial support and tools used. The definition of the attacker’s profile allows determining the type of the malefactor and the complexity of the countermeasures, and may significantly simplify the attacker attribution process when investigating security incidents. The goal of the paper is to analyze existing techniques of the attacker’s behaviour, the attacker’ profile specifications, and their application for the forecasting of the attack future steps. The implemented analysis allowed outlining the main advantages and limitations of the approaches to attack forecasting and attacker’s profile constructing, existing challenges, and prospects in the area. The approach for attack forecasting implementation is suggested that specifies further research steps and is the basis for the development of an attacker behaviour forecasting technique.
Action Recognition in Videos Using Pre-Trained 2D Convolutional Neural Networks

A pre-trained 2D CNN (Convolutional Neural Network) can be used for the spatial stream in the two-stream CNN structure for videos, treating the representative frame selected from the video as an input. However, the CNN for the temporal stream in the two-stream CNN needs training from scratch using the optical flow frames, which demands expensive computations. In this paper, we propose to adopt a pre-trained 2D CNN for the temporal stream to avoid the optical flow computations. Specifically, three RGB frames selected at three different times in the video sequence are converted into grayscale images and are assigned to three R(red), G(green), and B(blue) channels, respectively, to form a Stacked Grayscale 3-channel Image (SG3I). Then, the pre-trained 2D CNN is fine-tuned by SG3Is for the temporal stream CNN. Therefore, only pre-trained 2D CNNs are used for both spatial and temporal streams. To learn long-range temporal motions in videos, we can use multiple SG3Is by partitioning the video shot into sub-shots and a single SG3I is generated for each sub-shot. Experimental results show that our two-stream CNN with the proposed SG3Is is about 14.6 times faster than the first version of the two-stream CNN with the optical flow, and yet achieves a similar recognition accuracy for UCF-101 and a 5.7% better result for HMDB-51.
Location recommendation by combining geographical, categorical, and social preferences with location popularity

The primary aim of location recommendation is to predict users’ future movement by modeling user preference. Multiple types of information have been adopted in profiling users; however, simultaneously combining them for a better recommendation is challenging. In this study, a novel location recommendation method that incorporates geographical, categorical, and social preferences with location popularity is proposed. Experimental results on two public datasets show that the proposed method significantly outperforms two state-of-the-art recommendation methods. Geographical preference generally shows more importance than both categorical and social preferences. A category hierarchy that unleashes the independent assumption of location tags improves categorical preference. Location popularity proves to be a useful metric in ranking candidate locations. The findings of this study can provide practical guidelines for location recommendation services.
A Systematic Literature Review Method On AES Algorithm for Data Sharing Encryption On Cloud Computing

Cloud computing is one revolution in information technology (IT) that can share resources, services and data through a network among users. Because users have same rights on the network to transfer data, data are vulnerable to be attacked by unauthorized person. Lately, data security in a system only concentrates on data storage on cloud by utilizing internet security, but a little concentration is found during data transfer. By considering security as a serious problem, an encryption-based proposed system is presented to secure during data transfer. Authors propose an approach to boost system security during data transfer in order to prevent data theft by unauthorized person. To prevent an attack by unauthorized person, Advanced Encryption Standard (AES) will be proposed to secure data transfer and storage in cloud computing. For better future, authors will propose Systematic Literature Review (SLR) to generate suggestions and opportunities in AES cloud computing.
Public Key Encryption with Keyword Search in Cloud: A Survey

With the popularization of cloud computing, many business and individuals prefer to outsource their data to cloud in encrypted form to protect data confidentiality. However, how to search over encrypted data becomes a concern for users. To address this issue, searchable encryption is a novel cryptographic primitive that enables user to search queries over encrypted data stored on an untrusted server while guaranteeing the privacy of the data. Public key encryption with keyword search (PEKS) has received a lot of attention as an important branch. In this paper, we focus on the development of PEKS in cloud by providing a comprehensive research survey. From a technological viewpoint, the existing PEKS schemes can be classified into several variants: PEKS based on public key infrastructure, PEKS based on identity-based encryption, PEKS based on attribute-based encryption, PEKS based on predicate encryption, PEKS based on certificateless encryption, and PEKS supporting proxy re-encryption. Moreover, we propose some potential applications and valuable future research directions in PEKS.
ADMIR–Affine and Deformable Medical Image Registration for Drug-Addicted Brain Images

We proposed an unsupervised end-to-end Affine and Deformable Medical Image Registration (ADMIR) method based on convolutional neural network (ConvNet). ADMIR includes three key components: an affine registration module for learning the affine transformation parameters, a deformable registration module for learning the displacement vector field, and a spatial transformer for getting the final warped image from both affine and deformable transformation parameters. To evaluate its performance, the magnetic resonance images of drug-addicted brains were used to train and test the model, and we compared it with two state-of-art methods in terms of Dice score, Hausdorff distance, and average symmetric surface distance. The experimental results demonstrated that our proposed ADMIR model outperforms existing methods even with the images without pre-alignment, which suggests that the ADMIR model can be used to achieve quick medical image registration with high accuracy.
Extending the Frontier of Quantum Computers With Qutrits

We advocate for a fundamentally different way to perform quantum computation by using three-level qutrits instead of qubits. In particular, we substantially reduce the resource requirements of quantum computations by exploiting a third state for temporary variables (ancilla) in quantum circuits. Past work with qutrits has demonstrated only constant factor improvements, owing to the log2(3) binary-to-ternary compression factor. We present a novel technique using qutrits to achieve a logarithmic runtime decomposition of the Generalized Toffoli gate using no ancilla---an exponential improvement over the best qubit-only equivalent. Our approach features a 70x improvement in total two-qudit gate count over the qubit-only decomposition. This results in improvements for important algorithms for arithmetic and QRAM. Simulation results under realistic noise models indicate over 90% mean reliability (fidelity) for our circuit, versus under 30% for the qubit-only baseline. These results suggest that qutrits offer a promising path toward extending the frontier of quantum computers.
OSCD: A one-shot conditional object detection framework

The current advances in object detection depend on large-scale datasets to get good performance. However, there may not always be sufficient samples in many scenarios, resulting in the performance degradation of the current deep learning based object detection models. To overcome this problem, we propose a novel one-shot conditional detection framework (OSCD). Given a support image of the target object and a query image as input, OSCD can detect all objects belonging to the target object category in the query image. Specifically, OSCD is composed of a Siamese network and a two-stage detection model. In each stage of the two-stage detection pipeline, a feature fusion module and a learnable metric module are designed for effective conditional detection respectively. Once trained, OSCD can detect objects of both seen and unseen classes without further training, which also has advantages including class-agnostic, training-free for unseen classes, and without catastrophic forgetting. Experiments show that the proposed approach achieves state-of-the-art performance on the proposed datasets based on Fashion-MNIST and Pascal VOC.
Memory and forecasting capacities of nonlinear recurrent networks

The notion of memory capacity, originally introduced for echo state and linear networks with independent inputs, is generalized to nonlinear recurrent networks with stationary but dependent inputs. The presence of dependence in the inputs makes natural the introduction of the network forecasting capacity, that measures the possibility of forecasting time series values using network states. Generic bounds for memory and forecasting capacities are formulated in terms of the number of neurons of the nonlinear recurrent network and the autocovariance function or the spectral density of the input. These bounds generalize well-known estimates in the literature to a dependent inputs setup. Finally, for the particular case of linear recurrent networks with independent inputs it is proved that the memory capacity is given by the rank of the associated controllability matrix, a fact that has been for a long time assumed to be true without proof by the community.
Self-supervised Keypoint Correspondences for Multi-person Pose Estimation and Tracking in Videos

Video annotation is expensive and time consuming. Consequently, datasets for multi-person pose estimation and tracking are less diverse and have more sparse annotations compared to large scale image datasets for human pose estimation. This makes it challenging to learn deep learning based models for associating keypoints across frames that are robust to nuisance factors such as motion blur and occlusions for the task of multi-person pose tracking. To address this issue, we propose an approach that relies on keypoint correspondences for associating persons in videos. Instead of training the network for estimating keypoint correspondences on video data, it is trained on a large scale image dataset for human pose estimation using self-supervision. Combined with a top-down framework for human pose estimation, we use keypoint correspondences to (i) recover missed pose detections and to (ii) associate pose detections across video frames. Our approach achieves state-of-the-art results for multi-frame pose estimation and multi-person pose tracking on the PoseTrack 2017 and 2018 datasets .
Superpixel Boundary-Based Edge Description Algorithm for SAR Image Segmentation

Although various methods can effectively segment synthetic aperture radar (SAR) images, we found that the method combining superpixel and image edge information can get better results. To solve the problem that common SAR image segmentation methods often segment pixels incorrectly in edge region, a superpixel boundary-based edge description algorithm (SpBED) is proposed. First, an edge detection method with three edge detectors is used. Therefore, accurate strong edges of SAR images can be extracted, and false edges that are easy to appear in a single detection method can be effectively eliminated. Then the weak edges of the image are extracted by superpixel generation algorithm. The extracted weak edges can supplement the edge information that is difficult to extract by edge detection. Superpixel boundaries are also used to carry the strong edges, so that the strong and weak edges can be completely represented by superpixel boundaries. Finally, boundary constraint superpixel smoothing is used to reduce the effects of noise, and k-means algorithm is performed on superpixels. Since edge information is carried by superpixels, it effectively guarantees the segmentation accuracy in edge region. Compared with seven state-of-the-art algorithms, segmentation results on simulated images and real images demonstrate the effectiveness of the proposed SpBED.
The Effectiveness of Reading English Learning Process Based on Blended Learning through "Absyak" Website Media in Higher Education

The fastest development of technology and the internet in the 4.0 revolution era that influenced the world of education, especially higher education. Currently education focuses on outcome-based education, thus forcing all lecturers to innovate in teaching and learning. One form is by on-line learning for English learning materials developed through the website. This study analyzes the effectiveness of on-line based learning on websites on the ability to read students in the process of learning English at Universitas Brawijaya through "absyak" media websites. The use of the blended learning model has been done in several subjects, but for English subjects (reading) has never been done before. This research is quantitative descriptive. The research sample used a sample of 65 students through a questionnaire on students of the English language study program at Brawijaya University in academic year 2018/2019. The results showed that 81.99% agreed with the application of English reading learning based on blended learning through "absyak" media websites and the remaining 18.01% did not agree on learning English reading based on blended learning through "absyak" media websites. The implementation of online-based learning through "absyak" learning in the media can be effectively contained in high student responses, increased student scores and student attitudes towards variation and learning innovation.
HGM-4: A new multi-cameras dataset for hand gesture recognition

Gesture recognition technology is rapidly growing in the recent years due to the demands of many application such as computer game and sport, human robot interaction, assistant systems, sign language interpretation and e-commerce. One of the most important of gesture recognition is hand-gesture recognition. For example, it can be used to control all devices (television, radio, air-condition, and doors) by just hand gestures for smart home application. The HGM-4 dataset is built for hand gesture recognition (the full dataset is available from: https://data.mendeley.com/datasets/jzy8zngkbg/4) which contains total 4,160 color images (1280 × 700 pixels) of 26 hand gestures captured by four cameras at different position. The training and testing set are defined to create a benchmark framework for comparing the experimental results.
Random error sampling-based recurrent neural network architecture optimization

Recurrent neural networks are good at solving prediction problems. However, finding a network that suits a problem is quite hard because their performance is strongly affected by their architecture configuration. Automatic architecture optimization methods help to find the most suitable design, but they are not extensively adopted because of their high computational cost. In this work, we introduce the Random Error Sampling-based Neuroevolution (RESN), an evolutionary algorithm that uses the mean absolute error random sampling, a training-free approach to predict the expected performance of an artificial neural network, to optimize the architecture of a network. We empirically validate our proposal on four prediction problems, and compare our technique to training-based architecture optimization techniques, neuroevolutionary approaches, and expert designed solutions. Our findings show that we can achieve state-of-the-art error performance and that we reduce by half the time needed to perform the optimization.