Reviewerly/exHarmony huggingface dataset:
Investigating the use of innovative mobile pedagogies for school-aged students: A systematic literature review

The empirical evidence surrounding innovative technology-enhanced-learning practices in K-12 education is at best patchy. This study seeks to address this problem by investigating innovative mobile learning pedagogies for school-aged learners, including the extent to which these approaches may be disruptive to traditional school structures and practices. It adopts a rigorous Systematic Literature Review (SLR) methodology to interrogate this phenomenon, with fifty-seven high quality articles published between 2010 and 2017 meeting the stringent selection criteria. Content and thematic analysis showed low to medium degrees of innovation in most studies, with only 3 articles showing radically innovative, disruptive mobile learning practices. Illustrative examples are discussed across this 'innovation spectrum' and the notion of feasible innovation is proposed in the context of mobile learning in schools. The findings have implications for educators interested in designing and enacting effective innovative mobile learning practices, and researchers interested in the nature of innovation in mobile pedagogies.
Sentiment Analysis of Twitter Data

Nowadays, people from all around the world use social media sites to share information. Twitter for example is a platform in which users send, read posts known as `tweets' and interact with different communities. Users share their daily lives, post their opinions on everything such as brands and places. Companies can benefit from this massive platform by collecting data related to opinions on them. The aim of this paper is to present a model that can perform sentiment analysis of real data collected from Twitter. Data in Twitter is highly unstructured which makes it difficult to analyze. However, our proposed model is different from prior work in this field because it combined the use of supervised and unsupervised machine learning algorithms. The process of performing sentiment analysis as follows: Tweet extracted directly from Twitter API, then cleaning and discovery of data performed. After that the data were fed into several models for the purpose of training. Each tweet extracted classified based on its sentiment whether it is a positive, negative or neutral. Data were collected on two subjects McDonalds and KFC to show which restaurant has more popularity. Different machine learning algorithms were used. The result from these models were tested using various testing metrics like cross validation and f-score. Moreover, our model demonstrates strong performance on mining texts extracted directly from Twitter.
Deep Learning in Digital Modulation Recognition Using High Order Cumulants

By considering the different cumulant combinations of the 2FSK, 4FSK, 2PSK, 4PSK, 2ASK, and 4ASK, this paper established new identification parameters to achieve the recognition of those digital modulations. The deep neural network (DNN) was also employed to improve the recognition rate, which was designed to classify the signal based on the distinct feature of each signal type that was extracted with high order cumulants. The extensive simulations demonstrated the exceptional classification performance for new key features based on high order cumulants. The overall success rate of the proposed algorithm was over 99% at the signal to noise ratio (SNR) of −5 dB and 100% at the SNR of −2 dB. The results of the experiments also showed the robustness of the proposed method for a variety of conditions, such as frequency offset, multi-path, and so on.
Extra-Tree Classifier with Metaheuristics Approach for Email Classification

It is very normal for any user to receive hundreds of emails every day. Almost 93% of them are spam messages which include mainly advertisements from the industries like software, phishing, gambling, stocks, electronics, pharmaceutical, loan, and malware attempts etc. Spams messages not only waste user’s time but also eats up user valuable space. In this paper, a nature inspired metaheuristics technique has been used for email classification which emphasizes on reducing false-positive problem of treating spam messages as ham. It uses metaheuristics-based feature selection methods and employs extra-tree classifier to classify emails into spam and ham. The proposed model has accuracy of 95.5%, specificity of 93.7%, and F1-score of 96.3%, which is clearly a major improvement over the previous researches which have been conducted in this field using decision trees. The comparative analysis of extra-tree classifiers with other classifiers like decision trees and random forest has also been studied.
Application of Ising Machines and a Software Development for Ising Machines

An online advertisement optimization, which can be represented by a combinatorial optimization problem is performed using D-Wave 2000Q, a quantum annealing machine. To optimize the online advertisement allocation optimization, we introduce a generalized version of the Markowitz mean-variance model which is a basic model of portfolio optimization. The obtained optimization performance using D-Wave 2000Q is higher than that using the greedy method which is a conventional method. Additionally, to conveniently use Ising machines including a quantum annealing machine, new software called PyQUBO is developed. The first half of the paper gives a review of several combinatorial optimization problems and how to represent them using the Ising model or the quadratic unconstrained binary optimization (QUBO) form. We show the results of the online advertisement allocation optimization and the explanation of PyQUBO in the last half of the paper.
Quantum Search Algorithms for Wireless Communications

Faster, ultra-reliable, low-power, and secure communications has always been high on the wireless evolutionary agenda. However, the appetite for faster, more reliable, greener, and more secure communications continues to grow. The state-of-the-art methods conceived for achieving the performance targets of the associated processes may be accompanied by an increase in computational complexity. Alternatively, a degraded performance may have to be accepted due to the lack of jointly optimized system components. In this survey we investigate the employment of quantum computing for solving problems in wireless communication systems. By exploiting the inherent parallelism of quantum computing, quantum algorithms may be invoked for approaching the optimal performance of classical wireless processes, despite their reduced number of cost-function evaluations. In this contribution we discuss the basics of quantum computing using linear algebra, before presenting the operation of the major quantum algorithms, which have been proposed in the literature for improving wireless communications systems. Furthermore, we investigate a number of optimization problems encountered both in the physical and network layer of wireless communications, while comparing their classical and quantum-assisted solutions. Finally, we state a number of open problems in wireless communications that may benefit from quantum computing.
Self-Supervised Representation Learning From Videos for Facial Action Unit Detection

In this paper, we aim to learn discriminative representation for facial action unit (AU) detection from large amount of videos without manual annotations. Inspired by the fact that facial actions are the movements of facial muscles, we depict the movements as the transformation between two face images in different frames and use it as the self-supervisory signal to learn the representations. However, under the uncontrolled condition, the transformation is caused by both facial actions and head motions. To remove the influence by head motions, we propose a Twin-Cycle Autoencoder (TCAE) that can disentangle the facial action related movements and the head motion related ones. Specifically, TCAE is trained to respectively change the facial actions and head poses of the source face to those of the target face. Our experiments validate TCAE's capability of decoupling the movements. Experimental results also demonstrate that the learned representation is discriminative for AU detection, where TCAE outperforms or is comparable with the state-of-the-art self-supervised learning methods and supervised AU detection methods.
Survey on Private Blockchain Consensus Algorithms

A blockchain is a distributed ledger of records called as blocks. These blocks are linked using cryptographic hash. Each block contains a hash of the previous block, a timestamp, and transaction data. Consensus layer is the main layer in Blockchain Architecture, in which consensus protocol is configured to decide how new block is added in blockchain. Consensus algorithm solves the problem of trust in blockchain. Consensus algorithms can be classified into two classes. The first class is voting-based consensus, which requires nodes in the blockchain network to broadcast their results of mining a new block or transaction, before appending the block to blockchain. The second class is proof-based consensus, which requires the nodes joining the blockchain network to solve and mathematical puzzle to show that they are more eligible than the others to do the appending or mining work. Performance of blockchain can be increased with the use of suitable consensus algorithm. However, theory and data support for the selecting suitable consensus in private blockchain is very limited. This paper contributes theory and data used for selecting suitable consensus algorithm and would help researchers for further exploring of consensus in private blockchain environment.
Breaking Winner-Takes-All: Iterative-Winners-Out Networks for Weakly Supervised Temporal Action Localization

We address the challenging problem of weakly supervised temporal action localization from unconstrained web videos, where only the video-level action labels are available during training. Inspired by the adversarial erasing strategy in weakly supervised semantic segmentation, we propose a novel iterative-winners-out network. Specifically, we make two technical contributions: we propose an iterative training strategy, namely, winners-out, to select the most discriminative action instances in each training iteration and remove them in the next training iteration. This iterative process alleviates the "winner-takes-all" phenomenon that existing approaches tend to choose the video segments that strongly correspond to the video label but neglects other less discriminative video segments. With this strategy, our network is able to localize not only the most discriminative instances but also the less discriminative ones. To better select the target action instances in winners-out, we devise a class-discriminative localization technique. By employing the attention mechanism and the information learned from data, our technique is able to identify the most discriminative action instances effectively. The two key components are integrated into an end-to-end network to localize actions without using the frame-level annotations. Extensive experimental results demonstrate that our method outperforms the state-of-the-art weakly supervised approaches on ActivityNet1.3 and improves mAP from 16.9% to 20.5% on THUMOS14. Notably, even with weak video-level supervision, our method attains comparable accuracy to those employing frame-level supervisions.
WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild

Pedestrian detection has achieved significant progress with the availability of existing benchmark datasets. However, there is a gap in the diversity and density between real world requirements and current pedestrian detection benchmarks: first, most existing datasets are taken from a vehicle driving through the regular traffic scenario, usually leading to insufficient diversity; second, crowd scenarios with highly occluded pedestrians are still underrepresented, resulting in low density. To narrow this gap and facilitate future pedestrian detection research, we introduce a large and diverse dataset named WiderPerson for dense pedestrian detection in the wild. This dataset involves five types of annotations in a wide range of scenarios, no longer limited to the traffic scenario. There are a total of 13 382 images with 399 786 annotations, that is, 29.87 annotations per image, which means this dataset contains dense pedestrians with various kinds of occlusions. Hence, pedestrians in the proposed dataset are extremely challenging due to large variations in the scenario and occlusion, which is suitable to evaluate pedestrian detectors in the wild. We introduce an improved Faster R-CNN and the vanilla RetinaNet to serve as baselines for the new pedestrian detection benchmark. Several experiments are conducted on previous datasets including Caltech-USA and CityPersons to analyze the generalization capabilities of the proposed dataset, and we achieve state-of-the-art performances on these previous datasets without bells and whistles. Finally, we analyze common failure cases and find the classification ability of pedestrian detector needs to be improved to reduce false alarm and misdetection rates. The proposed dataset is available at http://www.cbsr.ia.ac.cn/users/sfzhang/WiderPerson.
A new comprehensive eye-tracking test battery concurrently evaluating the Pupil Labs glasses and the EyeLink 1000

Eye-tracking experiments rely heavily on good data quality of eye-trackers. Unfortunately, it is often the case that only the spatial accuracy and precision values are available from the manufacturers. These two values alone are not sufficient to serve as a benchmark for an eye-tracker: Eye-tracking quality deteriorates during an experimental session due to head movements, changing illumination or calibration decay. Additionally, different experimental paradigms require the analysis of different types of eye movements; for instance, smooth pursuit movements, blinks or microsaccades, which themselves cannot readily be evaluated by using spatial accuracy or precision alone. To obtain a more comprehensive description of properties, we developed an extensive eye-tracking test battery. In 10 different tasks, we evaluated eye-tracking related measures such as: the decay of accuracy, fixation durations, pupil dilation, smooth pursuit movement, microsaccade classification, blink classification, or the influence of head motion. For some measures, true theoretical values exist. For others, a relative comparison to a reference eye-tracker is needed. Therefore, we collected our gaze data simultaneously from a remote EyeLink 1000 eye-tracker as the reference and compared it with the mobile Pupil Labs glasses. As expected, the average spatial accuracy of 0.57° for the EyeLink 1000 eye-tracker was better than the 0.82° for the Pupil Labs glasses ( N = 15). Furthermore, we classified less fixations and shorter saccade durations for the Pupil Labs glasses. Similarly, we found fewer microsaccades using the Pupil Labs glasses. The accuracy over time decayed only slightly for the EyeLink 1000, but strongly for the Pupil Labs glasses. Finally, we observed that the measured pupil diameters differed between eye-trackers on the individual subject level but not on the group level. To conclude, our eye-tracking test battery offers 10 tasks that allow us to benchmark the many parameters of interest in stereotypical eye-tracking situations and addresses a common source of confounds in measurement errors (e.g., yaw and roll head movements). All recorded eye-tracking data (including Pupil Labs’ eye videos), the stimulus code for the test battery, and the modular analysis pipeline are freely available ( https://github.com/behinger/etcomp ).
Dual-Inception Network for Cross-Database Micro-Expression Recognition

This paper presents the technique for our contribution to 2019 Micro-Expression Grand Challenge (MEGC 2019). One sub-challenge of MEGC 2019 named Cross-Database (Cross-DB) challenge aims to classify three main classes (Negative, Positive and Surprise) in the task of Composite Database Evaluation (CDE). Our proposed method utilizes Inception technique to overcome the challenge for the cross-database micro-expression recognition and can be divided into three steps. (1) In the preprocessing stage, onset and mid-position frames of each micro-expression sample are selected for the feature extraction. (2) TV-L1 optical flow features are calculated by the two frames obtained in the first step. (3) The horizontal and vertical components of TV-L1 optical flow features are fed to a Dual-Inception network for the micro-expression recognition. Our experiment results on three benchmark databases show that our proposed mechanism archives the overall unweighted F1 score (UF1) of 0.7322 and unweighted average recall (UAR) of 0.7278, which significantly outperform those metrics of the baseline method ( UF1: 0.5882, UAR: 0.5785). Code is publicly available on GitHub: https://github.com/xly135846/MEGC2019.
Category-Based Deep CCA for Fine-Grained Venue Discovery From Multimodal Data

In this work, travel destinations and business locations are taken as venues. Discovering a venue by a photograph is very important for visual context-aware applications. Unfortunately, few efforts paid attention to complicated real images such as venue photographs generated by users. Our goal is fine-grained venue discovery from heterogeneous social multimodal data. To this end, we propose a novel deep learning model, category-based deep canonical correlation analysis. Given a photograph as input, this model performs: 1) exact venue search (find the venue where the photograph was taken) and 2) group venue search (find relevant venues that have the same category as the photograph), by the cross-modal correlation between the input photograph and textual description of venues. In this model, data in different modalities are projected to a same space via deep networks. Pairwise correlation (between different modality data from the same venue) for exact venue search and category-based correlation (between different modality data from different venues with the same category) for group venue search are jointly optimized. Because a photograph cannot fully reflect rich text description of a venue, the number of photographs per venue in the training phase is increased to capture more aspects of a venue. We build a new venue-aware multimodal data set by integrating Wikipedia featured articles and Foursquare venue photographs. Experimental results on this data set confirm the feasibility of the proposed method. Moreover, the evaluation over another publicly available data set confirms that the proposed method outperforms state of the arts for cross-modal retrieval between image and text.
A Systematic Evaluation of Static API-Misuse Detectors

Application Programming Interfaces (APIs) often have usage constraints, such as restrictions on call order or call conditions. API misuses, i.e., violations of these constraints, may lead to software crashes, bugs, and vulnerabilities. Though researchers developed many API-misuse detectors over the last two decades, recent studies show that API misuses are still prevalent. Therefore, we need to understand the capabilities and limitations of existing detectors in order to advance the state of the art. In this paper, we present the first-ever qualitative and quantitative evaluation that compares static API-misuse detectors along the same dimensions, and with original author validation. To accomplish this, we develop MUC, a classification of API misuses, and MUBENCHPIPE, an automated benchmark for detector comparison, on top of our misuse dataset, MUBENCH. Our results show that the capabilities of existing detectors vary greatly and that existing detectors, though capable of detecting misuses, suffer from extremely low precision and recall. A systematic root-cause analysis reveals that, most importantly, detectors need to go beyond the naive assumption that a deviation from the most-frequent usage corresponds to a misuse and need to obtain additional usage examples to train their models. We present possible directions towards more-powerful API-misuse detectors.
A Multi-Scan Labeled Random Finite Set Model for Multi-Object State Estimation

State-space models in which the system state is a finite set-called the multi-object state-have generated considerable interest in recent years. Smoothing for state-space models provides better estimation performance than filtering. In multi-object state estimation, the multi-object filtering density can be efficiently propagated forward in time using an analytic recursion known as the generalized labeled multi-Bernoulli (GLMB) recursion. In this paper, we introduce a multi-scan version of the GLMB model to accommodate the multi-object posterior recursion, and develop efficient numerical algorithms for computing this so-called multi-scan GLMB posterior.
Moving Object Detection Method via ResNet-18 With Encoder–Decoder Structure in Complex Scenes

In complex scenes, dynamic background, illumination variation, and shadow are important factors, which make conventional moving object detection algorithms suffer from poor performance. To solve this problem, a moving object detection method via ResNet-18 with encoder-decoder structure is proposed to segment moving objects from complex scenes. ResNet-18 with encoder-decoder structure possesses pixel-level classification capability to divide pixels into foreground and background, and it performs well in feature extraction because of its layers are so shallow that many more low-scale features will be retained. First, the object frames and their corresponding artificial labels are input to the network. Then, feature vectors will be generated by the encoder, and they are converted into segmentation maps by the decoder through deconvolution processing. Third, a rough matching of the moving object regions will be obtained, and finally, the Euclidean distance is used to match the moving object regions accurately. The proposed method is suitable for the scenes where dynamic background, illumination variation, and shadow exist, and experimental results on the public standard CDnet2014 and I2R datasets, from both qualitative and quantitative comparison aspects, demonstrate that the proposed method outperforms state-of-the-art algorithms significantly, and its mean F-measure increased by 1.99%~29.17%.
PCCN: Parallel Cross Convolutional Neural Network for Abnormal Network Traffic Flows Detection in Multi-Class Imbalanced Network Traffic Flows

Network attack behavior detection using deep learning is an important research topic in the field of network security. Currently, there are still many challenges in detecting multi-class imbalanced abnormal traffic data. This paper proposed a new intrusion detection network based on deep learning, named parallel cross convolutional neural network (PCCN), to improve the detection performance of imbalanced abnormal flows. By fusing the flow features learned from the two branch convolutional neural networks (CNN), PCCN can better learn the flow features with fewer samples, to improve the detection results of the imbalanced abnormal flows. We proposed an improved feature extraction method of the original flow to extract multi-class flow features at the same time. The proposed algorithm not only reduces the number of useless elements for network learning, but also accelerates network convergence. In addition, we proposed four improved versions of the PCCN network structure to meet the real-time requirements of network intrusion detection in the current big data computing. These networks can achieve almost the same detection results as the PCCN, but greatly reduce the detection time of data. Through the analysis of high-order evaluation metrics, the proposed PCCN algorithm is significantly better than the traditional machine learning algorithms. Compared with the current hierarchical network model, PCCN can also achieve better performance in term of overall accuracy.