Reviewerly/exHarmony huggingface dataset:
Hierarchical Sequence-to-Sequence Model for Multi-Label Text Classification

We propose a novel sequence-to-sequence model for multi-label text classification, based on a “parallel encoding, serial decoding” strategy. The model combines a convolutional neural network and self-attention in parallel as the encoder to extract fine-grained local neighborhood information and global interaction information from the source text. We design a hierarchical decoder to decode and generate the label sequence. Our method not only gives full consideration to the interpretable fine-gained information in the source text but also effectively utilizes the information to generate the label sequence. We conducted a large number of comparative experiments on three datasets. The results show that the proposed model has significant advantages over the state-of-the-art baseline model. In addition, our analysis demonstrates that our model is competitive with the RNN-based Seq2Seq models and that it is more robust at handling datasets with a high label/sample ratio.
Developing Agriculture Land Mapping using Rapid Application Development (RAD): A Case Study from Indonesia

The use of Information and Communication Technology (ICT) in agriculture has become one of the steps to improve agricultural efficiency, effectiveness, productivity, and also expected to encourage the creation of Precision Agriculture.Precision agriculture has an impact on the efficiency of operational costs to increase margins in the production of agricultural products using ICTs.One of the problems that often arise in agriculture is related to the management of agricultural land in each farmer group area.This information is closely related to the needs of agricultural production facilities and infrastructure, such as the need for fertilizers, seeds, and other resources.Web Mapping System is one of the systems to assist in land or area mapping.In this study, the Web Mapping System is expected to be used to help at agricultural land mapping, owned by farmer members of farmer groups.The developed system will store spatial data from farmland members and farmer groups.The Web Mapping System was developed using the Rapid Application Development (RAD) method, where there are several iterative processes.The result of this study is the Web Mapping System for agricultural land.With this application, farmers can find out the status of the land being cultivated or owned.In addition, the Web Mapping System can record the status of the existing land in a farmer group and the need for agricultural production facilities and infrastructure.Further, the Web Mapping System also provides information in a dashboard that can help farmer groups to manage the land owned by each farmer who is a member of the group.
Spatiotemporal Feature Descriptor for Micro-Expression Recognition Using Local Cube Binary Pattern

Micro-expression recognition has been an active research area in recent years, it plays an important role in psychology and public security. Due to the aspects of short duration and subtle movement, it is challenging to extract spatiotemporal features of micro-expressions. The existing methods only extract features in the three-dimensional orthogonal plane and fail to make full use of that information. To solve this problem, we propose a new Local Cubes Binary Patterns (LCBP) method for micro-expression recognition. LCBP is cascaded by the motion information LCBP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">direction</sub> , the amplitude information LCBP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">amplitudes</sub> , and the spatial information LCBP3D to obtain the spatiotemporal features. The advantage of LCBP is its ability to preserve the spatiotemporal information and the low feature dimension. Furthermore, to increase the discrimination of features in micro-expression sequences, we apply a differential calculation energy map to find regions of interest (ROI) for getting a weighted energy map. The final micro-expression feature acquired by fusing the LCBP features and the weighted energy map are classified through the Support Vector Machine (SVM). We evaluate the proposed method on four published micro-expression databases including SMIC, CASME, CASME2, SAMM. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition.
Nonequilibrium effects on quantum correlations: Discord, mutual information, and entanglement of a two-fermionic system in bosonic and fermionic environments

We study the steady-state entanglement and correlations of an open system comprising two coupled fermions in the equilibrium or nonequilibrium environments and distill the nonequilibrium contribution to the quantum correlations. We show that in the equilibrium condition, the steady-state quantum correlations exhibit nonmonotonic behavior, while in the nonequilibrium case, the monotonicity is determined by many parameters. The entanglement vanishes abruptly upon the increase of the temperature (bias) and chemical potential bias, and it witnesses a critical chemical potential above which the concurrence always remains positive. In the fermionic reservoirs, quantum correlations reach the maximal values when one chemical potential of the reservoirs matches the system frequency. We separate the quantum correlation generation due to the averaged effect from the pure nonequilibrium effect. In contrast with previous results, when the averaged effect is separated out, the nonequilibrium generation of quantum correlation shows a distinctive monotonic behavior. The difference between the large-tunneling regime with decaying correlations and the small-tunneling with increasing correlations is discussed. Near the boundary of the two regimes, the entanglement behavior is a mixture of two extremes, and it resurrects with the increase of chemical potential bias after its previous drop to zero.
A Big Data Security Layer Meta-Model Proposition

In Big Data, several solution providers offer distributions to handle this large amount of data.Given the variety of these solutions, we are working to provide universal metamodeling for all layers of a Big Data system in order to address the issue of interoperability and portability between these solutions.As part of our continuous efforts to standardize concepts in Big Data world, we apply in this paper techniques related to Model-Driven Engineering "MDE" to propose a meta-model for the security layer in Big Data.This metamodel with the others that we have already proposed for the other layers of the Big Data system, will be used as a platform-independent according to Model-Driven Architecture pattern, which describes the structures of Big Data layers independently of any specific platform.
SmartCrowd: Decentralized and Automated Incentives for Distributed IoT System Detection

Internet of Things (IoT) devices achieve the rapid development and have been widely deployed recently. Meanwhile, inherent vulnerabilities of IoT systems (including firmware and software) have been continually uncovered and thus the systems are always exposed to various attacks. The root cause of the issue is that IoT systems always have design flaws and implementation bugs. In particular, the released systems (e.g., by third-party marketplaces and IoT vendors) may be maliciously repackaged with malware. Unfortunately, IoT consumers are not able to effectively capture such vulnerabilities because of the limited detection capabilities. In this paper, we propose SmartCrowd, a blockchain-based platform that aims to outsource security detection of IoT systems to distributed detectors with strong detection incentives. SmartCrowd enables built-in accountability for IoT providers and authoritative references of detection results for IoT consumers. By building smart contracts, we can incentivize the efficient and high-coverage security detection of IoT systems, while providing decentralized and automated incentives for both IoT providers releasing secure IoT systems and detectors uncovering vulnerabilities. We present the security and theoretical analysis that demonstrates the security of SmartCrowd and the incentives for participators. We prototype SmartCrowd by using Ethereum and the experimental results show that SmartCrowd has both technical feasibility and financial benefits, which can be applied to build a secure IoT ecosystem.
Promoting Academic Writing Proficiency of Iranian EFL Learners through Blended Learning

Blended learning is a combination of traditional and technology-enhanced learning. Social networks services can be used in blended learning settings as they support collaboration, resource sharing, and socialization. Edmodo is an educational social network which can facilitate the development of second or foreign language skills. This study intended to investigate the effects of blended learning through implementing Edmodo mobile application on the academic writing proficiency and perceptions of Iranian intermediate EFL learners in higher education. This quasi-experimental research followed a mixed method design. The results indicated that it was possible to integrate Edmodo mobile application into essay writing class successfully since the experimental group who practiced collaborative essay writing through Edmodo mobile application outperformed the control group regarding writing proficiency in general as well as such aspects as organization, vocabulary, and mechanics of writing. The qualitative analysis of follow-up interviews revealed that majority of students had positive opinions and perceived it as an innovative, engaging and helpful activity. Blended Language Learning is a promising area in the field of TEFL, and as such it is crucial for teachers to keep their teaching methodologies and techniques compliant with the technology developments of 21st century.
Exposing Fake Bitrate Videos Using Hybrid Deep-Learning Network From Recompression Error

Bitrate is generally regarded as an important criterion of video quality. However, with sophisticated video editing software, forgers can create fake bitrate videos by up-converting the bitrate of original videos with lower video quality to attract more viewers on video sharing websites. In this work, we first model the generation process of fake bitrate videos and analyze the dominant sources of information loss. It is found that the recompression error generated by the proposed one-step-further recompression operation is an efficient measurement to expose distinguishable quality variation tendencies between true and fake bitrate videos. Based on this analysis, we propose a detection method for fake bitrate videos using a hybrid deep-learning network from recompression error. For an input video, the patch-wise recompression errors are first calculated to increase the learning capability of the network. To learn robust representations of recompression errors in local regions with different degrees of predictability, a hybrid deep-learning network that contains two branches with heterogeneous structures is designed. For noise-like recompression errors, the first branch has a shallow CNN structure initialized with an Inception-like module using multisize convolutional kernels. For zero-element clustered recompression errors, the second branch has a multi-layer perceptron structure equipped with a unique layer that extracts the histogram of zero-element clustered square regions. The output vectors of different branches are concatenated and then jointly optimized to obtain the patch-wise detection results. Finally, the majority voting (local-to-global) strategy is applied to obtain the final detection result. Extensive experiments are conducted to evaluate the detection performance under various coding parameter settings, such as different bitrates, rate-distortion optimization strategies and so on. The experimental results demonstrate the superiority of the proposed method compared with several state-of-the-art methods to provide more fine-grained forensic clues.
A high-N00N output of harmonically driven cavity QED

Abstract A harmonically driven cavity QED system consisting of two cavities and a two-level qubit is shown to enable the generation of a vast class of maximally entangled states suitable for measurements with a Heisenberg-limit precision. As one of its modalities, this system can serve as a quantum beam splitter, converting an | N 〉 ⊗ |0〉 input into a maximally entangled N00N state (| N 〉 ⊗ |0〉 + |0〉 ⊗ | N 〉)/ $$\sqrt{{\bf{2}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msqrt><mml:mn>2</mml:mn></mml:msqrt></mml:math> at its output. A network of such quantum beam splitters is shown to provide a source of multimode N00N-type entanglement.
Using Sparse Representation to Detect Anomalies in Complex WSNs

In recent years, wireless sensor networks (WSNs) have become an active area of research for monitoring physical and environmental conditions. Due to the interdependence of sensors, a functional anomaly in one sensor can cause a functional anomaly in another sensor, which can further lead to the malfunctioning of the entire sensor network. Existing research work has analysed faulty sensor anomalies but fails to show the effectiveness throughout the entire interdependent network system. In this article, a dictionary learning algorithm based on a non-negative constraint is developed, and a sparse representation anomaly node detection method for sensor networks is proposed based on the dictionary learning. Through experiment on a specific thermal power plant in China, we verify the robustness of our proposed method in detecting abnormal nodes against four state of the art approaches and proved our method is more robust. Furthermore, the experiments are conducted on the obtained abnormal nodes to prove the interdependence of multi-layer sensor networks and reveal the conditions and causes of a system crash.
Detection of Propaganda Using Logistic Regression

Various propaganda techniques are used to manipulate peoples perspectives in order to foster a predetermined agenda such as by the use of logical fallacies or appealing to the emotions of the audience.In this paper, we develop a Logistic Regression-based tool that automatically classifies whether a sentence is propagandistic or not.We utilize features like TF-IDF, BERT vector, sentence length, readability grade level, emotion feature, LIWC feature and emphatic content feature to help us differentiate these two categories.The linguistic and semantic features combination results in 66.16% of F1 score, which outperforms the baseline hugely.
An Improved Two-stream 3D Convolutional Neural Network for Human Action Recognition

In order to obtain global contextual information precisely from videos with heavy camera motions and scene changes, this study proposes an improved spatiotemporal two-stream neural network architecture with a novel convolutional fusion layer. The three main improvements of this study are: 1) the Resnet-101 network has been integrated into the two streams of the target network independently; 2) two kinds of feature maps (i.e., the optical flow motion and RGB-channel information) obtained by the corresponding convolution layer of two streams respectively are superimposed on each other; 3) the temporal information is combined with the spatial information by the integrated three-dimensional (3D) convolutional neural network (CNN) to extract more latent information from the videos. The proposed approach was tested by using UCF-101 and HMDB51 benchmarking datasets and the experimental results show that the proposed two-stream 3D CNN model can gain substantial improvement on the recognition rate in video-based analysis.
Improving Web Image Search with Contextual Information

In web image search, items users search for are images instead of Web pages or online services. Web image search constitutes a very important part of web search. Re-ranking is a trusted technique to improve retrieval effectiveness in web search. Previous work on re-ranking web image search results mainly focuses on intra-query information (e.g., human interactions with the initial list of the current query). Contextual information such as the query sequence and implicit user feedback provided during a search session prior to the current query is known to improve the performance of general web search but has so far not been used in web image search. The differences in result placement and interaction mechanisms of image search make the search process rather different from general Web search engines. Because of these differences, context-aware re-ranking models that have originally been developed for general web search cannot simply be applied to web image search. We propose CARM, a context-aware re-ranking model, a neural network-based framework to re-rank web image search results for a query based on previous interaction behavior in the search session in which the query was submitted. Specifically, we explore a hybrid encoder with an attention mechanism to model intra-query and inter-query user preferences for image results in a two-stage structure. We train context-aware re-ranking model (CARM) to jointly learn query and image representations so as to be able to deal with the multimodal characteristics of web image search. Extensive experiments are carried out on a commercial web image search dataset. The results show that CARM outperforms state-of-the-art baseline models in terms of personalized evaluation metrics. Also, CARM combines the original ranking can improve the original ranking on personalized ranking and relevance estimation. We make the implementation of CARM and relevant datasets publicly available to facilitate future studies.
Self-Normalizing Generative Adversarial Network for Super-Resolution Reconstruction of SAR Images

High-resolution images with abundant detailed information are necessary elements for various applications of synthetic aperture radar (SAR). In this paper, a novel super-resolution image reconstruction method based on self-normalizing generative adversarial network (SNGAN) is proposed. Compared with other published GAN-based super-resolution algorithms, the proposed method reflects its superiority in two aspects. First, the scaled exponential linear units (SeLU) is introduced as the activation function of generator to give the GAN system self-normalization ability and make it more suitable for SAR images. Second, the batch normalization layers after convolution are canceled to reduce the computational requirement and model oscillation. Experiment results on the images of TerraSAR and MSTAR dataset demonstrate that the proposed method acquires satisfactory performance on the resolution enhancement and target recognition of SAR images.
Pufferfish

Data-intensive applications often suffer from significant memory pressure, resulting in excessive garbage collection (GC) and out-of-memory (OOM) errors, harming system performance and reliability. In this paper, we demonstrate how lightweight virtualization via OS containers opens up opportunities to address memory pressure and realize memory elasticity: 1) tasks running in a container can be set to a large heap size to avoid OutOfMemory (OOM) errors, and 2) tasks that are under memory pressure and incur significant swapping activities can be temporarily "suspended" by depriving resources from the hosting containers, and be "resumed" when resources are available. We propose and develop Pufferfish, an elastic memory manager, that leverages containers to flexibly allocate memory for tasks. Memory elasticity achieved by Pufferfish can be exploited by a cluster scheduler to improve cluster utilization and task parallelism. We implement Pufferfish on the cluster scheduler Apache Yarn. Experiments with Spark and MapReduce on real-world traces show Pufferfish is able to avoid OOM errors, improve cluster memory utilization by 2.7x and the median job runtime by 5.5x compared to a memory over-provisioning solution.
Collaborative Localization for Occluded Objects in Connected Vehicular Platform

Localizing occluded object is a long-term challenge in Advanced Driving Assistant System (ADAS) and autonomous driving research. In this paper, we propose a novel graph-matching based approach that leverages the challenge by adopting the deep learning and multiple-view geometry analysis. Specifically, the 3D scene reconstruction is firstly built by associating the comprehensive graph representations of the multiple-view observations, incorporated with the spatial relationship of the co-visible objects so as their discriminant appearance features. Followed by, the localization for occluded object is achieved by inferring from the reconstructed 3D geometry. We conduct experiments to validate the system in connected vehicular platform in the advanced traffic simulation dataset. The experimental results convincingly indicate the effectiveness of the proposed system in real- time object detection, graph generation, matching and location inference for occluded objects.
A new model for iris data set classification based on linear support vector machine parameter's optimization

Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
Octave stretching phenomenon with complex tones of orchestral instruments

For decades, the phenomenon of subjectively enlarged octaves has been investigated using sinusoidal and synthesized complex tones. The present study elaborates the topic with samples of real orchestra instruments in successive tone listening experiments. Compared to previous research, this study also included a substantially larger number of subjects (N = 36). Examined instrument tones were categorized into five groups based on their acoustic principles. In addition, each group was assessed at three dynamic levels (pp-mf-ff). Collected data were analyzed with tuning stretch curves by applying generalized additive models in the manner of the Railsback curve used to characterize piano tuning. Although the tuning curve modeled for the orchestra instruments was observed to differ slightly from the Railsback curve and typical Steinway D grand piano tuning (Steinway, New York), the stretching trends were qualitatively similar. Deviation from a mathematical equal-tempered scale was prominent. According to statistical analyses, dynamics or musical background of the participant did not affect results significantly, but some instrument groups exhibited differences in the curve extremities. In conclusion, the stretched scale is natural for a human listener and should be used as a reference scale in tuning machines instead of the mathematical equal-tempered scale.