Reviewerly/exHarmony huggingface dataset:
A comprehensive study for Arabic Sentiment Analysis (Challenges and Applications)

Arabic language processing works on multiple levels; less often, these complementary levels synergize well with each other. Arabic Language Sentiment Analysis (ALSA) levels consist of phonetics, morphology, syntax, lexicology, semantics, and figurative nature. The analysis of opinions and feelings is of interest in English and Indo-European languages, with little emphasis in the Arabic language, which is a language full of rhetorical characteristics and implicit meanings that have positive and negative connotations and meanings across the six linguistic levels. This paper presents a comprehensive and full proposal of a strategy for ALSA. The ALSA framework analyzes the opinions and feelings at all levels of language, in addition to the importance of building an annotated corpus, which helps to understand an Arabic sentence from the level of phonetics to the rhetorical and metonymy levels.
A machine learning approach based on generative topographic mapping for disruption prevention and avoidance at JET

The need for predictive capabilities greater than 95% with very limited false alarms are demanding requirements for reliable disruption prediction systems in tokamaks such as JET or, in the near future, ITER.The prediction of an upcoming disruption must be provided sufficiently in advance in order to apply effective disruption avoidance or mitigation actions to prevent the machine from being damaged.In this paper, following the typical machine learning workflow, a generative topographic mapping (GTM) of the operational space of JET has been built using a set of disrupted and regularly terminated discharges.In order to build the predictive model, a suitable set of dimensionless, machine-independent, physics-based features have been synthesized, which make use of 1D plasma profile information, rather than simple zero-D time series.The use of such predicting features, together with the power of the GTM in fitting the model to the data, obtains, in an unsupervised way, a 2D map of the multi-dimensional parameter space of JET, where it is possible to identify a boundary separating the region free from disruption from the disruption region.In addition to helping in operational boundaries studies, the GTM map can also be used for disruption prediction exploiting the potential of the developed GTM toolbox to monitor the discharge dynamics.Following the trajectory of a discharge on the map throughout the different regions, an alarm is triggered depending on the disruption risk of these regions.The proposed approach to predict disruptions has been evaluated on a training and an independent test set and achieves very good performance with only one tardive detection and a limited number of false detections.The warning times are suitable for avoidance purposes and, more important, the detections are consistent with physical causes and mechanisms that destabilize the plasma leading to disruptions.
Single Shot Anchor Refinement Network for Oriented Object Detection in Optical Remote Sensing Imagery

Object detection is a challenging task in the field of remote sensing applications due to the complex backgrounds and uncertain orientation of targets. Compared with the horizontal bounding box, the oriented bounding box can provide orientation information while retaining the true size. Most existing oriented object detection methods are based on Faster-RCNN and the other one-stage methods that can achieve real-time speed but have shortcomings in localization and detection accuracy. To further enhance the performance of one-stage methods, we propose an oriented object detection framework that is based on the single shot detector, namely, single shot anchor refinement network (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ARN). The S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ARN obtains the accurate detection results by performing two consecutive regressions. More precisely, the multilevel features of the backbone are used to regress the coordinate offsets between the predefined rotated anchors and the ground-truth boxes to generate the refined anchors. The classification and regression subnetworks assigned to the output features are used to perform the second regression to determine the class labels and further adjust the location of the refined anchors. In addition, receptive field amplification modules (RFAMs) are inserted to enlarge the receptive field and extract more discriminative features. Furthermore, in the anchor matching step, angle-related Intersection over Union (ArIoU) is used to calculate the Intersection over Union (IoU) score instead of the traditional method. Benefiting from the multiple regressions and the insensitivity of the ArIoU score to the angle deviation, the angle sampling interval of the rotated anchor can be reduced. The experimental results for the two public datasets, HRSC2016 and UCAS-AOD, demonstrate the effectiveness of the proposed network.
Generating Commit Messages from Diffs using Pointer-Generator Network

The commit messages in source code repositories are valuable but not easy to be generated manually in time for tracking issues, reporting bugs, and understanding codes. Recently published works indicated that the deep neural machine translation approaches have drawn considerable attentions on automatic generation of commit messages. However, they could not deal with out-of-vocabulary (OOV) words, which are essential context-specific identifiers such as class names and method names in code diffs. In this paper, we propose PtrGNCMsg, a novel approach which is based on an improved sequence-to-sequence model with the pointer-generator network to translate code diffs into commit messages. By searching the smallest identifier set with the highest probability, PtrGNCMsg outperforms recent approaches based on neural machine translation, and first enables the prediction of OOV words. The experimental results based on the corpus of diffs and manual commit messages from the top 2,000 Java projects in GitHub show that PtrGNCMsg outperforms the state-of-the-art approach with improved BLEU by 1.02, ROUGE-1 by 4.00 and ROUGE-L by 3.78, respectively.
Learning with Type-2 Fuzzy activation functions to improve the performance of Deep Neural Networks

In this study, we propose a novel Interval Type-2 (IT2) Fuzzy activation layer that is composed of Single input IT2 (SIT2) Fuzzy Rectifying Units (FRUs) to improve the learning performances of Deep Neural Networks (DNNs). The novel SIT2-FRU has tunable parameters that not only define the slopes of the positive and negative quadrants but also the characteristic of the input–output mapping of the activation function. The novel SIT2-FRU also alleviates vanishing gradient problem and has a fast convergence rate since it can push the mean activation to around zero by processing the inputs defined in the negative quadrant. Thus, SIT2-FRU gives the opportunity to the DNN to have a better learning behavior as it is capable to express linear or sophisticated input–output mapping by simply tuning the footprint of uncertainty of its IT2 fuzzy sets. In order to examine the performance of the SIT2-FRU, comparative experimental studies are performed on the MNIST, Quickdraw Pictionary and CIFAR-10 benchmark datasets. The proposed SIT2-FRU is compared with the state of the art activation functions which are the Rectified Linear Unit (ReLU), Parametric ReLU (PReLU) and Exponential Linear Unit (ELU). Comparative experimental results and analyses clearly show the enhancement in the learning performance of DNNs that include activation layer(s) composed of SIT2-FRUs. It is shown that the learning performance of the SIT2-FRU is robust to different parameter settings of the learning rates and mini batch sizes. Furthermore, the experimental results show that SIT2-FRU can result with a high performance with or without batch normalization layers unlike the other employed activation units. It is concluded that DNNs with SIT2-FRUs have a satisfactory generalization capability, a robust and high learning performance when compared to the ReLU, PReLU and ELU activation functions.
Multi-document extractive text summarization: A comparative assessment on features

Text summarization is the process of generating a brief version of a text that preserves the salient information of the text. For information retrieval, it is a good dimension reduction solution. In addition, it reduces the required reading time. This study focused on extracting informative summaries from multiple documents using commonly used hand-crafted features from the literature. The first investigation focused on the generation of a feature vector. The features were the number of sentences, term frequency, similarity with the title, term frequency-inverse sentence frequency, sentence position, sentence length, sentence–sentence similarity, bushy-path results, phrases of the sentence, proper nouns, n-gram co-occurrence, and length of the document. Secondly, several combinations of these features were examined and a shallow multi-layer perceptron and two differently modeled fuzzy inference systems were used to extract salient sentences from texts in the Document Understanding Conference (DUC) dataset. The summarization performances of these models were evaluated using original classification performance metrics, and recall-oriented understudy for gisting evaluation (ROUGE)-n. This study recommended the use of fuzzy systems based on a feature vector and a fuzzy rule set for extractive text summarization. The extraction methods were evaluated against a changing compression ratio. Results of experiments showed that the implemented neural model tended to incorrectly infer sentences that were not considered salient by human annotators. However, for distinguishing between summary-worthy and summary-unworthy sentences, the fuzzy inference systems performed better than the utilized neural network, as well as better than the existing fuzzy inference-based text summarization approaches in the literature.
Virtual reality's effect on children's inhibitory control, social compliance, and sharing

We compared the effects of different immersive technologies on four- to six-year-olds' inhibitory control skills, social compliance (i.e., walking upon request), and sharing (i.e., physical stickers) with a children's media character (Grover from Sesame Street©). Children (N = 52) completed an inhibitory control task, Simon Says, with Grover either via TV or VR. Children using VR were less likely to suppress a dominant motoric response during Simon Says (i.e., not imitating Grover's actions at the appropriate time) compared to children using TV. More children in the VR condition approached Grover, and they shared a greater number of stickers with Grover compared to the TV condition (among those that shared). There were no differences between conditions for emotional or physical distress or children's enjoyment of the experience. These preliminary findings suggest that VR may elicit differential cognitive and social responses compared to less immersive technology.
LayerCode

With the advance of personal and customized fabrication techniques, the capability to embed information in physical objects becomes evermore crucial. We present LayerCode , a tagging scheme that embeds a carefully designed barcode pattern in 3D printed objects as a deliberate byproduct of the 3D printing process. The LayerCode concept is inspired by the structural resemblance between the parallel black and white bars of the standard barcode and the universal layer-by-layer approach of 3D printing. We introduce an encoding algorithm that enables the 3D printing layers to carry information without altering the object geometry. We also introduce a decoding algorithm that reads the LayerCode tag of a physical object by just taking a photo. The physical deployment of LayerCode tags is realized on various types of 3D printers, including Fused Deposition Modeling printers as well as Stereolithography based printers. Each offers its own advantages and tradeoffs. We show that LayerCode tags can work on complex, nontrivial shapes, on which all previous tagging mechanisms may fail. To evaluate LayerCode thoroughly, we further stress test it with a large dataset of complex shapes using virtual rendering. Among 4,835 tested shapes, we successfully encode and decode on more than 99% of the shapes.
Fast Dynamic Routing Based on Weighted Kernel Density Estimation

Capsules as well as dynamic routing between them are most recently proposed structures for deep neural networks. A capsule groups data into vectors or matrices as poses rather than conventional scalars to represent specific properties of target instance. Besides of pose, a capsule should be attached with a probability (often denoted as activation) for its presence. The dynamic routing helps capsules achieve more generalization capacity with many fewer model parameters. However, the bottleneck that prevents widespread applications of capsule is the expense of computation during routing. To address this problem, we generalize existing routing methods within the framework of weighted kernel density estimation, and propose a fast routing methods. Our method prompts the time efficiency of routing by nearly 40% with negligible performance degradation. By stacking a hybrid of convolutional layers and capsule layers, we construct a network architecture to handle inputs at a resolution of $$64\times {64}$$ pixels. The proposed models achieve a parallel performance with other leading methods in multiple benchmarks.
Digital Forensic Practices and Methodologies for AI Speaker Ecosystems

Various Internet of Things (IoT) devices, such as AI speakers, are being released with different functions to improve user convenience and better life. An AI speaker ecosystem is a cloud-based IoT system built around an AI speaker and IoT devices. In the near future, citizens in whole countries worldwide will be helped in real life when AI-equipped devices are deployed in their homes. Typically, because AI speakers are always operating, they can be used to provide vital evidence for digital forensics; however, privacy issues may arise. AI speakers have provided evidence of murders in the United States and Mexico and are being released without specific regulatory guidelines. In this study, we propose five digital forensic analysis methods for four AI speaker models from different manufacturers released in the Republic of Korea. The five proposed methods are applied to all the AI speaker models, and the results are presented in the Appendix. In particular, we developed a forensic tool for collecting user command history for NAVER Clova.
Structure-Based Sybil Detection in Social Networks via Local Rule-Based Propagation

Social networks are known to be vulnerable to the so-called Sybil attack, in which an attacker maintains massive Sybils and uses them to perform various malicious activities. Therefore, Sybil detection in social networks is a basic security research problem. Structure-based methods have been shown to be promising at detecting Sybils. Existing structure-based methods can be classified into two categories: Random Walk (RW)-based methods and Loop Belief Propagation (LBP)-based methods. RW-based methods cannot leverage labeled Sybils and labeled benign users simultaneously, which limits their detection accuracy, and/or they are not robust to noisy labels. LBP-based methods are not scalable, and they cannot guarantee convergence. In this work, we propose SybilSCAR, a novel structure-based method to detect Sybils in social networks. SybilSCAR is Scalable, Convergent, Accurate, and Robust to label noise. We first propose a framework to unify RW-based and LBP-based methods. Under our framework, these methods can be viewed as iteratively applying a (different) local rule to every user, which propagates label information among a social graph. Second, we design a new local rule, which SybilSCAR iteratively applies to every user to detect Sybils. We compare SybilSCAR with state-of-the-art RW-based methods and LBP-based methods both theoretically and empirically. Theoretically, we show that, with proper parameter settings, SybilSCAR has a tighter asymptotical bound on the number of Sybils that are falsely accepted into a social network than existing structure-based methods. Empirically, we perform evaluation using both social networks with synthesized Sybils and a large-scale Twitter dataset (41.7M nodes and 1.2B edges) with real Sybils, and our results show that 1) SybilSCAR is substantially more accurate and more robust to label noise than state-of-the-art RW-based methods; and 2) SybilSCAR is more accurate and one order of magnitude more scalable than state-of-the-art LBP-based methods.
Building quantum neural networks based on a swap test

An artificial neural network, consisting of many neurons in different layers, is an important method to simulate the human brain. Usually, one neuron has two operations: one is linear, the other is nonlinear. The linear operation is the inner product and the nonlinear operation is represented by an activation function. In this work, we introduce a kind of quantum neuron whose inputs and outputs are quantum states. The inner product and activation operator of the quantum neurons can be realized by quantum circuits. Based on the quantum neuron, we propose a model of a quantum neural network in which the weights between neurons are all quantum states. We also construct a quantum circuit to realize this quantum neural network model. A learning algorithm is proposed meanwhile. We show the validity of the learning algorithm theoretically and demonstrate the potential of the quantum neural network numerically.
Style-Controlled Synthesis of Clothing Segments for Fashion Image Manipulation

We propose an approach for digitally altering people's outfits in images. Given images of a person and a desired clothing style, our method generates a new clothing item image. The new item displays the color and pattern of the desired style while geometrically mimicking the person's original item. Through superimposition, the altered image is made to look as if the person is wearing the new item. Unlike recent works with full-image synthesis, our work relies on segment synthesis, yielding benefits in virtual try-on. For the synthesis process, we assume two underlying factors characterizing clothing segments: geometry and style. These two factors are disentangled via preprocessing and combined using a neural network. We explore several networks and introduce important aspects of the architecture and learning process. Our experimental results are three-fold: 1) on images from fashion-parsing datasets, we demonstrate the generation of high-quality clothing segments with fine-level style control; 2) on a virtual try-on benchmark, our method shows superiority over prior synthesis methods; and 3) in transferring clothing styles, we visualize the differences between our method and neural style transfer.
Quantum Annealing of Vehicle Routing Problem with Time, State and Capacity

We propose a brand-new formulation for capacitated vehicle routing problem (CVRP) as a quadratic unconstrained binary optimization (QUBO). The formulated CVRP is equipped with time-table which describes time-evolution of each vehicle. Therefore, various constraints associated with time can be successfully realized. Similarly, constraints of capacities are also introduced, where capacitated quantities are allowed to increase and decrease according to the cities in which vehicles arrive. As a bonus of capacity-qubits, one can also obtain a description of state, which allows us to set various traveling rules, depending on the state of each vehicle. As a consistency check, the proposed QUBO formulation is also evaluated using a quantum annealing machine, D-Wave 2000Q.
Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Model

In this paper we proposed a novel Adversarial Training (AT) approach for end-to-end speech recognition using a Criticizing Language Model (CLM). In this way the CLM and the automatic speech recognition (ASR) model can challenge and learn from each other iteratively to improve the performance. Since the CLM only takes the text as input, huge quantities of unpaired text data can be utilized in this approach within end-to-end training. Moreover, AT can be applied to any end-to-end ASR model using any deep-learning-based language modeling frameworks, and compatible with any existing end-to-end decoding method. Initial results with an example experimental setup demonstrated the proposed approach is able to gain consistent improvements efficiently from auxiliary text data under different scenarios.
Interactive Deep Colorization Using Simultaneous Global and Local Inputs

Colorization methods using deep neural networks have become a recent trend. However, most of them do not allow user inputs, or only allow limited user inputs (only global inputs or only local inputs), to control the output colorful images. The possible reason is that it's difficult to differentiate the influence of different kind of user inputs in network training. To solve this problem, we propose a novel deep colorization method allowing inputting global and local inputs simultaneously or individually, which is not supported in previous deep colorization methods. The key steps include designing a neural network model that can appropriately combine the different inputs, and designing an appropriate loss function that can differentiate the influence of different inputs. Experimental results show that our method can magnificently control the colorized results and generate state-of-art results.
Spatiotemporal-Textual Co-Attention Network for Video Question Answering

Visual Question Answering (VQA) is to provide a natural language answer for a pair of an image or video and a natural language question. Despite recent progress on VQA, existing works primarily focus on image question answering and are suboptimal for video question answering. This article presents a novel Spatiotemporal-Textual Co-Attention Network (STCA-Net) for video question answering. The STCA-Net jointly learns spatially and temporally visual attention on videos as well as textual attention on questions. It concentrates on the essential cues in both visual and textual spaces for answering question, leading to effective question-video representation. In particular, a question-guided attention network is designed to learn question-aware video representation with a spatial-temporal attention module. It concentrates the network on regions of interest within the frames of interest across the entire video. A video-guided attention network is proposed to learn video-aware question representation with a textual attention module, leading to fine-grained understanding of question. The learned video and question representations are used by an answer predictor to generate answers. Extensive experiments on two challenging datasets of video question answering, i.e., MSVD-QA and MSRVTT-QA, have shown the effectiveness of the proposed approach.