Reviewerly/exHarmony huggingface dataset:
Random Forest Twitter Bot Classifier

As social media usage continues to grow, so does the number of automated bot accounts that either spread malicious content or generate fraudulent popularity for political and social figures. Twitter, one of the more popular social media sites, has been plagued by bot armies and needs to find a way to rid itself of these infestations. Studies have shown that currently there are no accurate ways to detect Twitter bot accounts regularly. Using datasets from IIT (Institute of Informatics and Telematics), a feature set is created that allows a classifier to be both accurate and generalized. This feature set includes accessible features from the Twitter API as well as derivative ratio features that give a different perspective of the account. We then decide upon the Random Forest machine learning algorithm because of its ability to prevent most overfitting as well as create a generalized model that can be deployed for accurate use directly after training. In this paper, we propose a set of attributes for a Random Forest classifier that results in high accuracy (90.25%) and generalizability. To prove our derived feature set outperforms basic feature sets and grants valuable insight, we test our derived features against the most important of the basic features. Our derived ratio features outperform these basic account features.
Entanglement and quantum teleportation via dissipative cavities

A maximum entangled atomic system consists of 2-two level atoms interacts locally with their own dissipative cavity, is discussed. We show that, the amount of the survival entanglement of the atomic system decays as the detuning parameter increases or the coupling constant decreases. A theoretical teleportation is suggest to teleport a state of one cavity initially prepared in a superposition coherent state to the second cavity is discussed, by using the atomic system as a channel. It is shown that, the efficiency of the suggested teleportation may be improved by increasing the detuning parameter or decreasing the coupling constant between the atomic systems and the cavities.
Generative Adversarial Network with Policy Gradient for Text Summarization

Abstractive text summarization is the task of generating meaningful summary from a given document (short or long). This is a very challenging task for longer documents, since they suffer from repetitions (redundancy) when the given document is long and the generated summary should contain multi-sentences. In this paper we present an approach for applying generative adversarial networks in abstractive text summarization tasks with a novel time-decay attention mechanism. The data generator is modeled as a stochastic policy in reinforcement learning. The generator's goal is to generate summaries which are difficult to be discriminated from real summaries. The discriminator aims to estimate the probability that a summary came from the training data rather than the generator to guide the training of the generative model. This framework corresponds to a minimax two-player game. Qualitatively and quantitatively experimental results (human evaluations and ROUGE scores) show that our model can generate more relevant, less repetitive, grammatically correct, preferable by humans and is promising in solving the abstractive text summarization task.
A Comparative Analysis of Trust Requirements in Decentralized Identity Management

Identity management is a fundamental component in securing online services. Isolated and centralized identity models have been applied within organizations. Moreover, identity federations connect digital identities across trust domain boundaries. These traditional models have been thoroughly studied with regard to trust requirements. The recently emerging blockchain technology enables a novel decentralized identity management model that targets user-centricity and eliminates the identity provider as a trusted third party. The result is a substantially different set of entities with mutual trust requirements. In this paper, we analyze decentralized identity management based on blockchain through defining topology patterns. These patterns depict schematically the decentralized setting and its main actors. We study trust requirements for the devised patterns and, finally, compare the result to traditional models. Our contribution enables a clear view of differences in trust requirements within the various models.
Video deblurring via motion compensation and adaptive information fusion

Non-uniform motion blur caused by camera shake or object motion is a common artifact in videos captured by hand-held devices. Recent advances in video deblurring have shown that convolutional neural networks (CNNs) are able to aggregate information from multiple unaligned consecutive frames to generate sharper images. However, without explicit image alignment, most of the existing CNN-based methods often introduce temporal artifacts, especially when the input frames are severely blurred. To this end, we propose a novel video deblurring method to handle spatially varying blur in dynamic scenes. In particular, we introduce a motion estimation and motion compensation module which estimates the optical flow from the blurry images and then warps the previously deblurred frame to restore the current frame. Thus, the previous processing results benefit the restoration of the subsequent frames. This recurrent scheme is able to utilize contextual information efficiently and can facilitate the temporal coherence of the results. Furthermore, to suppress the negative effect of alignment error, we propose an adaptive information fusion module that can filter the temporal information adaptively. The experimental results obtained in this study confirm that the proposed method is both effective and efficient.
Augmenting LIDC dataset using 3D generative adversarial networks to improve lung nodule detection

One drawback of Computer Aided Detection (CADe) systems is the large amount of data needed to train them, which may be expensive in the medical field. We propose using a generative adversarial network (GAN) as a potential data augmentation strategy to generate more training data to improve CADe. In our preliminary results, using the NIH/NCI Lung Image Database Consortium, we obtained a higher sensitivity when training a CADe system on our augmented lung nodule 3D data than training it without. We show that GANs are a viable method of data augmentation for lung nodule detection and are a promising area of potential research in the CADe domain.
in5

The human body is a complex system itself composed of complex systems; its state influences all aspects of our health, wellbeing including our cognitive to physical performance. In HCI most of us are not well versed in how this complex system works. The following paper proposes in5, a model to help make that physiology accessible for design. The model has two parts: (1) the MEECS dichotomies: five fundamental-to-life, volitional processes - move, eat, engage, cogitate, sleep - that are affected by parameters of quality, quantity, time and context, and (2) tuning: an approach to adjust the parameters of these dichotomies toward "dialing in" health, wellbeing, performance. The paper also offers examples for how this model can be explored for design research.
Text categorisation in Quran and Hadith: Overcoming the interrelation challenges using machine learning and term weighting

Quran and Al-Hadith are interrelated in the sense that both often complement each other in interpreting Islamic teachings. In order to gain comprehension of the Quran in detail, it is vital for a Muslim to refer to Al-Hadith in clarifying ambiguities from the Quran. Al-Hadith offers explanations and lends certainties to the abstract concepts depicted in the Quran. With that, this research proposes a method using text categorisation to classify selected categories by determining the interrelation between the resources. Several interrelated cases were simulated by using a combination of different Islamic resources datasets comprising Quran and Hadiths. The selected three categories; Hajj, Prayer, and Zakat, were compared using three classification methods (Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN)) via term weighting; Term Frequency – Inverse Document Frequency (TF-IDF). As a result, the SVM, regardless of being used alone or with term weighting, successfully addressed the interrelationship for single- and multi-label classifications. Additionally, SVM attained better accuracy with 10%–20% improvement, when compared to the other methods that had managed to exhibit slight improvement accuracy wise.
An improved evidence-based aggregation method for sentiment analysis

Sentiment analysis is one of the natural language processing tasks used to find reviews expressed in online texts and classify them into different classes. One of the most important factors affecting the efficiency of sentiment analysis methods is the aggregation algorithm used for scores combination. Recently, Dempster–Shafer algorithm has been used for scores aggregation. This algorithm has a higher precision than common methods such as average, weighed average, product and voting, but the problem with this algorithm is the aggregation of a dominant high or low score that is always selected by the algorithm as the overall score. In the current research, a new method is proposed for scores aggregation that employs both the most and the second probable classes to predict the final score. The proposed approach considers every review as a set of sentences each of which has its own sentiment orientation and score and computes the probability of belonging of every sentence to different classes in a five-star scale using a pure lexicon-based system. These probabilities are then used for document-level sentiment detection. To this aim, two-point structure is used to improve the Dempster–Shafer aggregation algorithm. The proposed method is applied to review datasets of TripAdvisor and CitySearch which have been used in previous studies. The obtained results show that in comparison with the original Dempster–Shafer aggregation method, the precision of the proposed method for both datasets is 23% and 27% higher, respectively.
Distributed Join Processing Between Streaming and Stored Big Data Under the Micro-Batch Model

In order to interpret, enrich, and analyze the streaming data, stream applications often access the data stored in an external database. Although there has been a lot of studies on stream processing, little attention has been paid so far to the join between streaming data and stored data. In this paper, we propose a comprehensive solution called DS-join for distributed processing of the join under the micro-batch model of recently distributed stream processing engines (SPEs), such as spark streaming. The micro-batch model performs stream processing as a series of very small batch jobs and is more fault-tolerant in a distributed environment compared with the record-at-a-time model. The DS-join reduces the number of database accesses by using micro-batching. Furthermore, the DS-join optimizes the join operation by minimizing the data shuffling, managing a cache in a distributed SPE, parallelizing the join processing, and balancing the load between the SPE and the external database system. The experimental results using real and synthetic datasets show that, compared with the state-of-the-art methods, the DS-join significantly improves throughput, especially for large databases.
Face Swapping: Realistic Image Synthesis Based on Facial Landmarks Alignment

We propose an image-based face swapping algorithm, which can be used to replace the face in the reference image with the same facial shape and features as the input face. First, a face alignment is made based on a group of detected facial landmarks, so that the aligned input face and the reference face are consistent in size and posture. Secondly, an image warping algorithm based on triangulation is presented to adjust the reference face and its background according to the aligned input faces. In order to achieve more accurate face swapping, a face parsing algorithm is introduced to realize the accurate detection of the face-ROIs, and then the face-ROI in the reference image is replaced with the input face-ROI. Finally, a Poisson image editing algorithm is adopted to realize the boundary processing and color correction between the replacement region and the original background, and then the final face swapping result is obtained. In the experiments, we compare our method with other face swapping algorithms and make a qualitative and quantitative analysis to evaluate the reality and the fidelity of the replaced face. The analysis results show that our method has some advantages in the overall performance of swapping effect.
A Blockchain-based Trust Management Approach for Connected Autonomous Vehicles in Smart Cities

Advancement in communication technologies and the Internet of Things (IoT) is driving smart cities adoption that aims to increase operational efficiency and improve the quality of services and citizen welfare. It is estimated that by 2020, 75% of cars shipped globally will be equipped with hardware to facilitate vehicle connectivity. The privacy, reliability and integrity of communication must be ensured so that actions can be accurate and implemented promptly after receiving actionable information. Because vehicles are equipped with the ability to compute, communicate, and sense their environment, there is a concomitant critical need to create and maintain trust among network entities in the context of the network's dynamism, an issue that requires building and validating the trust between entities in a small amount of time before entities leave each other's range. In this work, we present a multi-tier scheme consisting of an authentication and trust building/distribution framework designed to ensure the safety and validity of the information exchanged in the system.
Orientation-Aware Saliency Guided JND Model for Robust Image Watermarking

The visual saliency-based just noticeable distortion (VS-JND) model has been widely used in the quantization-based watermarking framework, but it remains a challenge that how to reflect the characteristics of the human visual system (HVS) to the greatest extent in the processing of detecting salient objects. In this paper, given that the HVS has a different perception sensitivity in the different orientation of the image, a compound orientation feature map, which contains the vertical, horizontal, and diagonal information for directional patch extraction, is computed to improve the visual saliency map within just noticeable distortion (JND) profile. First, the DC and three low-frequency coefficients of each block are used to calculate the luminance and texture feature map, respectively. Then, the feature map based on the complex directional feature includes the horizontal, vertical, and diagonal information contained in the image is calculated by three low-frequency coefficients. Finally, we will be able to make a linear fusion of the same three different visual features to get the final VS map, and the JND model is improved according to the new VS model. Later, the new proposed VS-JND model is employed in the quantization watermarking (Q-watermarking) framework. The simulation results show that the proposed watermarking scheme is effective and produces superior robustness compared with existing monochrome image watermarking schemes.
Recommendation Mechanism for Patent Trading Empowered by Heterogeneous Information Networks

The emerging patent trading platforms help to ease information asymmetry and trust issues during transaction, but a proactive recommendation mechanism that intelligently helps patent buyers identify relevant patents is still absent in the literature. This study proposes a recommendation mechanism for patent trading empowered by heterogeneous information networks (HIN) that integrates various patent information such as patent trading, patent invention, patent citation, patent ontology, and patent contents. Further, the meta-path-based similarity measure (i.e., AvgSim) is employed to calculate relevance and identify the different motivations of potential buyers in buying patents. We conducted two experiments to examine the performance of a proposed mechanism. An offline experiment on Public PatentsView database and Patent Assignment database show that the HIN-empowered recommendation outperforms baseline methods. We also implemented the proposed mechanism on a real-world trading platform (www.InnoCity.com). The recommendation function achieves satisfying results by tracking users' feedback, which further validates the usability of HIN-empowered recommendation in a patent trading context.
Simultaneously precise estimations of phase and amplitude variations based on weak-value amplification

In this paper, we theoretically and experimentally present a precision method to simultaneously estimate the phase and amplitude variations of light based on the weak-value amplification. We demonstrate that the imaginary and real components of the weak-value correspond to the amplification of the phase and amplitude variations, respectively. If both the phase and the amplitude are varied, the phase measurement using the imaginary weak-value is not influenced by the amplitude shift and the amplitude measurement using the real weak value is not influenced by the phase shift. Thus, the tiny changes of the phase and amplitude of light can be obtained simultaneously with different post-selections, and the measurement precision reaches ∼10−6 rad. These findings may be favorable for highly sensitive optical polarization-dependent effects, including optical activity and magneto-optic effects.
Instance-Level Embedding Adaptation for Few-Shot Learning

Few-shot learning aims to recognize novel categories from just a few labeled instances.Existing metric learning-based approaches perform classifications by nearest neighbor search in the embedding space.The embedding function is a deep neural network and usually shared by all novel categories.However, these brute approaches lack a fast adaptation mechanism like meta-learning when dealing with novel categories.To tackle this, we present a novel instance-level embedding adaptation mechanism, aiming at rapidly adapting embedding deep features to improve their generalization ability in recognizing novel categories.To this end, we design an Attention Adaptation Module to pull a query instance and its corresponding class center as close as possible.Note that, each query instance is pulled closer to its corresponding class center before performing nearest neighbor classifications.This instance-level reduction of intra-class distance increases the probability of correct classifications, and thus improves the generalization ability to embed deep features and promoting the performance.The extensive experiments are conducted on two benchmark datasets: miniImageNet and CUB.Our approach yields very promising results on both datasets.In addition, in a realistic cross-domain evaluation setting, our method also achieves the-state-of-the-art performance.
Examining the quality of social support messages produced face-to-face and in computer-mediated communication: The effects of hyperpersonal communication

The hyperpersonal communication model was used to investigate the implications of the reduced social cues in computer-mediated communication (CMC) for the production of social support messages. Participants were randomly assigned to interact with a confederate seeking help about a problem for which the confederate was or was not responsible. The interactions took place either face-to-face or in one of two CMC conditions. The results were partially consistent with the intensification effect proposed in the hyperpersonal model. Participants evaluated the confederate most negatively, but produced the highest quality support messages, in the CMC condition with visual anonymity followed by the CMC condition and face-to-face condition. Participants’ evaluations of the confederate were also influenced by the confederate’s responsibility for their problem.
Inductive t-SNE via deep learning to visualize multi-label images

This work presents a methodology for dimensionality reduction of images with multiple occurrences of multiple objects, such that they can be placed on a 2-dimensional plane under the constrain that nearby images are similar in terms of visual content and semantics. The first part of this methodology adds inductive capabilities to the well known t-SNE method used for visualization, thus making possible its generalization for unseen data, as opposed to previous extensions with only transductive capabilities. This is achieved by pairing the base t-SNE with a Deep Neural Network. The second part exploits semantic information to perform supervised dimensionality reduction, which results in better separability of the low-dimensional space, this is, it separates better images with no relevance, while retaining the proximity of those images with partial relevance. Since dealing with images having multiple occurrences of multiple objects requires the consideration of partial relevance, additionally we present a definition of partial relevance for the evaluation of classification and retrieval scenarios on images, or other documents, that share contents, at least partially.
Type Migration in Ultra-Large-Scale Codebases

Type migration is a refactoring activity in which an existing type is replaced with another one throughout the source code. Manually performing type migration is tedious as programmers need to find all instances of the type to be migrated, along with its dependencies that propagate over assignment operations, method hierarchies, and subtypes. Existing automated approaches for type migration are not adequate for ultra-large-codebases - they perform an intensive whole-program analysis that does not scale. If we could represent the type structure of the program as graphs, then we could employ a MAPREDUCE parallel and distributed process that scales to hundreds of millions of LOC. We implemented this approach as an IDE-independent tool called T2R, which integrates with most build systems. We evaluated T2R's accuracy, usefulness and scalability on seven open source projects and one proprietary codebase of 300M LOC. T2R generated 130 type migration patches, of which the original developers accepted 98%.
Broad Learning Model Based on Enhanced Features Learning

With the continuous development of deep learning, its drawbacks are also beginning to appear.As an alternative to deep learning, broad learning is emerging.However, the level of broad learning model is shallow, so feature learning is not sufficient.In order to solve two problems of small samples whose dimensions are not very high in network model training, it cannot be adequately trained in the deep learning model and the features of input data cannot be fully learned in the broad learning model.This paper attempts to add a hidden layer on the enhancement nodes of the broad learning model and do shallow learning for the enhanced features to learn the hierarchical features again.This improved broad learning model also provides a new idea for solving the problem of small samples.From the perspective of regression and classification, this paper proves that for small samples whose dimensions are not very high, the effect of the improved broad learning model is better than that of the original broad learning model and also proves that the improved broad learning model has a good ability of application.This shows that the broad learning model based on enhanced features learning has the necessity and feasibility of further research.