Reviewerly/exHarmony huggingface dataset:
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search

Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have mostly been applied to “standard” ad hoc retrieval tasks over web pages and newswire articles. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network), a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A poolingbased similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011–2014 show that our model significantly outperforms prior feature-based as well as existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models. Our code and data are publicly available.1
LAGraph: A Community Effort to Collect Graph Algorithms Built on Top of the GraphBLAS

In 2013, we released a position paper to launch a community effort to define a common set of building blocks for constructing graph algorithms in the language of linear algebra. This led to the GraphBLAS. We released a specification for the C programming language binding to the GraphBLAS in 2017. Since that release, multiple libraries that conform to the GraphBLAS C specification have been produced. In this position paper, we launch the next phase of this ongoing community effort: a project to assemble a set of high level graph algorithms built on top of the GraphBLAS. While many of these algorithms are well-known with high quality implementations available, they have not been assembled in one place and integrated with the GraphBLAS. We call this project the LAGraph graph algorithms project and with this position paper, we put out a call for collaborators to join us. While the initial goal is to just assemble these algorithms into a single framework, the long term goal is a library of production-worthy code, with the LAGraph library serving as an open source repository of verified graph algorithms that use the GraphBLAS.
GANs for Semi-Supervised Opinion Spam Detection

Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews, only a few of them have been labeled spam or non-spam. We propose spamGAN, a generative adversarial network which relies on limited labeled data as well as unlabeled data for opinion spam detection. spamGAN improves the state-of-the-art GAN based techniques for text classification. Experiments on TripAdvisor data show that spamGAN outperforms existing techniques when labeled data is limited. spamGAN can also generate reviews with reasonable perplexity.
Know your Big Data Trade-offs when Classifying Encrypted Mobile Traffic with Deep Learning

The spread of handheld devices has led to the unprecedented growth of traffic volumes traversing both local networks and the Internet, appointing mobile traffic classification as a key tool for gathering highly-valuable profiling information, other than traffic engineering and service management. However, the nature of mobile traffic severely challenges state-of-art Machine-Learning (ML) approaches, since the quickly evolving and expanding set of apps generating traffic hinders ML-based approaches, that require domain-expert design. Deep Learning (DL) represents a promising solution to this issue, but results in higher completion times, in turn suggesting the application of the Big-Data (BD) paradigm. In this paper, we investigate for the first time BD-enabled classification of encrypted mobile traffic using DL from a general standpoint, (a) defining general design guidelines, (b) leveraging a public-cloud platform, and (c) resorting to a realistic experimental setup. We found that, while BD represents a transparent accelerator for some tasks, this is not the case for the training phase of DL architectures for traffic classification, requiring a specific BD-informed design. The experimental setup is built upon a three-dimensional investigation path in the BD adoption, namely: (i) completion time, (ii) deployment costs, and (iii) classification performance, highlighting relevant non-trivial trade-offs.
Spotting Collective Behaviour of Online Frauds in Customer Reviews

Online reviews play a crucial role in deciding the quality before purchasing any product. Unfortunately, spammers often take advantage of online review forums by writing fraud reviews to promote/demote certain products. It may turn out to be more detrimental when such spammers collude and collectively inject spam reviews as they can take complete control of users' sentiment due to the volume of fraud reviews they inject. Group spam detection is thus more challenging than individual-level fraud detection due to unclear definition of a group, variation of inter-group dynamics, scarcity of labeled group-level spam data, etc. Here, we propose DeFrauder, an unsupervised method to detect online fraud reviewer groups. It first detects candidate fraud groups by leveraging the underlying product review graph and incorporating several behavioral signals which model multi-faceted collaboration among reviewers. It then maps reviewers into an embedding space and assigns a spam score to each group such that groups comprising spammers with highly similar behavioral traits achieve high spam score. While comparing with five baselines on four real-world datasets (two of them were curated by us), DeFrauder shows superior performance by outperforming the best baseline with 17.11% higher NDCG@50 (on average) across datasets.
Multi-Granularity Self-Attention for Neural Machine Translation

Current state-of-the-art neural machine translation (NMT) uses a deep multi-head selfattention network with no explicit phrase information.However, prior work on statistical machine translation has shown that extending the basic translation unit from words to phrases has produced substantial improvements, suggesting the possibility of improving NMT performance from explicit modeling of phrases.In this work, we present multi-granularity self-attention (MG-SA): a neural network that combines multi-head selfattention and phrase modeling.Specifically, we train several attention heads to attend to phrases in either n-gram or syntactic formalism.Moreover, we exploit interactions among phrases to enhance the strength of structure modeling -a commonly-cited weakness of self-attention.Experimental results on WMT14 English-to-German and NIST Chinese-to-English translation tasks show the proposed approach consistently improves performance.Targeted linguistic analysis reveals that MG-SA indeed captures useful phrase information at various levels of granularities.
Drone Forensic Investigation: DJI Spark Drone as A Case Study

Carrying capabilities of drones and their easy accessibility to the public have led to an increase in drone crimes. The need for drone forensic analysis is paramount. The paper presents the collection, examination, correction, and analysis of important artifacts from the recorded flight data. The crime reconstruction for temporal analysis and relational artifacts is presented for exploring and evaluating the association among the drone, the mobile phone and SD card.
Topological Frontier-Based Exploration and Map-Building Using Semantic Information

Exploration of unknown environments is a fundamental problem in autonomous robotics that deals with the complexity of autonomously traversing an unknown area while acquiring the most important information of the environment. In this work, a mobile robot exploration algorithm for indoor environments is proposed. It combines frontier-based concepts with behavior-based strategies in order to build a topological representation of the environment. Frontier-based approaches assume that, to gain the most information of an environment, the robot has to move to the regions on the boundary between open space and unexplored space. The novelty of this work is in the semantic frontier classification and frontier selection according to a cost–utility function. In addition, a probabilistic loop closure algorithm is proposed to solve cyclic situations. The system outputs a topological map of the free areas of the environment for further navigation. Finally, simulated and real-world experiments have been carried out, their results and the comparison to other state-of-the-art algorithms show the feasibility of the exploration algorithm proposed and the improvement that it offers with regards to execution time and travelled distance.
Experimental investigation of entropic uncertainty relations and coherence uncertainty relations

The uncertainty relation usually is one of the most important features in quantum mechanics and is the backbone of quantum theory, which distinguishes it from the rule in its classical counterpart. Specifically, entropy-based uncertainty relations are of fundamental importance in the region of quantum information theory, offering one nontrivial bound of key rate towards quantum key distribution. In this work, we experimentally demonstrate the entropic uncertainty relations and coherence-based uncertainty relations in an all-optics platform. By means of preparing two kinds of bipartite initial states with high fidelity, i.e., Bell-like states and Bell-like diagonal states, we carry out local projective measurements over a complete set of mutually unbiased bases on the measured subsystem. In terms of quantum tomography, the density matrices of the initial states and the postmeasurement states are reconstructed. It shows that our experimental results coincide with the theoretical predictions very well. Additionally, we also verify that the lower bounds of both the entropy-based and coherence-based uncertainty can be tightened by imposing the Holevo quantity and mutual information, and the entropic uncertainty is inversely correlated with the coherence. Our demonstrations might offer an insight into their uncertainty relations and their connection to quantum coherence in quantum information science, which might be applicable to the security analysis of quantum key distributions.
Averaging Is Probably Not the Optimum Way of Aggregating Parameters in Federated Learning

Federated learning is a decentralized topology of deep learning, that trains a shared model through data distributed among each client (like mobile phones, wearable devices), in order to ensure data privacy by avoiding raw data exposed in data center (server). After each client computes a new model parameter by stochastic gradient descent (SGD) based on their own local data, these locally-computed parameters will be aggregated to generate an updated global model. Many current state-of-the-art studies aggregate different client-computed parameters by averaging them, but none theoretically explains why averaging parameters is a good approach. In this paper, we treat each client computed parameter as a random vector because of the stochastic properties of SGD, and estimate mutual information between two client computed parameters at different training phases using two methods in two learning tasks. The results confirm the correlation between different clients and show an increasing trend of mutual information with training iteration. However, when we further compute the distance between client computed parameters, we find that parameters are getting more correlated while not getting closer. This phenomenon suggests that averaging parameters may not be the optimum way of aggregating trained parameters.
The Future of Higher Education in the Light of Artificial Intelligence Transformations

This study discussed the potential effects of artificial intelligence on higher education at Prince Sattam Bin Abdulaziz University. To achieve this goal, a qualitative research methodology was used by asking an open question on a sample of academics. The results of the analysis showed that there is a decrease in the level of awareness of the mechanisms of applying artificial intelligence, and that there is a need to further spread awareness in The Saudi environment on the possibilities of using artificial intelligence applications in education.
Too Real to Be Virtual: Autonomic and EEG Responses to Extreme Stress Scenarios in Virtual Reality

The evolution of virtual reality (VR) technologies requires setting boundaries of its use. In this study, 3 female participants were experiencing VR scenarios with stressful content and their activity of the autonomic nervous system and EEG were recorded. It has been discovered that virtual reality can evoke acute stress reactions accompanied by activation of the sympathetic nervous system and a decrease in the activity of the parasympathetic nervous system. The high-stress response is accompanied by a decrease in the power of the EEG, and, on the contrary, the activation of the avoidance reaction is accompanied by an increase in the power of the EEG alpha waves. Therefore, the use of stressful VR content can cause high emotional stress to a user and restrictions should be considered.
Image Encryption Based on Hash Table Scrambling and DNA Substitution

This paper proposes an image encryption algorithm based on hash table structure scrambling and DNA substitution.The algorithm uses the classical 'scrambling-diffusion' process, and the pseudo-random sequence used in each process is generated by the hyper-chaotic Chen system.Firstly, in the process of scrambling, two sequences with no repeated values are constructed by using the closed hash method in the hash table structure for chaotic sequences, and the plain image is scrambled twice according to the two sequences.Then, in the process of diffusion, DNA coding and decoding rules and DNA substitution rules are made according to the chaotic sequences.The image is dynamically encoded, substituted and decoded with DNA.In this way, while modifying the gray value of image pixels, it also destroys the strong correlation between adjacent pixels.Through a series of verifications, it is concluded that the algorithm not only has good encryption effect, high security and large key space, but also has the advantages of high key sensitivity and strong anti-attack capability.
Swapped face detection using deep learning and subjective assessment

Abstract The tremendous success of deep learning for imaging applications has resulted in numerous beneficial advances. Unfortunately, this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent. In this study, we use deep transfer learning for face swapping detection, showing true positive rates greater than 96% with very few false alarms. Distinguished from existing methods that only provide detection accuracy, we also provide uncertainty for each prediction, which is critical for trust in the deployment of such detection systems. Moreover, we provide a comparison to human subjects. To capture human recognition performance, we build a website to collect pairwise comparisons of images from human subjects. Based on these comparisons, we infer a consensus ranking from the image perceived as most real to the image perceived as most fake. Overall, the results show the effectiveness of our method. As part of this study, we create a novel dataset that is, to the best of our knowledge, the largest swapped face dataset created using still images. This dataset will be available for academic research use per request. Our goal of this study is to inspire more research in the field of image forensics through the creation of a dataset and initial analysis.
Reliable Fidelity and Diversity Metrics for Generative Models

Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated images has been the Frechet Inception Distance (FID) score. Because it does not differentiate the fidelity and diversity aspects of the generated images, recent papers have introduced variants of precision and recall metrics to diagnose those properties separately. In this paper, we show that even the latest version of the precision and recall metrics are not reliable yet. For example, they fail to detect the match between two identical distributions, they are not robust against outliers, and the evaluation hyperparameters are selected arbitrarily. We propose density and coverage metrics that solve the above issues. We analytically and experimentally show that density and coverage provide more interpretable and reliable signals for practitioners than the existing metrics. Code: this https URL.
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup

While deep neural networks achieve great performance on fitting the training distribution, the learned networks are prone to overfitting and are susceptible to adversarial attacks. In this regard, a number of mixup based augmentation methods have been recently proposed. However, these approaches mainly focus on creating previously unseen virtual examples and can sometimes provide misleading supervisory signal to the network. To this end, we propose Puzzle Mix, a mixup method for explicitly utilizing the saliency information and the underlying statistics of the natural examples. This leads to an interesting optimization problem alternating between the multi-label objective for optimal mixing mask and saliency discounted optimal transport objective. Our experiments show Puzzle Mix achieves the state of the art generalization and the adversarial robustness results compared to other mixup methods on CIFAR-100, Tiny-ImageNet, and ImageNet datasets. The source code is available at this https URL.
Disrupting Healthcare Silos: Addressing Data Volume, Velocity and Variety With a Cloud-Native Healthcare Data Ingestion Service

Healthcare enterprises are starting to adopt cloud computing due to its numerous advantages over traditional infrastructures. This has become a necessity because of the increased volume, velocity and variety of healthcare data, and the need to facilitate data correlation and large-scale analysis. Cloud computing infrastructures have the power to offer continuous acquisition of data from multiple heterogeneous sources, efficient data integration, and big data analysis. At the same time, security, availability, and disaster recovery are critical factors aiding towards the adoption of cloud computing. However, the migration of healthcare workloads to cloud is not straightforward due to the vagueness in healthcare data standards, heterogeneity and sensitive nature of healthcare data, and many regulations that govern its usage. This paper highlights the need for providing healthcare data acquisition using cloud infrastructures and presents the challenges, requirements, use-cases, and best practices for building a state-of-the-art healthcare data ingestion service on cloud.
Stable polarization entanglement based quantum key distribution over a deployed metropolitan fiber

We demonstrate a quantum key distribution implementation over deployed dark telecom fibers with polarisation-entangled photons generated at the O-band. One of the photons in the pairs are propagated through 10km of deployed fiber while the others are detected locally. Polarisation drifts experienced by the photons propagating through the fibers are compensated with liquid crystal variable retarders. This ensures continuous and stable QKD operation with an average QBER of 6.4% and a final key rate of 109 bits/s.
Novel and efficient randomized algorithms for feature selection

Feature selection is a crucial problem in efficient machine learning, and it also greatly contributes to the explainability of machine-driven decisions. Methods, like decision trees and Least Absolute Shrinkage and Selection Operator (LASSO), can select features during training. However, these embedded approaches can only be applied to a small subset of machine learning models. Wrapper based methods can select features independently from machine learning models but they often suffer from a high computational cost. To enhance their efficiency, many randomized algorithms have been designed. In this paper, we propose automatic breadth searching and attention searching adjustment approaches to further speedup randomized wrapper based feature selection. We conduct theoretical computational complexity analysis and further explain our algorithms' generic parallelizability. We conduct experiments on both synthetic and real datasets with different machine learning base models. Results show that, compared with existing approaches, our proposed techniques can locate a more meaningful set of features with a high efficiency.
Diaspora micro-influencers and COVID-19 communication on social media: The case of Chinese-speaking YouTube vloggers

Abstract Diaspora vloggers–migrants who produce video blogs in the language of their home countries for a transnational diaspora community–have been a largely overlooked group in the studies of social media. This paper focuses on the unique role of Chinese diaspora vloggers during an unprecedented global event—the COVID-19 pandemic. Using manual keyword search (e.g., zhaijia riji , faguo yiqing ) and chance sampling (i.e., following platform recommendation), we collected 26 videos (07:44:30) from six Chinese YouTube micro-influencers (1–100k followers) located in Germany, the US, Australia, France, Italy, and Korea. Drawing on theories of narrative and stance-taking, we analyzed how these diaspora vloggers relate their experience during the COVID-19 pandemic. Results show that vloggers display both universal (e.g., fears) and culturally specific (e.g., mask-wearing) feelings, and invite their viewers to co-construe the emotional experience (e.g., the pronoun ni and address term dajia ). Moreover, through different ways of “being Chinese”, vloggers orient their discourse to a unique audience—transnational Chinese-speaking diaspora. Our findings point to the emergence of a new form of migrant identity in the age of social media and highlight the importance of understanding such identities in delivering public health information in global emergencies such as the COVID-19 pandemic.
Coherent Dynamics in Quantum Emitters under Dichromatic Excitation

We characterize the coherent dynamics of a two-level quantum emitter driven by a pair of symmetrically detuned phase-locked pulses. The promise of dichromatic excitation is to spectrally isolate the excitation laser from the quantum emission, enabling background-free photon extraction from the emitter. While excitation is not possible without spectral overlap between the exciting pulse and the quantum emitter transition for ideal two-level systems due to cancellation of the accumulated pulse area, we find that any additional interactions that interfere with cancellation of the accumulated pulse area may lead to a finite stationary population inversion. Our spectroscopic results of a solid-state two-level system show that, while coupling to lattice vibrations helps to improve the inversion efficiency up to 50% under symmetric driving, coherent population control and a larger amount of inversion are possible using asymmetric dichromatic excitation, which we achieve by adjusting the ratio of the intensities between the red- and blue-detuned pulses. Our measured results, supported by simulations using a real-time path-integral method, offer a new perspective toward realizing efficient, background-free photon generation and extraction.