Reviewerly/exHarmony huggingface dataset:
BAMB

The discovery of Markov blanket (MB) for feature selection has attracted much attention in recent years, since the MB of the class attribute is the optimal feature subset for feature selection. However, almost all existing MB discovery algorithms focus on either improving computational efficiency or boosting learning accuracy, instead of both. In this article, we propose a novel MB discovery algorithm for balancing efficiency and accuracy, called &lt;underline&gt;BA&lt;/underline&gt;lanced &lt;underline&gt;M&lt;/underline&gt;arkov &lt;underline&gt;B&lt;/underline&gt;lanket (BAMB) discovery. To achieve this goal, given a class attribute of interest, BAMB finds candidate PC (parents and children) and spouses and removes false positives from the candidate MB set in one go. Specifically, once a feature is successfully added to the current PC set, BAMB finds the spouses with regard to this feature, then uses the updated PC and the spouse set to remove false positives from the current MB set. This makes the PC and spouses of the target as small as possible and thus achieves a trade-off between computational efficiency and learning accuracy. In the experiments, we first compare BAMB with 8 state-of-the-art MB discovery algorithms on 7 benchmark Bayesian networks, then we use 10 real-world datasets and compare BAMB with 12 feature selection algorithms, including 8 state-of-the-art MB discovery algorithms and 4 other well-established feature selection methods. On prediction accuracy, BAMB outperforms 12 feature selection algorithms compared. On computational efficiency, BAMB is close to the IAMB algorithm while it is much faster than the remaining seven MB discovery algorithms.
Stacked Memory Network for Video Summarization

In recent years, supervised video summarization has achieved promising progress with various recurrent neural networks (RNNs) based methods, which treats video summarization as a sequence-to-sequence learning problem to exploit temporal dependency among video frames across variable ranges. However, RNN has limitations in modelling the long-term temporal dependency for summarizing videos with thousands of frames due to the restricted memory storage unit. Therefore, in this paper we propose a stacked memory network called SMN to explicitly model the long dependency among video frames so that redundancy could be minimized in the video summaries produced. Our proposed SMN consists of two key components: Long Short-Term Memory (LSTM) layer and memory layer, where each LSTM layer is augmented with an external memory layer. In particular, we stack multiple LSTM layers and memory layers hierarchically to integrate the learned representation from prior layers. By combining the hidden states of the LSTM layers and the read representations of the memory layers, our SMN is able to derive more accurate video summaries for individual video frames. Compared with the existing RNN based methods, our SMN is particularly good at capturing long temporal dependency among frames with few additional training parameters. Experimental results on two widely used public benchmark datasets: SumMe and TVsum, demonstrate that our proposed model is able to clearly outperform a number of state-of-the-art ones under various settings.
When and Why is Document-level Context Useful in Neural Machine Translation?

Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences.However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets.We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT.We show that most of the improvements are not interpretable as utilizing the context.We also show that a minimal encoding is sufficient for the context modeling and very long context is not helpful for NMT.
Self-Knowledge Distillation in Natural Language Processing

Since deep learning became a key player in natural language processing (NLP), many deep learning models have been showing remarkable performances in a variety of NLP tasks, and in some cases, they are even outperforming humans.Such high performance can be explained by efficient knowledge representation of deep learning models.While many methods have been proposed to learn more efficient representation, knowledge distillation from pretrained deep networks suggest that we can use more information from the soft target probability to train other neural networks.In this paper, we propose a new knowledge distillation method self-knowledge distillation, based on the soft target probabilities of the training model itself, where multimode information is distilled from the word embedding space right below the softmax layer.Due to the time complexity, our method approximates the soft target probabilities.In experiments, we applied the proposed method to two different and fundamental NLP tasks: language model and neural machine translation.The experiment results show that our proposed method improves performance on the tasks.
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation

While models for Visual Question Answering (VQA) have steadily improved over the years, interacting with one quickly reveals that these models lack consistency.For instance, if a model answers "red" to "What color is the balloon?", it might answer "no" if asked, "Is the balloon red?".These responses violate simple notions of entailment and raise questions about how effectively VQA models ground language.In this work, we introduce a dataset, ConVQA, and metrics that enable quantitative evaluation of consistency in VQA.For a given observable fact in an image (e.g. the balloon's color), we generate a set of logically consistent question-answer (QA) pairs (e.g.Is the balloon red?) and also collect a humanannotated set of common-sense based consistent QA pairs (e.g.Is the balloon the same color as tomato sauce?).Further, we propose a consistency-improving data augmentation module, a Consistency Teacher Module (CTM).CTM automatically generates entailed (or similar-intent) questions for a source QA pair and fine-tunes the VQA model if the VQA's answer to the entailed question is consistent with the source QA pair.We demonstrate that our CTM-based training improves the consistency of VQA models on the Con-VQA datasets and is a strong baseline for further research.
An integrated data-driven framework for surface water quality anomaly detection and early warning

The surface water quality anomaly detection for rapid early warning is essential to prevent potential harmful compounds, resulting from river environmental spills or intentional releases, from dispersing in large scale. In this study, an effective data-driven framework for surface water quality anomaly detection is developed to provide early warnings for dealing with river environmental pollution in advance. The developed framework is constructed by an integration of Bayesian autoregressive (BAR) model for water quality variation prediction and Isolation Forest (IF) algorithm for water quality anomaly detection. First, an autoregressive method based on Bayesian inference is used to forecast the tendencies of water quality variations. Second, an IF algorithm is applied to identify the features of water quality anomalies using the prediction residuals obtained in the previous stage. The integration framework is then applied to analyze and detect the surface water quality variations and anomalies in Potomac River of West Virginia, USA, comparing with prediction-based anomaly detection method, classification-based anomaly detection method, and different scenarios. The results demonstrate that the developed integration framework not only could enhance water quality anomaly detection accuracy, but also effectively provide early warning for emergency operations in a quick response.
Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification Over Encrypted Wi-Fi Traffic

The consumer UAV (unmanned aerial vehicle) market has grown significantly over the past few years. Despite its huge potential in spurring economic growth by supporting various applications, the increase of consumer UAVs poses potential risks to public security and personal privacy. To minimize the risks, efficiently detecting and identifying invading UAVs is in urgent need for both invasion detection and forensics purposes. Given the fact that consumer UAVs are usually used in a civilian environment, existing physical detection methods (such as radar, vision, and sound) may become ineffective in many scenarios. Aiming to complement the existing physical detection mechanisms, we propose a machine learning-based framework for fast UAV identification over encrypted Wi-Fi traffic. It is motivated by the observation that many consumer UAVs use Wi-Fi links for control and video streaming. The proposed framework extracts features derived only from packet size and inter-arrival time of encrypted Wi-Fi traffic, and can efficiently detect UAVs and identify their operation modes. In order to reduce the online identification time, our framework adopts a re-weighted $\ell_1$-norm regularization, which considers the number of samples and computation cost of different features. This framework jointly optimizes feature selection and prediction performance in a unified objective function. To tackle the packet inter-arrival time uncertainty when optimizing the trade-off between the detection accuracy and delay, we utilize Maximum Likelihood Estimation (MLE) method to estimate the packet inter-arrival time. We collect a large number of real-world Wi-Fi data traffic of eight types of consumer UAVs and conduct extensive evaluation on the performance of our proposed method.
Classification of White Blood Cells by Deep Learning Methods for Diagnosing Disease

Leukocytes, also known as white blood cells, are a group of cells that protect the body against infections, which is an important part of the immune system.The classification of white blood cells is widely used to diagnose various diseases, such as AIDS, leukemia, myeloma and anemia.However, the conventional methods to classify white blood cells are time consuming and prone to errors.In this paper, one of the most popular neural networks, convolutional neural network (CNN) is selected to differentiate between different types of white blood cells, namely, eosinophil, lymphocyte, monocyte and neutrophil.The CNN was coupled with Alexnet, Resnet50, Densenet201 and GoogleNet in turn, and trained with the Kaggle Dataset.Then, Gaussian and median filters were applied separately to the images in the database.The new images were classified again by the CNN with each of the four networks.The results obtained after applying the two filters to the images were better than the results obtained with the original data.The research results make it easier to diagnose blood related diseases.
Automatic Data Clustering Using Hybrid Firefly Particle Swarm Optimization Algorithm

The firefly algorithm is a nature-inspired metaheuristic optimization algorithm that has become an important tool for solving most of the toughest optimization problems in almost all areas of global optimization and engineering practices.However, as with other metaheuristic algorithms, the performance of the firefly algorithm depends on adequate parameter tuning.In addition, its diversification as a global metaheuristic can lead to reduced speed, as well as an associated decrease in the rate of convergence when applied to solve problems with large number of variables such as data clustering problems.Clustering is an unsupervised data analysis technique used for identifying homogeneous groups of objects based on the values of their attributes.To mitigate the aforementioned drawbacks, an improved firefly algorithm is hybridized with the well-known particle swarm optimization algorithm to solve automatic data clustering problems.To investigate the performance of the proposed hybrid algorithm, it is compared with four popular metaheuristic methods from literature using twelve standard datasets from the UCI Machine Learning Repository and the two moons dataset.The extensive computational experiments and results analysis carried out shows that the proposed algorithm not only achieves superior performance over the standard firefly and particle swarm optimization algorithms, but also exhibits high level of stability and can be efficiently utilized to solve other clustering problems with high dimensionality.
Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study

While neural network-based models have achieved impressive performance on a large body of NLP tasks, the generalization behavior of different models remains poorly understood: Does this excellent performance imply a perfect generalization model, or are there still some limitations? In this paper, we take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives and characterize the differences of their generalization abilities through the lens of our proposed measures, which guides us to better design models and training methods. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models in terms of breakdown performance analysis, annotation errors, dataset bias, and category relationships, which suggest directions for improvement. We have released the datasets: (ReCoNLL, PLONER) for the future research at our project page: http://pfliu.com/InterpretNER/.
On Measuring and Mitigating Biased Inferences of Word Embeddings

Word embeddings carry stereotypical connotations from the text they are trained on, which can lead to invalid inferences in downstream models that rely on them. We use this observation to design a mechanism for measuring stereotypes using the task of natural language inference. We demonstrate a reduction in invalid inferences via bias mitigation strategies on static word embeddings (GloVe). Further, we show that for gender bias, these techniques extend to contextualized embeddings when applied selectively only to the static components of contextualized embeddings (ELMo, BERT).
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction

Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision. Most prior works adopt a selective attention mechanism over sentences in a bag to denoise from wrongly labeled data, which however could be incompetent when there is only one sentence in a bag. In this paper, we propose a brand-new light-weight neural framework to address the distantly supervised relation extraction problem and alleviate the defects in previous selective attention framework. Specifically, in the proposed framework, 1) we use an entity-aware word embedding method to integrate both relative position information and head/tail entity embeddings, aiming to highlight the essence of entities for this task; 2) we develop a self-attention mechanism to capture the rich contextual dependencies as a complement for local dependencies captured by piecewise CNN; and 3) instead of using selective attention, we design a pooling-equipped gate, which is based on rich contextual representations, as an aggregator to generate bag-level representation for final relation classification. Compared to selective attention, one major advantage of the proposed gating mechanism is that, it performs stably and promisingly even if only one sentence appears in a bag and thus keeps the consistency across all training examples. The experiments on NYT dataset demonstrate that our approach achieves a new state-of-the-art performance in terms of both AUC and top-n precision metrics.
Expressing Objects Just Like Words: Recurrent Visual Embedding for Image-Text Matching

Existing image-text matching approaches typically infer the similarity of an image-text pair by capturing and aggregating the affinities between the text and each independent object of the image. However, they ignore the connections between the objects that are semantically related. These objects may collectively determine whether the image corresponds to a text or not. To address this problem, we propose a Dual Path Recurrent Neural Network (DP-RNN) which processes images and sentences symmetrically by recurrent neural networks (RNN). In particular, given an input image-text pair, our model reorders the image objects based on the positions of their most related words in the text. In the same way as extracting the hidden features from word embeddings, the model leverages RNN to extract high-level object features from the reordered object inputs. We validate that the high-level object features contain useful joint information of semantically related objects, which benefit the retrieval task. To compute the image-text similarity, we incorporate a Multi-attention Cross Matching Model into DP-RNN. It aggregates the affinity between objects and words with cross-modality guided attention and self-attention. Our model achieves the state-of-the-art performance on Flickr30K dataset and competitive performance on MS-COCO dataset. Extensive experiments demonstrate the effectiveness of our model.
Conducting online virtual environment experiments with uncompensated, unsupervised samples

Web-based experimentation with uncompensated and unsupervised samples allows for a larger and more diverse sample population, more generalizable results, and faster theory to experiment cycle. Given that participants are unsupervised, it is still unknown whether the data collected in such settings would be of sufficiently high quality to support robust conclusions. Therefore, we investigated the feasibility of conducting such experiments online using virtual environment technologies. We conducted a conceptual replication of two prior experiments that have been conducted in virtual environments. Our results replicate findings previously obtained in conventional laboratory settings. These results hold across different device types of participants (ranging from desktop, through mobile devices to immersive virtual reality headsets), suggesting that experiments can be conducted online with uncompensated samples in virtual environments.
A Comprehensive survey on Blockchain: Working, security analysis, privacy threats and potential applications

Recently, Blockchain a decentralized as well as distributed public ledger technology in (P2P) peer-to-peer network, has received considerable attention. It applies a block structure (linked) used to store as well as verify data and also applies the trusted mechanism for synchronization of changes in data in order to possibly create a tamper-proof digital platform for sharing as well as storing data. Blockchain can also be used to diverse the interactive system of internet (e.g. supply chain system, Internet of things). The blockchain model of bitcoin has been used in wide range of services like from asset trading to transaction of real estate, from services of escrow to national income distribution system in some countries. In this paper we present a comprehensive survey on blockchain, working of blockchain, security analysis on blockchain, privacy threats for blockchain and potential applications of blockchain.
Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep Sparse Residual Model

In this article, we propose a novel and simple automatic model based on multimodal anomaly feature learning in a residual space, aiming at solving the binary classification problem of temporal change detection (CD) between pairs of heterogeneous remote sensing images.The model starts by learning from image pairs the normal existing patterns in the before and after images to come up with a suitable representation of the normal (nonchange) class.To achieve this, we employ a stacked sparse autoencoder trained on a large number of temporal image features (training data) in an unsupervised manner.To classify pixels of new unseen image-pairs, the built anomaly detection model reconstructs the input from its representation in the latent space.First, the probe (new) image (i.e., the bitemporal heterogeneous image pair as the input request) is encoded in this compact normal space from a stacked hidden representation.The reconstruction error is computed using the L2 norm in what we call the residual normal space.In which, the nonchange patterns are characterized by small reconstruction errors as a normal class while the change patterns are quantified by high reconstruction errors categorizing the abnormal class.The dichotomic (changed/unchanged) classification map is generated in the residual space by clustering the reconstructed errors using a Gaussian mixture model.Experimental results on different real heterogeneous images, reflecting a mixture of imaging and land surface CD conditions, confirm the robustness of the proposed anomaly detection model.
Implementation of a Walsh-Hadamard Gate in a Superconducting Qutrit

We have implemented a Walsh-Hadamard gate, which performs a quantum Fourier transform, in a superconducting qutrit. The qutrit is encoded in the lowest three energy levels of a capacitively shunted flux device, operated at the optimal flux-symmetry point. We use an efficient decomposition of the Walsh-Hadamard gate into two unitaries, generated by off-diagonal and diagonal Hamiltonians, respectively. The gate implementation utilizes simultaneous driving of all three transitions between the three pairs of energy levels of the qutrit, one of which is implemented with a two-photon process. The gate has a duration of 35 ns and an average fidelity over a representative set of states, including preparation and tomography errors, of 99.2%, characterized with quantum-state tomography. Compensation of ac-Stark and Bloch-Siegert shifts is essential for reaching high gate fidelities.
Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph

Nowadays, scholar recommender systems often recommend academic papers based on users’ personalized retrieval demands. Typically, a recommender system analyzes the keywords typed by a user and then returns his or her preferred papers, in an efficient and economic manner. In practice, one paper often contains partial keywords that a user is interested in. Therefore, the recommender system needs to return the user a set of papers that collectively covers all the queried keywords. However, existing recommender systems only use the exact keyword matching technique for recommendation decisions, while neglecting the correlation relationships among different papers. As a consequence, it may output a set of papers from multiple disciplines that are different from the user’s real research field. In view of this shortcoming, we propose a keyword-driven and popularity-aware paper recommendation approach based on an undirected paper citation graph, named PR keyword+pop . At last, we conduct large-scale experiments on the real-life Hep-Th dataset to further demonstrate the usefulness and feasibility of PR keyword+pop . Experimental results prove the advantages of PR keyword+pop in searching for a set of satisfactory papers compared with other competitive approaches.