Reviewerly/exHarmony huggingface dataset:
Exploiting semantic relationships for unsupervised expansion of sentiment lexicons

The literature in sentiment analysis has widely assumed that semantic relationships between words cannot be effectively exploited to produce satisfactory sentiment lexicon expansions. This assumption stems from the fact that words considered to be "close" in a semantic space (e.g., word embeddings) may present completely opposite polarities, which might suggest that sentiment information in such spaces is either too faint, or at least not readily exploitable. Our main contribution in this paper is a rigorous and robust challenge to this assumption: by proposing a set of theoretical hypotheses and corroborating them with strong experimental evidence, we demonstrate that semantic relationships can be effectively used for good lexicon expansion. Based on these results, our second contribution is a novel, simple, and yet effective lexicon-expansion strategy based on semantic relationships extracted from word embeddings. This strategy is able to substantially enhance the lexicons, whilst overcoming the major problem of lexicon coverage. We present an extensive experimental evaluation of sentence-level sentiment analysis, comparing our approach to sixteen state-of-the-art (SOTA) lexicon-based and five lexicon expansion methods, over twenty datasets. Results show that in the vast majority of cases our approach outperforms the alternatives, achieving coverage of almost 100% and gains of about 26% against the best baselines. Moreover, our unsupervised approach performed competitively against SOTA supervised sentiment analysis methods, mainly in scenarios with scarce information. Finally, in a cross-dataset comparison, our approach turned out to be as competitive as (i.e., statistically tie with) state-of-the-art supervised solutions such as pre-trained transformers (BERT), even without relying on any training (labeled) data. Indeed in small datasets or in datasets with scarce information (short messages), our solution outperformed the supervised ones by large margins.
A Method to Detect and Track Moving Airplanes from a Satellite Video

In recent years, satellites capable of capturing videos have been developed and launched to provide high definition satellite videos that enable applications far beyond the capabilities of remotely sensed imagery. Moving object detection and moving object tracking are among the most essential and challenging tasks, but existing studies have mainly focused on vehicles. To accurately detect and then track more complex moving objects, specifically airplanes, we need to address the challenges posed by the new data. First, slow-moving airplanes may cause foreground aperture problem during detection. Second, various disturbances, especially parallax motion, may cause false detection. Third, airplanes may perform complex motions, which requires a rotation-invariant and scale-invariant tracking algorithm. To tackle these difficulties, we first develop an Improved Gaussian-based Background Subtractor (IPGBBS) algorithm for moving airplane detection. This algorithm adopts a novel strategy for background and foreground adaptation, which can effectively deal with the foreground aperture problem. Then, the detected moving airplanes are tracked by a Primary Scale Invariant Feature Transform (P-SIFT) keypoint matching algorithm. The P-SIFT keypoint of an airplane exhibits high distinctiveness and repeatability. More importantly, it provides a highly rotation-invariant and scale-invariant feature vector that can be used in the matching process to determine the new locations of the airplane in the frame sequence. The method was tested on a satellite video with eight moving airplanes. Compared with state-of-the-art algorithms, our IPGBBS algorithm achieved the best detection accuracy with the highest F1 score of 0.94 and also demonstrated its superiority on parallax motion suppression. The P-SIFT keypoint matching algorithm could successfully track seven out of the eight airplanes. Based on the tracking results, movement trajectories of the airplanes and their dynamic properties were also estimated.
Privacy-Preserving Personalized Federated Learning

To provide intelligent and personalized services on smart devices, machine learning techniques have been widely used to learn from data, identify patterns, and make automated decisions. Machine learning processes typically require a large amount of representative data that are often collected through crowdsourcing from end users. However, user data could be sensitive in nature, and learning machine learning models on these data may expose sensitive information of users, violating their privacy. Moreover, to meet the increasing demand of personalized services, these learned models should capture their individual characteristics. This paper proposes a privacy-preserving approach for learning effective personalized models on distributed user data while guaranteeing the differential privacy of user data. Practical issues in a distributed learning system such as user heterogeneity are considered in the proposed approach. Moreover, the convergence property and privacy guarantee of the proposed approach are rigorously analyzed. Experiments on realistic mobile sensing data demonstrate that the proposed approach is robust to high user heterogeneity and offer a trade-off between accuracy and privacy.
Parallel and Distributed Training of Deep Neural Networks: A brief overview

Deep neural networks and deep learning are becoming important and popular techniques in modern services and applications. The training of these networks is computationally intensive, because of the extreme number of trainable parameters and the large amount of training samples. In this brief overview, current solutions aiming to speed up this training process via parallel and distributed computation are introduced. The necessary components and strategies are described from the low-level communication protocols to the high-level frameworks for the distributed deep learning. The current implementations of the deep learning frameworks with distributed computational capabilities are compared and key parameters are identified to help design effective solutions.
Group Sampling for Scale Invariant Face Detection

Detectors based on deep learning tend to detect multi-scale objects on a single input image for efficiency. Recent works, such as FPN and SSD, generally use feature maps from multiple layers with different spatial resolutions to detect objects at different scales, e.g., high-resolution feature maps for small objects. However, we find that objects at all scales can also be well detected with features from a single layer of the network. In this paper, we carefully examine the factors affecting detection performance across a large range of scales, and conclude that the balance of training samples, including both positive and negative ones, at different scales is the key. We propose a group sampling method which divides the anchors into several groups according to the scale, and ensure that the number of samples for each group is the same during training. Our approach using only one single layer of FPN as features is able to advance the state-of-the-arts. Comprehensive analysis and extensive experiments have been conducted to show the effectiveness of the proposed method. Moreover, we show that our approach is favorably applicable to other tasks, such as object detection on COCO dataset, and to other detection pipelines, such as YOLOv3, SSD and R-FCN. Our approach, evaluated on face detection benchmarks including FDDB and WIDER FACE datasets, achieves state-of-the-art results without bells and whistles.
Fusing Knowledge and Aspect Sentiment for Explainable Recommendation

The current research of recommender system mainly focuses on improving the accuracy of the recommendation, but pays less attention to explainability.Explainability is essential to enhance users' trust and satisfaction, which can even increase the likelihood of buying items.In the existing explainable recommender systems, the mining of explicit and implicit features is not comprehensive, and the interaction among these features is not considered plentifully.In addition, the generated recommended reason text is not personalized and rich enough.It is necessary to improve the quality of recommended reason text because it is difficult to meet the needs of different users by using low-quality text.In this paper, we propose a new method that fuses external knowledge and aspect sentiment to predict rating and generate personalized, content-rich recommended reasons, which applies fine-tuning BERT to solve aspect-based sentiment analysis and extends Transformer to generate recommended reason by using bi-directional attention.The experiment results on real-world datasets demonstrated that our method was effective, and our model was superior to the baseline models on various metrics.For rating prediction task, our model can achieve an improvement of 0.6% on average in terms of RMSE.For recommended reason generation task, our model can achieve an improvement of 9.2% to 11.3% over state-of-the-arts in terms of BLEU.
Improving Generative Adversarial Networks With Local Coordinate Coding

Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution (e.g., Gaussian noises). However, such prior distribution is often independent of real data and thus may lose semantic information (e.g., geometric structure or content in images) of data. In practice, the semantic information might be represented by some latent distribution learned from data. However, such latent distribution may incur difficulties in data sampling for GAN methods. In this paper, rather than sampling from the predefined prior distribution, we propose a GAN model with local coordinate coding (LCC), termed LCCGAN, to improve the performance of the image generation. First, we propose an LCC sampling method in LCCGAN to sample meaningful points from the latent manifold. With the LCC sampling method, we can explicitly exploit the local information on the latent manifold and thus produce new data with promising quality. Second, we propose an improved version, namely LCCGAN++, by introducing a higher-order term in the generator approximation. This term is able to achieve better approximation and thus further improve the performance. More critically, we derive the generalization bound for both LCCGAN and LCCGAN++ and prove that a low-dimensional input is sufficient to achieve good generalization performance. Extensive experiments on several benchmark datasets demonstrate the superiority of the proposed method over existing GAN methods.
Synthesizing Camera Noise Using Generative Adversarial Networks

We present a technique for synthesizing realistic noise for digital photographs. It can adjust the noise level of an input photograph, either increasing or decreasing it, to match a target ISO level. Our solution learns the mappings among different ISO levels from unpaired data using generative adversarial networks. We demonstrate its effectiveness both quantitatively, using Kullback-Leibler divergence and Kolmogorov-Smirnov test, and qualitatively through a large number of examples. We also demonstrate its practical applicability by using its results to significantly improve the performance of a state-of-the-art trainable denoising method. Our technique should benefit several computer-vision applications that seek robustness to noisy scenarios.
Rethinking Segmentation Guidance for Weakly Supervised Object Detection

Weakly supervised object detection aims at learning object detectors with only image-level category labels. Most existing methods tend to solve this problem by using a multiple instance learning detector which is usually trapped to discriminate object parts, rather than the entire object. In order to select high-quality proposals, recent works lever-age objectness scores derived from weakly-supervised segmentation maps to rank the object proposals. Base our observation, this kind of segmentation guided method always fails due to neglect of the fact that objectness of all proposals inside the ground-truth box should be consistent. In this paper, we propose a novel object representation named Objectness Consistent Representation (OCR) to meet the consistency criterion of objectness. Specifically, we project the segmentation confidence scores into two orthogonal directions, namely vertical and horizontal, to get the OCR. With the novel object representation, more high-quality proposals can be mined for learning a much stronger object detector. We obtain 54.6% and 51.1% mAP scores on VOC 2007 and 2012 datasets, significantly outperforming the state-of-the-arts and demonstrating the superiority of OCR for weakly supervised object detection.
FaceFilter: Face Identification with Deep Learning and Filter Algorithm

Although significant advances have been made recently in the field of face recognition, these have some limitations, especially when faces are in different poses or have different levels of illumination, or when the face is blurred. In this study, we present a system that can directly identify an individual under all conditions by extracting the most important features and using them to identify a person. Our method uses a deep convolutional network that is trained to extract the most important features. A filter is then used to select the most significant of these features by finding features greater than zero, storing their indices, and comparing the features of other identities with the same indices as the original image. Finally, the selected features of each identity in the dataset are subtracted from features of the original image to find the minimum number that refers to that identity. This method gives good results, as we only extract the most important features using the filter to recognize the face in different poses. We achieve state-of-the-art face recognition performance using only half of the 128 bytes per face. The system has an accuracy of 99.7% on the Labeled Faces in the Wild dataset and 94.02% on YouTube Faces DB.
Context-Driven Image Caption With Global Semantic Relations of the Named Entities

Automatic image captioning has achieved a great progress.However, the existing captioning frameworks basically enumerate the objects in the image.The generated captions lack the real-world knowledge about named entities and their relations, such as the relations among famous persons, organizations and buildings.On the contrary, humans interpret images in a specific way by providing real-world knowledge with relations of the aforementioned named entities.To generate human-like captions, we focus on captioning the images of news, which could provide real-world knowledge of the whole story behind the images.Then we propose a novel model that makes captions closer to the human-like description of the image, by leveraging the semantic relevance of the named entities.The named entities are not only extracted from news under the guidance of the image content, but also extended with external knowledge based on the semantic relations.In detail, we propose a sentence correlation analysis algorithm to selectively draw the contextual information from news, and use entity-linking algorithm based on knowledge graph to discover the relations of entities with a global sight.The results of extensive experiments based on real-world dataset which is collected from the news show that our model generates image captions closer to the corresponding real-world captions.
Multiscale Methods for Optical Remote-Sensing Image Captioning

Recently, the optical remote-sensing image-captioning task has gradually become a research hotspot because of its application prospects in the military and civil fields. Many different methods along with data sets have been proposed. Among them, the models following the encoder–decoder framework have better performance in many aspects like generating more accurate and flexible sentences. However, almost all these methods are of a single fixed receptive field and could not put enough attention on grabbing the multiscale information, which leads to incomplete image representation. In this letter, we deal with the multiscale problem and propose two multiscale methods named multiscale attention (MSA) method and multifeat attention (MFA) method, to obtain better representations for the captioning task in the remote-sensing field. The MSA method extracts features from different layers and uses the multihead attention mechanism to obtain the context feature, respectively. The MFA method combines the target-level features and the scene-level features by using the target-detection task as the auxiliary task to enrich the context feature. The experimental results demonstrate that both of them perform better with regard to the metrics like BLEU, METEOR, ROUGE_L, and CIDEr than the benchmark method.
Generic Superlight Client for Permissionless Blockchains

We initiate a systematic study on the light-client protocol of permissionless blockchains, in the setting where full nodes and light clients are rational. In the game-theoretic model, we design a superlight-client protocol to enable a light client to employ some relaying full nodes (e.g., two or one) to read the blockchain. The protocol is “generic”, i.e., it can be deployed disregarding underlying consensuses, and it is also “superlight”, i.e., the computational cost of the light client to predicate the (non)existence of a transaction in the blockchain becomes a small constant. Since our protocol resolves a fundamental challenge of broadening the usage of blockchain technology, it captures a wide variety of important use-cases such as multi-chain wallets, DApp browsers and more.
Adversarial machine learning for spam filters

Email spam filters based on machine learning techniques are widely deployed in today's organizations. As our society relies more on artificial intelligence (AI), the security of AI, especially the machine learning algorithms, becomes increasingly important and remains largely untested. Adversarial machine learning, on the other hand, attempts to defeat machine learning models through malicious input. In this paper, we experiment how adversarial scenario may impact the security of machine learning based mechanisms such as email spam filters. Using natural language processing (NLP) and Baysian model as an example, we developed and tested three invasive techniques, i.e., synonym replacement, ham word injection and spam word spacing. Our adversarial examples and results suggest that these techniques are effective in fooling the machine learning models. The study calls for more research on understanding and safeguarding machine learning based security mechanisms in the presence of adversaries.
Channel Estimation Enhancement With Generative Adversarial Networks

Improving the accuracy of channel estimation is a significant topic in the context of wireless communications. For training-based channel estimations, increasing the length of a training sequence may improve the accuracy of channel estimation but causes a higher overhead. Nevertheless, this paper shows that benefiting from generative adversarial networks (GANs), which is an emerging deep learning framework, the accuracy of channel estimation can be improved without transmitting a longer training sequence. To this end, this paper proposes a GAN-based channel estimation enhancement algorithm, where GANs are trained online with receive sequence so as to obtain a longer mimic sequence and enhance channel estimation. In order to address the problem of improving the training stability and the learning ability of GANs, this paper proposes a novel framework by integrating a conditional GAN with an improved Wasserstein GAN. Furthermore, a strategy based on a lookup table is proposed to alleviate overfitting that may occur during the training of GANs. Simulation results indicate that the proposed GAN-based channel estimation enhancement algorithm can benefit the conventional training-based channel estimation, yielding lower relative error performance, especially in the low SNR regions.
Autonomous Task Dropping Mechanism to Achieve Robustness in Heterogeneous Computing Systems

Robustness of a distributed computing system is defined as the ability to maintain its performance in the presence of uncertain parameters. Uncertainty is a key problem in heterogeneous (and even homogeneous) distributed computing systems that perturbs system robustness. Notably, the performance of these systems is perturbed by uncertainty in both task execution time and arrival. Accordingly, our goal is to make the system robust against these uncertainties. Considering task execution time as a random variable, we use probabilistic analysis to develop an autonomous proactive task dropping mechanism to attain our robustness goal. Specifically, we provide a mathematical model that identifies the optimality of a task dropping decision, so that the system robustness is maximized. Then, we leverage the mathematical model to develop a task dropping heuristic that achieves the system robustness within a feasible time complexity. Although the proposed model is generic and can be applied to any distributed system, we concentrate on heterogeneous computing (HC) systems that have a higher degree of exposure to uncertainty than homogeneous systems. Experimental results demonstrate that the autonomous proactive dropping mechanism can improve the system robustness by up to 20%.
Enhancing Seismic P-Wave Arrival Picking by Target-Oriented Detection of the Local Windows Using Faster-RCNN

The accuracy of P-wave arrival picking is essential for seismic analysis. The improvement in the accuracy of P-wave arrival picking is generally achieved through improved algorithms and the processing of waveforms. Therefore, we propose a method that uses deep learning to detect local windows to enhance the accuracy of P-wave arrival picking. The local window is defined as a short time window containing the main components of the signal. The faster-RCNN model is trained on the dataset with the calibrated local window. The trained faster-RCNN model is used for the local window detection of new records, and the existing algorithm is going to work in the local window. As a validation, four kinds of automatic P-wave arrival picking algorithms (wavelet-transform-based approach, PphasePicker algorithm, STAFD/LTAFD algorithm, and deep learning method) are used to conduct experiments in synthetic seismic records and field seismic records, respectively. The field experimental results show that the method proposed in this article can improve the picking capacity of the four methods by 17.5%, 37.6%, 62.4%, and 46.8%, respectively. No matter which algorithm is used, the accuracy of P-wave arrival picking in the local window is generally enhanced. The method presented in this article has a positive effect on improving the accuracy of seismic records.