Reviewerly/exHarmony huggingface dataset:
How to Make an RGBD Tracker?

We propose a generic framework for converting an arbitrary short-term RGB tracker into an RGBD tracker. The proposed framework has two mild requirements – the short-term tracker provides a bounding box and its object model update can be stopped and resumed. The core of the framework is a depth augmented foreground segmentation which is formulated as an energy minimization problem solved by graph cuts. The proposed framework offers two levels of integration. The first requires that the RGB tracker can be stopped and resumed according to the decision on target visibility. The level-two integration requires that the tracker accept an external mask (foreground region) in the target update. We integrate in the proposed framework the Discriminative Correlation Filter (DCF), and three state-of-the-art trackers – Efficient Convolution Operators for Tracking (ECOhc, ECOgpu) and Discriminative Correlation Filter with Channel and Spatial Reliability (CSR-DCF). Comprehensive experiments on Princeton Tracking Benchmark (PTB) show that level-one integration provides significant improvements for all trackers: DCF average rank improves from 18th to 17th, ECOgpu from 16th to 10th, ECOhc from 15th to 5th and CSR-DCF from 19th to 14th. CSR-DCF with level-two integration achieves the top rank by a clear margin on PTB. Our framework is particularly powerful in occlusion scenarios where it provides 13.5% average improvement and 26% for the best tracker (CSR-DCF).
Fuzzy K-Means Using Non-Linear S-Distance

A considerable amount of research has been done since long to select an appropriate similarity or dissimilarity measure in cluster analysis for exposing the natural grouping in an input dataset. Still, it is an open problem. In recent years, the research community is focusing on divergence-based non-Euclidean similarity measure in partitional clustering for grouping. In this paper, the Euclidean distance of traditional Fuzzy k-means (FKM) algorithm is replaced by the S-distance, which is derived from the newly introduced S-divergence. Few imperative properties of S-distance and modified FKM are presented in this study. The performance of the proposed FKM is compared with the conventional FKM with Euclidean distance and its variants with the help of several synthetic and real-world datasets. This study focuses on how the proposed clustering algorithm performs on the adopted datasets empirically. The comparative study illustrates that the obtained results are convincing. Moreover, the achieved results denote that the modified FKM outperforms some state-of-the-art FKM algorithms.
Scene Context-Driven Vehicle Detection in High-Resolution Aerial Images

As the spatial resolution of remote sensing images is improving gradually, it is feasible to realize “scene-object” collaborative image interpretation. Unfortunately, this idea is not fully utilized in vehicle detection from high-resolution aerial images, and most of the existing methods may be promoted by considering the variability of vehicle spatial distribution in different image scenes and treating vehicle detection tasks scene-specific. With this motivation, a scene context-driven vehicle detection method is proposed in this paper. At first, we perform scene classification using the deep learning method and, then, detect vehicles in roads and parking lots separately through different vehicle detectors. Afterward, we further optimize the detection results using different postprocessing rules according to different scene types. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms in terms of higher detection accuracy rate and lower false alarm rate.
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English

Sentiment analysis has a range of corpora available across multiple languages.For emotion analysis, the situation is more limited, which hinders potential research on crosslingual modeling and the development of predictive models for other languages.In this paper, we fill this gap for German by constructing deISEAR, a corpus designed in analogy to the well-established English ISEAR emotion dataset.Motivated by Scherer's appraisal theory, we implement a crowdsourcing experiment which consists of two steps.In step 1, participants create descriptions of emotional events for a given emotion.In step 2, five annotators assess the emotion expressed by the texts.We show that transferring an emotion classification model from the original English ISEAR to the German crowdsourced deISEAR via machine translation does not, on average, cause a performance drop.
Towards Fine-grained Text Sentiment Transfer

In this paper, we focus on the task of finegrained text sentiment transfer (FGST).This task aims to revise an input sequence to satisfy a given sentiment intensity, while preserving the original semantic content.Different from conventional sentiment transfer task that only reverses the sentiment polarity (positive/negative) of text, the FTST task requires more nuanced and fine-grained control of sentiment.To remedy this, we propose a novel Seq2SentiSeq model.Specifically, the numeric sentiment intensity value is incorporated into the decoder via a Gaussian kernel layer to finely control the sentiment intensity of the output.Moreover, to tackle the problem of lacking parallel data, we propose a cycle reinforcement learning algorithm to guide the model training.In this framework, the elaborately designed rewards can balance both sentiment transformation and content preservation, while not requiring any ground truth output.Experimental results show that our approach can outperform existing methods by a large margin in both automatic evaluation and human evaluation.Our code and data, including outputs of all baselines and our model are available at https://github.com/luofuli/Fine-grained-Sentiment-Transfer.
Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation

Pretrained contextual and non-contextual subword embeddings have become available in over 250 languages, allowing massively multilingual NLP.However, while there is no dearth of pretrained embeddings, the distinct lack of systematic evaluations makes it difficult for practitioners to choose between them.In this work, we conduct an extensive evaluation comparing non-contextual subword embeddings, namely FastText and BPEmb, and a contextual representation method, namely BERT, on multilingual named entity recognition and part-of-speech tagging.We find that overall, a combination of BERT, BPEmb, and character representations works well across languages and tasks.A more detailed analysis reveals different strengths and weaknesses: Multilingual BERT performs well in medium-to high-resource languages, but is outperformed by non-contextual subword embeddings in a low-resource setting.* Work done while at HITS. 1 While language-agnostic, these approaches are not language-independent.See Appendix B for a discussion.
Multi-Organ Gland Segmentation Using Deep Learning

Clinical morphological analysis of histopathology samples is an effective method in cancer diagnosis. Computational pathology methods can be employed to automate this analysis, providing improved objectivity and scalability. More specifically, computational techniques can be used in segmenting glands, which is an essential factor in cancer diagnosis. Automatic delineation of glands is a challenging task considering a large variability in glandular morphology across tissues and pathological subtypes. A deep learning based gland segmentation method can be developed to address the above task, but it requires a large number of accurate gland annotations from several tissue slides. Such a large dataset need to be generated manually by experienced pathologists, which is laborious, time-consuming, expensive and suffers from the subjectivity of the annotator. So far, deep learning techniques have produced promising results on a few organ-specific gland segmentation tasks, however, the demand for organ-specific gland annotations hinder the extensibility of these techniques to other organs. This work investigates the idea of cross-domain (-organ type) approximation that aims at reducing the need for organ-specific annotations. Unlike parenchyma, the stromal component of tissues, that lies between the glands, is more consistent across several organs. It is hypothesized that an automatic method, that can precisely segment the stroma, would pave the way for a cross-organ gland segmentation. Two proposed Dense-U-Nets are trained on hematoxylin and eosin (H&E) stained colon adenocarcinoma samples focusing on the gland and stroma segmentation. The trained networks are evaluated on two independent datasets, they are, a H&E stained colon adenocarcinoma dataset and a H&E stained breast invasive cancer dataset. The trained network targeting the stroma segmentation performs similar to the network targeting the gland segmentation on the colon dataset. Whereas, the former approach performs significantly better compared to the latter approach on the breast dataset, showcasing the higher generalization capacity of the stroma segmentation approach. The networks are evaluated using Dice coefficient and Hausdorff distance computed between the ground truth gland masks and the predicted gland masks. The conducted experiments validate the efficacy of the proposed stoma segmentation approach towards multi-organ gland segmentation.
FuCoLoT – A Fully-Correlational Long-Term Tracker

We propose FuCoLoT – a Fully Correlational Long-term Tracker. It exploits the novel DCF constrained filter learning method to design a detector that is able to re-detect the target in the whole image efficiently. FuCoLoT maintains several correlation filters trained on different time scales that act as the detector components. A novel mechanism based on the correlation response is used for tracking failure estimation. FuCoLoT achieves state-of-the-art results on standard short-term benchmarks and it outperforms the current best-performing tracker on the long-term UAV20L benchmark by over 19%. It has an order of magnitude smaller memory footprint than its best-performing competitors and runs at 15 fps in a single CPU thread.
Biologically Motivated Algorithms for Propagating Local Target Representations

Finding biologically plausible alternatives to back-propagation of errors is a fundamentally important challenge in artificial neural network research. In this paper, we propose a learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery. In addition, we propose an improved variant of Difference Target Propagation, another procedure that comes from the same family of algorithms as LRA-E. We compare our procedures to several other biologicallymotivated algorithms, including two feedback alignment algorithms and Equilibrium Propagation. In two benchmarks, we find that both of our proposed algorithms yield stable performance and strong generalization compared to other competing back-propagation alternatives when training deeper, highly nonlinear networks, with LRA-E performing the best overall.
DeepMellow: Removing the Need for a Target Network in Deep Q-Learning

Deep Q-Network (DQN) is an algorithm that achieves human-level performance in complex domains like Atari games. One of the important elements of DQN is its use of a target network, which is necessary to stabilize learning. We argue that using a target network is incompatible with online reinforcement learning, and it is possible to achieve faster and more stable learning without a target network when we use Mellowmax, an alternative softmax operator. We derive novel properties of Mellowmax, and empirically show that the combination of DQN and Mellowmax, but without a target network, outperforms DQN with a target network.
Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop

Online discussions often derail into toxic exchanges between participants.Recent efforts mostly focused on detecting antisocial behavior after the fact, by analyzing single comments in isolation.To provide more timely notice to human moderators, a system needs to preemptively detect that a conversation is heading towards derailment before it actually turns toxic.This means modeling derailment as an emerging property of a conversation rather than as an isolated utterance-level event.Forecasting emerging conversational properties, however, poses several inherent modeling challenges.First, since conversations are dynamic, a forecasting model needs to capture the flow of the discussion, rather than properties of individual comments.Second, real conversations have an unknown horizon: they can end or derail at any time; thus a practical forecasting model needs to assess the risk in an online fashion, as the conversation develops.In this work we introduce a conversational forecasting model that learns an unsupervised representation of conversational dynamics and exploits it to predict future derailment as the conversation develops.By applying this model to two new diverse datasets of online conversations with labels for antisocial events, we show that it outperforms state-of-the-art systems at forecasting derailment.
Deep Set Prediction Networks

Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result. We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our model is able to auto-encode point sets, predict the set of bounding boxes of objects in an image, and predict the set of attributes of these objects.
KnowledgeNet: A Benchmark Dataset for Knowledge Base Population

KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web.KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).We discuss five baseline approaches, where the best approach achieves an F1 score of 0.50, significantly outperforming a traditional approach by 79% (0.28).However, our best baseline is far from reaching human performance (0.82), indicating our dataset is challenging.The KnowledgeNet dataset and baselines are available at https:
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis

Aspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspectindependent (weakly associative) encoder for sentence representation generation.In this paper, we propose a novel Aspect-Guided Deep Transition model, named AGDT, which utilizes the given aspect to guide the sentence encoding from scratch with the speciallydesigned deep transition architecture.Furthermore, an aspect-oriented objective is designed to enforce AGDT to reconstruct the given aspect with the generated sentence representation.In doing so, our AGDT can accurately generate aspect-specific sentence representation, and thus conduct more accurate sentiment predictions.Experimental results on multiple SemEval datasets demonstrate the effectiveness of our proposed approach, which significantly outperforms the best reported results with the same setting.
Dual Stego-imaging Based Reversible Data Hiding Using Improved LSB Matching

Since the inception of the reversible data hiding (RDH) concept, it has been a compelling topic in the field of data hiding.Being reversible, it has the ability to restore the original image followed by the successful retrieval of the secret data, at the receiving side.The concept of the dual stego-image based RDH technique utilizes two identical images of the original image for concealing the secret data, has gained wide compliance.Therefore, someone with both the stego-images can only extract the concealed data.In this paper, two improved dual imaging based RDH techniques, such as (1) dual stego-image based pixel pair LSB matching with reversibility, and (2) dual stego-image based modified LSB matching with reversibility, are proposed.In technique 1, at first two mirrored images are obtained from the original image.Then, using the pair of two consecutive pixels from the original image, the mirrored images pixels are modified using LSB matching technique.Later, these pixel pairs are readjusted to ensure reversibility at the receiving side.Similarly, technique 2 utilizes each original pixel to generate two distinct stegopixels using modified LSB matching.The experimental result shows that the technique 1 maintains excellent peak signal-to-noise ratio (PSNR) of 51.29 dB and 51.30 dB for the two stego-images with hiding capacity (HC) of 524288 bits.At the same time, technique 2 offers 51.19 dB and 49.44 dB of PSNR while exhibiting the equal HC.Further, investigation with various image quality assessment (IQA) metrics like quality index (QI), and structural similarity index (SSIIM) are proven to be competent over the other existing works considered in this paper.In addition, both the proposed techniques have shown excellent anti-steganalytic ability against RS and pixel difference histogram (PDH) attack.
Towards a secure incremental proxy re‐encryption for e‐healthcare data sharing in mobile cloud computing

Summary Cloud computing provides universal access to a pool of shared resources to numerous stakeholders/shareholders of the e‐healthcare industry. The speedy adoption of cloud computing has inevitably raised security concerns for the outsourced data. Since mobile devices are resource constrained, the security solutions must discharge the computing comprehensive operations on the cloud for implementation. Conventionally, any modification to uploaded record would compel the mobile client to encrypt and compute the hash value from scratch. Through this paper, we intend to propose a pairing‐free incremental proxy re‐encryption scheme, without certificates, which would run proportionate to the number of modifications in time, instead of the document length for improvement in the file modification tasks. The proposed scheme shows a significant improvement in the file modification system regarding the energy consumption and the turnaround timer taken. The proposed scheme has been verified through a formal method using Z 3 solver.
A Framework for Effective Known-item Search in Video

Searching for one particular scene in a large video collection (known-item search) represents a challenging task for video retrieval systems. According to the recent results reached at evaluation campaigns, even respected approaches based on machine learning do not help to solve the task easily in many cases. Hence, in addition to effective automatic multimedia annotation and embedding, interactive search is recommended as well. This paper presents a comprehensive description of an interactive video retrieval framework VIRET that successfully participated at several recent evaluation campaigns. Utilized video analysis, feature extraction and retrieval models are detailed as well as several experiments evaluating effectiveness of selected system components. The results of the prototype at the Video Browser Showdown 2019 are highlighted in connection with an analysis of collected query logs. We conclude that the framework comprise a set of effective and efficient models for most of the evaluated known-item search tasks in 1000 hours of video and could serve as a baseline reference approach. The analysis also reveals that the result presentation interface needs improvements for better performance of future VIRET prototypes.
Robust Optimized Discrete Wavelet Transform-Singular Value Decomposition Based Video Watermarking

The effortless editing, interchanging and replication of multimedia data on the internet is growing exponentially and has created copyright protection uncertainties for content providers.Thus, in order to discourage illegal duplication and to attain the required level of protection to digital data, digital watermarking is found to be a feasible solution.Thus, this paper proposes a video watermarking technique by exploring Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) transform in addition to Artificial Bee colony (ABC) Optimization algorithm.In this paper, DWT is applied on every luminance frame which is divided into 8x8 blocks of the video 'V' thus producing distinct frequency sub-bands.Out of them, LL band is selected for watermark insertion.Later SVD transform is implemented on the selected dwt blocks of LL bands of all frames.The starting indices of best blocks are obtained adaptively rather than manually through ABC algorithm.At the receiving part, retrieval of watermark contents is achieved by a similar evaluation scheme practiced during the embedding procedure.The proposed optimized DWT-SVD based video watermarking method has been evaluated in the presence of video processing attacks and simulation results proved that due to cascading of two powerful mathematical transforms DWT and SVD in addition to ABC algorithm the proposed video watermarking method endures all attacks and aptly extracts the concealed watermark without significant degradation in the video quality of the watermarked video.Thus when the Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) performance of the proposed algorithm is correlated with other related techniques it is found that the PSNR of the proposed method is above 53 dB for all set of videos and Robustness of the scheme is superior than the existing schemes for similar set of videos in terms of NC.
Anomaly detection based on one-class intelligent techniques over a control level plant

Abstract A large part of technological advances, especially in the field of industry, have been focused on the optimization of productive processes. However, the detection of anomalies has turned out to be a great challenge in fields like industry, medicine or stock markets. The present work addresses anomaly detection on a control level plant. We propose the application of different intelligent techniques, which allow to obtain one-class classifiers using real data taken from the correct plant operation. The performance of each classifier is assessed and validated with real created faults, achieving successful overall results.
A Learning Approach for Physical Layer Authentication Using Adaptive Neural Network

In communications, innovative paradigm shifts have emerged in integrating various devices into the network to provide advanced and intelligent services. However, various security threats may occur that may not always be detected using traditional cryptographic techniques. Secure authentication is of paramount importance in modern wireless systems. This paper focusses on robust authentication in a time-varying communication environment where conventional authentication mechanisms are severely limited. We propose an Adaptive Neural Network (ANN) as an intelligent authentication process to improve detection accuracy. Specifically, a Data-Adaptive Matrix (DAM) is designed to track time-varying channel features. By utilizing a convolutional neural network as an intelligent authenticator, the proposed approach integrates deep feature extraction and attack detection, hence, leading to effective physical layer security. To evaluate the system, the ANN is prototyped on a universal software radio peripheral (USRP) and its authentication performance is evaluated in a conference room environment. Experimental results show that the ANN is effective in tackling the challenges of physical layer authentication under interference conditions, and is effective in time-varying environments.