Reviewerly/exHarmony huggingface dataset:
SPFTN: A Joint Learning Framework for Localizing and Segmenting Objects in Weakly Labeled Videos

Object localization and segmentation in weakly labeled videos are two interesting yet challenging tasks. Models built for simultaneous object localization and segmentation have been explored in the conventional fully supervised learning scenario to boost the performance of each task. However, none of the existing works has attempted to jointly learn object localization and segmentation models under weak supervision. To this end, we propose a joint learning framework called Self-Paced Fine-Tuning Network (SPFTN) for localizing and segmenting objects in weakly labelled videos. Learning the deep model jointly for object localization and segmentation under weak supervision is very challenging as the learning process of each single task would face serious ambiguity issue due to the lack of bounding-box or pixel-level supervision. To address this problem, our proposed deep SPFTN model is carefully designed with a novel multi-task self-paced learning objective, which leverages the task-specific prior knowledge and the knowledge that has been already captured to infer the confident training samples for each task. By aggregating the confident knowledge from each single task to mine reliable patterns and learning deep feature representation for both tasks, the proposed learning framework can address the ambiguity issue under weak supervision with simple optimization. Comprehensive experiments on the large-scale YouTube-Objects and DAVIS datasets demonstrate that the proposed approach achieves superior performance when compared with other state-of-the-art methods and the baseline networks/models.
Deep Integration: A Multi-Label Architecture for Road Scene Recognition

Deep convolutional neural networks have been applied by automobile industries, Internet giants, and academic institutes to boost autonomous driving technologies; while progress has been witnessed in environmental perception tasks, such as object detection and driver state recognition, the scene-centric understanding and identification still remain a virgin land. This mainly encompasses two key issues: 1) the lack of shared large datasets with comprehensively annotated road scene information and 2) the difficulty to find effective ways to train networks concerning the bias of category samples, image resolutions, scene dynamics, and capturing conditions. In this paper, we make two contributions: 1) we introduce a large-scale dataset with over 110 k images, dubbed DrivingScene, covering traffic scenarios under different weather conditions, road structures, and environmental instances and driving places, which is the first large-scale dataset for multi-class traffic scenes classification and 2) we propose a multi-label neural network for road scene recognition, which incorporates both single- and multi-class classification modes into a multi-level cost function for training with imbalanced categories and utilizes a deep data integration strategy to improve the classification ability on hard samples. The experimental results on DrivingScene and PASCAL VOC demonstrate the effectiveness of the proposed approach in handling the challenge of data imbalance.
SPB: A Secure Private Blockchain-Based Solution for Distributed Energy Trading

Blockchain is increasingly being used to provide a distributed, secure, trusted, and private framework for energy trading in smart grids. However, existing solutions suffer from a lack of privacy, processing and packet overheads, and reliance on trusted third party (TTP) to secure the trade. To address these challenges, we propose a secure private blockchain (SPB) framework. SPB enables energy producers and consumers to directly negotiate the energy price. To reduce the associated overheads, we propose a routing method which routes packets based on the destination public key (PK). SPB eliminates the reliance on TTP to ensure both energy producer and consumer commit to their obligations by introducing atomic meta-transactions. The latter consists of two transactions: first the consumer generates a CTP transaction, committing to pay the energy price to the producer. On receipt of the energy, the smart meter of the consumer generates an energy receipt confirmation (ERC) which triggers a smart contract to transfer the committed price in CTP to the energy producer. To verify that the ERC is generated by a genuine smart meter, SPB supports authentication of anonymous smart meters to prevent malicious nodes from linking ERC transactions and thus enhance the user privacy. Qualitative security analysis shows the resilience of SPB against a range of attacks. Implementation results demonstrate that SPB reduces monetary cost and delay compared to existing solutions.
Data Augmentation for Spoken Language Understanding via Joint Variational Generation

Data scarcity is one of the main obstacles of domain adaptation in spoken language understanding (SLU) due to the high cost of creating manually tagged SLU datasets. Recent works in neural text generative models, particularly latent variable models such as variational autoencoder (VAE), have shown promising results in regards to generating plausible and natural sentences. In this paper, we propose a novel generative architecture which leverages the generative power of latent variable models to jointly synthesize fully annotated utterances. Our experiments show that existing SLU models trained on the additional synthetic examples achieve performance gains. Our approach not only helps alleviate the data scarcity issue in the SLU task for many datasets but also indiscriminately improves language understanding performances for various SLU models, supported by extensive experiments and rigorous statistical testing.
A Hybrid Differential Evolution Algorithm and Its Application in Unmanned Combat Aerial Vehicle Path Planning

CIPDE and JADE are two powerful and effective Differential Evolution (DE) algorithms with strong exploration and exploitation capabilities. In order to take advantage of these two algorithms, we present a hybrid differential evolution algorithm combining modified CIPDE (MCIPDE) with modified JADE (MJADE) called CIJADE. In CIJADE, the population is first partitioned into two subpopulations according to the fitness value, i.e., superior and inferior subpopulations, to maintain the population diversity. The superior subpopulation evolves using the operation defined in MCIPDE. The MCIPDE adds an external archive to the mutation scheme to enhance the population diversity and exploration capability of original CIPDE. While the inferior subpopulation evolves using the operation defined in MJADE. The MJADE modifies the original JADE by adjusting the parameter p in linear decreasing way to balance the exploration and exploitation ability of original JADE. A new crossover operation is designed to original JADE to deal with the problem of stagnation. Furthermore, the parameters CR and F values of CIJADE are updated according to a modified parameter adaptation strategy in each generation. We validate the performance of the proposed CIJADE algorithm over 28 benchmark functions of the CEC2013 benchmark set. The experimental results indicate that the proposed CIJADE performs better than the eleven popular stateof-the-art DE variants. What's more, we apply the proposed CIJADE to deal with Unmanned Combat Aerial Vehicle (UCAV) path planning problem. The simulation results show that the proposed CIJADE can efficiently find the optimal or near optimal flight path for UCAV.
Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition

With the rapid advancement of technology and the necessity of processing large amounts of data, biomedical Named Entity Recognition (NER) has become an essential technique for information extraction in the biomedical field. NER, which is a sequence-labeling task, has been performed using various traditional techniques including dictionary-, rule-, machine learning-, and deep learning-based methods. However, as existing biomedical NER models are insufficient to handle new and unseen entity types from the growing biomedical data, the development of more effective and accurate biomedical NER models is being widely researched. Among biomedical NER models utilizing deep learning approaches, there have been only a few studies involving the design of high-level features in the embedding layer. In this regard, herein, we propose a deep learning NER model that effectively represents biomedical word tokens through the design of a combinatorial feature embedding. The proposed model is based on Bidirectional Long Short-Term Memory (bi-LSTM) with Conditional Random Field (CRF) and enhanced by integrating two different character-level representations extracted from a Convolutional Neural Network (CNN) and bi-LSTM. Additionally, an attention mechanism is applied to the model to focus on the relevant tokens in the sentence, which alleviates the long-term dependency problem of the LSTM model and allows effective recognition of entities. The proposed model was evaluated on two benchmark datasets, the JNLPBA and NCBI-Disease, and a comparative analysis with the existing models is performed. The proposed model achieved a relatively higher performance with an F1-score of 86.93% in case of NCBI-Disease, and a competitive performance for the JNLPBA with an F1-score of 75.31%.
Imbalanced Breast Cancer Classification Using Transfer Learning

Accurate breast cancer detection using automated algorithms remains a problem within the literature. Although a plethora of work has tried to address this issue, an exact solution is yet to be found. This problem is further exacerbated by the fact that most of the existing datasets are imbalanced, i.e., the number of instances of a particular class far exceeds that of the others. In this paper, we propose a framework based on the notion of transfer learning to address this issue and focus our efforts on histopathological and imbalanced image classification. We use the popular VGG-19 as the base model and complement it with several state-of-the-art techniques to improve the overall performance of the system. With the ImageNet dataset taken as the source domain, we apply the learned knowledge in the target domain consisting of histopathological images. With experimentation performed on a large-scale dataset consisting of 277,524 images, we show that the framework proposed in this paper gives superior performance than those available in the existing literature. Through numerical simulations conducted on a supercomputer, we also present guidelines for work in transfer learning and imbalanced image classification.
Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction

Opinion entity extraction is a fundamental task in fine-grained opinion mining.Related studies generally extract aspects and/or opinion expressions without recognizing the relations between them.However, the relations are crucial for downstream tasks, including sentiment classification, opinion summarization, etc.In this paper, we explore Aspect-Opinion Pair Extraction (AOPE) task, which aims at extracting aspects and opinion expressions in pairs.To deal with this task, we propose Synchronous Double-channel Recurrent Network (SDRN) mainly consisting of an opinion entity extraction unit, a relation detection unit, and a synchronization unit.The opinion entity extraction unit and the relation detection unit are developed as two channels to extract opinion entities and relations simultaneously.Furthermore, within the synchronization unit, we design Entity Synchronization Mechanism (ESM) and Relation Synchronization Mechanism (RSM) to enhance the mutual benefit on the above two channels.To verify the performance of SDRN, we manually build three datasets based on SemEval 2014 and 2015 benchmarks.Extensive experiments demonstrate that SDRN achieves state-of-the-art performances.
ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost

We describe a new deep learning model - Convolutional eXtreme Gradient Boosting (ConvXGB) for classification problems based on convolutional neural nets and Chen et al.’s XGBoost. As well as image data, ConvXGB also supports the general classification problems, with a data preprocessing module. ConvXGB consists of several stacked convolutional layers to learn the features of the input and is able to learn features automatically, followed by XGBoost in the last layer for predicting the class labels. The ConvXGB model is simplified by reducing the number of parameters under appropriate conditions, since it is not necessary re-adjust the weight values in a back propagation cycle. Experiments on several data sets from UCL Repository, including images and general data sets, showed that our model handled the classification problems, for all the tested data sets, slightly better than CNN and XGBoost alone and was sometimes significantly better.
Guiding Monocular Depth Estimation Using Depth-Attention Volume

Recovering the scene depth from a single image is an ill-posed problem that requires additional priors, often referred to as monocular depth cues, to disambiguate different 3D interpretations. In recent works, those priors have been learned in an end-to-end manner from large datasets by using deep neural networks. In this paper, we propose guiding depth estimation to favor planar structures that are ubiquitous especially in indoor environments. This is achieved by incorporating a non-local coplanarity constraint to the network with a novel attention mechanism called depth-attention volume (DAV). Experiments on two popular indoor datasets, namely NYU-Depth-v2 and ScanNet, show that our method achieves state-of-the-art depth estimation results while using only a fraction of the number of parameters needed by the competing methods. Code is available at: https://github.com/HuynhLam/DAV .
SelfReg: Self-supervised Contrastive Regularization for Domain Generalization

In general, an experimental environment for deep learning assumes that the training and the test dataset are sampled from the same distribution. However, in real-world situations, a difference in the distribution between two datasets, i.e. domain shift, may occur, which becomes a major factor impeding the generalization performance of the model. The research field to solve this problem is called domain generalization, and it alleviates the domain shift problem by extracting domain-invariant features explicitly or implicitly. In recent studies, contrastive learning-based domain generalization approaches have been proposed and achieved high performance. These approaches require sampling of the negative data pair. However, the performance of contrastive learning fundamentally depends on quality and quantity of negative data pairs. To address this issue, we propose a new regularization method for domain generalization based on contrastive learning, called self-supervised contrastive regularization (SelfReg). The proposed approach use only positive data pairs, thus it resolves various problems caused by negative pair sampling. Moreover, we propose a class-specific domain perturbation layer (CDPL), which makes it possible to effectively apply mixup augmentation even when only positive data pairs are used. The experimental results show that the techniques incorporated by SelfReg contributed to the performance in a compatible manner. In the recent benchmark, DomainBed, the proposed method shows comparable performance to the conventional state-of-the-art alternatives.
Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks

There are two approaches for pairwise sentence scoring: Cross-encoders, which perform full-attention over the input pair, and Bi-encoders, which map each input independently to a dense vector space.While crossencoders often achieve higher performance, they are too slow for many practical use cases.Bi-encoders, on the other hand, require substantial training data and fine-tuning over the target task to achieve competitive performance.We present a simple yet efficient data augmentation strategy called Augmented SBERT, where we use the cross-encoder to label a larger set of input pairs to augment the training data for the bi-encoder.We show that, in this process, selecting the sentence pairs is non-trivial and crucial for the success of the method.We evaluate our approach on multiple tasks (in-domain) as well as on a domain adaptation task.Augmented SBERT achieves an improvement of up to 6 points for in-domain and of up to 37 points for domain adaptation tasks compared to the original bi-encoder performance. 1
A Systematic Framework to Generate Invariants for Anomaly Detection in Industrial Control Systems

Industrial Control Systems (ICS) consisting of integrated hardware and software components designed to monitor and control a variety of industrial processes, are typically deployed in critical infrastructures such as water treatment plants, power grids and gas pipelines.Unlike conventional IT systems, the consequences of deviations from normal operation in ICS have the potential to cause significant physical damage to equipment, the environment and even human life.The active monitoring of invariant rules that define the physical conditions that must be maintained for the normal operation of ICS provides a means to improve the security and dependability of such systems by which early detection of anomalous system states may be achieved, allowing for timely mitigating actions -such as fault checking, system shutdown -to be taken.Generally, invariant rules are predefined by system engineers during the design phase of a given ICS build.However, this manually intensive process is costly, error-prone and, in typically complex systems, sub-optimal.In this paper we propose a novel framework that is designed to systematically generate invariant rules from information contained within ICS operational data logs, using a combination of several machine learning and data mining techniques.The effectiveness of our approach is demonstrated by experiments on two real world ICS testbeds: a water distribution system and a water treatment plant.We show that sets of invariant rules, far larger than those defined manually, can be successfully derived by our framework and that they may be used to deliver significant improvements in anomaly detection compared with the invariant rules defined by system engineers as well as the commonly used residual errorbased anomaly detection model for ICS.
Using Blockchain in a Reputation-Based Model for Grouping Agents in the Internet of Things

The Internet of Things (IoT) makes attractive services available to smart objects and humans. To aim this, IoT devices need high sensing, reasoning, and real-time acting capabilities that can be also obtained by promoting adaptive forms of cooperation machine-to-machine among smart objects. The convergence of IoT and multiagent systems also relies on the association between software agents with IoT devices for exploiting their social attitude of interacting and cooperating for services. However, the choice of reliable partners for cooperation can be very difficult when IoT devices migrate across different environments, where the most part of their members will be unreferenced with respect to their trustworthiness. It is well known that agents reputation can be a viable aspect to consider to form social groups; therefore, a possible solution to this problem is to form groups of agents in each IoT environment, based on their social capabilities. In this respect, the first contribution of this paper is represented by a reputation model focused on building the reputation capital of each agent. Second, an algorithm capable to form groups of agents in IoT environments on the basis of their reputation capital was designed. Finally, since in this contest, it is important to spread reliable and certified information about the device/agent reputation in a distributed environment, the third contribution is represented by the adoption of the blockchain technology to certify the reputation capital. Some experiments we have performed show that the model is capable to detect almost all the misleading agents if their percentage is under a high enough threshold, and that good results in term of group composition are obtained. Moreover, the simulations show that, by adopting our model, malicious devices always pay for services significantly more than honest ones. We argue that the individual reputation capital of devices and, consequently, the overall reputation capital of the IoT community can take benefit from the adoption of the proposed approach.
Information Technology and the pandemic: a preliminary multinational analysis of the impact of mobile tracking technology on the COVID-19 contagion control

This paper explores the benefits and drawbacks of government surveillance within a public health crisis, specifically the COVID-19 outbreak of 2020. We review the current state of COVID-19 infection tracking by public health authorities, and then we examine the effectiveness of voluntary and mandatory mobile contact-tracing apps by COVID-19-positive or suspected positive individuals in China, Germany, Italy, Singapore, South Korea, and the United States. Through a Difference-In-Differences test, the apps were found to be highly significantly correlated with a reduction in the spread of COVID-19 in their countries. Robustness tests were run with four alternative models and the results are kept and presented within. In light of the success of these apps, ethical implications for their use during and beyond this public health crisis are discussed, including data governance and individual privacy issues.
Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features

Fall causes trauma or critical injury among the geriatric population which is a second leading accidental cause of post-injury mortality around the world. It is crucial to keep elderly people under supervision by ensuring proper privacy and comfort. Thus the elderly fall detection and prediction using wearable/ non-wearable sensors become an active field of research. In this work, a novel pipeline for fall detection based on wearable accelerometer data has been proposed. Three publicly available datasets have been used to validate our proposed method, and more than 7700 cross-disciplinary time-series features were investigated for each of the datasets. After following a series of feature reduction techniques such as mutual information, removing highly correlated features using the Pearson correlation coefficient, Boruta algorithm, we have obtained the dominant features for each dataset. Different classical machine learning (ML) algorithms were utilized to detect falls based on the obtained features. For individual datasets, the simple ML classifiers achieved very good accuracy. We trained our pipeline with two of the three datasets and tested with the remaining one dataset until all three datasets were used as the test set to show the generalization capability of our proposed pipeline. A set of 39 high-performing features is selected, and the classifiers were trained with them. For all the cases, the proposed pipeline showed excellent efficiency in detecting falls. This architecture performed better than most of the existing works in all the used publicly available datasets, proving the supremacy of the proposed data analysis pipeline.