Reviewerly/exHarmony huggingface dataset:
A recurrent video quality enhancement framework with multi-granularity frame-fusion and frame difference based attention

In recent years, deep learning has attracted substantial research attention for video restoration. Among the existing contributions, the single-frame based approaches purely rely on one reference frame and neglect the rest neighboring frames when enhancing a target frame. By contrast, the multi-frame based contributions exploit temporal information in a sliding window and the existing recurrent design only employ a single preceding enhanced frame. It is intuitive to exploit both multiple original neighboring frames and the preceding enhanced frames for video quality enhancement. In this paper, we propose a Recurrent video quality Enhancement framework with Multi-granularity frame-fusion and frame Difference based attention (REMD). Firstly, we devise a three-dimensional convolutional neural network based encoder-decoder fusion model, which fuses multiple frames in multi-granularity. Secondly, severe compression artifacts tend to emerge on the edges and textures of the compressed frames. We propose a frame difference based spatial attention method to intensify the edges and textures of motioning regions. Finally, a recurrent sliding window design is conceived for exploiting the temporal information in preceding enhanced frames and subsequent neighboring frames. Experiments demonstrate that our method achieves superior performance in comparison to the state-of-the-art contributions with substantially reduced spatial and computational complexity.
CrowdGAN: Identity-Free Interactive Crowd Video Generation and Beyond

In this paper, we introduce a novel yet challenging research problem, interactive crowd video generation, committed to producing diverse and continuous crowd video, and relieving the difficulty of insufficient annotated real-world datasets in crowd analysis. Our goal is to recursively generate realistic future crowd video frames given few context frames, under the user-specified guidance, namely individual positions of the crowd. To this end, we propose a deep network architecture specifically designed for crowd video generation that is composed of two complementary modules, each of which combats the problems of crowd dynamic synthesis and appearance preservation respectively. Particularly, a spatio-temporal transfer module is proposed to infer the crowd position and structure from guidance and temporal information, and a point-aware flow prediction module is presented to preserve appearance consistency by flow-based warping. Then, the outputs of the two modules are integrated by a self-selective fusion unit to produce an identity-preserved and continuous video. Unlike previous works, we generate continuous crowd behaviors beyond identity annotations or matching. Extensive experiments show that our method is effective for crowd video generation. More importantly, we demonstrate the generated video can produce diverse crowd behaviors and be used for augmenting different crowd analysis tasks, i.e., crowd counting, anomaly detection, crowd video prediction. Code is available at https://github.com/Icep2020/CrowdGAN.
Analyzing the depression and suicidal tendencies of people affected by COVID-19’s lockdown using sentiment analysis on social networking websites

AbstractCoronavirus 2019, also known by name Corona Virus Disease (COVID-19), is the newer type of pandemic and communicable disease that has erupted and is spreading at alarming rates. This pandemic has shocked the whole world and has affected all the sectors ranging from healthcare, science, economy, government, trade. The rate and scale at which this virus is spreading are so rapid that it has become almost impossible to contain and scientists, and researchers across the world are working hard to stop and contain its spread. Till now, since there is no cure and vaccines for COVID-19, it is the need of the hour to find out ways and techniques to combat and contain its impact and spread. It has claimed the lives of around three lakh people, and this number is still growing. It has impacted not only the health of people but also it has shattered the economies of various countries leading to shutting down of various companies and making people jobless. Lockdown has been imposed by the government of various countries to contain the spread of this pandemic. This lockdown has forced the people to remain at their homes jobless, without money and food. The probability of developing depression and suicidal tendencies among people is growing. It is very important to identify such people and to provide the required assistance in this hour of emergency. In this article, a Sentiment Analysis Framework has been proposed which will extract the emotions and sentiments of people from the social networking website like twitter and report the location as well as the required available details to the authority to save the lives of people from this pandemic.Subject Classification: 68Keywords: CoronavirusCOVID-19FacebookLockdownSentiment analysisSuicidal tendenciesTwitter
Fairness-Aware Mechanism for Load Balancing in Distributed Systems

When a set of self-interested users shares multiple resources in a distributed system, we face the problem of allocating resources, called the load balancing problem. In particular, load balancing is defined as allocating the load to the servers of the distributed system such that jobs’ response time is minimized, and the utilization of servers is improved. In this article, the load balancing problem in a distributed system consists of a finite set of servers, and a finite set of users is studied. The load balancing problem considered here is a bi-objective problem with two highly probable conflicting objectives: (i) minimizing jobs’ response time (ii) providing the fair utilization of servers. In order to satisfy these two objectives simultaneously, both the objectives are considered in an integrated manner. Next, the load balancing problem is formulated as a noncooperative game; and to solve the game (i.e., to find the Nash equilibrium), a distributed load balancing algorithm (DLBA) is proposed. An experimental study is carried out to ascertain the efficacy of the proposed DLBA. Further, we compare DBLA with three existing load balancing approaches to evaluate its comparative effectiveness. The experimental results validate the effectiveness of the DLBA over the existing approaches.
Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images

Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) usually requires sufficient annotated image data, which is prohibitively expensive or unavailable in some applications. Additionally, when applying a CNN to new datasets, it is common to annotate individual cells/nuclei in those target datasets for model re-learning, leading to inefficient and low-throughput image analysis. To tackle these problems, we present a bidirectional, adversarial domain adaptation method for nucleus detection on cross-modality microscopy image data. Specifically, the method learns a deep regression model for individual nucleus detection with both source-to-target and target-to-source image translation. In addition, we explicitly extend this unsupervised domain adaptation method to a semi-supervised learning situation and further boost the nucleus detection performance. We evaluate the proposed method on three cross-modality microscopy image datasets, which cover a wide variety of microscopy imaging protocols or modalities, and obtain a significant improvement in nucleus detection compared to reference baseline approaches. In addition, our semi-supervised method is very competitive with recent fully supervised learning models trained with all real target training labels.
A Secure Personal-Data Trading System Based on Blockchain, Trust, and Reputation

Every citizen has the right to privacy and, therefore, the right to control their personal information, deciding to whom, when, and where their information is available. This paper proposes a secure, agile, and effective system for a distributed, automatic, and transparent data trading between domains using blockchain, smart contracts, trust, and reputation. We develop and implement a prototype of a trust and reputation system based on real-life interactions. The results show that the proposed system provides security and privacy in a quick and distributed way, performing hundreds of transactions per second, and effectively punishing malicious behavior.
Classification of three-photon states in waveguide quantum electrodynamics

We provide the first classification of three-photon eigenstates in a finite periodic array of two-level atoms coupled to a waveguide. We focus on the strongly subwavelength limit and show the hierarchical structure of the eigenstates in the complex plane. The main characteristic eigenstates are explored using entanglement entropy as a distinguishing feature. We show that the rich interplay of effects from order, chaos to localisation found in two-photon systems extends naturally to three-photon systems. There also exist interaction-induced localised states unique to three-photon systems such as bound trimers, corner states and trimer edge states.
Domain Priori Knowledge based Integrated Solution Design for Internet of Services

Various types of services, such as web APIs, IoT services, O2O services, and many others, have flooded on the Internet. Interconnections among these services have resulted in a new phenomenon called “Internet of Services” (IoS). By IoS, people don’t need to request multiple services by themselves to fulfill their daily requirements, but it is an IoS platform that is responsible for constructing integrated solutions for them. Since user requirements (URs) are usually coarse-grained and transboundary, IoS platforms have to integrate services from multiple domains to fulfill the requirements. Considering there are too many available services in IoS, a big challenge is how to look for a tradeoff between the construction efficiency and the precision of final solutions. For this challenge, we introduce a framework and a platform for transboundary user requirement oriented solution design in IoS. The main idea is to make use of domain priori knowledge derived from the commonness and similarities among massive historical URs and among historical integrated service solutions(ISSs). Priori knowledge is classified into three types: requirement patterns (RPs), service patterns (SPs), and probabilistic matching matrix (PMM) between RPs and SPs. A UR is modeled in the form of an intention tree (I-Tree) along with a set of constraints on intention nodes, and then optimal RPs are selected to cover the I-Tree as much as possible. By taking advantage of the PMM, a set of SPs are filtered out and composed together to form the final ISS. Finally, the design of a platform supporting the above process is introduced.
Accelerating Life Sciences Research with Blockchain

As life sciences research becomes increasingly focused on patient-centered technologies that allow for remote participation and greater access, distributed ledger technologies (“blockchain”) are being developed to address these needs. Blockchain-based applications range from basic functions, such as securing electronic data with audit trails, to honoring research participants’ informed consent for secondary uses of their data, and to the advanced features of aggregating data on a single platform for sophisticated machine learning, and hundreds of examples in between. There are many questions, however, about the best uses of blockchain and implementation strategies for life sciences research. This chapter introduces uses of blockchain for life sciences research and offers ethical, regulatory, and practical considerations for implementation. Recommendations are pertinent for blockchain developers, researchers, and life sciences organizations considering blockchain solutions for their research.
Counteracting adversarial attacks in autonomous driving

In this paper, we focus on studying robust deep stereo vision of autonomous driving systems and counteracting adversarial attacks against it. Autonomous system operation requires real-time processing of measurement data which often contain significant uncertainties and noise. Adversarial attacks have been widely studied to simulate these perturbations in recent years. To counteract these attacks in autonomous systems, a novel defense method is proposed in this paper. A stereo-regularizer is proposed to guide the model to learn the implicit relationship between the left and right images of the stereo-vision system. Univariate and multivariate functions are adopted to characterize the relationships between the two input images and the object detection model. The regularizer is then relaxed to its upper bound to improve adversarial robustness. Furthermore, the upper bound is approximated by the remainder of its Taylor expansion to improve the local smoothness of the loss surface. The model parameters are trained via adversarial training with the novel regularization term. Our method exploits basic knowledge from the physical world, i.e., the mutual constraints of the two images in the stereo-based system. As such, outliers can be detected and defended with high accuracy and efficiency. Numerical experiments demonstrate that the proposed method offers superior performance when compared with traditional adversarial training methods in state-of-the-art stereo-based 3D object detection models for autonomous vehicles.
Robust distribution-based nonnegative matrix factorizations for dimensionality reduction

As a popular dimensionality-reduction technique, nonnegative matrix factorization (NMF) has been widely researched since it is consistent with human cognitive processes in the psychology and physiology. This paper presents a novel NMF framework, called robust distribution-based NMF (RDNMF), to learn the robustly discriminative representations for data. In this RDNMF, a Kullback–Leibler divergence to measure the similarity between the data and representations is introduced, which fully preserves the geometrical structure of data. Meanwhile, this RDNMF employs the l2,1-norm loss to reduce the influence of noise and outliers. This paper further proposes a semi-supervised RDNMF (SRDNMF) by enforcing the representations of labeled points in the same class to be aligned on the same axis. The proposed RDNMF and SRDNMF are solved by the modified multiplicative update rules. Clustering experiments on seven benchmark datasets demonstrate the effectiveness of our methods in comparison to other state-of-the-art methods.
Synergies between synaptic and intrinsic plasticity in echo state networks

Synaptic plasticity and intrinsic plasticity, as two of the most common neural plasticity mechanisms, occur in all neural circuits throughout life. Neurobiological studies indicated that the interplay between synaptic and intrinsic plasticity contributes to the adaptation of the nervous system to different synaptic input signals. However, most existing computational models of neural plasticity consider these two plasticity mechanisms separately, which is biologically implausible. In this paper, a synergistic plasticity learning rule is proposed to adapt the reservoir connections in echo state networks (ESNs), which not only takes into account the regulation of synaptic weights, but also considers the adjustment of neuronal intrinsic excitability. The proposed synergetic plasticity rule is verified on a number of prediction and classification benchmark problems and our empirical results demonstrate that the ESN with synergistic plasticity learning rule performs much better than the state-of-the-art ESN models, and an ESN with a single neural plasticity rule.
A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition

Smartphone-sensors-based human activity recognition is attracting increasing interest due to the popularization of smartphones. It is a difficult long-range temporal recognition problem, especially with large intraclass distances such as carrying smartphones at different locations and small interclass distances such as taking a train or subway. To address this problem, we propose a new framework of combining short-term spatial/frequency feature extraction and a long-term independently recurrent neural network (IndRNN) for activity recognition. Considering the periodic characteristics of the sensor data, short-term temporal features are first extracted in the spatial and frequency domains. Then, the IndRNN, which can capture long-term patterns, is used to further obtain the long-term features for classification. Given the large differences when the smartphone is carried at different locations, a group-based location recognition is first developed to pinpoint the location of the smartphone. The Sussex-Huawei Locomotion (SHL) dataset from the SHL Challenge is used for evaluation. An earlier version of the proposed method won the second place award in the SHL Challenge 2020 (first place if not considering the multiple models fusion approach). The proposed method is further improved in this paper and achieves 80.72% accuracy, better than the existing methods using a single model.
Online cloud resource prediction via scalable window waveform sampling on classified workloads

Accurate prediction on the utilization of cloud resources is increasingly important for public cloud users, as it relates to the reasonable reservation of resources for minimizing the usage costs. However, the existing relevant approaches fail to predict the usage amount of cloud resources on the basis of the requested workloads of users’ applications, and the characteristics of changing workload data are rarely considered for the real-time prediction. To address these challenges, we propose an online cloud resource prediction model (OCRPM) to timely predict the proper resource usage amount. Firstly, all of the requested workloads are classified into three types of waveform trend patterns using the trend degree (TD). Next, a scalable window waveform sampling method (SWWS) on the classified patterns is devised to extend the suitable workload waveform interval window for supporting the subsequent high accurate prediction on the cloud resources. Finally, an optimal error gradient boosting regression (OEGBR) algorithm is given to train the data model and to predict the reasonable cloud resource usage amount in light of the requested workloads. The simulation results indicate that the proposed method can adjust the suitable workload waveform sampling window, and achieve higher prediction accuracy than the state-of-the-art relevant approaches and existing statistical learning models.
CCA Updatable Encryption Against Malicious Re-encryption Attacks

Updatable encryption (UE) is an attractive primitive, which allows the secret key of the outsourced encrypted data to be updated to a fresh one periodically. Several elegant works exist studying various security properties. We notice several major issues in existing security models of (ciphertext dependent) updatable encryption, in particular, integrity and CCA security. The adversary in the models is only allowed to request the server to re-encrypt honestly generated ciphertext, while in practice, an attacker could try to inject arbitrary ciphertexts into the server as she wishes. Those malformed ciphertext could be updated and leveraged by the adversary and cause serious security issues. In this paper, we fill the gap and strengthen the security definitions in multiple aspects: most importantly our integrity and CCA security models remove the restriction in previous models and achieve standard notions of integrity and CCA security in the setting of updatable encryption. Along the way, we refine the security model to capture post-compromise security and enhance the re-encryption indistinguishability to the CCA style. Guided by the new models, we provide a novel construction ReCrypt $$^+$$ , which satisfies our strengthened security definitions. The technical building block of homomorphic hash from a group may be of independent interests. We also study the relations among security notions; and a bit surprisingly, the folklore result in authenticated encryption that IND-CPA plus ciphertext integrity imply IND-CCA security does not hold for ciphertext dependent updatable encryption.
A Bibliometric and Content Analysis of Articles in Remote Sensing From Vietnam Indexed in Scopus for the 2000–2019 Period

In this study, a bibliometric analysis is conducted to evaluate bibliographic content of all scientific publications in the field of remote sensing published by scholars with Vietnamese affiliations, indexed in the Scopus database, for the 2000–2019 period. Results indicated that the number of scientific publications in this research field increased gradually by years and then rapidly from 2015 with at least 50 articles per year. Most of the studies were concentrated in a few research institutions, and 65% of the publications were from top-10 Vietnamese research institutions. Vietnamese scholars had the most international collaboration with the USA, followed by Germany and Japan. The Mekong Delta is the most frequently studied area. Climate change, land-use change, groundwater, and landslides were the most popular research topics; machine learning and artificial neural networks were the most popular techniques studied in recent years.
Why is this an anomaly? Explaining anomalies using sequential explanations

In most applications, anomaly detection operates in an unsupervised mode by looking for outliers hoping that they are anomalies. Unfortunately, most anomaly detectors do not come with explanations about which features make a detected outlier point anomalous. Therefore, it requires human analysts to manually browse through each detected outlier point’s feature space to obtain the subset of features that will help them determine whether they are genuinely anomalous or not. This paper introduces sequential explanation (SE) methods that sequentially explain to the analyst which features make the detected outlier anomalous. We present two methods for computing SEs called the outlier and sample-based SE that will work alongside any anomaly detector. The outlier-based SE methods use an anomaly detector’s outlier scoring measure guided by a search algorithm to compute the SEs. Meanwhile, the sample-based SE methods employ sampling to turn the problem into a classical feature selection problem. In our experiments, we compare the performances of the different outlier- and sample-based SEs. Our results show that both the outlier and sample-based methods compute SEs that perform well and outperform sequential feature explanations.
A Tripartite Theory of Trustworthiness for Autonomous Systems

It is recognized that system trustworthiness is a hyperstructure embodied by the structural, behavioral, and system dimensions with a set of coherent attributes. We explore a theoretical framework of tripartite trustworthiness that can be applied to real-world autonomous systems. We present a formal study of the essences and mathematical models of system trustworthiness and their quantitative measurements in the contexts of autonomous and mission-critical intelligent systems where humans and machines interact in a hybrid environment.
An adaptive defense mechanism to prevent advanced persistent threats

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