Reviewerly/exHarmony huggingface dataset:
Gaussian Affinity for Max-Margin Class Imbalanced Learning

Real-world object classes appear in imbalanced ratios. This poses a significant challenge for classifiers which get biased towards frequent classes. We hypothesize that improving the generalization capability of a classifier should improve learning on imbalanced datasets. Here, we introduce the first hybrid loss function that jointly performs classification and clustering in a single formulation. Our approach is based on an 'affinity measure' in Euclidean space that leads to the following benefits: (1) direct enforcement of maximum margin constraints on classification boundaries, (2) a tractable way to ensure uniformly spaced and equidistant cluster centers, (3) flexibility to learn multiple class prototypes to support diversity and discriminability in feature space. Our extensive experiments demonstrate the significant performance improvements on visual classification and verification tasks on multiple imbalanced datasets. The proposed loss can easily be plugged in any deep architecture as a differentiable block and demonstrates robustness against different levels of data imbalance and corrupted labels.
Active Adversarial Domain Adaptation

We propose an active learning approach for transferring representations across domains. Our approach, active adversarial domain adaptation (AADA), explores a duality between two related problems: adversarial domain alignment and importance sampling for adapting models across domains. The former uses a domain discriminative model to align domains, while the latter utilizes the model to weigh samples to account for distribution shifts. Specifically, our importance weight promotes unlabeled samples with large uncertainty in classification and diversity compared to la-beled examples, thus serving as a sample selection scheme for active learning. We show that these two views can be unified in one framework for domain adaptation and transfer learning when the source domain has many labeled examples while the target domain does not. AADA provides significant improvements over fine-tuning based approaches and other sampling methods when the two domains are closely related. Results on challenging domain adaptation tasks such as object detection demonstrate that the advantage over baseline approaches is retained even after hundreds of examples being actively annotated.
Cloud Based Secure Service Providing for IoTs Using Blockchain

Internet of Things (IoTs) is widely growing domain of the modern era. With the advancement in technologies, the use of IoTs devices also increases. However, security risks regarding service provisioning and data sharing also increases. There are many existing security approaches. However, these approaches are not suitable for IoTs devices due to their limited storage and computation resources. These secure approaches also require a specific hardware. With the invention of blockchain technologies, many security risks are eliminated. Blockchain also supports data sharing mechanism. In this paper, we proposed a secure service providing mechanism for IoTs using blockchain. We introduced cloud nodes for maintaining the validity states of edge service providers. The edge node reputation is considered as a service rating given by end users. Incentive is given to edge servers after validation of service codes. Incentive is in the form of cryptocurrency. Incentive and edge node reputation values are stored in cloud node and are updated with respect to time. Smart contract is proposed to check the validity state of the edge servers. Smart contract is also used for the comparison and verification of the service codes provided by edge servers. In our proposed system, we perform service authentication at both cloud and edge server layer. Moreover, Proof of Authority (PoA) is used as a consensus mechanism. PoA enhanced overall performance of our proposed system. By experimental analysis, it is shown that our proposed model is suitable for resource constrained devices.
Blockchain for 5G: Opportunities and Challenges

5G is a revolutionary technology in mobile telecommunications that promises to be 20x faster than today's 4G technology. The novel characteristics of 5G can be exploited to support new business models and services that require seamless interactions among multiple parties that may include mobile operators, enterprises, telecom providers, government regulators, and infrastructure providers. Meanwhile, blockchain technology has evolved as an enabling, disruptive, and transformational technology that has started to be adopted across many industry vertical domains. Blockchain has been increasingly used to register, authenticate and validate assets and transactions, govern interactions, record data and manage the identification among multiple parties, in a trusted, decentralized, and secure manner. In this paper, we discuss and highlight how blockchain can be leveraged for 5G networks. First, an overview of blockchain capabilities as well as smart contracts, decentralized storage and trusted oracles are presented. Second, potential opportunities in which blockchain features are used to enable 5G services are outlined. Third, examples of system integration architecture and sequence flow diagrams to illustrate how blockchain along with other supporting decentralized technologies can support and facilitate such opportunities are discussed. Finally, key challenges and open research problems are identified and discussed.
Nonlocal CNN SAR Image Despeckling

We propose a new method for SAR image despeckling, which performs nonlocal filtering with a deep learning engine. Nonlocal filtering has proven very effective for SAR despeckling. The key idea is to exploit image self-similarities to estimate the hidden signal. In its simplest form, pixel-wise nonlocal means, the target pixel is estimated through a weighted average of neighbors, with weights chosen on the basis of a patch-wise measure of similarity. Here, we keep the very same structure of plain nonlocal means, to ensure interpretability of results, but use a convolutional neural network to assign weights to estimators. Suitable nonlocal layers are used in the network to take into account information in a large analysis window. Experiments on both simulated and real-world SAR images show that the proposed method exhibits state-of-the-art performance. In addition, the comparison of weights generated by conventional and deep learning-based nonlocal means provides new insight into the potential and limits of nonlocal information for SAR despeckling.
Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison

In today's economic scenario, credit card use has become extremely commonplace. These cards allow the user to make payments of large sums of money without the need to carry large sums of cash. They have revolutionized the way of making cashless payments and made making any sort of payments convenient for the buyer. This electronic form of payment is extremely useful but comes with its own set of risks. With the increasing number of users, credit card frauds are also increasing at a similar pace. The credit card information of a particular individual can be collected illegally and can be used for fraudulent transactions. Some Machine Learning Algorithms can be applied to collect data to tackle this problem. This paper presents a comparison of some established supervised learning algorithms to differentiate between genuine and fraudulent transactions.
Analysis of Political Sentiment Orientations on Twitter

The dramatic increase in the number of users on social media platform leads to the generation of huge amount of unstructured text in the form of messages, chats, posts and blogs. Besides the exchange of information, social media is a remarkably convenient medium to express the ideas and opinions which gain popularity when liked by a large set of users. This popularity may reflect the sentiment of people towards that person, organization or a place. The social media platform, such as Twitter, generates huge amounts of the text containing political insights, which can be mined to analyze the people’s opinion and predict the future trends in the elections. In this work, an attempt is made to the mine tweets, capture the political sentiments from it and model it as a supervised learning problem. The extraction of tweets pertaining to the General Elections of India in 2019 is carried out along with the study of sentiments among Twitter users towards the major national political parties participating in the electoral process. Subsequently, the classification model based on sentiments is prepared to predict the inclination of tweets to infer the results of the elections. The Long Short Term Memory (LSTM) is employed to prepare the classification model and compare it with the classical machine learning models.
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study

Research has shown that recommender systems are typically biased towards popular items, which leads to less popular items being underrepresented in recommendations. The recent work of Abdollahpouri et al. in the context of movie recommendations has shown that this popularity bias leads to unfair treatment of both long-tail items as well as users with little interest in popular items. In this paper, we reproduce the analyses of Abdollahpouri et al. in the context of music recommendation. Specifically, we investigate three user groups from the Last.fm music platform that are categorized based on how much their listening preferences deviate from the most popular music among all Last.fm users in the dataset: (i) low-mainstream users, (ii) medium-mainstream users, and (iii) high-mainstream users. In line with Abdollahpouri et al., we find that state-of-the-art recommendation algorithms favor popular items also in the music domain. However, their proposed Group Average Popularity metric yields different results for Last.fm than for the movie domain, presumably due to the larger number of available items (i.e., music artists) in the Last.fm dataset we use. Finally, we compare the accuracy results of the recommendation algorithms for the three user groups and find that the low-mainstreaminess group significantly receives the worst recommendations.
Using location semantics to realize personalized road network location privacy protection

Abstract With the rapid development of location-based services in the field of mobile network applications, users enjoy the convenience of location-based services on one side, and they are exposed to the risk of privacy disclosure on the other side. Attackers may attack based on the semantic of the user’s location and user’s query location. A few of existing works on location privacy protection consider to protect the user’s location and his query location simultaneously, while the query location may reflect his requirement. In this paper, based on the existing location privacy protection framework, we first generate sensitive weight documents based on the user’s sensitivity to different location semantics automatically, then obtain the best collaborative segment for k -anonymity of the user’s location by using the reinforcement learning algorithm, and finally, the bidirectional k -disturbance of the user’s location and query location is performed based on the location semantics in real road network environment. The experiment verifies the effectiveness of the proposed method.
FalconDB: Blockchain-based Collaborative Database

Nowadays an emerging class of applications are based oncollaboration over a shared database among different entities. However, the existing solutions on shared database may require trust on others, have high hardware demand that is unaffordable for individual users, or have relatively low performance. In other words, there is a trilemma among security, compatibility and efficiency. In this paper, we present FalconDB, which enables different parties with limited hardware resources to efficiently and securely collaborate on a database. FalconDB adopts database servers with verification interfaces accessible to clients and stores the digests for query/update authentications on a blockchain. Using blockchain as a consensus platform and a distributed ledger, FalconDB is able to work without any trust on each other. Meanwhile, FalconDB requires only minimal storage cost on each client, and provides anywhere-available, real-time and concurrent access to the database. As a result, FalconDB over-comes the disadvantages of previous solutions, and enables individual users to participate in the collaboration with high efficiency, low storage cost and blockchain-level security guarantees.
Multiple-strategy learning particle swarm optimization for large-scale optimization problems

Abstract The balance between the exploration and the exploitation plays a significant role in the meta-heuristic algorithms, especially when they are used to solve large-scale optimization problems. In this paper, we propose a multiple-strategy learning particle swarm optimization algorithm, called MSL-PSO, to solve problems with large-scale variables, in which different learning strategies are utilized in different stages. At the first stage, each individual tries to probe some positions by learning from the demonstrators who have better performance on the fitness value and the mean position of the population. All the best probed positions, each of which has the best fitness among all positions probed by its corresponding individual, will compose a new temporary population. The temporary population will be sorted on the fitness values in a descending order, and will be used for each individual to find its demonstrators, which is based on the rank of the best probed solution in the temporary population and the rank of the individual in the current population, to learn using a new strategy in the second stage. The first stage is used to improve the exploration capability, and the second one is expected to balance the convergence and diversity of the population. To verify the effectiveness of MSL-PSO for solving large-scale optimization problems, some empirical experiments are conducted, which include CEC2008 problems with 100, 500, and 1000 dimensions, and CEC2010 problems with 1000 dimensions. Experimental results show that our proposed MSL-PSO is competitive or has a better performance compared with ten state-of-the-art algorithms.
A Comprehensive Review of Shepherding as a Bio-Inspired Swarm-Robotics Guidance Approach

The simultaneous control of multiple coordinated robotic agents represents an elaborate problem. If solved, however, the interaction between the agents can lead to solutions to sophisticated problems. The concept of swarming, inspired by nature, can be described as the emergence of complex system-level behaviors from the interactions of relatively elementary agents. Due to the effectiveness of solutions found in nature, bio-inspired swarming-based control techniques are receiving a lot of attention in robotics. One method, known as swarm shepherding, is founded on the sheep herding behavior exhibited by sheepdogs, where a swarm of relatively simple agents are governed by a shepherd (or shepherds) which is responsible for high-level guidance and planning. Many studies have been conducted on shepherding as a control technique, ranging from the replication of sheep herding via simulation, to the control of uninhabited vehicles and robots for a variety of applications. A comprehensive review of the literature on swarm shepherding is presented in order to reveal the advantages and potential of the approach to be applied to a plethora of robotic systems in the future.
Taking a Deeper Look at Co-Salient Object Detection

Co-salient object detection (CoSOD) is a newly emerging and rapidly growing branch of salient object detection (SOD), which aims to detect the co-occurring salient objects in multiple images. However, existing CoSOD datasets often have a serious data bias, which assumes that each group of images contains salient objects of similar visual appearances. This bias results in the ideal settings and the effectiveness of the models, trained on existing datasets, may be impaired in real-life situations, where the similarity is usually semantic or conceptual. To tackle this issue, we first collect a new high-quality dataset, named CoSOD3k, which contains 3,316 images divided into 160 groups with multiple level annotations, i.e., category, bounding box, object, and instance levels. CoSOD3k makes a significant leap in terms of diversity, difficulty and scalability, benefiting related vision tasks. Besides, we comprehensively summarize 34 cutting-edge algorithms, benchmarking 19 of them over four existing CoSOD datasets (MSRC, iCoSeg, Image Pair and CoSal2015) and our CoSOD3k with a total of ~61K images (largest scale), and reporting group-level performance analysis. Finally, we discuss the challenge and future work of CoSOD. Our study would give a strong boost to growth in the CoSOD community. Benchmark toolbox and results are available on our project page.
Differential privacy in blockchain technology: A futuristic approach

Blockchain has received a widespread attention because of its decentralized, tamper-proof, and transparent nature. Blockchain works over the principle of distributed, secured, and shared ledger, which is used to record, and track data within a decentralized network. This technology has successfully replaced certain systems of economic transactions in organizations and has the potential to overtake various industrial business models in future. Blockchain works over peer-to-peer (P2P) phenomenon for its operation and does not require any trusted-third party authorization for data tracking and storage. The information stored in blockchain is distributed throughout the decentralized network and is usually protected using cryptographic hash functions. Since the beginning of blockchain technology, its use in different applications is increasing exponentially, but this increased use has also raised some questions regarding privacy and security of data being stored in it. Protecting privacy of blockchain data using data perturbation strategy such as differential privacy could be a novel approach to overcome privacy issues in blockchain. In this article, we cover the topic of integration of differential privacy in each layer of blockchain and in certain blockchain based scenarios. Moreover, we highlight some future challenges and application scenarios in which integration of differential privacy in blockchain can produce fruitful results.
User experiences of virtual reality technologies for healthcare in learning: an integrative review

The aim of this integrative review was to analyse the usage of different virtual reality (VR) technologies in learning and user experiences (UXs) of these technologies in healthcare practice and education. The integrative review was conducted in spring 2019 by searching eight international databases. The searches retrieved n = 26 original articles that were quality checked and included for the review. Three different VR technologies used in the field of healthcare education and practice were identified: haptic device simulators, computer-based simulations and head-mounted displays (HMDs). The haptic simulators were the most often used, whereas the HMD devices were the least-used technology in the field of healthcare. In immersive virtual environments, UX includes ten components. Most of the components were observed in the context of haptic devices and HMD devices, with all ten components being observed with the HMD devices. Almost all of the components were rated as positive. In conclusion, the development of VR technology has enabled the creation of the most comprehensive UXs, thus enhancing skill development, enabling remote access to training and, ultimately, improving patient safety. This review is important as it highlights the need for far more UX research within immersive virtual environments.
Preserving the Privacy of Electronic Health Records using Blockchain

Electronic health records (EHRs) are health information of patients that are saved digitally in a network. Various opportunities to enhance patient care, performance measures in clinical practice and contribute to clinical research in the future are provided by EHRs. The schemes used to store EHRs have been very insecure in the present era of smart cities and homes. The data can be easily breached by hackers and unauthorized external parties. Also, the data is not accessible to patients and care providers. These schemes are unable to create a balance between data security and data accessibility. But blockchain can resolve these issues. Blockchain creates a ledger system that is immutable and allows the transactions to take place in a decentralized manner. The three main features of blockchain technology - Security, Decentralization, and Transparency make any application built using it secure and not accessible by unauthorized parties. The manipulation of data is almost impossible to do in a blockchain network. In this project, we propose a system to implement EHRs using blockchain technology and make EHRs more secure and private. The blockchain technology will keep control over access to information using its cryptographic techniques and decentralization. It will also maintain the balance between data privacy and data accessibility. Our main objective of this project is the framing of data privacy and security issues in electronic healthcare
Color Cast Dependent Image Dehazing via Adaptive Airlight Refinement and Non-Linear Color Balancing

Hazy images suffer from low visibility since the light gets scattered as it passes through various atmospheric particles. Moreover, such images are prone to color distortion, particularly in real weather conditions like sandstorms. In this letter, an effective dehazing technique is proposed using weighted least squares filtering on dark channel prior and color correction that involves automatic detection of color cast images. We show that the spread of the hue in a hazy image can differentiate a color cast image from a non-cast one. We propose a measure using the same for categorizing hazy images as cast and non-cast ones. Our novel color correction is performed by color balancing using a non-linear transformation followed by a cast-adaptive airlight refinement. Subjective and quantitative evaluations show that our method outperforms the state-of-the-art. It removes cast satisfactorily and reduces haze substantially while maintaining the naturalness of the image. Moreover, it produces visually pleasing images without halo artifacts.
A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection

Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing. This is not a problem for high-level vision problems, where discriminative features are barely affected by resizing. On the contrary, in image forensics, resizing tends to destroy precious high-frequency details, impacting heavily on performance. One can avoid resizing by means of patch-wise processing, at the cost of renouncing whole-image analysis. In this work, we propose a CNN-based image forgery detection framework which makes decisions based on full-resolution information gathered from the whole image. Thanks to gradient checkpointing, the framework is trainable end-to-end with limited memory resources and weak (image-level) supervision, allowing for the joint optimization of all parameters. Experiments on widespread image forensics datasets prove the good performance of the proposed approach, which largely outperforms all baselines and all reference methods.