Reviewerly/exHarmony huggingface dataset:
Dual-Triplet Metric Learning for Unsupervised Domain Adaptation in Video Face Recognition

The scalability and complexity of deep learning models remains a key issue in many of visual recognition applications. For instance, in video surveillance, fine tuning of a model with labeled image data from each new camera is required to reduce the domain shift between videos captured from the source domain (laboratory setting) and the target domain (operational environment). In many video surveillance applications, like face recognition and person re-identification, a pair-wise matcher is typically employed to assign a query image captured using a video camera to the corresponding reference images in a gallery. The different configuration, viewpoint, and operational conditions of each camera can introduce significant shifts in pair-wise distance distributions, resulting in a decline in recognition performance for new cameras. In this paper, a new deep domain adaptation (DA) method is proposed to adapt the CNN embedding of a Siamese network using unlabeled tracklets captured with a new video camera. To this end, a dual-triplet loss is introduced for metric learning, where two triplets are constructed using video data from a source camera, and a new target camera. In order to constitute the dual triplets, a mutual-supervised learning approach is introduced where the source camera acts as a teacher, providing the target camera with an initial embedding. Then, the student relies on the teacher to iteratively label the positive and negative pairs collected during, e.g., initial camera calibration. Both source and target embeddings continue to simultaneously learn such that their pair-wise distance distributions become aligned. For validation, the proposed metric learning technique is used to train deep Siamese networks under different training scenarios, and is compared to state-of-the-art techniques for still-to-video FR on the COXS2V and a private video-based FR dataset. Results indicate that the proposed method can provide a level of accuracy that is comparable to the upper bound performance, in training scenario where labeled target data is employed to fine-tune the Siamese network.
No-Reference Video Quality Assessment Based On Similarity Map Estimation

One of the key challenges in no-reference video quality assessment (NR-VQA) is the absence of the reference video to measure the similarity or difference between the distorted video and the original one. In this paper, an encoder-decoder model is proposed to predict pixel-by-pixel similarity maps from the distorted video. The model takes multiple frames as input since correlated pixels of adjacent frames can be exploited to recover the similarity map of the middle frame of the distorted video clip. In addition, to further exploit the temporal perception mechanism of the human visual system (HVS), which is relevant to the perceptual video distortion measurement, visual persistence and temporal memory effects are considered in the spatio-temporal pooling network design. Experimental results demonstrate that our proposed method outperforms state-of-the-art NR-VQA metrics.
Performance Evaluation of Distributed Ledger Technologies for IoT data registry : A Comparative Study

Internet-of-Things (IoT) technologies have gained prevalence in various areas including Smart Vehicles, Smart Buildings, and Smart Health. Especially on these domains, the use of IoT involves certain challenges around security, privacy and trust, which can be characterized as sensitive issues. Blockchain technologies have emerged as the potential solution to address these issues. However, Blockchain can be inefficient in terms of both performance and cost due to high bandwidth overhead and delays. In this paper, we have examined four different popular Blockchain platforms (Hyperledger Fabric, Hyperledger Burrow, Hyperledger Sawtooth, and BigchainDB) to identify what are the overheads around the use of Blockchain and to study whether there is a single optimal solution with respect to time and computation overheads, or if there are trade-offs between the four platforms for IoT applications.
OvA-INN: Continual Learning with Invertible Neural Networks

In the field of Continual Learning, the objective is to learn several tasks one after the other without access to the data from previous tasks. Several solutions have been proposed to tackle this problem but they usually assume that the user knows which of the tasks to perform at test time on a particular sample, or rely on small samples from previous data and most of them suffer of a substantial drop in accuracy when updated with batches of only one class at a time. In this article, we propose a new method, OvA-INN, which is able to learn one class at a time and without storing any of the previous data. To achieve this, for each class, we train a specific Invertible Neural Network to extract the relevant features to compute the likelihood on this class. At test time, we can predict the class of a sample by identifying the network which predicted the highest likelihood. With this method, we show that we can take advantage of pretrained models by stacking an Invertible Network on top of a feature extractor. This way, we are able to outperform state-of-the-art approaches that rely on features learning for the Continual Learning of MNIST and CIFAR-100 datasets. In our experiments, we reach 72% accuracy on CIFAR-100 after training our model one class at a time.
Composite firefly algorithm for breast cancer recognition

Summary Breast cancer is the most common tumor that seriously threatens the life of women. However, with imprecise measure methods, the detection results are not reliable enough, and this will bring more pain and cost to patients. Therefore, accurate identification of breast cancer is a very important issue. To tackle this problem, a composite firefly algorithm (named CoFA) is proposed, in which each firefly is attracted compositely by the best and two randomly selected fireflies. First, the composite attraction method increases the probability that the current firefly generates better solution. In addition, the two fireflies are randomly selected, whatever they are better or worse than current firefly, the population diversity can be improved. The proposed CoFA has been tested on several breast cancer datasets derived from UCI. Experimental results verified that CoFA significantly improves the recognition accuracy.
Multi-Range Gated Graph Neural Network for Telecommunication Fraud Detection

With the expansion of the mobile communication technology, telecommunication fraud is increasing dramatically which results in the loss of billions of dollars worldwide every year. In recent years, some detection methods utilize data mining and statistical techniques to detect fraud from large amount of subscriber content data, and some methods transform the social relation into a set of topological features, such as degree, k-core etc. However, both content and relation have not been fully explored for identifying fraudsters. In this paper, we propose the Multi-Range Gated Graph Neural Network (MRG-GNN) for learning latent features of social network. Specifically, we first model a social network as a directed graph where vertices with subscriber features represent subscribers and edges with relational features represent activities between them. Then, a novel method based on efficient short walks and node-merging is proposed to structure the convolutions, and graph convolution block captures content information and relation information between users. The multi-range gated readout operation is proposed to aggregate informative features in multiple nodes and automatically learns the representation of user social network. Finally, experiments on a real-world telecommunication network show that our MRG-GNN achieves the state-of-the-art results.
Scalable Decentralized Multi-Robot Trajectory Optimization in Continuous-Time

This article presents a decentralized algorithm that generates continuous-time trajectory online for a swarm of robots based upon model predictive control. To generate collision-free trajectory, temporally distinct safe regions are formed such that the robots are confined to move within these safe regions to avoid collisions with one another. The distinct safe regions are temporally linked by generating a B-spline. Additionally, to ensure that collisions are avoided, collision-regions that the robots have to stay outside are also generated distinctly. A non linear program (NLP) with an objective to make the robots stay outside the collision-regions and stay within the safe regions is formulated. The algorithm was tested in simulations on Gazebo with aerial robots. The simulated results suggest that the proposed algorithm is computationally efficient and can be used for online planning in moderate sized multi-robot systems.
Algorithmic Support for Rabin Cryptosystem Implementation Based on Addition

This paper deals with algorithmic support for Rabin cryptosystem implementation based on addition without performing computationally expensive arithmetic operations. Due to this, there is a reduction in time and hardware complexity of the encryption and decryption processes. The proposed approach allows us to increase the amount of input data and the size of the keys in order to improve the security of the cryptosystem not affecting its efficiency. An example of Rabin cryptosystem implementation based on addition is given.
A Particular Character Speech Synthesis System Based on Deep Learning

The speech synthesis system of a particular character is a TTS (text-to-speech) synthetic system, which can obtain voice with the specific speaker’s voice characteristics. The traditional method, based on machine learning, requires a great amount of training samples and large iterations. In this paper, we proposed a novel TTS system based on fully convolutional neural networks and attention mechanism. The system can be trained start from scratch with random initialization and realize end-to-end output. By adding the attention layer and the loss of attention, it can better adapt to the features of the pronunciation, intonation and accent of a specific speaker. Experimental results show that our speech synthesis framework demonstrates a stronger model performance by synthesizing higher quality forged specific character audio with a smaller training set and lesser iterations.
Mining the Characteristics of the Ethereum P2P Network

Ethereum is the typical representative of the second generation of Blockchain technology. To achieve anonymity and decentralization, Ethereum utilizes a structured P2P network for data broadcasting, replication, and synchronization. The Ethereum P2P network topology construction profoundly affects the overall Ethereum system performance and reliability. However, there are few studies on the Ethereum underlying P2P overlay network topology. In this paper, we perform a preliminary investigation on this extensive Ethereum P2P system with a comprehensive measurement method combining active and passive methods. We gather Ethereum nodes information by active method for nodes crawling and verification, and we use the passive network traffic monitoring for routing data gathering. After a period of data collection for more than one month, we get a massive data set that has more than 50 million routing data entries. This data set allows us to gain knowledge on the Ethereum network scale and to derive mathematical characteristics of overlay network structure. By analyzing the data set comprehensively, we find that the Ethereum network exhibits both small-world and scale-free characteristics. There are a few critical nodes that have a significant impact on the robustness and vulnerability of the whole system. The Ethereum network also has a very high concentration in terms of network routing, which is very vulnerable to routing attacks.
Blockchain and the Future of Business Data Analytics

ABSTRACT Blockchain and distributed ledger technologies are changing the way financial and business records are created and stored. New approaches to collaboration within and across industries enabled by this technology will increasingly result in new opportunities for data analysis and enable fundamental changes in accounting and auditing. The importance of this technology is increasing and “CPAs will need to learn, adjust, and adapt to emerging uses of blockchain” (AICPA 2020). This paper presents a pencil-and-paper activity that can help students unfamiliar with blockchain-related technologies understand these systems, the inter-organizational databases that result from their use, and their potential impacts for the accounting profession. We include optional reference materials that can be used as background reading for faculty members unfamiliar with blockchain or that can be integrated into a course.
Kyrix-S: Authoring Scalable Scatterplot Visualizations of Big Data

Static scatterplots often suffer from the overdraw problem on big datasets where object overlap causes undesirable visual clutter. The use of zooming in scatterplots can help alleviate this problem. With multiple zoom levels, more screen real estate is available, allowing objects to be placed in a less crowded way. We call this type of visualization scalable scatterplot visualizations, or SSV for short. Despite the potential of SSVs, existing systems and toolkits fall short in supporting the authoring of SSVs due to three limitations. First, many systems have limited scalability, assuming that data fits in the memory of one computer. Second, too much developer work, e.g., using custom code to generate mark layouts or render objects, is required. Third, many systems focus on only a small subset of the SSV design space (e.g. supporting a specific type of visual marks). To address these limitations, we have developed Kyrix-S, a system for easy authoring of SSVs at scale. Kyrix-S derives a declarative grammar that enables specification of a variety of SSVs in a few tens of lines of code, based on an existing survey of scatterplot tasks and designs. The declarative grammar is supported by a distributed layout algorithm which automatically places visual marks onto zoom levels. We store data in a multi-node database and use multi-node spatial indexes to achieve interactive browsing of large SSVs. Extensive experiments show that 1) Kyrix-S enables interactive browsing of SSVs of billions of objects, with response times under 500ms and 2) Kyrix-S achieves 4X-9X reduction in specification compared to a state-of-the-art authoring system.
The Use of e-Learning Technologies in the Russian University in the Training of Engineers of the XXI Century

The paper deals with the main problems and prospects of using e-learning technologies in the Russian education in the training of engineers of the XXI century. The factors hindering the development of a clear and consistent conceptual framework for the research are revealed as well as contradictions in the results of studies on the use of e-learning, the lack of justification for the effectiveness of the new e-learning technologies. The revealed contradictions allowed to formulate the purpose of the study, associated with the need to create a technology in order to overcome the observed problems for students of technical specialties. A technology to overcome motivational, didactic and activity barriers in the blended training of engineers is offered. The research methodology is based on the competence approach to the implementation of the educational process. Scientific methods include the analysis and content generalization of pedagogical and methodical literature. The results of the experiment proved the fact that e-learning is an effective instrument to level motivational, didactic and activity barriers. The results of the study and materials can be used in the preparation of educational programs aimed at the development of e-learning technologies, as well as for the adjustment of their content.
A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.
A Descriptive Study on Homomorphic Encryption Schemes for Enhancing Security in Fog Computing

Nowadays, Fog Computing gets more attention due to its characteristics. Fog computing provides more advantages in related to apply with the latest technology. On the other hand, there is an issue about the data security over processing of data. Fog Computing encounters many security challenges like false data injection, violating privacy in edge devices and integrity of data, etc. An encryption scheme called Homomorphic Encryption (HME) technique is used to protect the data from the various security threats. This homomorphic encryption scheme allows doing manipulation over the encrypted data without decrypting it. This scheme can be implemented in many systems with various crypto-algorithms. This homomorphic encryption technique is mainly used to retain the privacy and to process the stored encrypted data on a remote server. This paper addresses the terminologies of Fog Computing, work flow and properties of the homomorphic encryption algorithm, followed by exploring the application of homomorphic encryption in various public key cryptosystems such as RSA and Pailier. It focuses on various homomorphic encryption schemes implemented by various researchers such as Brakerski-Gentry-Vaikuntanathan model, Improved Homomorphic Cryptosystem, Upgraded ElGamal based Algebric homomorphic encryption scheme, In-Direct rapid homomorphic encryption scheme which provides integrity of data.
Shop and Go: An innovative approach towards shopping using Deep Learning and Computer Vision

Today, technology plays a vital role in developing the nation and it is changing day-by-day to make people lives much simpler, without this change it is hard to imagine our life. Everyday new innovation is taking place, and one such innovation made by amazon is Amazon Go. Amazon Go is enhancement to shopping experience which employees' self-checkout technique. Amazon Go currently uses the technology such as sensor fusion and deep learning. It introduces a complete new kind of shopping style with just Walk-out technology. It is designed for those who do not like to stand in checkout queue. Customer needs to install an app that needs to be scanned at entry point. It then uses sophisticated technology to track the customer's purchase and add them to customer's cart. Customer can exit the store without physical checkout and the purchase amount will be deducted from their account directly. Our implementation focuses on building a shopping system to ease the users experience with respect to shopping. It further discusses the challenges faced during implementing this new technology and suggest some ways through which we can overcome it. These technologies have a vast application in various fields that will play important role in near future.
GUIM‐SMD: guilty user identification model using summation matrix‐based distribution

Data sharing across multiple different entities is on-demand to upgrade an enterprise's performance. However, some malicious entity can reveal this data to an unauthorised third party that may result in heavy loss to the enterprises in terms of finance, reputation, and long-term stability. This study presents a novel model GUIM-SMD for the identification of the guilty entity which is responsible for the data leakage to the unauthorised party in the shared environment. An effective distribution strategy to allocate the data among the users based on the access control mechanism is proposed in this model. The approach introduces the summation matrix which is computed using D-score and U-score that are assigned to the classified data and user, correspondingly. Furthermore, D-score and U-score are based on the data sensitivity and user guilty record relatively; and their values vary between 0 and 1. The evaluated summation matrix is used for data distribution among various users. The results show improvement up to 98.74, 236.38, and 252.39% for average probability, average success rate, and detection rate, respectively, as compared to the prior work.
Generating ROS-based Software for Industrial Cyber-Physical Systems from UML/MARTE

This work proposes an approach to generate automatically the embedded software for distributed Cyber-Physical Systems implemented using the Robotic Operating System (ROS) framework. For that, the Aspect-oriented Model Driven Engineering for Real-Time systems (AMoDE-RT) design approach has been extended in order to support the C++ code generation using the semantics and libraries available in ROS framework which is widely used in both academia and industry to implement the embedded software for robotic systems. The system architecture, behavior, requirements and constraints are specified in a UML/MARTE model. The information specified in the high-level model is used as input for a tool that generates a great part of the embedded software for all distributed computing devices. The main goal is to foster the use of Model-Driven Engineering in the context of cyber-physical systems design aiming the rapid prototyping via simulation and also the generation of the actual implementation of the system components. The proposed approach has been validated through a case study that demonstrates the feasibility to implement a ROS/C++ software for industrial systems. The results indicate that the proposed approach can be applied to complex systems comprising a larger number of interacting devices, whereas keeping the high-level of abstraction for system specification in UML/MARTE models.