Reviewerly/exHarmony huggingface dataset:
NOMARO: Defending Against Adversarial Attacks by NOMA-Inspired Reconstruction Operation

In this work, a non-orthogonal multiple access (NOMA)-inspired defense method is proposed to mitigate the effect of adversarial attacks, which pose a major challenge toward deep neural networks (DNNs) in multimedia networks. The novel defense method, namely NOMA-inspired reconstruction operation (NOMARO), incorporates a copy of the input image generated by applying the untargeted adversarial attack. The copy and input images are superposed with a power allocation factor inversely proportional to the correlation between the considered images. To the best of our knowledge, this is the first communication theory based approach to design an adversarial defense method to be useful in multimedia applications. A comparative study with the existing defense techniques shows the superior performance of the proposed NOMARO defense against the state-of-the-art Carlini & Wargner (C&W) and Square attacks in white-box and black-box settings, respectively, on popular DNN models.
Modulation Recognition Based on Lightweight Residual Network via Hybrid Pruning

Signal modulation recognition is an important and complicated problem in spectrum monitoring. In recent years, many advanced deep learning methods have been proposed for this problem. However, the high computational cost of deep neural networks largely hinders practical deployment. In this paper, we consider the signal modulation recognition problem via the deep residual network and propose a hybrid Pruning scheme to reduce the model size and memory cost. This is achieved by simultaneously enforcing channel-level and layer-level sparsity in residual network in a simple way. We take wide and large networks as input models, but during training, insignificant channels and layers are automatically identified and pruned afterwards, yielding compact and shallower networks with comparable accuracy. The results show that with 0 dB snr, the trainable parameters and multiply–accumulate operations are both reduced by 2/3. In summary, the proposed method can effectively reduce the parameters and complexity of recognition model.
The impact of e-service quality and ease of use of e-customer relationship management: a study of mobile phone services in Jordan

In the use of technology and human resources, e-customer relationship management (e-CRM) plays a key role in clarifying customer behaviour and their value. With the use of such systems, businesses can reap the benefits of customer repeat purchase, word-of-mouth, customer retention, brand loyalty and customer satisfaction. This study tested two components of customer factor namely e-service quality (ESQ) and ease of use (EOU) in their role as antecedents of e-CRM performance from the point of view of the employees of food industry companies (FIC) in Jordan. Based on the analysed data both variables positively and significantly related to e-CRM performance. The study conducted a review of relevant studies and presented an extensive discussion of the development of the research model underpinned by the technology acceptance model (TAM), upon which the formulation of hypotheses was made. The study provided a detailed description of data collection, study sample, results and discussions of findings.
A Swarm Reinforcement Learning Method for dynamic reconfiguration with end-to-end constraints in composite web services

Service composition is an efficient way to fulfill user requirements in service-oriented architecture by combining several web services to perform a specific task. As a result of the dynamic environment of web services, the emergence of many unexpected events can disrupt or affect the quality of services composing a web service and, hence violate the end-to-end constraints. Dynamically reconfiguring the composite web service is essential to dealing with such issues. However, recent reconfiguration methods failed to meet the end-to-end constraints due to the vast number of web services with the same functionality and different nonfunctional features (QoS). This paper proposes a Swarm Reinforcement Learning approach to replace multiple failed services and maintain the original end-to-end constraints. ACO (Ant Colony Optimization) is used to improve the exchange of information between agents based on the Pheromone-Q values, inspired by real ants’ behavior. Experiments are conducted on real data sets and compared with related work methods to prove the efficiency of the proposed approach in terms of constraint satisfaction.
A Blockchain Based Decentralized Transaction Settlement System in Banking Sector

Blockchain, the underlying technology behind Bit-coin, is an emerging technology in industry. Blockchain has the power to reform the existing business processes more democratic, transparent, secure, and efficient. Banking industries are the first movers that capitalize the disruptive potential of this technology. The indian banking system is one of the complex bank payment system in this world. The current infrastructure that is used by indian bank system is real time gross settlement system based and it follows a centralized architecture. Due to this centralized architecture the processing of transactions are slow and cum-bersome. It also causes large amount for security and recovery purposes. The real time gross settlement based system demands high need for security, resilience, and performance. Moving from traditional system to blockchain platform is not the prior concern but making a system that provide security, confidentiality, and decentralized money lending mechanism is the core idea. Here proposed a novel system that enable a decentralized Banking system and services based on Ethereum blockchain platform. The system support different services including money deposit, money transfer and loan checking etc. using the distributed ledger technology.
Syntax-Aware Multi-Spans Generation for Reading Comprehension

This paper presents a novel method to generate answers for non-extraction machine reading comprehension (MRC) tasks whose answers cannot be simply extracted as one span from the given passages. Using a pointer network-style extractive decoder for such type of MRC may result in unsatisfactory performance when the ground-truth answers are given by human annotators or highly re-paraphrased from parts of the passages. On the other hand, using a generative decoder cannot well guarantee the resulted answers with well-formed syntax and semantics when encountering long sentences. Therefore, to alleviate the obvious drawbacks of both sides, we propose an answer making-up method from extracted multi-spans that are learned by our model as highly confident <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -gram candidates in the given passage. That is, the returned answers are composed of discontinuous multi-spans but not just one consecutive span in the given passages anymore. The proposed method is simple but effective: empirical experiments on MS MARCO show that the proposed method has a better performance on accurately generating long answers and substantially outperforms two typical competitive one-span and Seq2Seq baseline decoders.
Dangerous Object Detection for Visually Impaired People using Computer Vision

In this contemporary world, Artificial Intelligence and Machine Learning are one of the leading technologies creating an impact in the world by mimicking human behaviour to solve a particular problem. Hence, these technologies are widely used to aid different obstacles encountered by humans. One such problem widely faced by the mankind is visual impairment. According to World Health Organization, approximately 285 million people suffer with vision impairment. Therefore, applications of machine learning and computer vision can be applied to guide the people with such problems. This paper presents the idea of using object detection to aid the visually impaired people. In this paper, an experiment has been proposed which uses a custom-built image dataset of various dangerous objects. The objects have been categorized into 5 broad categories: Sharp objects, Danger signs, Broken glass, Manhole and Fires. A number of different algorithms have been trained on this custom image dataset containing the menacing objects and their performances have been evaluated. The evaluation indicators for the models are the validation error in terms mean Average Precision (mAP) and the processing time for each model. The models have also been tested in real world scenario by evaluating on a custom video to gauge their performance in terms of accuracy in detection of different objects as well as their ease in deployment by suggesting their frame rate handling capacity. The results are discussed and the most robust and balanced model is suggested at the end of the paper.
Improving image classification robustness using self‐supervision

Self‐supervised learning allows training of neural networks without immense, high‐quality or labelled data sets. We demonstrate that self‐supervision furthermore improves robustness of models using small, imbalanced or incomplete data sets which pose severe difficulties to supervised models. For small data sets, the accuracy of our approach is up to 12.5% higher using MNIST and 15.2% using Fashion‐MNIST compared to random initialization. Moreover, self‐supervision influences the way of learning itself, which means that in case of small or strongly imbalanced data sets, it can be prevented that classes are not or insufficiently learned. Even if input data are corrupted and large image regions are missing from the training set, self‐supervision significantly improves classification accuracy (up to 7.3% for MNIST and 2.2% for Fashion‐MNIST). In addition, we analyse combinations of data manipulations and seek to generate a better understanding of how pretext accuracy and downstream accuracy are related. This is not only important to ensure optimal pretraining but also for training with unlabelled data in order to find an appropriate evaluation measure. As such, we make an important contribution to learning with realistic data sets and making machine learning accessible to application areas that require expensive and difficult data collection.
Decentralized Inner-Product Encryption with Constant-Size Ciphertext

With the rise of technology in recent years, more people are studying distributed system architecture, such as the e-government system. The advantage of this architecture is that when a single point of failure occurs, it does not cause the system to be invaded by other attackers, making the entire system more secure. On the other hand, inner product encryption (IPE) provides fine-grained access control, and can be used as a fundamental tool to construct other cryptographic primitives. Lots of studies for IPE have been proposed recently. The first and only existing decentralized IPE was proposed by Michalevsky and Joye in 2018. However, some restrictions in their scheme may make it impractical. First, the ciphertext size is linear to the length of the corresponding attribute vector; second, the number of authorities should be the same as the length of predicate vector. To cope with the aforementioned issues, we design the first decentralized IPE with constant-size ciphertext. The security of our scheme is proven under the ℓ-DBDHE assumption in the random oracle model. Compared with Michalevsky and Joye’s work, ours achieves better efficiency in ciphertext length and encryption/decryption cost.
Multi-Robot Workspace Division Based on Compact Polygon Decomposition

In this work, we tackle the problem of multi-robot convex workspace division. We present an algorithm to split a convex area among several robots into the corresponding number of parts based on the area requirements for each part. The core idea of the algorithm is a sequence of divisions into pairs with the lowest possible perimeters. In this way, the compactness of the partitions obtained is maximized. The performance of the algorithm, as well as the quality of the obtained parts, are analyzed in comparison with two different algorithms. The presented approach yields better results in all metrics compared to other algorithms.
The Ins and Outs of Decentralized Autonomous Organizations (Daos)

Despite the increase in number of blockchain-based Decentralized Autonomous Organizations (DAOs), there is no consensus of what constitutes a DAO. This paper provides an in-depth study of DAOs by analyzing definitions, characteristics and emerging developments. Existing definitions in literature hardly recognize common functionalities and intermingle coded DAOs, platforms and blockchain DAOs. We develop a comprehensive DAO definition based on key characteristics, current definitions and empirical analysis of 1,859 DAOs. We found that many DAOs are inactive and provided the first empirical evidence regarding decentralization characteristic of DAOs: a threshold of 20 tokenholders seems to be crucial for sustaining DAOs’ activity levels. Finally, based on empirical analysis of 4,592 perceived DAOs, the emerging development of off-chain voting is identified, which challenges the automatic execution of decisions, thus a violation of the autonomous nature of DAOs. We suggest for future research on the effect of DAO governance structures on their long-term survivability.
Unsupervised real-time induction and interactive visualization of taxonomies over domain-specific concepts

Given a domain-specific set of concept labels, taxonomy induction is the problem of inducing a taxonomy over the concept labels. Despite its importance in problems such as e-commerce, and some algorithmic research as a consequence, practical tools for taxonomy induction and interactive visualization do not currently exist. To be truly useful, such a tool must permit a reasonable solution in a relatively unsupervised setting, and be applicable to general subsets of concept labels. In this paper, we present an unsupervised, end-to-end taxonomy induction system for arbitrary concept-labels from the e-commerce domain. Our system only takes a simple text file as input and yields a tree-like taxonomy that can be rendered on a browser, and that a non-technical user can interact with. Important components of the system can also be customized by a technically experienced user.
Uncle Incentive Mechanism Adjustment against Selfish Mining in Ethereum

Selfish mining has potential hazards to blockchain systems by hiding mined blocks and broadcasting them strategically. It lets the adversary gain additional rewards in the mining process which was first proposed in Bitcoin recommended by Satoshi Nakamoto. The blockchain-based application Ethereum using GHOST (Greedy Heaviest-Observed Sub-Tree) protocol with regard to Bitcoin to alleviate the loss of honest miners of stale blocks and compensate uncle blocks which will increase if selfish mining occurs. But it only makes things worse since uncle incentive mechanism reduces the cost of the failure of selfish mining and decreases the threshold of selfish mining pool needed to be profitable. There is no research which focuses on the rationale behind uncle incentive mechanism in Ethereum. This paper focuses on the feasible modifications on uncle incentive mechanism against selfish mining in Ethereum. The uncle block reference behaviors of miners are analyzed when selfish mining occurs in Ethereum by building models. Furthermore, a feasible uncle incentive mechanism against selfish mining is proposed. It is not necessary to have a strategy with a monotonous order with regard to generations of uncle blocks. The uncle incentive mechanism suggested in this paper can efficiently raise the threshold for selfish mining to be profitable by around 3.17%, an increase by around 9.76%. It offers an alternative uncle incentive mechanism for cryptocurrencies which are based on GHOST protocol.
Stages of arc atomic emission spectrometry development as applied to the solid geological samples’ analysis

The atomic emission spectrometry (AES) with arc discharge method evolution is inextricably linked with the fundamental scientific discoveries made in the 19th and 20th centuries, and it also reflects the change of scientific paradigms in a specific field of natural science – analytical chemistry. Theoretical comprehension and generalization of experimental data, along with the improving spectral equipment and methodological techniques for determining the elemental and material composition of solid geological samples, increased the accuracy of the analysis results i.e. the results were translated from qualitative to semi-quantitative and quantitative. Modern computerized equipment for direct AES with arc discharge provides minimal errors in measuring the spectral intensity due to the high stability of the excitation source of the spectra of atoms and molecules, the use of high-power polychromators and express digital recording of spectra by multi-channel detectors. However, in the commercial software of spectrometers, only the methods of manual spectra processing proposed in the 30s of the last century are programmed. That limits the possibilities of improving the analysis quality. The time has come to use the developed concept of computer processing of big spectral data, which is based on the information models of chemical analysis and the back propagation of error, in order to select the best models. Current article shows that the information models of computer spectra interpretation obtained from direct arc AES multi-element techniques of geological samples’ analysis using the injection-spillage method provide better quantitative results (category III of accuracy) due to a more complete account of spectral and matrix influences compared to the routine processing techniques.
End-to-End Performance Optimization for Training Streaming Convolutional Neural Networks using Billion-Pixel Whole-Slide Images

The combination of digital pathology and artificial intelligence attracts more and more attention. In order to take into account the global and detailed textural information, recent works proposed to use whole-slide images (WSI) with more than 10 million pixels to train artificial neural networks in pursuit of a high-precision model. However, such an approach faces new technical challenges. When researchers conduct neural network training on a graphics processing unit (GPU), the extremely high spatial resolution of WSI and the large amount of intermediate data generated during the training process exceed the memory capacity of the GPU. Therefore, streaming convolutional neural network (SCNN) was proposed to disassemble a large image into multiple smaller patches that can be forward and backward propagated under the limitation of GPU memory during the training process. However, in a multi-GPU high-performance computing (HPC) system, when the central processing unit (CPU) performs data preprocessing for the distributed training of multiple GPUs, decoding large images and data augmentation consume a lot of memory and CPU time, resulting in a performance bottleneck.This paper first presents a multithreaded image decoder optimized for multi-core CPUs to speedup WSI loading. We also propose a patch-level data augmentation algorithm and implement it on a dedicated GPU to distribute the augmented data to other GPUs for training the SCNN. We use the above method to train the ResNet-50 SCNN model by learning from the TCGA-LUAD and TCGA-LUSC datasets published by The Cancer Genome Atlas. In comparison to training the SCNN on multiple GPUs using the regular distributed data parallelism, our method can save up to 92.8% of memory and increase the training speed by up to 242.5% when training with 8 A100 GPUs on the NVIDIA DGX-A100 system.
FilterNet: Self-Supervised Learning for High-Resolution Photo Enhancement

We propose an end-to-end model for high-resolution photographic enhancement in a self-supervised learning approach.Bad quality photos are created from an unlabelled dataset of good quality images.Our deep network model is presented with pairs of bad and good images, and learns the parameters of 6 photographic filters that improve the bad photos by trying to make them similar to the high-quality references.Custom rendering layers apply the photographic filters and compute their derivatives during the forward training pass, so loss attribution can be performed during the backward pass.Our experiments confirm that loss functions based on feature-extraction networks achieve better quality than pixel-comparison metrics.To mimic professional editing applications, our filters are based on curve mapping and alpha blending, and they are rendered using a linear RGB colorspace for mathematical accuracy.At inference time, the custom rendering layers are removed so the model's output is just the set of filter parameters that best improve the input image.We achieve high-resolution results by applying the predicted filters to the photo captured by the user, even though training and prediction take place using downscaled thumbnails.Our approach has been validated in a professional mobile application.
Multi-information fusion based few-shot Web service classification

Automatic Web service classification becomes an essential topic in the services computing field. The distribution of Web services over various categories usually follows the long-tail distribution, suggesting that many categories (i.e., the tail categories) contain very limited services. An empirical experiment shows that the classification performance of tail categories is much worse than that of head categories due to the limited training samples. Existing works on Web service classification usually ignore this problem. Towards this issue, we propose a few-shot Web service classification approach called MIF-FWSC (multi-information fusion based few-shot Web service classification), which exploits both the knowledge learned from head categories and the information contained by category names to improve the classification of tail categories. Experiments show that our proposed approach for few-shot Web service classification achieves state-of-the-art accuracy on two real-world Web service datasets. • Reporting that the categories of Web services follow the long-tail distribution. • Analyzing the few-shot service classification problem ignored in existing works. • Proposing a multi-information fusion based few-shot service classification method. • Constructing transferable representations for services using word distributions.
Aerial Image Object Detection Method Based on Adaptive ClusDet Network

Object detection in aerial images is more difficult than general object detection. The main reasons are as follows: (1) aerial image object pixels are small and difficult to detect; (2) aerial image objects are sparsely distributed, which increases the difficulty of detection. In this paper, we propose an adaptive Clustered Detection network based on Clustered Detection, which can better solve the above-mentioned problems. The whole process of image detection is as follows: (1) given an aerial image, the adaptive clustering sub-network will output the coordinates of several candidate clustering areas and the corresponding confidence, and also output a suggested candidate clustering area the number of N; (2) the fusion network merges and divides the candidate clustering area according to the coordinates, confidence and N of the candidate clustering area, and outputs N segmented pictures; (3) these segmented pictures are accurately detected, the original pictures are globally detected, and the results of the precise detection and the global detection are merged to obtain the final detection result. Compared with the Clustered Detection, our network can adaptively determine the number of clustering regions in the picture, thereby improving the detection efficiency and accuracy.