Reviewerly/exHarmony huggingface dataset:
Secure Cloud Data Storage System using Hybrid Paillier-Blowfish Algorithm

Cloud computing utilizes enormous clusters of serviceable and manageable resources that can be virtually and dynamically reconfigured in order to deliver optimum resource utilization by exploiting the pay-per-use model. However, concerns around security have been an impediment in the extensive adoption of the cloud computing model. In this regard, advancements in cryptography, accelerated by the wide usage of the internet worldwide, has emerged as a key area in addressing some of these security concerns. In this document, a hybrid cryptographic protocol deploying Blowfish and Paillier encryption algorithms has been presented and its strength compared with the existing hybrid Advanced Encryption Standard (AES) and Rivest Shamir Adleman (RSA) techniques. Algorithms for secure data storage protocol in two phases have been presented. The proposed hybrid protocol endeavors to improve the power of cloud storage through a decrease in computation time and cipher-text size. Simulations have been carried out with Oracle Virtual Box and Fog server used on an Ubuntu 16.04 platform. This grouping of asymmetric and homomorphic procedures has demonstrated enhanced security. Compression usage has helped in decreasing the storage space and computation time. Performance analysis in terms of computation overhead and quality of service parameters like loads of parameters with and without attacks, throughput, and stream length for different modes of block cipher mode has been carried out. Security analysis has been carried out by utilizing the Hardening Index as an audit parameter using Lynis 2.7.1. Similarly, for halting the aforementioned approaches and for regulating traffic, firewall protection has been generated in the chosen hybrid algorithms. Finally, enhancements in the performance of the Paillier and Blowfish hybrid scheme with and without compression compared to the existing schemes using RSA and AES procedures have been demonstrated.
CFFNN: Cross Feature Fusion Neural Network for Collaborative Filtering

Numerous state-of-the-art recommendation frameworks employ deep neural networks in Collaborative Filtering (CF). In this paper, we propose a cross feature fusion neural network (CFFNN) for the enhancement of CF. Existing studies overlook either user preferences for various item features or the relationship between item features and user features. To solve this problem, we construct a cross feature fusion network to enable the fusion of user features and item features as well as a self-attention network to determine users’ preferences for items. Specifically, we design a feature extraction layer with multiple MLP (Multilayer Perceptrons) modules to extract both user features and item features. Then, we introduce a cross feature fusion mechanism for an accurate determination of the relationship between different user-item interactions. The features of users and items are crossly embedded and then fed into a prediction network. The attention mechanism enables the model to focus on more effective features. The effectiveness of CFFNN model is demonstrated through extensive experiments on four real-world datasets. The experimental results indicate that CFFNN significantly outperforms the existing state-of-the-art models, with a relative improvement of 3.0 to 12.1 percent on hit ratio (HR) and normalized discounted cumulative gain (NDCG) compared with the baselines.
New Insights Into the Control of Visible Gold Fineness and Deposition: A Case Study of the Sanshandao Gold Deposit, Jiaodong, China

Abstract Mineralogical distribution, textures, electron probe microanalysis of visible gold, laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) trace element analysis of pyrite, and LA-multicollector (MC-)ICP-MS sulfur isotope analysis of sulfide minerals are examined in an ore zone extending obliquely to –4 km depth in the Sanshandao gold deposit Jiaodong, China. We relate these results to the temporal and spatial ore-forming processes in the deposit to further elucidate the controls on the deposition of visible Au and fineness variation. Two generations of Au mineralization are identified. The early generation is represented by beresitization and quartz-pyrite veins in which visible Au grains are associated with pyrite (Py1 and Py2) and are characterized by high fineness [729–961; fineness = 1000×Au/(Au+Ag)]. Py1 and Py2 are both enriched in Co, Ni, and Bi and depleted in As and Au. Texturally, gold and pyrite are pristine crystals, homogeneous in composition. These features are attributed to the sulfidation of the granitic wallrock (fluid/rock interaction) that effectively destabilizes Au in the ore-forming fluids during pyrite deposition. Fineness decreases continuously from 870 at –2650 m depth to 752 at –420 m depth. The Co and Ni contents of Py1 and Py2 decrease significantly from –4000 m to –420 m depth, whereas the As contents increase. The mean δ34S values of Py1 increase from 10.5 to 11.8‰. The spatial variations are interpreted to be related to gradual cooling, decompression, and an enhanced degree of fluid/rock interaction with decreasing depth, which facilitated the initiation of visible gold mineralization at ca. –2700 m depth. The late generation of Au mineralization is represented by quartz-polysulfide veins in which visible Au grains are associated with multiple sulfide minerals (Py3, galena, chalcopyrite, arsenopyrite, and sphalerite). It is characterized by low fineness (549–719), and heterogeneous textures with Ag-rich parts (218–421). Py3, occurring as the rim of pyrite grain, is interpreted to form by replacement via a dissolution-reprecipitation reaction. Py3 is distinctly enriched in As (median of 10 000 ppm) and Au (2.2 ppm), but depleted in Co, Ni, and Bi. The δ34S values of the polysulfide minerals decrease sharply by 4 to 5‰ at depths from –1909 to –1450 m. These features are interpreted to be generated by significant decompression and phase separation of fluid, where most ore elements (e.g., Au, Ag, As, and base metal elements) are destabilized. Our study suggests that remobilization did not affect the generation of visible Au mineralization at Sanshandao.
Secure CT-Image Encryption for COVID-19 Infections Using HBBS-Based Multiple Key-Streams

The task of preserving patient data is becoming more sophisticated with the evolution of technology and its integration with the medical sector in the form of telemedicine and electronic health (e-health). Secured medical image transmission requires adequate techniques for protecting patient privacy. This study aims at encrypting Coronavirus (COVID-19) images of Computed Tomography (CT) chest scan into cipherimages for secure real-world data transmission of infected patients. Provably safe pseudo-random generators are used for the production of a "key-stream" to achieve high privacy of patient data. The Blum Blum Shub (BBS) generator is a powerful generator of pseudo-random bit-strings. In this article, a hashing version of BBS, namely Hash-BBS (HBBS) generator, is presented to exploit the benefits of a hash function to reinforce the integrity of extracted binary sequences for creating multiple key-streams. The NIST-test-suite has been used to analyze and verify the statistical properties of resulted key bit-strings of all tested operations. The obtained bit-strings showed good randomness properties; consequently, uniform distributed binary sequence was achieved over the key length. Based on the obtained key-streams, an encryption scheme of four COVID-19 CT-images is proposed and designed to attain a high grade of confidentiality and integrity in transmission of medical data. In addition, a comprehensive performance analysis was done using different evaluation metrics. The evaluation results of this study demonstrated that the proposed key-stream generator outperforms the other security methods of previous studies. Therefore, it can be successfully applied to satisfy security requirements of transmitting CT-images for COVID-19 patients.
Do “Good Citizens” fight hate speech online? Effects of solidarity citizenship norms on user responses to hate comments

(2021). Do "Good Citizens" fight hate speech online? Effects of solidarity citizenship norms on user responses to hate comments. Journal of Information Technology & Politics: Vol. 18, No. 3, pp. 258-273.
Robust Visual Object Tracking Via Adaptive Attribute-Aware Discriminative Correlation Filters

In recent years, attention mechanisms have been widely studied in Discriminative Correlation Filter (DCF) based visual object tracking.To realise spatial attention and discriminative feature mining, existing approaches usually apply regularisation terms to the spatial dimension of multi-channel features.However, these spatial regularisation approaches construct a shared spatial attention pattern for all multi-channel features, without considering the diversity across channels.As each feature map (channel) focuses on a specific visual attribute, a shared spatial attention pattern limits the capability for mining important information from different channels.To address this issue, we advocate channel-specific spatial attention for DCFbased trackers.The key ingredient of the proposed method is an Adaptive Attribute-Aware spatial attention mechanism for constructing a novel DCF-based tracker (A 3 DCF).To highlight the discriminative elements in each feature map, spatial sparsity is imposed in the filter learning stage, moderated by the prior knowledge regarding the expected concentration of signal energy.In addition, we perform a post processing of the identified spatial patterns to alleviate the impact of less significant channels.The net effect is that the irrelevant and inconsistent channels are removed by the proposed method.The results obtained on a number of well-known benchmarking datasets, including OTB2015, DTB70, UAV123, VOT2018, LaSOT, GOT-10K and TrackingNet, demonstrate the merits of the proposed A 3 DCF tracker, with improved performance compared to the state-ofthe-art methods.
A Study on Different Functionalities and Performances among Different Activation Functions across Different ANNs for Image Classification

Abstract Activation functions are very essential in artificial neural networks (ANNs), since they are non-linear functions and they have been proved necessary to implement deep learning. Recently, ReLU is one of the most well-known activation functions; however, several competitors – e.g. LReLU and SWISH – have nowadays been proposed or ‘discovered’. In this paper, the authors perform a detailed comparison of five activation functions over two image classification datasets. We found the overall performances of accuracy rates ranked from the best GELU, RELU, SWISH, SELU, down to Sigmoid. Such the observation would result in the improvement of future image classification via designing new state-of-the-art activation functions.
HoloFlows: modelling of processes for the Internet of Things in mixed reality

Abstract Our everyday lives are increasingly pervaded by digital assistants and smart devices forming the Internet of Things (IoT). While user interfaces to directly monitor and control individual IoT devices are becoming more sophisticated and end-user friendly, applications to connect standalone IoT devices and create more complex IoT processes for automating and assisting users with repetitive tasks still require a high level of technical expertise and programming knowledge. Related approaches for process modelling in IoT mostly suggest extensions to complex modelling languages, require high levels of abstraction and technical knowledge, and rely on unintuitive tools. We present a novel approach for end-user oriented- no-code -IoT process modelling using Mixed Reality (MR) technology: HoloFlows . Users are able to explore the IoT environment and model processes among sensors and actuators as first-class citizens by simply “drawing” virtual wires among physical IoT devices. MR technology hereby facilitates the understanding of the physical contexts and relations among the IoT devices and provides a new and more intuitive way of modelling IoT processes. The results of a user study comparing HoloFlows with classical modelling approaches show an increased user experience and decrease in required modelling knowledge and technical expertise to create IoT processes.
Daedalus: Breaking Nonmaximum Suppression in Object Detection via Adversarial Examples

This article demonstrates that nonmaximum suppression (NMS), which is commonly used in object detection (OD) tasks to filter redundant detection results, is no longer secure. Considering that NMS has been an integral part of OD systems, thwarting the functionality of NMS can result in unexpected or even lethal consequences for such systems. In this article, an adversarial example attack that triggers malfunctioning of NMS in OD models is proposed. The attack, namely, Daedalus, compresses the dimensions of detection boxes to evade NMS. As a result, the final detection output contains extremely dense false positives. This can be fatal for many OD applications, such as autonomous vehicles and surveillance systems. The attack can be generalized to different OD models, such that the attack cripples various OD applications. Furthermore, a way of crafting robust adversarial examples is developed by using an ensemble of popular detection models as the substitutes. Considering the pervasive nature of model reuse in real-world OD scenarios, Daedalus examples crafted based on an ensemble of substitutes can launch attacks without knowing the parameters of the victim models. The experimental results demonstrate that the attack effectively stops NMS from filtering redundant bounding boxes. As the evaluation results suggest, Daedalus increases the false positive rate in detection results to 99.9% and reduces the mean average precision scores to 0, while maintaining a low cost of distortion on the original inputs. It also demonstrates that the attack can be practically launched against real-world OD systems via printed posters.
Generating and evaluating cross‐sectional synthetic electronic healthcare data: Preserving data utility and patient privacy

Abstract Electronic healthcare record data have been used to study risk factors of disease, treatment effectiveness and safety, and to inform healthcare service planning. There has been increasing interest in utilizing these data for new purposes such as for machine learning to develop predictive algorithms to aid diagnostic and treatment decisions. Synthetic data could potentially be an alternative to real‐world data for these purposes as well as reveal any biases in the data used for algorithm development. This article discusses the key requirements of synthetic data for multiple purposes and proposes an approach to generate and evaluate synthetic data focused on, but not limited to, cross‐sectional healthcare data. To our knowledge, this is the first article to propose a framework to generate and evaluate synthetic healthcare data with the aim of simultaneously preserving the complexities of ground truth data in the synthetic data while also ensuring privacy. We include findings and new insights from synthetic datasets modeled on both the Indian liver patient dataset and UK primary care dataset to demonstrate the application of this framework under different scenarios.
Accurate and decentralized timestamping using smart contracts on the Ethereum blockchain

Timestamps allow us to identify a date and time when a piece of data existed or an event took place. For example, we use timestamps to establish the date when we grant a patent. Services that offer trusted timestamps on the blockchain exist, where one creates a timestamp on a value by sending the blockchain a transaction containing the value, which is eventually confirmed in a block a miner creates and timestamps. Our work focuses on the accuracy of such timestamps created on the Ethereum blockchain. We estimate that their mean accuracy is half of the average time between consecutive blocks. However, Ethereum clients geth and parity allow accuracy to vary in up to a few tens of seconds. Surprisingly, we found out empirically that miners can backdate data using the timestamp on a block, which is a serious flaw in timestamping services. To address this problem, we propose a new decentralized timestamping service that combines smart contracts and distinct time providers. The service offers timestamps with higher accuracy of milliseconds. We conduct a cost analysis of our service, which shows that Ethereum transaction fees drive our timestamps costs when the service scales up. Finally, we discuss alternatives to cut costs.
CoMoDA: Continuous Monocular Depth Adaptation Using Past Experiences

While ground truth depth data remains hard to obtain, self-supervised monocular depth estimation methods enjoy growing attention. Much research in this area aims at improving loss functions or network architectures. Most works, however, do not leverage self-supervision to its full potential. They stick to the standard closed world train-test pipeline, assuming the network parameters to be fixed after the training is finished. Such an assumption does not allow to adapt to new scenes, whereas with self-supervision this becomes possible without extra annotations.In this paper, we propose a novel self-supervised Continuous Monocular Depth Adaptation method (CoMoDA), which adapts the pretrained model on a test video on the fly. As opposed to existing test-time refinement methods that use isolated frame triplets, we opt for continuous adaptation, making use of the previous experience from the same scene. We additionally augment the proposed procedure with the experience from the distant past, preventing the model from overfitting and thus forgetting already learnt information.We demonstrate that our method can be used for both intra- and cross-dataset adaptation. By adapting the model from train to test set of the Eigen split of KITTI, we achieve state-of-the-art depth estimation performance and surpass all existing methods using standard architectures. We also show that our method runs 15 times faster than existing test-time refinement methods. The code is available at https://github.com/Yevkuzn/CoMoDA.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e521" altimg="si326.svg"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:math> control for continuous-time Takagi–Sugeno fuzzy model by applying generalized Lyapunov function and introducing outer variables

For fuzzy systems, it is known that membership function dependent Lyapunov function can reduce the conservatism that has arisen from quadratic Lyapunov function but the time derivative of membership function is hard to be dealt with. In this technical paper, a kind of generalized Lyapunov function (called homogeneous polynomially membership functions dependent (HPMFD) Lyapunov function ) is designed and the corresponding HPMFD switching is proposed to deal with the time derivative of membership function. In addition, the generalized outer variable is constructed to further improve the results. The final example shows the effectiveness of the proposed method.
mCRF and mRD: Two Classification Methods Based on a Novel Multiclass Label Noise Filtering Learning Framework

Mitigating label noise is a crucial problem in classification. Noise filtering is an effective method of dealing with label noise which does not need to estimate the noise rate or rely on any loss function. However, most filtering methods focus mainly on binary classification, leaving the more difficult counterpart problem of multiclass classification relatively unexplored. To remedy this deficit, we present a definition for label noise in a multiclass setting and propose a general framework for a novel label noise filtering learning method for multiclass classification. Two examples of noise filtering methods for multiclass classification, multiclass complete random forest (mCRF) and multiclass relative density, are derived from their binary counterparts using our proposed framework. In addition, to optimize the NI_threshold hyperparameter in mCRF, we propose two new optimization methods: a new voting cross-validation method and an adaptive method that employs a 2-means clustering algorithm. Furthermore, we incorporate SMOTE into our label noise filtering learning framework to handle the ubiquitous problem of imbalanced data in multiclass classification. We report experiments on both synthetic data sets and UCI benchmarks to demonstrate our proposed methods are highly robust to label noise in comparison with state-of-the-art baselines. All code and data results are available at https://github.com/syxiaa/Multiclass-Label-Noise-Filtering-Learning.
Knowledge-Guided Sentiment Analysis Via Learning From Natural Language Explanations

Sentiment analysis is crucial for studying public opinion since it can provide us with valuable information.Existing sentiment analysis methods rely on finding the sentiment element from the content of user-generated.However, the question of why a message produces certain emotions has not been well explored or utilized in previous works.To address this challenge, we propose a natural language explanation framework for sentiment analysis that provides sufficient domain knowledge for generating additional labelled data for each new labelling decision.A rule-based semantic parser transforms these explanations into programmatic labelling functions that generate noisy labels for an arbitrary amount of unlabelled sentiment information to train a sentiment analysis classifier.Experiments on two sentiment analysis datasets demonstrate the superiority it achieves over baseline methods by leveraging explanations as external knowledge to joint training a sentiment analysis model rather than only labels.An ablation study is conducted to clarify the relative contribution of natural language explanations.
Deep Binocular Fixation Prediction Using a Hierarchical Multimodal Fusion Network

RGB-D data are increasingly being used for myriad computer vision tasks. For such tasks, most methods simply concatenate or add feature vectors from RGB images and depth maps and allow the two modalities to complement each other mutually. However, such a fusion strategy results in inefficient and inadequate performance. In this study, we propose deep binocular fixation prediction based on a hierarchical multimodal fusion network that suitably combines RGB and depth maps. In the proposed method, a novel convolutional block attention module completely extracts image texture features and retains spatial information. In addition, a pyramid dilated-convolution module refines feature information, further improving the fusion of RGB and depth maps. Experimental results indicate that the proposed network achieves state-of-the-art performance on the NUS and NCTU data sets.