Reviewerly/exHarmony huggingface dataset:
A Large-Scale Secure Image Retrieval Method in Cloud Environment

With the rapid development of cloud computing technology, more and more users choose to outsource image data to clouds. To protect data's confidentiality, images need to be encrypted before being outsourced to clouds, but this brings difficulties to some basic yet important data services, such as content-based image retrieval. Existing secure image retrieval methods generally have some problems such as low retrieval accuracy and low retrieval efficiency, which cannot meet requirements for large-scale image retrieval in cloud environment. In this paper, we propose a large-scale secure image retrieval method in cloud environment. The Hamming embedding algorithm is utilized to generate binary signatures of image descriptors. A frequency histogram combined with binary signatures is generated to provide a more precise representation of image features in an image and thus the retrieval accuracy is improved. Visual words are selected from the histogram by the random sampling method before the min-Hash algorithm is performed on binary signatures of selected visual words to generate a secure index. The random sampling method and min-Hash algorithm can not only ensure the security of the search index, but also greatly improve the image retrieval efficiency. This method achieves the balance among security, accuracy and efficiency of large-scale secure image retrieval in public clouds. The security analysis and experimental results show the effectiveness of the proposed method.
Understanding Commonsense Inference Aptitude of Deep Contextual Representations

Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of the capability of these models.Thus, we investigate and challenge several aspects of BERT's commonsense representation abilities.First, we probe BERT's ability to classify various object attributes, demonstrating that BERT shows a strong ability in encoding various commonsense features in its embedding space, but is still deficient in many areas.Next, we show that, by augmenting BERT's pretraining data with additional data related to the deficient attributes, we are able to improve performance on a downstream commonsense reasoning task while using a minimal amount of data.Finally, we develop a method of fine-tuning knowledge graphs embeddings alongside BERT and show the continued importance of explicit knowledge graphs.
Blockchain Applications and Sustainability Issues

Blockchain, a distributed and democratically-sustained public register of the transactions of the digital currency "Bitcoin", proposed by Satoshi Nakamoto a pseudonym of a hided developer in 2009, has been the driver of a huge number of initiatives devoted to develop and implement a peer-to-peer distributed database, and with no central authority created as an open source software.Expansion of Bitcoin, as well as other digital currencies, has been due to lower transaction costs, high security protocols and lack of inflation with respect to fiat money and no need of a clearing entity or a central bank.Although, environmental issues related to the use of this currency and, in particular, in the energy consumes, have been raised by the scientific community, but no signals of limiting factors have been detected until now.The hidden in the wings blockchain technology has been recognized the driver of innovation in various fields, contributing to create a more sustainable world.The purpose of this paper is to describe both the recent trends in the applications of the blockchain technology in the cryptocurrencies market and the new projects considering the environmental sustainability (energy consumption, materials depletion) and social impacts.The contribute of blockchain in reducing and accelerating bureaucracy and incentivizing environmentally friendly behaviour, has been discussed.Although the environmental issues related to the energy consumed in Bitcoin mining may scare companies in adopting the new technology, there is no evidence of limiting factors or carrying capacity of the entire system.In future the development of a less energy intensive alternative for validating the blocks to stack in the distributed database, would render the blockchain the ideal candidate for the applicability of sustainability paradigms in the economic, environmental and social sectors.
Multiclass Probabilistic Classification Vector Machine

The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems. However, the PCVM is currently only applicable to binary cases. Extending the PCVM to multiclass cases via heuristic voting strategies such as one-vs-rest or one-vs-one often results in a dilemma where classifiers make contradictory predictions, and those strategies might lose the benefits of probabilistic outputs. To overcome this problem, we extend the PCVM and propose a multiclass PCVM (mPCVM). Two learning algorithms, i.e., one top-down algorithm and one bottom-up algorithm, have been implemented in the mPCVM. The top-down algorithm obtains the maximum a posteriori (MAP) point estimates of the parameters based on an expectation-maximization algorithm, and the bottom-up algorithm is an incremental paradigm by maximizing the marginal likelihood. The superior performance of the mPCVMs, especially when the investigated problem has a large number of classes, is extensively evaluated on the synthetic and benchmark data sets.
KPI-TSAD: A Time-Series Anomaly Detector for KPI Monitoring in Cloud Applications

Accurately detecting anomalies and timely interventions are critical for cloud application maintenance. Traditional methods for performance anomaly detection based on thresholds and rules work well for simple key performance indicator (KPI) monitoring. Unfortunately, it is difficult to find the appropriate threshold levels when there are significant differences between KPI values at different times during the day or when there are significant fluctuations stemming from different usage patterns. Therefore, anomaly detection presents a challenge for all types of temporal data, particularly when non-stationary time series have special adaptability requirements or when the nature of potential anomalies is vaguely defined or unknown. To address this limitation, we propose a novel anomaly detector (called KPI-TSAD) for time-series KPIs based on supervised deep-learning models with convolution and long short-term memory (LSTM) neural networks, and a variational auto-encoder (VAE) oversampling model was used to address the imbalanced classification problem. Compared with other related research on Yahoo’s anomaly detection benchmark datasets, KPI-TSAD exhibited better performance, with both its accuracy and F-score exceeding 0.90 on the A1benchmark and A2Benchmark datasets. Finally, KPI-TSAD continued to perform well on several KPI monitoring datasets from real production environments, with the average F-score exceeding 0.72.
A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

As a significant subset of the family of discrete optimization problems, the 0-1 knapsack problem (0-1 KP) has received considerable attention among the relevant researchers. The monarch butterfly optimization (MBO) is a recent metaheuristic algorithm inspired by the migration behavior of monarch butterflies. The original MBO is proposed to solve continuous optimization problems. This paper presents a novel monarch butterfly optimization with a global position updating operator (GMBO), which can address 0-1 KP known as an NP-complete problem. The global position updating operator is incorporated to help all the monarch butterflies rapidly move towards the global best position. Moreover, a dichotomy encoding scheme is adopted to represent monarch butterflies for solving 0-1 KP. In addition, a specific two-stage repair operator is used to repair the infeasible solutions and further optimize the feasible solutions. Finally, Orthogonal Design (OD) is employed in order to find the most suitable parameters. Two sets of low-dimensional 0-1 KP instances and three kinds of 15 high-dimensional 0-1 KP instances are used to verify the ability of the proposed GMBO. An extensive comparative study of GMBO with five classical and two state-of-the-art algorithms is carried out. The experimental results clearly indicate that GMBO can achieve better solutions on almost all the 0-1 KP instances and significantly outperforms the rest.
Efficient Feature Selection Algorithm Based on Particle Swarm Optimization With Learning Memory

Feature selection is an important pre-processing step in machine learning and data mining tasks, which improves the performance of the learning models by removing redundant and irrelevant features.Many feature selection algorithms have been widely studied, including greedy and random search approaches, to find a subset of the most important features for fulfilling a particular task (i.e., classification and regression).As a powerful swarm-based meta-heuristic method, particle swarm optimization (PSO) is reported to be suitable for optimization problems with continuous search space.However, the traditional PSO has rarely been applied to feature selection as a discrete space search problem.In this paper, a novel feature selection algorithm based on PSO with learning memory (PSO-LM) is proposed.The goal of the learning memory strategy is designed to inherit much more useful knowledge from those individuals who have higher fitness and offer faster progress, and the genetic operation is used to balance the local exploitation and the global exploration of the algorithm.Moreover, the k-nearest neighbor method is used as a classifier to evaluate the classification accuracy of a particle.The proposed method has been evaluated on some international standard data sets, and the results demonstrated its superiority compared with those wrapper-based feature selection methods.
Power consumption prediction in cloud data center using machine learning

The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.
GANai: Standardizing CT Images using Generative Adversarial Network with Alternative Improvement

Computed tomography (CT) is a widely-used diagnostic image modality routinely used for assessing anatomical tissue characteristics. However, non-standardized imaging protocols are commonplace, which poses a fundamental challenge in large-scale cross-center CT image analysis. One approach to address the problem is to standardize and normalize CT images using image synthesis algorithms including generative adversarial network (GAN) models. GAN learns the data distribution of training images and generate synthesized images under the same distribution. However, existing GAN models are not directly applicable to this task mainly due to the lack of constraints on the mode of data to generate. Furthermore, they treat every image equally, but in real applications, certain images are more difficult to standardize than the others. All these may lead to the lack-of-detail problem in CT image synthesis. We present a new GAN model called GANai to mitigate the differences in radiomic features across CT images captured using non-standard imaging protocols. Given source images, GANai composes new images by specifying a high-level goal that the image features of the synthesized images should be similar to those of the standard images. GANai introduces a new alternative improvement training strategy to alternatively and gradually improve GAN model performance. The new training strategy enables a series of technical improvements, including phase-specific loss functions, phase-specific training data, and the adoption of ensemble learning, leading to better model performance. The experimental results show that efficiency and stability of GAN models have been much improved in GANai and our model is significantly better than the existing state-of-the-art image synthesis algorithms on CT image standardization.
Deep Convolution Neural Network for Malignancy Detection and Classification in Microscopic Uterine Cervix Cell Images

Objective: Automated Pap smear cervical screening is one of the most effective imaging based cancer detection tools used for categorizing cervical cell images as normal and abnormal. Traditional classification methods depend on hand-engineered features and show limitations in large, diverse datasets. Effective feature extraction requires an efficient image preprocessing and segmentation, which remains prominent challenge in the field of Pathology. In this paper, a deep learning concept is used for cell image classification in large datasets. Methods: This relatively proposed novel method, combines abstract and complicated representations of data acquired in a hierarchical architecture. Convolution Neural Network (CNN) learns meaningful kernels that simulate the extraction of visual features such as edges, size, shape and colors in image classification. A deep prediction model is built using such a CNN network to classify the various grades of cancer: normal, mild, moderate, severe and carcinoma. It is an effective computational model which uses multiple processing layers to learn complex features. A large dataset is prepared for this study by systematically augmenting the images in Herlev dataset. Result: Among the three sets considered for the study, the first set of single cell enhanced original images achieved an accuracy of 94.1% for 5 class, 96.2% for 4 class, 94.8% for 3 class and 95.7% for 2 class problems. The second set includes contour extracted images showed an accuracy of 92.14%, 92.9%, 94.7% and 89.9% for 5, 4, 3 and 2 class problems. The third set of binary images showed 85.07% for 5 class, 84% for 4 class, 92.07% for 3 class and highest accuracy of 99.97% for 2 class problems. Conclusion: The experimental results of the proposed model showed an effective classification of different grades of cancer in cervical cell images, exhibiting the extensive potential of deep learning in Pap smear cell image classification.
Transformer Based Memory Network for Sentiment Analysis of Web Comments

The boom in wireless networking technology has led to an exponential increase in the number of web comments.Therefore, sentiment analysis of web comments is vital, and aspect-based sentiment analysis(ABSA) is very useful for the sentiment feature extraction of web comments.Currently, context-dependent sentiment feature typically derives from recurrent neural networks (RNN), and an average target vector usually replaces the target vector.However, web comments have become increasingly complex, and RNN may lose some essential sentiment information.At the same time, the average target vector may be the wrong target feature.We propose a new Transformer based memory network (TF-MN) to correct the shortcomings of the previous method.In TF-MN, the task becomes the question-answering process, which optimizes context, question, and the memory module.We use a global self-attention mechanism and a local attention mechanism (memory network) to construct emotionally inclined web comment semantics.Since self-attention can only obtain global semantic links, words such as nouns, prepositions, and adverbs still affect the emotional extraction of comments.To shield the influence of unrelated vocabulary on classification, we propose to use improved memory networks to optimize the extraction of web comments semantics.We conduct experiments on two datasets, and experimental results show that our model exceeds the state-of-the-art model.
Fast-AIC Method for Automatic First Arrivals Picking of Microseismic Event With Multitrace Energy Stacking Envelope Summation

As the signal-to-noise ratio (SNR) of surface microseismic monitoring data is generally low and large, traditional detection and picking algorithms cannot satisfy the real-time and high accuracy to processing. Therefore, a Fast Akaike information criterion (Fast-AIC) algorithm is proposed for microseismic event automatic detection and first arrival time picking. First, an automatic detection method of microseismic events based on multitrace energy stacking is proposed, to avoid the missed detection and false detection in conventional automatic detection methods. Second, the Fast-AIC algorithm is developed by mathematical derivation from the Vector Auto-regressive (VAR-AIC algorithm), to improve the efficiency of first arrival time picking of microseismic signals. Finally, the new method and three other conventional first arrival time picking methods are tested on microseismic monitoring data from a hydraulic fracture site in Shanxi, China. We have found that the new method has the highest picking accuracy and computational efficiency.
Event-triggered state estimation for networked systems with correlated noises and packet losses

The event-triggered state estimation for the systems suffering from correlated noises and packet losses is considered. A communication mechanism that determines the measurements to be sent or not depending on a specific event-triggered condition is presented to reduce the additional data transmissions. Then a novel event-triggered state estimator related to the trigger threshold and correlation coefficient is proposed. An expected trade-off between the rate of transmission and the estimator performance can be obtained through adjusting the threshold properly, and the influence of noise correlation and packet losses is weakened effectively. The estimator performance is evaluated and certain boundedness conditions for the covariance expectation are obtained. Finally, a target tracking system is supplied to support the relevant results.
RTFM: Generalising to New Environment Dynamics via Reading

Obtaining policies that can generalise to new environments in reinforcement learning is challenging. In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments. We propose a grounded policy learning problem, Read to Fight Monsters (RTFM), in which the agent must jointly reason over a language goal, relevant dynamics described in a document, and environment observations. We procedurally generate environment dynamics and corresponding language descriptions of the dynamics, such that agents must read to understand new environment dynamics instead of memorising any particular information. In addition, we propose txt2π, a model that captures three-way interactions between the goal, document, and observations. On RTFM, txt2π generalises to new environments with dynamics not seen during training via reading. Furthermore, our model outperforms baselines such as FiLM and language-conditioned CNNs on RTFM. Through curriculum learning, txt2π produces policies that excel on complex RTFM tasks requiring several reasoning and coreference steps.
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth

Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns. In this work we investigate how to leverage more directly this semantic structure to guide geometric representation learning, while remaining in the self-supervised regime. Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions. Furthermore, we propose a two-stage training process to overcome a common semantic bias on dynamic objects via resampling. Our method improves upon the state of the art for self-supervised monocular depth prediction over all pixels, fine-grained details, and per semantic categories.
RainFlow: Optical Flow Under Rain Streaks and Rain Veiling Effect

Optical flow in heavy rainy scenes is challenging due to the presence of both rain steaks and rain veiling effect, which break the existing optical flow constraints. Concerning this, we propose a deep-learning based optical flow method designed to handle heavy rain. We introduce a feature multiplier in our network that transforms the features of an image affected by the rain veiling effect into features that are less affected by it, which we call veiling-invariant features. We establish a new mapping operation in the feature space to produce streak-invariant features. The operation is based on a feature pyramid structure of the input images, and the basic idea is to preserve the chromatic features of the background scenes while canceling the rain-streak patterns. Both the veiling-invariant and streak-invariant features are computed and optimized automatically based on the the accuracy of our optical flow estimation. Our network is end-to-end, and handles both rain streaks and the veiling effect in an integrated framework. Extensive experiments show the effectiveness of our method, which outperforms the state of the art method and other baseline methods. We also show that our network can robustly maintain good performance on clean (no rain) images even though it is trained under rain image data.