Reviewerly/exHarmony huggingface dataset:
VM consolidation improvement approach using heuristics granular rules in cloud computing environment

Since cloud computing has been developing as a successful business and growing interest of organizations to make use of the environment, the volume of provided services is highly demanding. Cloud computing environment has high maintenance costs, therefore, the resources are limited; due to rapid progress and the highly demanded volume of utilization of resources, ascertaining an appropriate approach for controlling and managing resources has become essential in this area. Therefore, an algorithm must be designed to perform better in all QoS. A significant factor in current exploratory methods is that each method owns distinctive features and perhaps does not estimate all QoSes. For example, one specific method has low energy consumption but does not consider SLA violation. Concerning the aims of the present study, the granular model is designed which uses various criteria involved in computations; in practice, by entering the values given in the past and discovering the relationships among them, the most desirable classification is produced. The membership function and the inference rule are derived from the data provided by the actual workload. Experimental analysis of the results showed that this method works out better than all other related methods and avails consistent performance in all QoS criteria.
Application of machine learning methods in the classification of corruption related content in Russian-speaking and English-speaking Internet media

The paper attempts to classify the corruption-related media content of Russian-language and English-language Internet media using machine learning methods. The methodological approach proposed in the article is very relevant and promising, since, according to our earlier data, corruption monitoring mechanisms used in foreign publications based on the use of advanced information technologies have rather limited potential effectiveness and are not always adequately interpreted. The study shows the principles and grounds for identifying identification parameters, and also describes in detail the layout scheme of the collected news array. In the course of automatic text processing, which took place in 2 stages (vectorization of the text and the use of a learning model), it was possible to solve the main 4 tasks: highlighting a significant quote from a news article to identify a text on corruption topics, predicting the type of news message, predicting a relevant article of the Criminal Code of the Russian Federation, which is used to determine responsibility for the described corruption offense, as well as predicting the type of relationship in corruption offenses. The results obtained showed that modern methods of automatic text processing successfully cope with the tasks of identification and classification of corruption-related content in both Russian and English.
Adaptive response maps fusion of correlation filters with anti-occlusion mechanism for visual object tracking

Abstract Despite the impressive performance of correlation filter-based trackers in terms of robustness and accuracy, the trackers have room for improvement. The majority of existing trackers use a single feature or fixed fusion weights, which makes it possible for tracking to fail in the case of deformation or severe occlusion. In this paper, we propose a multi-feature response map adaptive fusion strategy based on the consistency of individual features and fused feature. It is able to improve the robustness and accuracy by building the better object appearance model. Moreover, since the response map has multiple local peaks when the target is occluded, we propose an anti-occlusion mechanism. Specifically, if the nonmaximal local peak is satisfied with our proposed conditions, we generate a new response map which is obtained by moving the center of the region of interest to the nonmaximal local peak position of the response map and re-extracting features. We then select the response map with the largest response value as the final response map. This proposed anti-occlusion mechanism can effectively cope with the problem of tracking failure caused by occlusion. Finally, by adjusting the learning rate in different scenes, we designed a high-confidence model update strategy to deal with the problem of model pollution. Besides, we conducted experiments on OTB2013, OTB2015, TC128 and UAV123 datasets and compared them with the current state-of-the-art algorithms, and the proposed algorithms have impressive advantages in terms of accuracy and robustness.
One emoji, many meanings: A corpus for the prediction and disambiguation of emoji sense

In this work, we uncover a hidden linguistic property of emoji, namely that they are polysemous and can be used to form a semantic network of emoji meanings. Our key contributions to this direction of study are as follows: (1) We have developed a new corpus to help in the task of emoji sense prediction. This corpus contains tweets with single emojis, where each emoji has been labelled with an appropriate sense identifier from WordNet. (2) Experiments, which demonstrate that it is possible to predict the sense of an emoji using our corpus to a reasonable level of accuracy. We are able to report an average path-similarity score of 0.4146 for our best emoji sense prediction algorithm. (3) We further show that emoji sense is a useful feature in the emoji prediction task, where we report an accuracy of 58.8816 and macro-F1 score of 46.6640, beating reasonable baselines in this task. Our work demonstrates that importance of considering the meaning behind emoji, rather than ignoring them, or simply treating them as extra wordforms.
A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk

Predictive modeling of clinical data is fraught with challenges arising from the manner in which events are recorded. Patients typically fall ill at irregular intervals and experience dissimilar intervention trajectories. This results in irregularly sampled and uneven length data which poses a problem for standard multivariate tools. The alternative of feature extraction into equal-length vectors via methods like Bag-of-Words (BoW) potentially discards useful information. We propose an approach based on a kernel framework in which data is maintained in its native form: discrete sequences of symbols. Kernel functions derived from the edit distance between pairs of sequences may then be utilized in conjunction with support vector machines to classify the data. Our method is evaluated in the context of the prediction task of determining patients likely to develop type 2 diabetes following an earlier episode of elevated blood pressure of 130/80 mmHg. Kernels combined via multi kernel learning achieved an F1-score of 0.96, outperforming classification with SVM 0.63, logistic regression 0.63, Long Short Term Memory 0.61 and Multi-Layer Perceptron 0.54 applied to a BoW representation of the data. We achieved an F1-score of 0.97 on MKL on external dataset. The proposed approach is consequently able to overcome limitations associated with feature-based classification in the context of clinical data.
Identity-Based Public Auditing for Cloud Storage of Internet-of-Vehicles Data

The Internet of Vehicles (IoV) , with the help of cloud computing, can provide rich and powerful application services for vehicles and drivers by sharing and analysing various IoV data. However, how to ensure the integrity of IoV data with multiple sources and diversity outsourced in the cloud is still an open challenge. To address this concern, this paper first presents an identity-based public auditing scheme for cloud storage of IoV data, which can fully achieve the essential function and security requirements, such as classified auditing, multi-source auditing and privacy protection. Particularly, we design a new authenticated data structure, called data mapping table, to track the distribution of each type of IoV data to ensure fine and rapid audits. Moreover, our scheme can reduce the overheads for both the key management and the generation of block tags. We formally prove the security of the presented scheme and evaluate its performance by comprehensive comparisons with the state-of-the-art schemes designed for traditional scenarios. The theoretical analyses and experimental results demonstrate that our scheme can securely and efficiently realize public auditing for IoV data, and outperforms the previous ones in both the computation and communication overheads in most cases.
Quantitative characterization of non-DLVO factors in the aggregation of black soil colloids

The variable role and fate of soil colloids under different environmental conditions are derived from their dispersion and aggregation properties. In this work, dynamic and static light scattering were used to characterize the original size, aggregation kinetics of natural black soil colloids (BSCs) and structural features of aggregates in electrolytes with different cations (K+, Mg2+, Ca2+), respectively. For these three cations, the aggregation kinetics followed the trend of Ca2+ > Mg2+ > K+ and the critical coagulation concentration (CCC) followed the sequence: K+ (134.30 mmol L-1) > Mg2+ (13.27 mmol L-1) > Ca2+ (4.19 mmol L-1). The results indicated that the aggregation behavior in different valence cation systems followed the classical Derjaguin-Landau-Verwey-Overbeek (DLVO) model qualitatively. However, the quantitative differences of CCC suggest the existence of ion-specific effects. The effective ionic charge coefficient 1.31, 2.20, and 2.78 of K+, Mg2+ and Ca2+ were proposed to consider of all the non-DLVO factors, which were obtained by forming a relationship based on mathematic between the electrostatic repulsion and the van der Waals attractive interaction at the CCC. The non-classical polarization of cations in a strong soil electric field is a primary mechanism of cation effects on soil colloid interactions, causing the difference in colloid interaction energy and further affecting soil colloid aggregation. This result is crucial for enriching the theory of charged colloidal interactions.
Searching for Cryptographically Significant Rotation Symmetric Boolean Functions by Designing Heuristic Algorithms

It has been proved that the set of rotation symmetric Boolean functions (RSBFs) is abundant in cryptographically strong functions with multiple criteria. In this study, we design two genetic algorithms and apply them to search for balanced RSBFs with high nonlinearity. The experimental results show that our methods can generate cryptographically strong Boolean functions with high nonlinearity, 1-resilient functions, and optimal algebraic immunity. It shows that these functions have superiority from the view point of practical application in cryptosystems compared with known ones which are obtained by other heuristics.
An Ensemble Method for Phishing Websites Detection Based on XGBoost

N owadays, the internet is spreading widely around the world. Beside the benefits the internet brings to its users, there are many potential harms such as phishing scams. These scams act like normal websites, stealing confidential information from users. To protect the privacy of the internet users from being imperiled by scams, it is necessary to find a way to detect those phishing websites. When considering detection, Machine Learning methods that have the best possible performances are promising techniques to resolve the problem since they can make reliable predictions or classifications for unseen scenarios upon given data. In this paper, I present a ensemble model to detect phishing websites using URL features. I used the dataset named &#x201C;Phishing website Detector - phishing website dataset&#x201D; from Kaggle. Next, several models using all types of mainstream ensemble algorithms (such as stacking, boosting, and bagging) were built. Finally, different methods measuring the performance of models were used. The reason why only ensemble models are selected as the methods to be implemented is mainly due to their overall strong performance. As for the results, the proposed XGBoost model combining Random Forest and K-Nearest Neighbors outperforms all other models (Random Forest, AdaBoost, Parameter-tuned XGBoost, Stacking, and Voting), achieving an accuracy of 99.74 &#x0025; on training data and 96.44&#x0025; on testing data. The confusion matrix generated according to the performance of the XGBoost model shows that it correctly predicts 1523 positive terms out of 1594 in total and 1995 negative terms out of 2054 in total. This resulting method can effectively detect phishing web sites. Furthermore, if integrated into application or web extension, it can protect internet users&#x0027; privacy from being imperiled when they accidentally step into phishing web sites traps.
Transforming remote sensing images to textual descriptions

Remote sensing data is growing enormously by virtue of the advancements in satellite and drone technology. Generating description of these remote sensing images have gained much attention in recent past owing to its applications in remote sensing image retrieval (RSIR) and image analysis. Remote sensing images capture huge diversity in different perspectives and levels. Due to overhead perspective and significantly larger scale of the scene, extraction of visual features from the remote sensing images to generate descriptions has become a challenging task. In this work, a model is proposed to generate novel captions by considering multi-scale features processed through adaptive attention based decoder using topic sensitive word embedding. The proposed model has been quantitatively evaluated using the benchmark remote sensing image captioning datasets and ablation study has been conducted to investigate the reason behind its effectiveness. Experimental evaluation depicts that the proposed model shows promising results compared to the existing state of the art remote sensing image description models.
Android application development for push notification feature for Indonesian space weather service based on Google Cloud Messaging

Abstract The advances of digital technology bring the era where the communication platform of modern society facilitates the spreading of information which is effortless yet distracting. This paper describes the development of a mobile application on the android platform as a service that provides information on Indonesian space weather conditions where the pushed- notification method is used to deliver information to the service’s stakeholders as the targeted users. The notification which presents the message directly to the users’ device is considered the most effective way to engage the users. A cross-platform message data builder based on GSON and Retrofit interface is implemented to construct information contents in the notification as well as in the application, suitable for use cases that occurred to accommodate the user preference. Furthermore, the application uses mobile device token identification to specifically manage the engagement process served by Firebase, a Google cloud messaging system. The application analytic indicates that the user-application retention of average users is about 80 percent and the success rate of delivered instant messages with push notification is 96 percent respectively during one month of testing from 48 registered users.
Optimal prediction intervals for macroeconomic time series using chaos and evolutionary multi-objective optimization algorithms

In a first-of-its-kind study, this paper formulates the problem of estimating the prediction intervals (PIs) in a macroeconomic time series as a bi-objective optimization problem and solves it with three evolutionary algorithms namely, Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Sorting Particle Swarm Optimization (NSPSO) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA-D). We also proposed modeling the chaos present in the time series as a preprocessor, which we called stage-1. Accordingly, we proposed 2-stage models, where stage-1 is followed by obtaining the optimal point prediction using NSGA-II/NSPSO/MOEA-D and using these point predictions to obtain PIs (stage-2). We then proposed a 3-stage hybrid, which is built on the 2-stage model, wherein the 3rd stage also invokes NSGA-II/NSPSO/MOEA-D in order to estimate the PIs from the point predictions obtained in 2nd stage by simultaneously and explicitly optimizing (i) prediction interval coverage probability (PICP) and (ii) prediction interval average width (PIAW). The proposed models yielded better results in terms of both PICP and PIAW compared to the state-of-the-art Lower Upper Bound Estimation Method (LUBE) with Gradient Descent (GD) and LUBE with long short-term memory (LSTM) network. The 3-stage models outperformed the 2-stage models with respect to PICP but showed similar performance in PIAW at the cost of running NSGA-II/NSPSO/MOEA-D second time. Overall, MOEA-D yielded best PIs in two datasets and NSGA-II outperformed the other two in the third dataset. But, in terms of hypervolume, in 2-stage MOEA-D produced most diverse solutions in two datasets, while NSGA-II was the winner in the third dataset.
Learning from data streams for automation and orchestration of 6G industrial IoT: toward a semantic communication framework

Abstract Established methods of communication are based mainly on Shannon’s theory of information, which purposefully overlooks semantic elements of communication. The future wireless technology should promise to facilitate many services, based on content, needs, and semantics, precisely customized to network capabilities. This gave rise to significant concern for Semantic Communication (SC), a novel paradigm considering the message’s meaning during transmission. Federated learning (FL) and Asynchronous Advantage Actor Critic (A3C) are the two emerging distributed and artificially intelligent approaches that provide diverse and possibly massive network coverage for data-driven SC solutions of industry 4.0 automation. Although SC is still in an early development stage, FL-empowered architecture has been recognized as one of the most promising solutions to meet the ubiquitous intelligence in the anticipated sixth-generation (6G) networks. This paper identifies industry 4.0 automation needs that drive the convergence of artificial intelligence and 6G for learning from data streams. We develop a novel SC framework based on the FL and A3C networks and discuss its potential along with transfer learning to address most of the new difficulties anticipated in 6G for industrial communication networks. Our proposed framework has been evaluated with extensive simulation results.
An empirical study of the design choices for local citation recommendation systems

As the number of published research articles grows on a daily basis, it is becoming increasingly difficult for scientists to keep up with the published work. Local citation recommendation (LCR) systems, which produce a list of relevant articles to be cited in a given text passage, could help alleviate the burden on scientists and facilitate research. While research on LCR is gaining popularity, building such systems involves a number of important design choices that are often overlooked. We present an empirical study of the impact of the three design choices in two-stage LCR systems consisting of a prefiltering and a reranking phase. In particular, we investigate (1) the impact of the prefiltering models’ parameters on the model’s performance, as well as the impact of (2) the training regime and (3) negative sampling strategy on the performance of the reranking model. We evaluate various combinations of these parameters on two datasets commonly used for LCR and demonstrate that specific combinations improve the model’s performance over the widely used standard approaches. Specifically, we demonstrate that (1) optimizing prefiltering models’ parameters improves R@1000 in the range of 3% to 12% in absolute value, (2) using the strict training regime improves both R@10 and MRR (up to a maximum of 3.4% and 2.6%, respectively) in all combinations of dataset and prefiltering model, and (3) a careful choice of negative examples can further improve both R@10 and MRR (up to a maximum of 11.9% and 8%, respectively) in both datasets used Our results show that the design choices we considered are important and should be given greater consideration when building LCR systems.
Higher-order Context-free Session Types in System F

We present an extension of System F with higher-order context-free session types. The mixture of functional types with session types has proven to be a challenge for type equivalence formalization: whereas functional type equivalence is often rule-based, session type equivalence usually follows a semantic approach based on bisimulations. We propose a unifying approach that handles the equivalence of functional and session types together. We present three notions of type equivalence: a syntactic rule-based version, a semantic bisimulation-based version, and an algorithmic version by reduction to the problem of bisimulation of simple grammars. We prove that the three notions coincide and derive a decidability result for the type equivalence problem of higher-order context-free session types.
Advanced algorithms for abstract dialectical frameworks based on complexity analysis of subclasses and SAT solving

Abstract dialectical frameworks (ADFs) constitute one of the most powerful formalisms in abstract argumentation. Their high computational complexity poses, however, certain challenges when designing efficient systems. In this paper, we tackle this issue by (i) analyzing the complexity of ADFs under structural restrictions, (ii) presenting novel algorithms which make use of these insights, and (iii) implementing these algorithms via (multiple) calls to SAT solvers. An empirical evaluation of the resulting implementation on ADF benchmarks generated from ICCMA competitions shows that our solver is able to outperform state-of-the-art ADF systems.
Octave convolution-based vehicle detection using frame-difference as network input

Vehicle detection in video frames has been treated the same way detecting vehicle for an isolated image. However, the models designed for the isolated image are blind to fast-moving vehicles and cannot localize the moving targets partially occluded in the scene. In this case, we figure out a way to combine the classic moving target detection method with the neural network method. In this work, first, we propose to add three-differential-frames into the neural network of Yolov3 as the second input which contains the motion information on the front and back frames to detect vehicles partially occluded; second, we reform the network by using Octave Convolution to reduce memory and computational cost while boosting accuracy. We experimentally show that by using the aforementioned methods together, compared with using original YOLOv3 on UA-DETRAC data set, AP is increased by 2.31%, recall is increased by 4.01%, and precision is increased by 3.10%. We demonstrate that the proposed method is indeed effective.