Reviewerly/exHarmony huggingface dataset:
Task Scheduling Based Optimized Based Algorithm for Minimization of Energy Consumption in Cloud Computing Environment

Allocating virtual machineries in the cloud optimally for workloads is difficult. In the cloud, finding the best way to schedule tasks is an NP-hard issue due to the huge sizes of the tasks involved. The optimal strategy includes allocating work to a data centre full of virtual machines in such a way as to minimise energy consumption, make time, and cost. For that purpose, this paper introduces a hybrid optimisation approach for scheduling jobs. As the butterfly optimisation algorithm (BOA) only takes into account the scent perception criteria, it easily becomes stuck at a local maximum. The suggested butterfly optimisation algorithm (HFBOA) is more in accordance with the real foraging behaviours of butterflies in countryside since it incorporates an additional operator, namely, a colour perception rule, as opposed to the original BOA. In addition, the HFBOA uses logistic mapping to implement an up-to-date technique for controlling parameters, hence improving global optimisation. The simulation findings show that the cloud data canter’s energy consumption, maketime, and cost may all be reduced by improving the scheduling of tasks. In conclusion, the article suggests that using metaheuristic algorithms to agenda tasks on the cloud is a viable option. The simulation answers show that the suggested technique performs very well when used to the solution of difficult, real-world engineering constraints.
Cryptocurrency Price Prediction Using Deep Learning

As technology advances daily, are advancing our lives into the digital sphere. The introduction of these cryptocurrencies aims to prevent the financial crisis. Due to its decentralized nature, high level of security, and restrictions on the number of coins that may be created, cryptocurrencies have attracted investors. Predicting the future price includes several limits and determinants because it involves capital. It varies according to market share. Using block chain technology and encryption, the transactions are encrypted from the beginning to end. Predicting prices to encourage consumers to invest during a specific period and earn a profit. They include a variety of elements, such as market analysis, sentiment analysis on Twitter, trading volume, and open and closing prices. Typical models that can be used to forecast bitcoin prices include regression techniques, neural networks, and support vector machines. Predictions of cryptocurrency prices based on their closing prices give investors additional insight into whether to wait until the closing period if prices are low for the entire day or to invest the following day. Using deep learning and bidirectional Long Short-Term Memory (LSTM) suggested this model to anticipate the price of digital currencies including Bitcoin, Litecoin, Ethereum, and Cardano. In this model, predictions are made using historical price statistics, and the graph is created by evaluating several performance indicators.
The Process of Modeling

Abstract Models are required in any type of performance study, from the design and procurement of new systems, to data center and cloud capacity planning. This chapter describes the main input parameters of the models and the output measures that can be obtained from their solutions. The relationships among these quantities, i.e., the operational laws, are also introduced. The most important steps to implement models to be solved with simulation and analytical techniques are reviewed.
Topic Classification of Text-Based Lesson Questions in Turkish with BERTurk

With the corona virus pandemic, social contact has been kept to a minimum, and education in schools can be carried out remotely. As a result of this, the concept of distance education has gained importance. In this study, natural language processing (NLP) and its effects on distance education are discussed, and by using NLP, a topic classification system is proposed. Classification is applied to text-based lesson questions in Turkish language. In this way, the questions asked by the students can be quickly directed to the teachers in the relevant specialty through a system to be designed, and the processes can be accelerated. In the data preparation phase, the real-world lesson questions were collected and converted from image to text using the EasyOCR library, and topic classification was performed on the data set using the Berturk model. Since the image-to-text method was used in the data set preparation phase, we encountered some noise in the data. To clean the data, different data preprocessing and cleaning techniques are applied. Finally, the training has been performed, and accuracy rates are presented.
Classification of the Class Imbalanced Data Using Mahalanobis Distance with Feature Filtering

Class imbalance exists in many real-life applications. Classification of an imbalanced dataset without pre-processing is more complex. Existing literature has numerous solutions to get rid of this problem either through data level or algorithm level modification, but the imbalanced data classification still needs significant improvements. In the proposed work, a new method has been formulated to reduce the misclassification in the classes. The feature filtering technique is applied iteratively in an imbalanced dataset to use only significant features for the classification. The chi-square statistics is used for the filtering process and Mahalanobis distance (MD) is used for classification. The performance of the MD is being compared with the state-of-the-art classifiers and observed that the Mahalanobis distance performs better in 6 datasets and gives the same result in 3 datasets out of the 12 experimented datasets. The observations show that the association between the features is a major deciding factor during the classification. If each feature is independent, then the classification region is separable even though the dataset is imbalanced. The combination of the MD and the chi-square feature filtering helps in achieving a better AUC score.
Crime Prediction Using Modified Capsule Network with CrissCross Optimization on the Sentiment Analysis for Cyber-Security

Data mining from textual sources is known as social media text analytics. Social media, like any other text-based dataset, is amenable to text analysis. Today’s society has a huge challenge in the form of crime, with the routes of which having shifted almost entirely to social media. Because of crime, both the standard of living and economic development have suffered. By sifting through the available data, we may identify trends in criminal behaviour and foresee potential incidents in the future. Unfortunately, not all crimes are recorded or solved due of a lack of evidence. Hence, tracking these offenders remains challenging. We can keep an eye on illicit activities on social media. For the simple reason that social media users sometimes make observations about their immediate environment. In this study, we propose a model for crime categorization on a Twitter dataset that combines the recently proposed Capsule Network with the tried-and-true Multilayer Perceptron. The output of both networks is combined in a manner that makes the most of each network’s strengths using a rule-based technique. Moreover, the Criss-Cross Optimization (CCO) method is used to fine-tune the capsule network’s hyper-parameters. Crime statistics from reliable sources are used to make accurate comparisons between regions. We also evaluate recent changes in crime rates in both Jammu, India, and Ghaziabad, Uttar Pradesh. The most recent crime patterns during the last week have been documented (23, January 2019 to 30, January 2019). The studies show that the generated findings are consistent with the actual crime rate information. We anticipate that research of this kind will aid in gauging the current crime rate in various areas and in identifying criminal activity.
Performance Analysis of Different Classifiers Using HOG and LBP for Traffic Sign Detection

A critical requirement for a traffic assistance system that can detect and recognize traffic signs in challenging circumstances has arisen as a result of the increased use of electrically powered autonomous cars. In order to overcome this difficulty, we provide a real-time method that greatly improves the effectiveness of traffic sign identification. We examine two widely utilized modern object detectors, Faster RCNN and YOLO, for traffic signal detection. Our experimental study shows that the faster RCNN model and the YOLO model give better accuracy, but they cannot meet real-time performance. In this paper, we evaluate a method for detecting traffic signs that has the potential to greatly enhance traffic safety and avoid accidents, especially in the era of electrically powered automobiles. The suggested solution has undergone extensive testing on the GTSDB and is based on reliable and effective techniques for feature extraction, ROI detection, and subclass sign recognition. Our method represents a significant advancement in the creation of traffic assistance systems that can swiftly and reliably detect and recognize traffic signs.
Image Captioning Using Xception-Long Short-Term Memory

Deep Learning has shown great potential in developing applications capable of automatically generating captions or descriptions for images and video frames. The critical components of this process are image processing and natural language processing, which play a crucial role in captioning images and videos. These applications can be used in several areas, including robotic vision systems, assisting people with visual impairments, generating metadata for search engines, answering visual questions, visual grounding, and more. This paper discusses various working algorithms such as a combination of CNN-RNN, encoder-decoder, attention mechanisms, and transformation models with evaluation matrices, datasets, and limitations of existing models. Xception-LSTM shows great potential compared to the traditional encoding-decoding model using BLEU and METEOR evaluation matrics.
General theory of data, artificial intelligence and governance

Abstract Big Data (BD) and Artificial Intelligence (AI) play a fundamental role in today’s economy that traditional economic models fail to capture. This paper presents a theoretical conceptualisation of the data economy and derives implications for digital governance and data policies. It defines a hypothetical data-intensive economy where data are the main input of AI and in which the amount of knowledge generated is below the socially desired amount. Intervention could consist of favouring the creation of additional knowledge via data sharing. We show that the framework suggested describes many features of today’s data-intensive economy and provides a tool to assist academic, policy and governance discussions. Our conclusions support data sharing as a way of increasing knowledge production on societal challenges and dilemmas of data capitalism and transparency in AI.
Hacking DCNs

In this paper we study the problem of the security of Deep Convolutional Networks (DCN). DCN are gaining more and more integration into daily lives and technology even though they cannot be directly understood or interpreted: while the process of learning from data is relatively well known the process by which DCN learns is still not explained. This state of using DCN without full understanding creates several non trivial security gaps and deficiencies that have already been addressed and exploited. Some of them are adversarial attacks, Trojan insertion, catastrophic content loss, etc. We study the problem of the DCN’s sensitivity to noise attack insertion on the level of its components. We show, that it is possible to determine how to inject Gaussian noise into the network so that a reliable predictor can be trained as to what images will be misclassified. In addition we study the dependency of image misclassification as a function of inserted noise magnitude and the average accuracy of the DCN. The results of this paper show that it is possible to relate the magnitude and type of inserted noise to the type of samples that are the most probable to be misclassified and therefore predict the failure of the network. The methodology presented in this paper can thus be seen as a type of unstructured Trojan insertion with expected misclassification and minimal default accuracy change.
Humorous hate speech on social media: A mixed-methods investigation of users’ perceptions and processing of hateful memes

Humor that denigrates social groups can be just as harmful as hate speech. Despite research indicating the prevalence of humorous hate speech, how audiences perceive and process the combination of humor (e.g. irony as a humor cue) and hate speech (e.g. dehumanization as a hate cue) remains unclear. Using a sequential mixed-methods approach combining a qualitative think-aloud study (Study 1, N = 41) with an experiment involving implicit measurements of response times (Study 2, N = 65), it was examined how individuals perceive memes that contain both humor and hate cues. While think-aloud interviews indicated that processing humorous hate speech may require multiple steps, the relative time spent by participants in Study 2 to rate humorous and non-humorous hate speech as being hostile or not did not entirely support that conclusion. However, findings imply that hostile views may become more commonplace when hate speech is masked by humor.
The Impact of Key Stakeholders and the Computer Skills of Librarians on Research Data Management Support Services (Id so-21-1893.r2)

This study aims to investigate key stakeholders’ impact and to assess its effect on Research Data Management (RDM), thereby setting the agenda for sound local government Research Data Management (RDM) policy for maximum profit. This study relies mainly on the stakeholder’ theory. Our target population is stakeholders in RDM. The survey used the Partial Least Squares and Structural Equation Modeling tool with a sample of 295 received from respondents to find the effect among the stakeholders. The Statistical results confirmed there is a positive effect among stakeholders. The Funders agencies criteria influence researchers’ work. The effect of government support policies on librarians RDM support services was positive but not significant. The impact of computer literacy of librarians on the RDM support service delivered by librarians was insignificant. The finding is mainly due to overreliance on manual approach to work in most libraries in Ghana as a result inadequate fund support. Librarians need to take a leading role in RDM to implement RDM policy fully. This study contributes to the discourse by helping to strengthen the existing relationship and also to understand the role of various stakeholders in RDM. Stakeholders in Ghana and developing countries need to take greater responsibility to achieve success. Government can rely on it for policy formulation. The research can be used to expand the existing stakeholders’ effective support and interactions as well as in teaching. The survey results can also help identify best practices, improve current observation, and compare practices across different countries.
Embedding latent class regression and latent class distal outcome models into cluster‐weighted latent class analysis: a detailed simulation experiment

Summary Usually in latent class (LC) analysis, external predictors are taken to be cluster conditional probability predictors (LC models with external predictors), and/or score conditional probability predictors (LC regression models). In such cases, their distribution is not of interest. Class‐specific distribution is of interest in the distal outcome model, when the distribution of the external variables is assumed to depend on LC membership. In this paper, we consider a more general formulation, that embeds both the LC regression and the distal outcome models, as is typically done in cluster‐weighted modelling. This allows us to investigate (1) whether the distribution of the external variables differs across classes, (2) whether there are significant direct effects of the external variables on the indicators, by modelling jointly the relationship between the external and the latent variables. We show the advantages of the proposed modelling approach through a set of artificial examples, an extensive simulation study and an empirical application about psychological contracts among employees and employers in Belgium and the Netherlands.
Description: Its meaning, epistemology, and use with emphasis on information science

Abstract This study examines the concept of “description” and its theoretical foundations. The literature about it is surprisingly limited, and its usage is vague, sometimes even conflicting. Description should be considered in relation to other processes, such as representation, data capturing, and categorizing, which raises the question about what it means to describe something. Description is often used for any type of predication but may better be limited to predications based on observations. Research aims to establish criteria for making optimal descriptions; however, the problems involved in describing something have seldom been addressed. Specific ideals are often followed without examine their fruitfulness. This study provides evidence that description cannot be a neutral, objective activity; rather, it is a theory‐laden and interest‐based activity. In information science, description occurs in processes such as document description, descriptive metadata assignment, and information resource description. In this field, description has equally been used in conflicting ways that mostly do not evince a recognition of the value‐ and theory‐laden nature of descriptions. It is argued that descriptive activities in information science should always be based on consciously explicit considerations of the goals that descriptions are meant to serve.
<scp>GBNN</scp> algorithm enhanced by movement planner for <scp>UV‐C</scp> disinfection

Abstract In order to maintain adequate levels of cleanliness and sanitation in public facilities, prevent the buildup of viruses and other harmful pathogens, and ensure health and safety, health and labor authorities have repeatedly warned of the need to adhere to proper disinfection protocols in the workplace. This is particularly important in public places where food is handled, where there are more vulnerable people, including hospitals and health care centers, or where there is a large concentration of people. One promising approach is the combination of ultraviolet‐C (UV‐C) light and mobile robots to automate disinfection processes. Being this technology effective for disinfection, an excessive dose of UV can damage the materials, limiting its applicability. Therefore, a major challenge for automatic disinfection is to find a route that covers the entire surface, ensures cleanliness, and provides the correct radiation dose while preventing environmental materials from being damaged. To achieve this, in this paper a novel intelligent control approach is proposed. A bio‐inspired Glasius neural network with a motion planner, an UV estimation module, a speed regulator, and pure pursuit controller are combined into one intelligent system. The motion planner proposes a sequence of movements to go through the space in the most efficient way possible, avoiding obstacles of the environment. The speed controller adjusts the dose of UV‐C radiation and the pure pursuit regulator ensures the following of the path. This approach has been tested in various simulation scenarios of increasing complexity and in four different areas of dosing requirements. In simulation, a 44% reduction of the maximum dose is achieved, 17% less distance travelled by the robot and, what is more important, 229% more locations with the appropriate dose.
Selective real‐time adversarial perturbations against deep reinforcement learning agents

Abstract Recent work has shown that deep reinforcement learning (DRL) is vulnerable to adversarial attacks, so that exploiting vulnerabilities in DRL systems through adversarial attack techniques has become a necessary prerequisite for building robust DRL systems. Compared to traditional deep learning systems, DRL systems are characterised by long sequential decisions rather than one‐step decision, so attackers must perform multi‐step attacks on them. To successfully attack a DRL system, the number of attacks must be minimised to avoid detecting by the victim agent and to ensure the effectiveness of the attack. Some selective attack methods proposed in recent researches, that is, attacking an agent at partial time steps, are not applicable to real‐time attack scenarios, although they can avoid detecting by the victim agent. A real‐time selective attack method that is applicable to environments with discrete action spaces is proposed. Firstly, the optimal attack threshold T for performing selective attacks in the environment Env is determined. Then, the observation states corresponding to when the value of the action preference function of the victim agent in multiple eposides exceeds the threshold T are added to the training set according to this threshold. Finally, a universal perturbation is generated based on this training set, and it is used to perform real‐time selective attacks on the victim agent. Comparative experiments show that our attack method can perform real‐time attacks while maintaining the attack effect and stealthiness.
Continuous sign language recognition based on hierarchical memory sequence network

Abstract With the goal of solving the problem of feature extractors lacking strong supervision training and insufficient time information concerning single‐sequence model learning, a hierarchical sequence memory network with a multi‐level iterative optimisation strategy is proposed for continuous sign language recognition. This method uses the spatial‐temporal fusion convolution network (STFC‐Net) to extract the spatial‐temporal information of RGB and Optical flow video frames to obtain the multi‐modal visual features of a sign language video. Then, in order to enhance the temporal relationships of visual feature maps, the hierarchical memory sequence network is used to capture local utterance features and global context dependencies across time dimensions to obtain sequence features. Finally, the decoder decodes the final sentence sequence. In order to enhance the feature extractor, the authors adopted a multi‐level iterative optimisation strategy to fine‐tune STFC‐Net and the utterance feature extractor. The experimental results on the RWTH‐Phoenix‐Weather multi‐signer 2014 dataset and the Chinese sign language dataset show the effectiveness and superiority of this method.
ATL-DAS: Automatic Topology Learning for Differentiable Architecture Search

Differentiable architecture search (DARTS) provides a fast solution to Neural Architecture Search (NAS) by using gradient descent over a continuous search space. Despite its high search speed, DARTS suffers from several problems such as ambiguous topology selection, operation co-adaptation, incomplete NAS pipeline, and large memory consumption. To address these problems, we first introduce topology parameters into the search space to explicitly model the network topology, which ensures the searched network architecture is well-defined. Next, we propose two sampling strategies to sample independent child networks for training and evaluation, which solve the co-adaptation problem while making the NAS pipeline complete. Finally, we use hard pruning to avoid invalid computations, which greatly reduces memory consumption. The proposed Automatic Topology Learning for Differentiable Architecture Search (ATL-DAS) algorithm performs favorably against the state-of-the-art approaches on CIFAR10 and CIFAR100 with error rates of 2.49% and 16.8%, respectively. Moreover, the architectures searched perform well across various visual tasks, including ImageNet classification, COCO object detection, and Composition-1K image matting, highlighting the potential of ATL-DAS for real-world applications.