Reviewerly/exHarmony huggingface dataset:
A Chaotic Encryption Algorithm for Image Privacy Based on Two Pseudorandomly Enhanced Logistic Maps

Chaos-based cryptography has become in an interesting research topic in secure communications during last years, since chaos is strongly related with cryptography to design fast and secure cryptosystems for bulk and correlated data such as digital images. On the other hand, improve randomness of chaotic stream is desirable to enhance the security of the encryption, since such stream is used as main source for diffusion and confusion process in a cryptosystem. In this chapter, we introduce a chaotic encryption algorithm by using two pseudorandomly enhanced logistic maps (PELM) for color RGB image privacy. Experimental results with computer simulations at MATLAB are realized to validate the effectiveness, security, and performance of proposed scheme. Based on results, the propose RGB image cryptosystem is secure and speed to be used in applications such as in biometric systems, telemedicine, or satelital image in military affairs.
Infrared image adaptive inverse histogram enhancement technology

In infrared images, when the traditional histogram equalizes the image, the detail pixels are easily immerged by the background pixels, resulting in the image being too bright and too dark. Based on this situation, an adaptive inverse histogram equalization algorithm was proposed in this paper. The algorithm enhanced image details by inverse statistics, adaptive selection threshold and segmentation mapping. Compared with the traditional histogram equalization algorithm, the inverse histogram equalization algorithm significantly improve the image visual effect in different gray level distributions and enhance the details of different areas of the image to different degrees. Moreover, under the premise of achieving better image processing effects, this algorithm can still guarantee real-time performance and high efficiency by optimizing calculation methods, and is suitable for FPGA hardware transplantation.
A novel privacy-preserving matrix factorization recommendation system based on random perturbation

With the popularity of networks and the increasing number of online users, recommender systems have suffered from the privacy leakage of sensitive information. While people enjoy recommender services, their information is exposed to the networks. To protect the privacy of users when using the reco mmender services, we propose a multi-level combined privacy-preserving model that maintains high accuracy of recommendation with privacy protection and alleviates the data sparsity problem. Our scheme contains two steps of recommendation. First, a multi-level combined random perturbation (MCRP) model is proposed on the client side. Our model dynamically divides multiple disturbance levels and adds noise of different ranges to the rating matrix according to Gaussian and uniform mixed disturbances. Second, on the server side, we propose a pseudo rating prediction filling (PRPF) algorithm based on the matrix factorization model. Combining the PRPF algorithm with the MCRP method significantly improves the recommender accuracy and effectively increases privacy security. Sensitive analysis and comparison experiments show that the proposed privacy method has certain advantages in security and recommender accuracy by using three publicly available datasets.
Semantic Concept Recommendation for Continuously Evolving Knowledge Graphs

With the digitalization of many industrial processes and the increasing interconnection of devices, the number of data sources and associated data sets is constantly increasing. Due to the heterogeneity of these large amounts of data sources, finding, accessing and understanding them is a major challenge for data consumers who want to work with the data. In order to make these data sources searchable and understandable, the paradigms of Ontology-Based Data Access (OBDA) or Ontology-Based Data Integration (OBDI) are used today. An important part of these paradigms is the creation of a mapping, such as a semantic model, between a previously defined ontology and the existing data sources. Although there are already many approaches that automate the creation of this mapping by using data-driven or data-structure-driven approaches, none of them focuses on the fact that the underlying ontology evolves over time. However, this is essential in today’s age of large amounts of data and ever-growing number of data sources. In this paper, we propose an approach that allows the recommendation of semantic concepts for data attributes based on a constantly evolving knowledge graph. The approach allows the knowledge graph to learn data-driven representations for any concept that is available in the knowledge graph and that is already mapped to at least one data attribute. Instead of supporting a single method for recommending semantic concepts, we design the approach to be able to learn multiple data representatives per semantic concept, with each representative being trained on a different method, such as machine learning classifiers, rules, or statistical methods. In this way, for example, we are able to distinguish between different data types and data distributions. In order to evaluate our approach, we have trained it on several different publicly available data sets. In comparison to existing approaches, our evaluation shows that the accuracy of the recommendation improves through the use of our flexible and dedicated classification approach.
b-Bit Sketch Trie: Scalable Similarity Search on Integer Sketches

Recently, randomly mapping vectorial data to strings of discrete symbols (i.e., sketches) for fast and space-efficient similarity searches has become popular. Such random mapping is called similarity-preserving hashing and approximates a similarity metric by using the Hamming distance. Although many efficient similarity searches have been proposed, most of them are designed for binary sketches. Similarity searches on integer sketches are in their infancy. In this paper, we present a novel space-efficient trie named $b$-bit sketch trie on integer sketches for scalable similarity searches by leveraging the idea behind succinct data structures (i.e., space-efficient data structures while supporting various data operations in the compressed format) and a favorable property of integer sketches as fixed-length strings. Our experimental results obtained using real-world datasets show that a trie-based index is built from integer sketches and efficiently performs similarity searches on the index by pruning useless portions of the search space, which greatly improves the search time and space-efficiency of the similarity search. The experimental results show that our similarity search is at most one order of magnitude faster than state-of-the-art similarity searches. Besides, our method needs only 10 GiB of memory on a billion-scale database, while state-of-the-art similarity searches need 29 GiB of memory.
The Modelling and Optimization of Machine Management System with Computer Numerical Control

The process of modeling and optimization of machine management system with computer numerical control and its findings are given. The production process is presented as IDEF3 diagram in BPwin system, structural and simulation modeling is shown in Arena system. Mathematical model has been constructed in accordance with simulation modeling findings, by which optimization has been carried out and formulae for calculation of optimized factor values on objective factor values have been obtained. In the present study, we consider a complex system whose behavior is characterized by set of various time-dependent factors. Some of these factors can characterize the external influences on the system, whereas other factors contain information generated by system. We demonstrate that time dependence of these factors can be reproduced by the nonlinear regression model. This allows us to predict a possible behavior of the system and to identify the so-called significant factors that have a significant impact on the behavior of the system. To demonstrate validity of the method, we apply it to analyze the data characterizing a manufacturing company.
Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).
Digital Libraries

The Italian Research Conference on Digital Libraries (IRCDL) is the annual Italian forum to discuss research topics on Digital Libraries and related technical, practical, and social issues. Along the years, IRCDL touched several aspects underlying the ?Digital Library" domain and promptly adapted to the evolution of the field. Today, the ?Digital Library" field includes theory and practices reflecting the evolution of the role of libraries in the scholarly communication domain, and also embracing scholarly communication and open science. The theme of IRCDL 2019 was ?Digital Libraries: Supporting Open Science". Three main reasons motivated this theme: (i) science is increasingly becoming digital, meaning that research is performed using data services and digital tools; (ii) the results of the research are no longer just traditional scientific publications; (iii) the outcomes of science are increasingly encompassing datasets, software, and experiments. As digital artifacts, such products can be shared and re-used together with the article, thus enabling comprehensive research assessment and various degrees of reproducibility of science. Positive consequences of this shift towards Open Science are: accelerating science, optimizing the cost of research, fraud detection, and fullyfledged scientific reward. Digital Libraries are central in the evolution of research outputs by targeting findability, preservation, interlinking, and re-use of research products and by integrating the components of the scholarly communication process. The conference has been organized in Pisa, and the proceedings are published in the Springer CCIS series Vol. 988 [22]. Pre-print versions, research datasets, and research software relative to the accepted contributions are accessible via Zenodo.org.
KLASIFIKASI FAKTOR-FAKTOR PENYEBAB PENYAKIT DIABETES MELITUS DI RUMAH SAKIT UNHAS MENGGUNAKAN ALGORITMA C4.5

Diabetes mellitus (DM) is one of the chronic and deadly diseases that are widely observed in various countries today. This disease continues and is increasing to a very alarming stage. This study aims to identify and see the relationship between factors that influence DM disease. The method used in this research is C4.5 algorithm which is one of the algorithms used to make predictive classifications. Classification is one of the processes in data mining that aims to find patterns in relatively large data that use the representations in the form of decision trees. This method is applied to data from medical records of patients with DM in 2014-2018 taken from the Hasanuddin University Teaching Hospital. The results obtained indicate that there are four factors that influence the prediction of a patient's DM status namely; Fasting Blood Glucose (GDP), LDL Cholesterol, Triglycerides, and Body Weight.
Experiment of Execution Time in Decentralized Transaction System of Surplus PV Output using Blockchain

In Japan, a large amount of photovoltaics has been introduced because of Feed-in Tariff for renewable energy resources. As the results, it is necessary to suppress the output of photovoltaics to maintain supply and demand balance. In the future, however, it is expected to make more effective use of renewable energy by demand-side management. This paper proposes a method to realize power trading among consumers who buy surplus electricity from photovoltaics by smart contracts on the blockchain. According to the conventional method in Japan, the transmission and distribution company, which is responsible for supply reliability, forecasts the surplus of solar power the next day, directs PV producers to suppress output. Conversely, this paper proposes that the PV producers search for customers who can carry out storing electricity and the demand shift in the local power exchange using the blockchain, in response to the output suppression command from the transmission and distribution company.
Current Problems of the Higher Education Development in the Russian Federation Considering the Digitalization Processes

The authors of this article single out the main characteristic of the digital economy---the constantly changing volume of information, as a result of which the need to thoroughly disseminate digital technologies meant to stimulate the formation of new ways and methods of property management, the growth of the industry efficiency and the development of the economy as a whole arises.
BD-ELM: A Regularized Extreme Learning Machine Using Biased DropConnect and Biased Dropout

In order to prevent the overfitting and improve the generalization performance of Extreme Learning Machine (ELM), a new regularization method, Biased DropConnect, and a new regularized ELM using the Biased DropConnect and Biased Dropout (BD-ELM) are both proposed in this paper. Like the Biased Dropout to hidden nodes, the Biased DropConnect can utilize the difference of connection weights to keep more information of network after dropping. The regular Dropout and DropConnect set the connection weights and output of the hidden layer to 0 with a single fixed probability. But the Biased DropConnect and Biased Dropout divide the connection weights and hidden nodes into high and low groups by threshold, and set different groups to 0 with different probabilities. Connection weights with high value and hidden nodes with a high-activated value, which make more contribution to network performance, will be kept by a lower drop probability, while the weights and hidden nodes with a low value will be given a higher drop probability to keep the drop probability of the whole network to a fixed constant. Using Biased DropConnect and Biased Dropout regularization, in BD-ELM, the sparsity of parameters is enhanced and the structural complexity is reduced. Experiments on various benchmark datasets show that Biased DropConnect and Biased Dropout can effectively address the overfitting, and BD-ELM can provide higher classification accuracy than ELM, R-ELM, and Drop-ELM.
An approach to support the construction of adaptive Web applications

Purpose This paper aims to presents Real-time Usage Mining (RUM), an approach that exploits the rich information provided by client logs to support the construction of adaptive Web applications. The main goal of RUM is to provide useful information about the behavior of users that are currently browsing a Web application. By consuming this information, the application is able to adapt its user interface in real-time to enhance the user experience. RUM provides two types of services as follows: support for the detection of struggling users; and user profiling based on the detection of behavior patterns. Design/methodology/approach RUM leverages the previous study on usability evaluation to provide a service that evaluates the usability of tasks performed by users while they browse applications. This evaluation is based on a metric that allows the detection of struggling users, making it possible to identify these users as soon as few logs from their interaction are processed. RUM also exploits log mining techniques to detect usage patterns, which are then associated with user profiles previously defined by the application specialist. After associating usage patterns to user profiles, RUM is able to classify users as they browse applications, allowing the application developer to tailor the user interface according to the users’ needs and preferences. Findings The proposed approach was exploited to improve user experience in real-world Web applications. Experiments showed that RUM was effective to provide support for struggling users to complete tasks. Moreover, it was also effective to detect usage patterns and associate them with user profiles. Originality/value Although the literature reports studies that explore client logs to support both the detection of struggling users and the user profiling based on usage patterns, no existing solutions provide support for detecting users from specific profiles or struggling users, in real-time, while they are browsing Web applications. RUM also provides a toolkit that allows the approach to be easily deployed in any Web application.
On Implementing Autonomic Systems with a Serverless Computing Approach: The Case of Self-Partitioning Cloud Caches

The research community has made significant advances towards realizing self-tuning cloud caches; notwithstanding, existing products still require manual expert tuning to maximize performance. Cloud (software) caches are built to swiftly serve requests; thus, avoiding costly functionality additions not directly related to the request-serving control path is critical. We show that serverless computing cloud services can be leveraged to solve the complex optimization problems that arise during self-tuning loops and can be used to optimize cloud caches for free. To illustrate that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDiS), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. A cost analysis shows that the serverless computing approach can lead to significant cost savings: The cost of running the controller as a serverless microservice is 0.85% of the cost of the always-on alternative. Through this case study, we make a strong case for implementing the controller of autonomic systems using a serverless computing approach.
The THREAT-ARREST Cyber-Security Training Platform

Cyber security is always a main concern for critical infrastructures and nation-wide safety and sustainability. Thus, advanced cyber ranges and security training is becoming imperative for the involved organizations. This paper presets a cyber security training platform, called THREAT-ARREST. The various platform modules can analyze an organization’s system, identify the most critical threats, and tailor a training program to its personnel needs. Then, different training programmes are created based on the trainee types (i.e. administrator, simple operator, etc.), providing several teaching procedures and accomplishing diverse learning goals. One of the main novelties of THREAT-ARREST is the modelling of these programmes along with the runtime monitoring, management, and evaluation operations. The platform is generic. Nevertheless, its applicability in a smart energy case study is detailed.
A Stronger Aadaptive Local Dimming Method with Details Preservation

Local dimming technology focuses on improving the contrast ratio of the displayed images for a great visual perception. It consists of backlight extraction and pixel compensation. Considering a single existing backlight extraction algorithm can hardly adapt to images with diverse characteristics and rich details, we propose a stronger adaptive local dimming method with details preservation in this paper. This method, combining the advantages of some existing methods and introducing the combination of the subjective evaluation and the objective evaluation, obtains a stronger adaptation compared with others. Besides, to offset the luminance reduction caused in the backlight extraction process, we improve the bi-histogram equalization algorithm and propose a new pixel compensation method. To preserve image details, the Retinex theory is adopted to separate details. Experimental results demonstrate the effect of the proposed method on contrast ratio improvement and details preservation.
The vein-hosted copper deposits of the Allihies mining area, SW Ireland; a new structural and chronological evaluation

This paper presents new data for historical vein-hosted copper sulfide deposits in the Late Paleozoic Munster and South Munster basins of SW Ireland. Detailed mapping, 3D modelling, fluid inclusion microthermometry and geochronology from the Allihies area of the Beara Peninsula have led to a new interpretation of the timing and development of ore mineralization. Macro- and microstructural studies reveal that the ore-bearing, mainly east–west-striking quartz veins are directly related to early extensional, basinal normal faults. Molybdenite Re–Os dating of the main-stage Cu lode yields ages from 367.3 ± 5.5 to 366.4 ± 1.9 Ma. This early vein system experienced subsequent late Carboniferous Variscan deformation, including cleavage development, sinistral SW–NE strike-slip faulting, cataclastic deformation and recrystallization. The new timing of Cu mineralization in SW Ireland has major implications for its relationship to the base metal deposits of the Irish Midlands. Supplementary material: A video file showing a 3D model of the major lodes and the underground workings at Mountain Mine, and Excel spreadsheets showing fluid inclusion measurements and sample locations of the Allihies Copper Mining District are available at https://doi.org/10.6084/m9.figshare.c.4875015
Cross-View Feature Learning via Structures Unlocking Based on Robust Low-Rank Constraint

The cross-view multimedia are widely existed and attract many attentions in recent years. Nevertheless, it is noted that the phenomenon, that data in different classes from same view are more similar than that in same class from different views, is usually presented for cross-view multimedia data. The intrinsic imperfection leads disappointing performance for cross-view multimedia recognition or classification. To solve this problem, in this paper, we propose a novel discriminative learning framework with low-rank constraint, which can be applied for view-invariant low-dimensional subspace learning. The advantages of our framework include three aspects. Firstly, to unlock the latent class structure and view structure, a self-expressed model by dual low-rank constraints are presented, which can separate the two manifold structures in the learned subspace. Secondly, two effective discriminative graphs are constructed to guide the affinity relationship of data in the above two low-dimensional projected subspaces respectively. Finally, the joint semantic consensus constraint is designed to be integrated into the learning framework, which can explore the shared and view-specific information for enforcing the view-invariant character in semantic space. Experimental results on several public cross-view multimedia datasets demonstrate that our proposed method outperforms existing excellent subspace learning approaches.