Reviewerly/exHarmony huggingface dataset:
A Novel Color Image Encryption Scheme Using Logistic Map and Quadratic Map Systems

In this work, we propose a color image encryption scheme based on logistic map and quadratic map systems. First, the color image is divided into three components (red (R), green (G), and blue (B)). The three components are mixed as single grayscale image, to permute the pixels positions; we have used two permutation index vectors for changing rows and columns position. Finally, the permuted grayscale image is encrypted. In order to evaluate the security of the proposed scheme, the entropy, key space analysis, key sensitivity analysis, the correlation of two adjacent pixels and differential attack were performed. The Experimental results and analyses show that the proposed scheme enable to reduce computing effort, to allow efficient and outperform many schemes given in the literature.
A comparison of generalised linear models and compositional models for ordered categorical data

Ordered categorical data occur in many applied fields, such as geochemistry, econometrics, sociology and demography or even transportation research, for example, in the form of results from various questionnaires. There are different possibilities for modelling proportions of individual categories. Generalised linear models (GLMs) are traditionally used for this purpose, but also methods of compositional data analysis (CoDa) can be considered. Here, both approaches are compared in depth. Particularly, different assumptions of the models on variability are highlighted. Advantages and disadvantages of individual models are pointed out. While the CoDa model may be inappropriate when the variability of the compositional coordinates depends on the regressors, for example, due to different total counts on which the coordinates are based, the GLM may underestimate the uncertainty of the predictions considerably in case of large-scale data.
Big Streaming Graph Analysis

When data is received at an extreme scale, many of the problems require faster approaches to be applied involving bounded space. With the growth of streaming data in different real-life domains (e.g., social media, astronomy, etc.), the necessity of efficient algorithm design has become imperative. In this paper, we present some of our proposals toward big streaming data analysis, precisely the analysis of streaming graphs. As the graphs are used for modelling interactions, streaming graphs play a major role in modeling dynamic interactions happening in many real-world scenarios. We highlight that how the spectral analysis on streaming graphs can be useful to develop efficient algorithms for understanding their properties. In this paper, we precisely focus on the problem of estimating the clique number of a graph. We propose an approach to set an upper bound on the clique number of a streaming graph and show its possible extensions.
Shape Adaptable Medical Multimedia Processing

Introduction: Electronic medical healthcare systems are becoming the backbone of health organizations over the world. The huge amounts of medical multimedia produced by these systems especially images and videos are transmitted by the computer networks that connect these systems. The variability in the shape and texture of transferred medical multimedia data needs adaptable procedures to process these data efficiently. In other words, these procedures must adjust automatically based on the shape of region of interests in the medical multimedia images to cope with fast changes in the healthcare environments. In this paper, we have proposed shape adaptable watermarking approaches for medical multimedia processing systems. The medical images generated by X-rays, MRI and CT modalities are used in our experiments to test proposed approaches. In addition, these approaches were tested under different kinds of signal processing and geometric attacks. The comparative comparison of our proposed approaches with state-of-art approaches proved the superiority and capability of our approaches to adjust the number of selected subands of medical cover image to embed and extract the hospital watermark logos. Background and Objective: The development of an adjustable approach to process medical multimedia signals for healthcare system. The aim of this research is to select adaptably the number of subands of cover image to hide the information of hospital logo watermark inside them such that embedded watermarks can resist different kinds of attacks. Method: The proposed adjustable approach consists of suband selection method, criterion, embedding and extraction procedures, DWT transform, attacks, evaluation metrics, etc . Results &amp; Conclusion: It provides robust and adjustable method to embed and extract watermark logo at different resolution levels of cover medical image and uses with images of different sizes and modalities.
Algorithms for determining relative position between spheroids and hyperboloids with one sheet

In this work we present a new method for determining relative positions between one moving object, modelled by a bounding spheroid, and surrounding static objects, modelled by circular hyperboloids of one sheet. The proposed strategy is based on the real-time calculation of the coefficients of degree three polynomial. We propose several algorithms for two real applications of this geometric problem: the first one, oriented to the design of video games, and the second one, devoted to surveillance tasks of a quadcopter in industrial or commercial environments.
Twitter’s Experts Recommendation System Based on User Content

The Internet provides users with an overwhelming amount of information. For this reason they may not always be able to find the information they are looking for. Recommendation systems help users locate useful information and save time. Twitter is one of the social networks that implements this type of system in order to help its users in searching content. However, the traditional recommendation system implemented by Twitter only considers people from the user’s surroundings or it suggests the followees/followers of the user’s followees. Many use Twitter as a source of information, it is therefore necessary to create a recommendation system that would suggest experts profiles to other users. Experts must be capable of providing interesting information to users. The “expert” recommended to a users will be chosen on the basis of the content they publish and whether this content is of interest to the user. The proposed system offers accurate and suitable recommendations.
Recent Advances on Human-Robot Interface of Wheelchair-Mounted Robotic Arm

Background: Wheelchair mounted robotic arm is a typical assistive robot, which is widely used to help the elders and the disabled to complete the activities of daily life. But limited by the restrictions of the users’ athletic ability and cognitive ability, how to flexibly manipulate such robot is still a problem in front of them. The human-computer interaction technology is the core technology of the robot. Its performance directly affects the user&amp;#039;s acceptability, satisfaction and promotion of intelligent wheelchairs. Objective: The study aims to give a general summary of recent human-robot interface of wheelchair mounted robotic arm and introduce their respective characteristics. Methods: Based on various patents and research developments about the human-robot interface of the assistive robot at home and abroad, this paper puts forward the basic principle of designing the humanrobot interaction mode, divides the man-robot interface into two categories based on the perspective of user control robot arm, and describes in detail, the typical human-robot interface and its related characteristics contained in each classification. Results: The development trends of the human-robot interface in future are predicted, so as to provide some research reference for the related scientific researchers. Conclusion: Wheelchair mounted robotic arm has important practical significance. Further improvements are needed in the design of the human-robot interface. It can effectively improve the operation performance of the WMRA, and take full advantage of the user’s existing movement ability to meet the requirement of dominating the control process. Furthermore, these improvements in the human-robot interface will allow more and more users to accept the WMRA, manipulate the WMRA, and enjoy improvements in the quality of their life for these assistive robots.
Determination the optimal tilt and azimuth angle of photovoltaic system for maximum energy in Bucharest region, Romania - Case Study

This study was conducted to evaluate the feasibility of installing a photovoltaic system for supplying the electric load of an environmental institute and to determinate the optimal tilt and azimuth angle of an 100 kW photovoltaic power plant located on the roof of institute. Those parameters plays an important role in maximizing the solar radiation collected by a PV panel. Simulations were made with PVsyst V6.39 for south-east orientation (100; 450) and south-west orientation (100; 450), and for the tilt angle the simulations were made at 100, 150, 370, 600 and 900. The simulation has been done to study the influence of the photovoltaic panels orientation on the amount of electricity produced each month of the year as well as on the total annual amount of electricity. Was experimentally determined by repetitive simulations in the range 300-400 for tilt angle and zero azimuth, optimal parameters for the position of photovoltaic panels. Was defined as the optimal position, the pair of values [zero azimuth, tilt angle 370] where the plant generates maximum energy in one year compared to any other set of values that defines the position of the photovoltaic panels.
EasyDist

With the increasing number of artificial intelligence (AI) applications, complex neural network based modeling on large scale data is becoming quite common. Using distributed deep learning (DDL), a data scientist can harness the computational power of multiple graphics processing units (GPUs) spread across different machines and significantly reduce neural network training times. In this paper, we focus on a TensorFlow based distributed training approach, which, unfortunately, leaves the auxiliary modeling capabilities (e.g., resource deployment, data distribution, run-time control etc.) to user-discretion. Additionally, these training capabilities cannot be accessed directly through popular high-level interfaces such as Keras. As a result, the transition to DDL from a single node training setting involves a steep learning curve for data scientists. To bridge this gap, we present EasyDist, an end-to-end DDL tool that preserves the single-node programming model by leveraging distributed TensorFlow between a Keras interface and public Cloud infrastructure. EasyDist is model agnostic, i.e., any neural network model written in Keras can use EasyDist for distributed training. Internally, EasyDist incorporates resource deployment, data allocation, training orchestration and result aggregation within a clean abstraction which allows the data scientist to focus on model definition. Evaluation of EasyDist on publicly available benchmark datasets and models shows that the model accuracy is not compromised in any significant way while the training times can be reduced upto ~6-8x compared to single machine settings.
Quality of Service on Link Aggregation Network Virtualization for Docker Containers

Docker engine is an extremely powerful tool for PaaS platform of cloud computing. It gives benefits for large-scale of internet services. Web service is basic service for everyone who requires to access internet that web infrastructure must has scalability with load-balance web server called reverse proxy. The key answers for a large-scale web must have multiple web servers working together with high speed bandwidth. Moreover, multiple clusters can find in the same data center there are required to assign priority and quality of each cluster service. We investigate load-balance assign link aggregation with network QoS by using pipework script and traffic control tool in frontend reverse proxy server on each cluster. Our research evaluates scenario of network QoS ratios which include 50/50, 60/40, 70/30 and 80/20. We compare network bandwidth between both web reverse proxy clusters. The results present our designed and implementation tool not only can control network QoS on each web reverse proxy cluster in all load-balance link aggregation modes which include round-robin, XOR and ALB but also those of clusters can access multiple network interface. In experiment, average network bandwidths in all QoS cases are around 200 MB per second for link aggregation of 2 gigabit interface.
Controlling and Visualizing the Precision-Recall Tradeoff for External Performance Indices

In many machine learning problems, the performance of the results is measured by indices that often combine precision and recall. In this paper, we study the behavior of such indices in function of the tradeoff precision-recall. We present a new tool of performance visualization and analysis referred to the tradeoff space, which plots the performance index in function of the precision-recall tradeoff. We analyse the properties of this new space and show its advantages over the precision-recall space. Code related to this paper is available at: https://sites.google.com/site/bhanczarhomepage/prerec .
Outlier Detection in Categorical Data

This chapter delves on a specific research issue connected with outlier detection problem, namely type of data attributes. More specifically, the case of analyzing data described using categorical attributes/features is presented here. It is known that the performance of a detection algorithm directly depends on the way outliers are perceived. Typically, categorical data are processed by considering the occurrence frequencies of various attributes values. Accordingly, the objective here is to characterize the deviating nature of data objects with respect to individual attributes as well as in the joint distribution of two or more attributes. This can be achieved by defining the measure of deviation in terms of the attribute value frequencies. Also, cluster analysis provides valuable insights on the inherent grouping structure of the data that helps in identifying the deviating objects. Based on this understanding, this chapter presents algorithms developed for detection of outliers in categorical data.
HyPSo

The proliferation of RDF data has presented the need for distributed RDF storage solutions. RDF is increasingly being used to model data for the Semantic Web. This sudden increase in the amount of RDF data is a pressing issue that requires a scalable solution. Some of the common issues faced while handling Big RDF data are storage and query processing. In this paper, we introduce a hybrid RDF partitioning scheme to speed up SPARQL query processing for Big RDF data. The approach in this paper, HyPSo, combines the two popular RDF partitioning schemes i.e. vertical partitioning and hash partitioning. By doing so HyPSo intends to provide a scalable solution to storage and query processing for Big RDF data. Our objective is to speed up query processing by reducing the query execution time. This is possible owing to the partitioning done in HyPSo where only the requisite properties are read thus, reducing the I/O cost and the join cost is minimized as subject-predicate bound queries can be answered by a single table scan. The Big RDF data partitioned using HyPSo occupies less disk space compared to the vertical partitioning scheme. HyPSo is compared with two existing distributed RDF frameworks in terms of storage space and query execution time and, it can be concluded that HyPSo demonstrates significant improvement in performance.
An active learning approach to build adaptive cost models for web services

Delivering accurate estimates of query costs in web services is important in different contexts, e.g., to measure their Quality of Service. However, building a reliable cost model is difficult as (i) a web service is a black box often hiding a complex computation, (ii) a call to the same service can yield completely different costs by simply changing a parameter value, and (iii) execution costs can drift with time. In this paper we propose Tiresias, an approach that, given a web service exposing an interface with a fixed number of parameters, initializes and actively adapts a model to accurately predict query costs. The cost model is represented by a regression tree trained through two interleaved querying cycles: a passive one, where the costs measured for user-generated queries are used to update the tree, and an active one, where the service is probed through system-generated queries to cope with drifts in the cost function. Tiresias is finally evaluated in terms of effectiveness and efficiency through a set of experimental tests performed on both real and synthetic datasets.
Vibration source classification and propagation distance estimation system based on spectrogram and KELM

Earth surface vibration signals source classification and propagation distance estimation attract increasing attention in recent years due to the wide applications in many areas. In this study, the authors develop a hybrid classification and propagation distance estimation algorithm for general earth surface vibration sources. The spectrogram (SPEC) feature characterising the energy distribution of vibrations is first developed for signal representation in this study. The kernel-based extreme learning machine (KELM) algorithm is then adopted for the vibration source classification and propagation distance estimation. Comparing with the conventional approaches, the proposed KELM + SPEC algorithm is not only effective in characterising the time- and frequency-domain features of vibrations, but also superior in accuracy and efficiency. To test the effectiveness of the proposed KELM + SPEC algorithm, experiments on real collected vibration signals are presented, where simulations on both periodic and aperiodic vibrations are carried out in the study. Comparisons to various existing vibration signal extraction and classification algorithms are provided to show the advantages of the proposed KELM + SPEC algorithm.
Achieving Cognitive Skills in Multimedia Through Revised Bloom Taxonomy

Students in middle east are having innovative skills in multimedia and Human Computer Interaction (HCI) and hence self-learning and critical thinking can be enhanced through the teaching of this subject through proper design of the course. To achieve cognitive learning objectives in students, revised bloom taxonomy can be embedded while designing the curriculum. Teaching activities should be aligned to achieve self-learning and critical thinking in students according to the revised bloom taxonomy. Through a Project Based Learning (PBL) method in multimedia and HCI course, the above cognitive learning objectives can be developed in classroom. In this paper the design and practice of a multimedia and HCI curriculum which is taught in Muscat College, Sultanate of Oman is described and demonstrates how cognitive skills are achieved through the embedding of revised bloom taxonomy. The design of the course took the students to the various concepts of graphics techniques and practicals give them the exposure towards realization of those techniques. The final project is developed through the integration of all these practical and tutorial experiences. The observation shows students have applied their creativity with revised bloom taxonomy to obtain the goal in a wonderful way.
Selection and morphology of expletive subject clitics in northern Italian dialects

This paper aims to sketch a morphological typology of 3sg expletive subject clitics (ESCs) in northern Italian dialects (NIDs). In order to show a variation greater than that reported in descriptive studies, a preliminary examination of the ESCs of some varieties of Piedmontese is made, with particular reference to the impersonal verbs sembra (‘it seems’) and bisogna (an impersonal only verb meaning ‘it is necessary’). Keeping the same focus, such a microvariational analysis is then extended to the whole of NIDs, seeking to draw up a morphological typology linked to syntax-semantic interface hypotheses. Lastly, some suggestions about a crosslinguistic comparison and some diachronic facts are presented.
The Project Management Information System as Enabler for ICT4D Achievement at Capability Maturity Level 2 and Above

Current trends in project management promote Agile as a solution for challenges around project delivery, focusing more on individuals and interactions than on processes and tools. This paper postulates that any project method, especially agile, if undertaken with limited process focus below Capability Maturity (CM) Level 2, may well result in project delivery failures, despite best intentions from the project team. With dynamic Internet growth in developing countries, and advancements in virtual collaboration, projects ought to involve tailoring jointly-agreed project management processes at Capability Maturity level 2 and above. In addition, the utilisation of a Project Management Information System (PMIS) emplacement is essential for project management success. The use of a PMIS is in line with a process-driven approach to ICT4D, and will facilitate the ICT value proposition for a developing economy. Following a comprehensive literature review, the paper asserts that Capability Maturity Level 2 will remain relatively unattainable, unless the PMIS emplacement becomes a strategic driver for success. To this end, we propose a PMIS CM Improvement Framework to the PMBOK Process Group and Knowledge Area matrix, and establish the value proposition for ICT4D.
Stereovision monitoring of deflection of concrete beam strengthened with ultraviolet-cured glass-fiber reinforced polymer in a destructive test

To monitor three-dimensional structural displacements in civil engineering, a stereovision displacement measurement method based on structure coordinate system is proposed in the present paper, and the absolute displacements of structure can be obtained through establishing the structure coordinate system and coordinate transformation. The center identification algorithm for circular target is studied to acquire the subpixel coordinates of center by combining Canny algorithm and Zernike algorithm. The epipolar constraint is introduced to conduct stereo matching of initial image pairs, and Kalman filtering and neighborhood searching algorithm are both employed to track circular targets on the left and right sequence images. To validate the effectiveness of the proposed method, a destructive test of concrete beams strengthened with ultraviolet-cured glass fiber reinforced polymer is performed in lab. Results show that the load–displacement curves obtained by the proposed stereovision method and linear variable differential transformer agree with each other; this verifies that the proposed stereovision method is feasible and effective for monitoring structural displacement in a destructive test.
Selection and Application of Culture Media

This chapter has presented some of the common types of culture media used in the pharmaceutical and healthcare microbiology laboratory for the purpose of environmental monitoring, using conventional methods. Here the choice can be with two media (one for fungi and one for bacteria) or a single culture medium. Other choices required are with incubation times and temperatures. This chapter reviews available literature on the subject and provides guidance for making this important choice. The chapter additionally addresses culture media release and testing.