Reviewerly/exHarmony huggingface dataset:
SMDCC: Smart Multidimensional Data Capturing Codes for Pervasive Systems

The Cyber Physical Systems (CPS) and Internet of Things (IoTs) represent an evolution in computerised inter-connectivity. The vision of CPS is to allow things to be connected anytime, anyplace, with anything and anyone, ideally using any path, any network, and any service. The study in this paper proposes a concept of utilisation of a single two-dimensional code to enable multi-user Automatic Identification and Data Capturing techniques/codes (AIDC) termed as Smart Multidimensional Data Capturing Codes (SMDCC). The objective is to enable pervasive interaction between multifunctional CPS-entities. The contributions of this paper are three fold. First, using SMDCC the study demonstrates the labelling technique along with rule-based system to build situation-aware reasoning. Second, using a use-case the study demonstrates on how SMDCC facilitates the handling of data to build basic data-driven models in multi-tiered environment. Third, in order to segregate the access-levels in pervasive environments, SMDCC demonstrates a process of labelling the access hierarchy in multi-user and multi-functional environments where the generation of large datasets is inevitable. The paper concludes with a fact that SMDCC is a preliminary step to evolve with polymorphic process of labelling data and transactions in data-driven models to suit pervasive environments.
Medical Recovery System Based on Inertial Sensors

This paper presents an equipment that can be used in the medical recovery activity, including for people with special needs. The equipment is composed of two human body motion tracking systems. The first system is based on video tracking technology and uses a Kinect sensor. The data captured by this video sensor will be used to animate an avatar from a dedicated software application, after which the patient can analyze the accuracy of real-time movements on a display. The second tracking system uses inertial sensors mounted on the patient’s lower limbs. These sensors return Euler angles that are processed, recorded and used for later evaluate. For this analysis, a human body model, using non-deformable segments and joints, able to reproduce human body movements, was developed. This skeleton model can be superimposed on the CATIA software mannequin and can be animated using a macro based on the Euler angles read by the sensors so that it can analyze the patient’s movements using data records gathered during recovery session.
Beyond validation accuracy: incorporating out-of-distribution checks, explainability, and adversarial attacks into classifier design

Validation accuracy and test accuracy are necessary, but not sufficient, measures of a neural network classifier’s quality. A model judged successful by these metrics alone may nevertheless reveal serious flaws upon closer examination, such as vulnerability to adversarial attacks or a tendency to misclassify (with high confidence) real-world data different than that in its training set. It may also be incomprehensible to a human, basing its decisions on seemingly arbitrary criteria or overemphasizing one feature of the dataset while ignoring others of equal importance. While these problems have been the focus of a substantial amount of recent research, they are not prioritized during the model development process, which almost always maximizes validation accuracy to the exclusion of everything else. The product of such an approach is likely to fail in unexpected ways outside of the training environment. We believe that, in addition to validation accuracy, the model development process must give equal weight to other performance metrics such as explainability, resistance to adversarial attacks, and classification of out-of-distribution data. We incorporate these assessments into the model design process using free, readily available tools to differentiate between convolutional neural network classifiers trained on the notMNIST character dataset. Specifically, we show that ensemble and ensemble-like models with high cardinality outperform simpler models with identical validation accuracy by up to a factor of 5 on these other metrics.
Self-indexed motion planning

Motion planning is a central problem for robotics. A practical way to address it is building a graph-based representation (a roadmap) capturing the connectivity of the configuration space. The Probabilistic Road Map (PRM) is perhaps the most widely used method by the robotics community based on that idea. A key sub-problem for discovering and maintaining a collision-free path in the PRM is inserting new sample points and connecting them with the k-nearest neighbors in the previous set. Instead of following the usual solution of indexing the points and then building the PRM with successive k-NN queries, we propose an approximation of the k-Nearest Neighbors Graph using the PRM as a self-index. The motivation for this construction comes from the Approximate Proximity Graph (APG), which is an index for searching proximal objects in a metric space. Using this approach the estimation of the k-NN is improved while simultaneously reducing the total time and space needed to compute a PRM. We present simulations for high-dimensional configuration spaces with and without obstacles, showing significant improvement over the standard techniques used by the robotics community.
Retinex-Based Framework for Visibility Enhancement During Inclement Weather with Tracking and Estimating Distance of Vehicles

Inclement weather directly impacts the number of vehicle accidents and fatalities, due to low and no-visibility conditions. According to the U.S. Department of Transportation, there are more than 1,259,000 crashes each year. On average, 6,000 people are killed, and more than 445,000 people are injured annually due to severe weather conditions. These accidents could be significantly reduced if real-time visibility enhancement systems were made available. However, eliminating the impact of weather conditions on visibility is still lacking and beyond our control. In this paper, the Retinex technique was selected as the framework for developing a system capable of enhancing visibility for drivers. It was selected for its ability to achieve a good dynamic range compression and spectral rendition. Hence, an innovative system is proposed through a multistage framework that not only incorporates a modified Retinex technique but also integrates object detection and depth estimation to overcome some of the current algorithms' and systems' drawbacks. The performance of the proposed system, along with histogram equalization and the basic Retinex enhancement techniques, are presented. The Performance was assessed using Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM) parameters.
THE MODEL OF INFORMATION EDUCATIONAL ENVIRONMENT OF EDUCATIONAL ORGANIZATION ON THE BASIS OF THE 1С:ENTERPRISE SOFTWARE PLATFORM

The main approaches to the educational organization’s information educational environment model construction and implementation on the basis of 1C:Enterprise software platform are considered, the arisen problems and ways of their elimination and also further plans for system’s functionality expansion are considered. The problem of forming the model of information educational environment of an educational organization is complex and is not limited only to the issues of automation; at the same time the existence of a uniform integrated information system is obligatory for decision-making on the basis of full, reliable and relevant information. The features of educational organizations and all education system of the Russian Federation are diversity, regional distinctions, individual specifics of each educational organization. These individual traits display themselves during the realization of various activities and cause the need for the educational organizations management problem solution on the basis of formation of the corresponding model of digital information educational environment.
On Modelling Human Population Characteristics with Copulas

Copulas are fast gaining in popularity in fields requiring modelling of multivariate data. Population synthesis is one such domain where copulas hold much promise. Characteristics of a population are inherently a multivariate distribution, for example, age, education and employment have a dependent relationship with each other. However, characteristics of population are often discrete, sometimes even categorical, and rarely continuous. Further, inherently continuous variables like age are often discretized into bins or rounded down to the nearest integer. Although the Sklar’s representation theorem of copulas [23] is still valid for these discrete data, assessing the goodness of fit by rejecting the null hypothesis that an assumed family of copulas adequately represents the data may not be fool-proof. In this paper, through simulation we demonstrate the failure of accepted goodness of fit tests of copulas even when the copula family is able to capture the dependence in the data. The strong recommendation is to use additional methods to test that the copula can capture the dependence in the available data.
Adaptive Threshold-Based Algorithm for Multi-objective VM Placement in Cloud Data Centers

Data is grown exponentially day by day which increases demand in cloud storage, leads to setting up a large number of cloud data centers. Such data centers employ an immense amount of electrical energy has ended up in enormous operating cost. Dynamic virtual machine consolidation allows to optimize energy consumption and Service Level Agreement (SLA) Violations. The process of selecting which virtual machine should be located (i.e., executed) on each host of the data center is known as Virtual Machine Placement (VMP). The existing approaches focus on energy consumption, yet ignoring SLA violations at the time of Virtual Machine Placement. This paper addresses the problems in cloud data centers by proposing an Adaptive Four Threshold framework and KMI-MMT VM selection algorithm. The experimental results show that the proposed algorithm outperforms the existing algorithm in terms of maximum energy efficiency and minimum SLA Violation rate. It also reduces the number of VMs migrated while VM allocation.
RDF Graph Stores as Convergent Datatypes

This report describes a way to represent and operate on an RDF dataset such the it behaves as an instance of a conflict free replicable datatype. In this industry presentation, we describe how we accomplish this for the Dydra RDF graph storage service in a manner compatible with the SPARQL Graph Store HTTP Protocol (GSP). The standard GSP concerns the current store state only. Dydra retains previous store states as active addressable aspects analogous to named graphs in a quad store. It incorporates and addresses arbitrary revisions of target datasets according to ETag and Content-Disposition specifications in HTTP headers. Appropriate interpretation of these arguments permits to replicate datasets among cooperating participants.
On-chip demonstration of Hong-Ou-Mandel effect using quantum-optical ring resonators

Quantum information science aims to revolutionize existing methods for manipulating data by utilizing the unique features of nonclassical physical phenomena. This control is realized over several platforms, one particular being photonics which employs state of the art fabrication techniques that achieve integrated nanocircuit components. The Hong-Ou-Mandel effect underlies the basic entangling mechanism of linear optical quantum computing, and is a critical feature in the design of nanophotonic circuits used for quantum information processing. We will present some results from an on-chip Hong-Ou-Mandel (HOM) experiment that replaces the conventional beam splitter with a more compact and highly versatile ring resonator allowing greater functionality with an expanded parameter space dubbed Hong-Ou-Mandel Manifold (HOMM). The overarching goal of this work is to demonstrate on-chip, scalable, dynamically configurable quantum-optical interconnects for integration into photonic quantum information processing devices.
Cloud-based software services delivery from the perspective of scalability

Measuring and testing the scalability and performance of cloud-based software services is critical for the delivery of such services, and the development of cloud computing. There are three interconnected Cloud-based software services’ performance aspects; both elasticity and efficiency are depending on the delivery of a sufficient level of scalability performance. In this work, we focused on testing and measuring the cloud-based software services scalability from a technical perspective. This paper uses technical scalability metrics that address both volume and quality scaling, inspired by earlier technical metrics of elasticity. We demonstrate the application of the metrics using a practical example and three demand scenarios and discuss the importance of these metrics. We show how our technical scalability metrics integrated into an earlier utility-oriented metric of scalability, in order to enable the scalability analysis from technical and production-driven perspectives.This work is related to the delivery of cloud-based software services from scalability perspective. We demonstrate our metrics by testing a cloud-based software service connecting with both AWS auto-scaling and load-balancing services. Measurements have been collected and analysis, and then been used to calculate the values of our scalability metrics.
Domain Specific Classification of Malay Based Complaints using the Complaint Concept Ontologies

The complaint from users is an effective method to identify the quality of services and facilities provided by an organization. The efficiency to respond to users' complaint also depends on an effective workflow. By having an effective method and workflow, the action taken by the management to improve the quality of services and facilities can be done immediately and effectively. One of the ways is by classifying the complaints that will isolate related complaints. This paper presents the implementation of the classification system that combines the application of Complaint Concept Ontologies in Malay language as classifier rules with the BM25 model of Information Retrieval system. Experiments showed the semantic based elements such as Malay ontology may bring the improvement of the classification of the Malay Complaint. The result yielded showed that the proposed classifier produced better result in four category compared to BM25 original score that only produced better result in one category. OBMCS also outperformed the LDA model in all eight categories on the Recall, Precision and F-measure metrics. The finding proven the proposed system is very useful, especially to the Malay complaint in regards of classification for documents in the domain area.
Design of the In-depth Intelligent Learning System Based on the College English Teaching

At present, many colleges and universities are influenced by the traditional teaching mode, and still adopt the teachers’ classroom teaching, ignoring the students’ main position, and the orientation of the college English teaching targets is inaccurate. The teaching for non English majors in many Chinese colleges and universities only requires that the students should master the simple reading ability and that the students should master the daily basic dialogue after their graduation. This teaching goal of College English has influenced many teachers’ teaching orientation and students’ learning orientation. Students will blindly think that will do as long as they have mastered the basic ability to read and as long as they pass the exams, and other abilities of listening, speaking, writing and translation are not that important.
The Classification of the Documents Based on Word Embedding and 2-layer Spherical Self Organizing Maps

Due to a popularization of SNS and increase of web pages, many documents can be obtained from the internet. However, it is difficult to process a huge set of document data manually. Therefore, various classification methods based on machine learning have been proposed. In this paper, a classification method which can visualize the relationship among the documents using Word2Vec and Spherical SOM is proposed, and the performance is examined in experiments of visualization and numerical evaluation of classification accuracy.
Efficient correspondence search algorithm for GOBO projection-based real-time 3D measurement

Many robot-operated automation tasks require real-time reconstruction of accurate 3D data. While our sensors that are based on GOBO projection-aided stereo matching between two cameras allow for high acquisition frame rates, the 3D reconstruction calculation is really time consuming. In order to find corresponding pixels between cameras, it is necessary to search the best match amongst all pixels within the geometrically possible image area. The well-established method for this search is to compare each candidate pixel by temporal cross correlation of the brightness-value sequences of both pixels. This is computationally intensive and interdicts fast, real-time applications on standard PC hardware. We introduce a new algorithm, which minimizes the number of calculations needed to compare two pixels down to two binary operations per comparison. To achieve this, we pre-calculate a bit-string of binary features for each pixel of both cameras. Then, two pixels can be compared by counting the number of bits that differ between the two bit strings. Our algorithm's results are accurate to a few pixels and require a second, cross correlation-based refinement. In practice, our algorithm (including pre-calculation and refinement step) is much faster than traditional, purely cross correlation-based search, while maintaining a similar level of accuracy.
Information Fusion by Extended Power-Based Aggregation in Image Segmentation

In this paper we construct two extended aggregation functions, extended powers product and extended weighted arithmetic mean of powers.Their basic properties are examined, as well as certain properties of distance functions constructed by them.In addition, one pixel descriptor, which is motivated by Local Binary Pattern family of descriptors (LBPs), is introduced.We give an application of the introduced extended aggregation functions and descriptor, by a construction of a new distance function, used in Fuzzy c-Means Clustering Algorithm (FCM) for image segmentation.
Internet pragmatics and the future

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Generalised median of graph correspondences

A graph correspondence is defined as a function that maps the elements of two attributed graphs. Due to the increasing availability of methods to perform graph matching, numerous graph correspondences can be deducted for a pair of attributed graphs. To obtain a representative prototype for a set of data structures, the concept of the median has been largely employed, as it has proven to deliver a robust sample. Nonetheless, the calculation of the exact (or generalised) median is known to be an NP-complete problem for most domains. In this paper, we present a method based on an optimisation function to calculate the generalised median graph correspondence. This method makes use of the Correspondence Edit Distance, which is a metric that considers the attributes and the local structures of the graphs to obtain more interesting and meaningful results. Experimental validation shows that this approach is capable of obtaining the generalised median in a comparable runtime with respect to state-of-the-art methods on artificial data, while maintaining the success rate for a real-application case.
A Fusion Approach for Collaborative Filtering

Collaborative Filtering (CF) is one of the most important memory based techniques used in recommender systems. CF exploits interests of all users in the database similar to that of the target user to recommend him/her items. However as the number of users and items grow in the database, it becomes difficult for traditional CF based recommender systems to search for similar users in the entire user space. This gives rise to accuracy and scalability issues and the CF algorithm fails to give quality real time results. In this paper we propose a fusion approach of leveraging the geographical location of the users and their social network graph to increase the accuracy of CF algorithm. We first cluster users based on their geographic location with the intuition that users within a same region usually have similar preferences and habits. Secondly, we use the social network graph to find friends of the target user as it is likely that a person will have more inclination towards his/her friend's recommendation than a stranger's. Lastly, we form a fusion of the target user's social network friends and other users who fall in the cluster of the target user and execute the CF algorithm. A Distance Threshold Filter is applied to generate the final recommendation list for each user. Results using evaluation metrics like MAE and RMSE confirms that the fusion approach provides better quality recommendations than other state-of-art collaborative filtering algorithms.
Prioritizing Capabilities of Blockchain Technology in Telecommunication for Promoting Customer Satisfaction

Nowadays telecommunications industry is based on closed source software which prevents the developers to be innovative (which blocks the innovation of developers). Distributed and open sourced telecommunication systems coupled with blockchain technology may have the ability to prevent errors and provide integrated user experience. Failure to pay sufficient attention to privacy; a need for a third party to verify transactions; high costs of services and long transaction times has made current communication systems vulnerable and therefore reducing the satisfaction of customers. This situation forces businesses in the telecom industry to consider some technological measures. The aim of this research is prioritizing the capabilities of blockchain in telecommunication industry for promoting customer satisfaction. First the applications of this technology are introduced. Second, the criteria for telecommunication are extracted from literature and then 3 important criteria are selected in an expert panel with 6 experts. The target populations are the customers that receive the services of telecommunication industry businesses. In the next step, customer satisfactions of blockchain applications in the telecommunications industry are ranked by Analytic Hierarchy Process (AHP). The results show that data security and privacy are the top priorities for customers. The current research is conducted in a theoretical manner in Iran in 2018, so and the results of this research can be useful for the managers of telecommunication industry businesses.
Verification of Verifiability of Voting Protocols by Strand Space Analysis

With the widespread adoption of electronic voting, various voting protocols have been proposed. Voting protocols need to satisfy security requirements, including privacy protection and the prevention of illegal voting (e.g., double voting). Our research focuses on the most important property of voting protocols, namely whether all votes are reflected in the voting results accurately. We formalized and verified this for one voting protocol using strand space analysis. We can also consider multiple security requirements depending on the extent to which the voting result is reflected accurately. These properties are discussed.