Reviewerly/exHarmony huggingface dataset:
Computer-Based Rehabilitation of Cognitive Impairments in Patients with Multiple Sclerosis

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system, which leads to demyelinating lesions in brain and spinal cord and further neurodegeneration. It results in manifestation of motor, cognitive and neuropsychological symptoms, which can occur simultaneously or independently of each other. Approximately 2/3 of people with MS suffer from cognitive impairment which lead to deteriorated quality of life. In unison with the global world tendencies, for the first time in Bulgaria a com-puter system Rila Cogni Soft for assessment and rehabilitation of cognitive im-pairment has been developed. The system is composed of 3 modules: 1) electronic record of demographic and clinical data; 2) module for assessment of the cognitive status and 3) module for rehabilitation of the registered cognitive impairment. The rehabilitation training programs are developed on the basis of established international standards. Based on the affected cognitive domains and degree (severity) of damage, the therapist administers individual rehabilitation programs. Using the system, we create our own norm (based on results from 70 healthy persons) for each of the diagnostic tests. A statistically significant difference (p < 0.05) in the level of cognition was found between healthy controls and individuals with MS (n = 23). InterCriteria Analysis was used to study the relation-ships between the degree of cognitive impairment and clinical features. Improved cognitive functions were registered after 3-month period of rehabilitation (p > 0.05).
Recursive Multi-Relational Graph Convolutional Network for Automatic Photo Selection

Automatic Photo Selection (APS) is a fundamental and important task for further photo cropping and photo enhancement. As the images in a photo series normally have subtle differences, it remains challenging to surface the best photos among highly similar photos. In this work, we propose a Recursive Multi-Relational Graph Convolutional Network (RMGCN) for APS. Specifically, we explore and devise inner-relation and inter-relation graphs to learn informative representations in hierarchical manner. 1) Patch-aware Intra Graph Module (PIGM) captures visual and spatial relations between different patches to characterize the representations in an image. 2) Context-aware Inter Graph Module (CIGM) explicitly exploits mutual comparative relation between different images in a photo series. These two graphs are recursively refined each other by reasoning the graph representations. Then, our model aggregates the output of CIGM with multi-scale local features via the proposed Cross-domain Fusing Gate (CFG) to boost the discriminative ability. Besides, we formulate four companion objectives as soft constraints to improve convergence rate during training. Extensive experiments are conducted on photo-triage dataset, and superior results are reported on different metrics when comparing to the state-of-the-art methods. We also perform rigorous ablations and analysis to validate our approach.
Histological image color normalization using a skewed normal distribution mixed model

Color variation between histological images may influence the performance of computer-aided histological image analysis. Therefore, among the most essential and challenging tasks in histological image analysis are the reduction of the color variation between images and the preservation of the histological information contained in the images. In recent years, many methods have been introduced with respect to the color normalization of histological images. In this study, we introduce a new clustering method referred to as the skewed normal distribution mixed model clustering algorithm. Realizing that the color distribution of hue values approximates the combination of several skewed normal distributions, we propose to use the skewed normal distribution mixture model to analyze the hue distribution. The proposed skewed normal distribution mixture model clustering algorithm includes saturation-weighted hue histograms because it takes into account the saturation and hue information of a particular histogram image, which can diminish the influence of achromatic pixels. Finally, we conducted extensive experiments based on three data sets and compared them with commonly used color normalization methods. The experiments show that the proposed algorithm has better performance in stain separation and color normalization compared to other methods.
How Has Regulation of Water and Sanitation Responded to the Covid-19 Pandemic? An Analysis Viewed Through the Human Rights Eyeglass in Brazil

This paper provides an overview of the regulatory responses to Covid-19 from the regulatory authorities of Brazilian states. The aim of this paper is to provide new insights into the operationalization of the human rights to water and sanitation in the Brazilian regulatory authorities’ actions during a health emergency. We find that communities located in unserved areas were not mentioned in the regulatory responses, nor were people in vulnerability situations. This study has also identified the absence of responses related to access to sanitation facilities, with normative contents related to the issue not appearing in the content analysis.
Discontinuities Classification Using Texture Features and Support Vector Machine

In this paper, a new approach is proposed for discontinuities classification in radiographic images. Two types of discontinuities will be considered namely line and circular discontinuities. To locate the region of interest (ROI), several image processing techniques such as fuzzy c means clustering, region filling, mean filtering, edge detection, Otsu thresholding, and valley detection were used in the first step, followed by inverse surface thresholding to segment the discontinuities. Then, the features were extracted using Segmentation based Fractal Texture Analysis (SFTA). Based on the extracted features, the images were classified using Support Vector Machine (SVM). In this work, 45 images were used for training and 25 images were used for testing. The proposed approach obtained 96% classification rate.
Development of the Globelike Mobile Robot with the Manipulator Set

The article presents the concept of an innovative mobile robot with a set of manipulation arms. The peculiarity of the device is the way of moving in every direction without having to change the orientation of the structure. This effect was achieved thanks to the spherical shape of the body. A set of arms cooperating with it are used to manipulate objects within the operating range of the device. The control system uses microcontrollers, orientation and position sensors. The robot is controlled remotely by the user using a smartphone. Based on the literature review, no similar solution used in the industry was found.
Telugu Text Summarization Using HS and GA Particle Swarm Optimization Algorithms

For the extraction of summaries of individual Telugu papers in this study, we propose using the Particle Swarm Optimization (PSO) algorithm. The PSO technique is analogous to the development of genetics and harmony search approaches (HS). The proposed technique will be examined using the Telugu NLP docs and the ROUGE tool. Experimental tests have demonstrated that the suggested approach achieves competitive and higher ROUGE values, compared with conventional HS and GA techniques.
Joint 3D Human Shape Recovery and Pose Estimation from a Single Image with Bilayer Graph

The ability to estimate the 3D human shape and pose from images can be useful in many contexts. Recent approaches have explored using graph convolutional networks and achieved promising results. The fact that the 3D shape is represented by a mesh, an undirected graph, makes graph convolutional networks a natural fit for this problem. However, graph convolutional networks have limited representation power Information from nodes in the graph is passed to connected neighbors, and propagation of information requires successive graph convolutions. To overcome this limitation, we propose a dual-scale graph approach. We use a coarse graph, derived from a dense graph, to estimate the human’s 3D pose, and the dense graph to estimate the 3D shape. Information in coarse graphs can be propagated over longer distances compared to dense graphs. In addition, information about pose can guide to recover local shape detail and vice versa. We recognize that the connection between coarse and dense is itself a graph, and introduce graph fusion blocks to exchange information between graphs with different scales. We train our model end-to-end and show that we can achieve state-of-the-art results for several evaluation datasets. The code is available at the following link, https://github.com/yuxwind/BiGraphBody.
A Secure Temporary Keyword Search Scheme In Cloud

A protected key is framed with a data framework that controls the client's inquiry effectiveness in a key-strategy quality based impermanent catchphrase search framework; getting to the information in an open worker is dangerous because anybody with the entrance subtleties can enter the cloud workers and picture the data in; not settled that the main KP-ABTKS structure as of now being used is shaky. All through our innovation, and information client's feedback private key compares to an entrance tree, and an information proprietor can handle search consent by encoding the term with ascribes and delivering an inquiry token that relates to an association tree and a period, and an information proprietor can handle search authorization by scrambling the term with credits and creating a pursuit token that relates to an association tree and a period. The workforces might check the created private key against the information base and gain one-time admittance to the client. The key lapses after the span and access are ended and the association is required to be postponed. As far as anyone is concerned, this is the first run through open key encryption that has been utilized to give quality-based inquiry control and brief watchword search simultaneously.
Ontology-Based Question Answering over Corporate Structured Data

Ontology-based approach to the Natural Language Understanding (NLU) processing allows to improve questions answering quality in dialogue systems. We describe our NLU engine architecture and evaluate its implementation. The engine transforms user's input into the SPARQL SELECT, ASK or INSERT query to the knowledge graph provided by the ontology-based data virtualization platform. The transformation is based on the lexical level of the knowledge graph built according to the Ontolex ontology. The described approach can be applied for graph data population tasks and to the question answering systems implementation, including chat bots. We describe the dialogue engine for a chat bot which can keep the conversation context and ask clarifying questions, simulating some aspects of the human logical thinking. Our approach uses graph-based algorithms to avoid gathering datasets, required in the neural nets-based approaches, and provide better explainability of our models. Using question answering engine in conjunction with data virtualization layer over the corporate data sources allows extracting facts from the structured data to be used in conversation.
Electrooculography: A Proposed Methodology for Sensing Human Eye Movement

Bioelectric signals are emanations from living biological systems and have their origin in various electrical potentials in cells. In this context, electrooculography (EOG) is defined as a specific biopotential is generated by the eye and eyelids’ muscular movement. Here, we are dedicated to developing a methodology and an appropriate environment to detect the human eye’s movement by acquiring bioelectric signals. The study was conducted in 40 healthy individuals using the 4/5 configuration consisting of four main electrodes and an additional fifth reference electrode. The methodology is following standards and guidelines for data acquisition of ECG signals. During the acquisition, the volunteer remains seated and with his head comfortably sustained on a support of a tailored Ganzfeld box. On the back wall of the box were fixed five light-emitting devices (LEDs) in the form of a plus sign symbol positioned at a distance opposed and at the center. The LEDs were turned-on or turned-off according to a pre-established sequence protocol synchronized with data acquisition while the volunteer performed eye movements. As a result, were obtained 18 samples of EOG signals per subject of 60 s. A dedicated algorithm was used to separate the data by group according to the established methodology.
Using an accelerometer-based system for real-time structural monitoring and response prediction under extreme hazard loadings

As sensing technologies develop, structures are instrumented with relatively low-cost sensors, such as accelerometers, to monitor structural behavior under varying loadings. This chapter presents and experimentally evaluates two methods to use this sensor data as part of a smart building monitoring system to estimate and predict in real time the structural response under dynamic loadings the building may experience over its lifetime. The outcomes are used for two real-time building monitoring applications. The first is in real-time monitoring of structural responses under extreme loadings, to, for example, activate control systems in a smart structure to respond to experienced loading conditions and minimize expected structural responses. The second is in real-time response prediction under seismic hazard loadings for earthquake early warning, where building occupants would be able to take protective actions in anticipation of an impending seismic event or building control systems are engaged prior to the main impact of the event to both mitigate and respond to extreme loadings and increase smart city resilience. In the methods, the use of a sparsely instrumented accelerometer-based system is investigated. Data from accelerometers mounted on the structure is used to continuously estimate the structural state. The method uses a dynamic Bayesian network-based approach with an unscented Kalman filter formulation to estimate the displacement response using acceleration measurements without any knowledge of the excitation. The accuracy of the approach is evaluated in comparison to experimental measurements of the displacement response. The estimation methodology then provides the foundation for a method to efficiently predict the maximum structural displacement response under a dynamic loading, specifically an impending earthquake event. The method uses limited initial loading information inferred from the first few seconds of the measured building acceleration response. The objective is to provide a structure-specific earthquake early warning based on the predicted maximum interstory displacement response. The accuracy of the methods is assessed based on both the root mean square error and percentage error in the response between the estimated and predicted values and the measured experimental data. The lab-scale experimental data is collected from both random input excitations and scaled historical earthquake inputs with varying magnitude and frequency content. The methodologies are shown to be accurate within 3% of the measured responses. The effect of uncertainty in the structural parameters is also evaluated. Results show that the estimated mean maximum response is stable as uncertainty increases. The effects of increasing the amount of data utilized to perform response prediction in terms of increasing computation time and decreasing prediction error are also shown. The accelerometer-based systems are shown to be effective for real-time structural monitoring and response prediction under extreme hazard loadings.
Evolutionary and Ideation Concepts for Cybersecurity Education

Evolution is a well-established biological theory, but some basic concepts can be abstracted and applied to non-biological domains such as the education domain for the purposes of knowledge sharing. There is a gap in the literature regarding how evolutionary processes can be applied to cyber security education. This article presents the general evolutionary algorithm and pairs it with an ideation technique (SCAMPER) to illustrate how certain evolutionary processes can be applied to cyber security education and learning. This paper does not attempt to close the gap, but rather offer a theoretical approach to address the gap.
Group affinity weakly supervised segmentation from prior selected tissue in colorectal histopathology images

Precise tissue segmentation of histopathology images is often a crucial step in computational pathology pipelines. However, visual scoring by pathologists is sensitive and depends on their experience and perception. Therefore, there is a need for novel automatic systems to improve the accuracy and reproducibility of pathologists’ interpretations. Here, a group affinity weakly supervised segmentation method (GAWS) is proposed to conquer this task, with the following pipeline. First, we create a cluster image by extracting the visual feature of each pixel using CNN and clustering it into different classes. Then, we create a target image by refining this cluster image with the constraints on prior tissue, color, and spatial distribution of pixels. Finally, a backpropagation process with a segmentation loss is considered to evaluate the error signals between cluster and target images and update the network parameters. We validate our method with extracellular mucin-to-tumor area quantification using a colorectal cancer clinical dataset with 163 Hematoxylin Eosin (H&E) whole slide images from 97 patients. Inter-observer agreement between pathologists and the proposed algorithm is excellent (ICC=0.917) and more accurate compared with two state-of-the-art unsupervised segmentation methods. Our results show that the GAWS results in a high average performance and excellent reliability when applied to histopathology images and possibly is a promising method for inclusion into clinical practice. This approach takes advantage of weakly supervised learning without any pre-trained network to have a tumor quantification tool that could improve the pathologist’s workflow.
An Efficient Method to Build a Standard Data Entry System by Extracting OLAP Cubes from NoSQL Data Sources

Any relational database can be an electronic digital database by using the relational model of files/data since it is offered simply by E. F. Codd in 1970. Any computer software method is utilized to sustain relational sources can be a relational databases management system (RDBMS). Several relational databases methods provide an alternative regarding while using the queries and also keeping the particular databases. Any NoSQL (originally discussing "non-SQL or perhaps "non- relational") database offers a mechanism for safe-keeping of data and also a collection of info which is modeled. Because of NoSQL directories tend to be developing within recognition, integration associated with various NoSQL techniques as well as interoperability associated with NoSQL techniques along with SQL directories turn out to be an ever more essential issue. We have proposed the mixed method to extracting OLAP cubes from NoSQL data sources and make a standard data entry system with regard to NoSQL as well as SQL data source. Our proposed algorithm consists of 4 stages: shingling, chunk, minhashing as well as locality-sensitive hashing MapReduce (LSHMR). Every stage works an effective procedure upon entering NoSQL directories. Our proposed method and algorithm demonstrate over 75% involving productivity as opposed to other algorithms.
Training organization on vocational educational programs with the use of a network form by departmental educational organizations of the penitentiary system

The paper analyzes organizational aspects of higher, further and vocational training implementation using a network form of interaction. The authors have revealed conditions for professional training of employees who were first hired based on the results of questionnaires of employees working at educational organizations of the Federal Penitentiary Service, as well as interviews of employees working at professional and service training departments of the territorial bodies of the penitentiary service system. The analysis of the identified problems showed that at the current stage of network interaction there are two groups of problems: the first group (organizational problems) is the inclusion in the resources of the participating organization of the personnel potential of those responsible for training employees of penitentiary service institutions; the second group (methodical) is the effective use of methods and technologies for organizing synchronous and asynchronous training of young employees who are in the field of prospective professional activity. In the process of summarizing ways to solve the identified problems, the authors have formulated optimal techniques for conducting professional training of employees recruited into the penitentiary system using an online form of educational process; common approaches to organizing work on the use of various forms of students educational activities, current norms for calculating teachers work during lectures and seminars, during current, intermediate and final control, and when they develop educational materials.
Hybrid Clustering Algorithm for Cloud Server Information Management

Data clustering is an important branch of data mining. At present, most of the existing data clustering algorithms are limited to dealing with data with only continuous attributes. In addition, a small number of algorithms are limited to dealing with data with only nominal attributes. If only one kind of attributes are processed, data information will be lost under the condition of mixed attributes, which will affect the quality of data mining. How to cluster mixed attribute data is still a challenging field. In this paper, cloud server information management oriented hybrid clustering algorithm research, but also in order to effectively promote the project information management.
A Qualitative Approach to Conceptual Spaces: Prototypes as Qualitative Atoms

Abstract The aim of this paper is to propose a qualitative approach to the theory of conceptual spaces, in contrast to the usual metric framework. This requires qualitative notions of similarity, simple concepts, prototypes and conceptual categorisation. For this purpose, I will introduce three mathematical models for conceptual spaces. The first one is topological and has been proposed by Mormann. The other two are new and are based on atomistic orders and similarity relations. I will discuss how each of them deals with the Design Principles proposed by Douven and Gärdenfors and with further Adequacy Conditions. Despite being apparently different, I will show that these three models are mathematically equivalent. Finally, I will address three objections to the present approach. The first one says that the qualitative notion of a prototype is a bad analogue of the metric one. The second one suggests that, in contrast to the Voronoi construction, the function qualitatively representing the conceptual categorisation process is arbitrary. The last one appeals to Goodman’s companionship and imperfect community problems to show that there is a flaw in defining simple concepts from similarity relations.