Reviewerly/exHarmony huggingface dataset:
Effect of risk predictive intervention on perioperative period of patients with hypertensive intracerebral hemorrhage

ObjectiveTo explore the effect of risk predictive intervention on the recovery and nursing care of patients with hypertensive intracerebral hemorrhage during perioperative period, and to accumulate evidence-based data for follow-up quality diagnosis and treatment.MethodsUsing computer as the selection medium, 42 patients who met the inclusion requirements and who were admitted from January, 2015 to September, 2016 were named as a control group, and were given traditional nursing intervention. 42 patients who met the inclusion criteria and were admitted from October, 2016 to June, 2018 were named as a study group, and the risk prediction intervention was conducted on the basis of the control group; risk assessment was carried out before and after operation; the level of care safety was assessed; and corresponding interventions were implemented according to the assessment results. The hospitalization time, the number of emergency cases, and the number of emergency operation cases in the two groups were compared. The nursing satisfaction and the incidence of adverse events before and after first weeks of intervention were compared between the two groups.ResultsThe hospitalization time of the study group was (11.3±2.1)days, which was shorter than that of the control group [(16.7±2.8) days]. The study group found that the change rate of the disease was 40.5%, which was higher than the control group (19.1%). The proportion of emergency operation cases in the study group was 35.7%, which was higher than that in the control group (16.7%). The satisfaction rate of the nursing service received by the study group was 97.6%, which was higher than the control group's (83.3%). The incidence of adverse events in the study group was 16.7%, which was lower than that in the control group (45.2%). There were statistical differences between these two groups (all P<0.05).ConclusionThe application of risk predictive intervention in patients with hypertensive intracerebral hemorrhage can promote their recovery and the effectiveness of nursing work.Key words: Hypertensive intracerebral hemorrhage; Perioperative period; Risk prediction; Recovery of disease; Nursing work
Jingle Jigsaw - Playful Dance Scaffolding Through Motion Detection

Extended screen-time can have negative health effects in both children and adults. With the advent of motion de- tection sensors and other novel interaction methods, it is possible to envision digital games that move away from screen-centered design. However, such interaction is still underrepresented in both the mainstream and academia. To address this issue, this paper proposes a screenless dance game, called Jingle Jigsaw, that encourages players to physically explore the space around them by using spatial tracking and audio feedback. We conducted a preliminary usability evaluation that conveys the fact that such interaction is perceived as enjoyable by users and pointed to promising future work.
Source Model Selection as a Meta-learning Technique to learn Novel Concepts

Visual concept classification from a closed set has been successfully solved with supervised deep learning. However, learning a novel visual concept is challenging for deep models, especially when the concept is rare with only a few samples. Deep Transfer Learning addresses this lack of training samples by using a model that is pre-trained on several visual concepts, as a feature extractor, to improve the performance on target concept. The effectiveness of such transfer depends on selection of relevant pretrained model. If we have multiple pretrained models, trained on different genre of visual concepts, we intuitively choose a model trained on the source visual concepts that are similar to target concept. In this paper, we attempt to quantify this human intuition that is crucial in human meta-learning for retrieving relevant knowledge. We propose an approach to quantitatively measure the suitability of source model to learn a novel and rare target concept, to automate the model selection which in effect achieves meta-learning.
Secure Big Data Transmission with Trust Management for the Internet of Things (IoT)

Big data and Internet of Things (IoT) are the highly sought-after frameworks these days because IoT connects abundant amount of data which cannot be labeled and stored in the typical database system. Generating intelligent decisions from enormously increasing data in a real-time system is of major concern. Although big data seems to change our lives, it tries to make a burden in the computing environment due to the proliferation of data. In such a context, the efficient collection, processing, analyzing and secure storage are identified to be some of the crucial steps. The continuous flow of incoming data to the big data is the first and major challenge and this concern may play a key role in designing a viable and secure big data. In addition, the security issues can be even worse when the stored data include highly sensitive and confidential information. Therefore, if strong security measures are not applied in big data storage, it will cause some vital consequences. Trust management can be considered as a critical factor which operates seamlessly behind the scenes in IoT big data era to provide a reliable communication between devices. This chapter aims to disclose the trust management techniques prevailing in IoT with a special focus on big data technologies, and will outline the new developments and approaches that are applicable in these areas.
Graphical Representation of Text Semantics

Text is one of the most crucial information element present in our world. We still have more books and writings than sound and image data. This predominance engages the need for engineers in the field of computer science to enhance the comprehension of text by machines, and the capacity to retrieve or create information out of text data. Semantics is the study of the relationship between the meaning of signs in a particular language. The understanding of the different meaning of textual elements present in a sentence gives us the capacity to interpret text and give it a logical meaning. We have to note that in the absence of a link between those different meanings, a complete sentence cannot be formed and no meaning can, therefore, be extracted. Our goal is to make possible for a machine to grasp those meanings along with the semantic relationships and establish a knowledge, and once this knowledge is established into semantic sequences, converting it into a visual representation such as an image could deeply improve our capacity of comprehension of text and help individuals having trouble understanding a text. To achieve this, we decided to first define a text parsing and summarization technique using word embeddings and engage into a text to image conversion using a GAN. The logic behind this implementation is to give the computer the ability to grasp semantic sequences and define sequences of illustrations based on the text provided.
How Cognitive Computational Models Can Improve Information Search

This chapter discusses why and how a computational cognitive model that captures the broader set of processes of information search is important and useful. The first reason why this can be useful is when information search is only one component of a broader task. A better understanding of the broader process of information search can lead to better metrics of relevance that are specific to the broader task in which the user is engaged. The second reason is that it helps to develop better personalized tools that are more compatible with the individual users as they search information for different purposes. Two examples of such computational cognitive models are presented. The first model, SNIF-ACT, demonstrates the value of adopting a theory-based mechanism, called the Bayesian satisficing mechanism (BSM), that selects information search strategies based on ongoing assessment of the information scent cues encountered by a user as he or she navigates across Web pages. The second model, ESL, tracks both learning of knowledge structures and search behavior in a social tagging system over a period of eight weeks as they continuously search for Web documents. These computational cognitive models generate explicit predictions on what users will do when they interact with different information retrieval systems for different tasks in different contexts. Computational cognitive models therefore complement existing computational techniques that aim to improve information or document retrieval. At the same time, they allow researchers to develop and test unified theories of information search by integrating the vast literature on information search behavior in different contexts.
Tracing Apps to Fight Covid-19

Tracing apps to prevent the spread of Covid-19 have been implemented in various Asian countries, and Europe is also considering their use. But is (...)
Spacecraft onboard computers control automation

Abstract This paper concerns the task of onboard spacecraft’s computers functioning analysis problem. Existing approach is analyzed. Criteria of onboard computers normative functioning and source data for analysis implementation are defined. As a result, telemetry analysis algorithm for forming decisions about onboard computer normative functioning is offered. Program based on it is developed.
Designing Digital Payments for Next Billion Users

This SIG proposes starting a discussion on the CHI platform about the issues pertinent to the design of digital payments and digitization of financial services. Although there has been a lot of discussion in HCI around domains such as health and education, the domain of financial HCI is still nascent. The purpose of this SIG is to engage researchers and the broader community at CHI in the discussion and debate around digital payments for the next billion users. We propose creating a live working document starting before the SIG which continues to develop during and after the SIG. This live document will enable to engage with a wider audience of researchers, and industry practitioners outlining processes, methods, and tools that HCI4D researchers have created to work with emergent users to develop ICT interventions.
Garbage Collector Robot (Gacobot) Design For Dry Waste Distribution

Abstract GACOBOT is a garbage collection robot that is an integration of mobile robot systems and non-mobile robots, namely mobile robots like-a-car robot systems and robotic manipulators as garbage grabber systems. The GACOBOT system also integrates ultrasonic sensors and navigation systems as GACOBOT support systems, the garbage collection process has succeeded in the type of egg waste and bottle junk. The garbage collection process by conditioning the front ultrasonic sensor, then the grabber adjusts the trash and puts it into a garbage container while the robot is mounted by the robot. GACOBOT has been able to take garbage with iterations 40 times with 2 forms of garbage.
A Hybrid Slope One Collaborative Filtering Algorithm Based on Nonnegative Matrix Factorization

Collaborative Filtering algorithm is widely used in plentiful personal recommendation system. However, it has low accuracy prediction in sparse data set. Current mainstream collaborative filtering algorithm filter neighbor of target user by calculating similarity between users with co-rated ratings. Nonnegative Matrix factorization (NMF) has a good performance in solving sparsity problem. Manifold learning algorithms can identify and preserve the intrinsic geometrical structure of data. In order to get more accurate recommendation results, we propose a hybrid Slope One algorithm based on NMF. By constraining PNMF with graph regularization term, then we propose a weighted Slope One algorithm combined with neighborhood preserving PNMF. The hybrid algorithm has positive consequences for new data and can reduce computation complexity. Experimental show that optimized method has a good recommendation effect compared with tradition algorithm, it helps to solve the data sparsity problem and can improve the scalability.
The Integration of Retention Pond And Composite Sluice in Flood Prevention in Makassar City

Abstract in 2018, Badan Pengkajian dan Penerapan Teknologi (BPPT) has developed composite sluice for controlling flood goals. The integration of retention pond and composite sluice deemed necessary to be analyzed as the integrated system as a flood disaster mitigation effort. This study aims to give a brief overview of the suitable retention pond capacity, which is integrating with composite sluice as a flood prevention effort. The retention pond designed in this study requires rainfall data, land use data and topography map. This study method uses empirical formulation based on statistical parametric on hydrology analyze and hydraulic analyze as a basis of retention pond capacity calculation and composite sluice as flood controller. The result of this study are; the maximum flood discharge in 10 years return period is 103,28 m3/s, while the riverbed discharge capacity with 8 meters width is 68,29 m3/s. To prevent flooding the retention pond volume with capacity volume in 1.117.214,24 m3 has required. In sluice practical width calculation for the retention pond, the resulting amount is 28,70 meters. It takes 32 units of composite sluice if the current needs to be maintained. The side composite sluice width has obtained 1 meter while the total composite sluice building width with four units for the retention pond is 6,4 meters.
Eating Computers Considered Harmful

Contemporary computing devices contain a concoction of numerous hazardous materials. Though users are more or less protected from these substances, recycling and landfilling reintroduce them to the biosphere where they may be ingested by people. This paper calls on HCI researchers to consider these corporal interactions with computers and critiques HCI's existing responses to the e-waste problem. We propose that whether one would consider eating a particular electronic component offers a surprisingly useful heuristic for whether we ought to be producing it on mass with vanishingly short lifespans. We hypothesize that the adoption of this heuristic might affect user behaviour and present a diet plan for users who wish to take responsibility for their own e-waste by eating it. Finally we propose an alternative direction for HCI researchers to design and advocate for those affected by the material properties of e-waste.
ANALYSIS OF THE FACTORS OF INTENSIFICATION OF LABOR ACTIVITY OF SCIENTIFIC AND PEDAGOGICAL PERSONNEL OF HIGHER EDUCATION

In the conditions of development of the domestic market of educational services an important task of university leaders is to study the factors of motivation of the activity of scientific and pedagogical staff. Therefore, the purpose of the article was to analyse the factors that influence the work processes of operators of the market of educational services, and to study the motivational components of the effectiveness of scientific and pedagogical composition of higher education institutions. Structural and functional analysis was used; survey and observation methods; factor analysis; time studies; economic modelling, statistical methods and correlation analysis. Personnel vision of the motivational aspects of the activity is crucial, because it is education workers who directly carry out their work in higher education, who are best aware of the latent elements of departments and other structural units. Therefore, according to the results of the questionnaire developed by the author and the discovery of «Statistica» statistical analysis package, the factors that most stimulate work efficiency were worked out: 1) the nature of the works that make up the content of the work; 2) staff qualification and ability to use professional competencies in the work process; 3) the level of ownership and use of computer equipment by specific employees; 4) work experience of employees; 5) individual work intensity; 6) the level of mental activity of the staff. The method of estimation of degree of influence of factors of intensification of work activity of scientific and pedagogical workers is offered. It has been proved that the number of times of personnel training is not a factor in influencing the efficiency of their work. While the qualifications and quality of the work performed are significant parameters. There is a close inverse relationship between the duration of professional functions and the level of mental activity. According to the results of the research it is possible to improve the system of motivation of scientific and pedagogical workers. Keywords: institutions of higher education, scientific and pedagogical staff, factors of work efficiency, motivational elements.
Integrated Retrieval System Based on Medical Big Data

This paper presented an integrated retrieval system based on medical big data, aiming at the characteristics of medical data, such as diversity, large quantity and complex structure. The data center is built with data extraction module and data storage module in the system. The index module is built based on Solr and the data is presented in a webpage. The system keeps proper loose coupling among modules with high availability and extension. In a hospital application environment, this system realizes the function of real-time retrieval, and provides effective support for clinical decision.
An Effective Subgradient Method for Simultaneous Restoration and Segmentation of Blurred Images

The segmentation of blurred and noise images is of great importance. There have been several recent works to link the problems of image segmentation and image reconstruction. Here we describe the universal subgradient method for simultaneous restoration and segmentation of blurred and noise images. Our method is based on the universal subgradient construction. Our universal subgradient contains both the brightness function and the brightness function gradient. In the paper we demonstrate that our method is effective for simultaneous restorations and segmentations of blurred images.
Mobile Robot Motion Control Based on Static Hand Gesture

Abstract The hand gesture is a form of human expression in communication through hand movements. Hand gesture provides an alternative way for interaction between humans and machines, in this case, a mobile robot. This study discusses the application of static hand gestures to mobile robot motion, the hand used is the right hand. The static hand gesture used consists of six static hand gestures to stop, forward, reverse, turn right, turn left and spin. This system consists of a leap motion, a computer, a microcontroller, and a dc motor robot wheel. The results of this study the robot can respond properly by given hand gesture commands. The results of the response time of the robot to the hand gesture command an average of 1 second.
Genetically-enhanced Multi-dimensional Constellation Design for Wireless Systems

In wireless communications, the design of digital constellations and the associated bit labeling methods have a large impact on the robustness of transmissions. With the aid of increased minimum Euclidean distance (MED), high-dimension constellations have shown notable performance improvements against their conventional two-dimensional (2-D) counterparts, such as conventional quadrature amplitude modulation (QAM). In this paper, we propose a new method that exploits genetic algorithm (GA) to construct multi-dimensional constellations with optimized MED and bit labeling. More specifically, the GA-based design method is capable of producing both new constellations that have greater MED values and near-optimum labeling that yields improved performance, when compared with conventional schemes. A few design examples and simulation results are provided to demonstrate the benefits of the proposed constellation and labeling design method.
A Petrophysical Multivariate Approach Using Geophysical Well Logs and Laboratory Measurements to Characterize an Albian Carbonate Reservoir in the Campos Basin, Southeast Brazil

This study characterizes an Albian carbonate reservoir of oilfield B in the Campos Basin, based on geophysical well logs and laboratory petrophysical data. The use of current approaches to characterize reservoirs allowed us to estimate the porosity, permeability, and water saturation of this reservoir in a more reliable way than when this oilfield was discovered in 1976. To achieve this goal, the cluster analysis for rock typing module of the Interactive Petrophysics software was used to divide the succession into 11 electrofacies. Using log and laboratory data, an equation was derived to determine the porosity and the permeability of each electrofacies through the multiple linear regression technique. The results were compared with different models proposed by other authors, with the best results being found with multiple linear regression. Water saturation, on the other hand, was estimated by the Archie (The electrical resistivity log as an aid in determining some reservoir characteristics, Petroleum Transactions of AIME, 146:54–62, 1942) equation after identifying the cementation coefficient with the Pickett (A review of current techniques for determination of water saturation from logs, Journal of Petroleum Technology 18:1425–1433, 1966) cross plot. Finally, the porosity and permeability data were again used to identify three main flow units in the reservoir through the Winland (Oil accumulation in response to pore size changes, Weyburn field, Saskatchewan. Amoco Production Company Report F72-G-25, 20 p., 1972) graph. To verify the effectiveness of the adopted methodology, it was applied (successfully) in a test well, defining porosity, permeability, water saturation, and flow units, where laboratory data were absent.
A Classification Approach to Predicting Beef Knuckle Quality using the Decision Tree and Naïves Bayes Method: Case Study: Tiga Bersaudara Factory

Tiga Bersaudara factory is a small-scale traditional factory which is engaged in the food industry that produces beef knuckle. Business competition from year to year is getting tougher, every company is required to be able to participate in competition. According to data from the Central Agency on Statistics (CAS), business competition in beef commodities in Cianjur District is on an upward trend. With this competition forcing companies to look for various alternatives to excel in competition, one of the things that companies can do to compete is to improve the quality of their products. This research was conducted to predict the quality of the beef knuckle production at the Tiga Bersaudara factory. Data obtained manually by observation and interview methods; data obtained as many as 9 variables with a total of 110 data. The data obtained will be formed a prediction model using the Decision Tree and Naïve Bayes with the help of RapidMiner software. The results of data processing showed that the accuracy of the prediction model for Decision Tree reached 70% and Naïve Bayes reached 82%. Predictor variables that influence in determining the quality of beef knuckle are cooking water temperature, order time, second immersion water volume, and the third immersion water volume. This analysis will help beef knuckle producers to know important factors that need to be considered when producing good quality beef knuckle.
On Tree Substitution Grammars

Tree substitution grammars are formal models that are used extensively in natural language processing. It is demonstrated that their expressive power is located strictly between the local tree grammars and the regular tree grammars. A decision procedure for the problem of determining whether a tree substitution grammar generates a local tree language is provided. Unfortunately, the class of tree substitution languages is neither closed under union, nor intersection, nor complements. Indeed unions of tree substitution languages even generate an infinite hierarchy. However, all finite and all co-finite tree languages are tree substitution languages.