Reviewerly/exHarmony huggingface dataset:
Fall detection smart-shoe enabled with wireless IoT device

Purpose Fall detection is a primary necessity for elderly people with medically tested nervous problems. This paper aims important to detect fall and prevent fatal injuries and untreated attention for long hours. Design/methodology/approach The project is focused on developing a smart shoe with force-sensitive resistors placed at plantar pressure points to detect fall. This could draw immediate medical attention to the patient. The device is developed using sensors, microcontroller and accelerometer integrated into a compact module. A rule-based detection algorithm helps in transmitting the alert to an Internet of Things device when a fall is detected. Findings Based on the pressure applied, there is a change in resistive value of force sensitivity resistor. When it reaches the threshold value, fall gets detected and alert gets triggered through telegram bot with latitude and longitude details of the location. Originality/value The challenge in developing this device is to make it wearable reducing the overall hardware complexity. The entire module placed inside the sole of the shoe avoids inconvenience to the patients.
Comparison of Neural Networks with Feature Extraction Methods for Depth Map Classification

In this paper, a comparison between feature extraction methods (Radon Cosine Method, Canny Contour Method, Fourier Transform, SIFT descriptor, and Hough Lines Method) and Convolutional Neural Networks (proposed CNN and pre-trained AlexNet) is presented. For the evaluation of these methods, depth maps were used. The tested data were obtained by Microsoft Kinect camera (IR depth sensor). The feature vectors were classified by the Support Vector Machine (SVM). The confusion matrix for the evaluation of experimental results was used. The row of confusion matrix represents target class of tested data and the column represents predicted class. From the experimental results, it is evident that the best results were achieved by proposed CNN (97.4%). On the other hand, the pre-trained AlexNet scored 93.7%.
MENINGKATKAN KEMAMPUAN MEMBACA MELALUI PENERAPAN PEMBELAJARAN KOOPERATIF MODEL JIGSAW PADA SISWA KELAS IV SDN 2 UEBONE

One of the efforts to improve Indonesian language learning achievement for fourth grade students in elementary school is by applying the Jigsaw cooperative learning model. The purpose of this study is to determine whether there are differences in learning achievement between students whose learning uses the Jigsaw cooperative learning model. The study design used a pretest-posttest control group design experiment. The population is grade IV students of SDN 2 Uebone Bulan Ampana Tete District. The method of collecting data through tests, before the treatment of each group was given a pretest and after the treatment was given a posttest. From the results of the analysis prove that there are significant differences in the achievement of Indonesian language learning students who are taught using the Jigsaw Cooperative Learning model. There was also a significant difference in the learning achievement of students who were taught using the Jigsaw Cooperative Learning model. From the results of the analysis of this study it was concluded that the best learning model is the Jigsaw Cooperative Learning Model.
Interactive Proofs for Social Graphs

We consider interactive proofs for social graphs, where the verifier has only oracle access to the graph and can query for the $$i^{th}$$ neighbor of a vertex v, given i and v. In this model, we construct a doubly-efficient public-coin two-message interactive protocol for estimating the size of the graph to within a multiplicative factor $$\varepsilon >0$$ . The verifier performs $$\widetilde{O}(1/\varepsilon ^2 \cdot \tau _{mix}\cdot \varDelta )$$ queries to the graph, where $$\tau _{mix}$$ is the mixing time of the graph and $$\varDelta $$ is the average degree of the graph. The prover runs in quasi-linear time in the number of nodes in the graph. Furthermore, we develop a framework for computing the quantiles of essentially any (reasonable) function f of vertices/edges of the graph. Using this framework, we can estimate many health measures of social graphs such as the clustering coefficients and the average degree, where the verifier performs only a small number of queries to the graph. Using the Fiat-Shamir paradigm, we are able to transform the above protocols to a non-interactive argument in the random oracle model. The result is that social media companies (e.g., Facebook, Twitter, etc.) can publish, once and for all, a short proof for the size or health of their social network. This proof can be publicly verified by any single user using a small number of queries to the graph.
Blockchain and Conservation: Why Does It Matter

Blockchain has created an innovative framework for safe, efficient and transparent chain of transactions, with the impressive rise of more than 2.100 cryptocurrencies traded anytime, worldwide. These transactions are registered on the web, with no central server, or proprietors. Blockchain also offers exchange and data solutions for businesses and society with tokenized currency, immutable data registry, smart contracts and funding sources. The Amazon region and forest conservation have a positive benefit from Payment for Ecosystem Services (PES) models that reward landowners and users for keeping the standing forest, thus providing services for the climate and biodiversity. A Blockchain proposition addresses key difficulties for PES models, such as lack of trust, high transaction costs and distance among parties. In the Brazilian Amazon, Bolsa Floresta Program (Standing Forest Program) benefits nearly 9.500 families living in traditional riverine communities, rewarding them for their role as the tropical forest keepers. A Blockchain proposition for Bolsa Floresta indicates positive impacts for forest conservation. In project finance, it allows the alternative funding with cryptocoins, observed possible regulatory issues. In project activities, Blockchain provides platforms for a safe exchange of information, goods, and possibly, monetary values, among families and associations, thus improving the standing forest community model. The technical configurations for these proposals include Application Program Interface solutions with online and offline mobility among members. Overall, an analysis of key issues linked to a Blockchain proposition indicates a favourable deployment for pilot projects in the Amazon region, and likely, to conservation projects with forests and communities, worldwide.
APPLIED MACHINE LEARNING IN LOAD BALANCING

A common way to maintain the quality of service on systems that are growing rapidly is by increasing server specifications or by adding servers. The utility of servers can be balanced with the presence of a load balancer to manage server loads. In this paper, we propose a machine learning algorithm that utilizes server resources CPU and memory to forecast the future of resources server loads. We identify the timespan of forecasting should be long enough to avoid dispatcher's lack of information server distribution at runtime. Additionally, server profile pulling, forecasting server resources, and dispatching should be asynchronous with the request listener of the load balancer to minimize response delay. For production use, we recommend that the load balancer should have friendly user interface to make it easier to be configured, such as adding resources of servers as parameter criteria. We also recommended from beginning to start to save the log data server resources because the more data to process, the more accurate prediction of server load will be.
On Case-Based Reasoning for ETL Process Repairs: Making Cases Fine-Grained

Data sources (DSs) being integrated in a data warehouse frequently change their structures. As a consequence, in many cases, an already deployed ETL process stops its execution, generating errors. Since the number of deployed ETL processes may reach dozens of thousands and structural changes in DSs are frequent, being able to (semi-)automatically repair an ETL process after DS changes, would decrease ETL maintenance costs. In our approach, we developed the E-ETL framework, for ETL process repairs. In E-ETL, an ETL process is semi-automatically or automatically (depending on a case) repaired, so that it works with the changed DS. E-ETL supports two different repair methods: (1) user defined rules, (2) and Case-Based Reasoning (CBR). Having experimented with CBR, we learned that large cases do not frequently fit a given DS change, even though they include elements that could be applied to repair a given ETL process, and vice-versa - more complex DS changes cannot be handled by small cases. To solve this problem, in this paper, we contribute algorithms for decomposing detected structural changes in DSs. The purpose of the decomposition is to divide a set of detected structural DSs changes into smaller sets, to increase the probability of finding a suitable case by the CBR method.
Efficient Defense Decision-Making Approach for Multistep Attacks Based on the Attack Graph and Game Theory

In the multistep attack scenario, each rational attack-defense player tries to maximize his payoff, but the uncertainty about his adversary prevents him from taking the favorable actions. How to select the best strategy from the candidate strategies to maximize the defense payoff becomes the core issue. For this purpose, the paper innovatively designs a game theory model from the point of network survivability in combination with the attribute attack graph. The attack graph is created based on the network connectivity and known vulnerabilities using the MulVAL toolkit, which gives the full view of all the known vulnerabilities and their interdependence. Then, we use the attack graph to extract attack-defense actions, candidate attack-defense strategies, attack-defense payoffs, and network states, as well as other game modeling elements. Afterwards, the payoffs of attack-defense strategies are quantified by integrating attack-defense strength and network survivability. In addition, we input the above elements into the game model. Through repeated learning, deduction, and improvement, we can optimize the layout of defense strategies. Finally, the efficient strategy selection approach is designed on the tradeoff between defense cost and benefit. The simulation of attack-defense confrontation in small-scale LAN shows that the proposed approach is reliable and effective.
Networking of Educational Organizations of General and Higher Education: Infrastructure Project

The article deals with organizational details and results of the infrastructural project that has been implemented over 15 years in several Moscow schools with consulting support of a pedagogical university. The goal of the project: through the interaction of schools and the university, develop and test a comprehensive system of didactic and methodological support for the development of research culture in schoolchildren as a metasubject result. Such support has been designed as a complex intraschool didactic and methodological system that includes the priority values and goals of meta-subject education, the determination of the activity-based component of meta-subject education content, the projects of meta-subject courses, methodological recommendations on designing meta-subject lessons and organizing students’ research. This system of didactic and methodological support requires managerial assistance that ensures coordination and consistency in the work of additional education pedagogues, teachers, university teachers who consult student research.
Proactivity: The Next Step in Voice Assistants for the TV Ecosystem

Intelligent Personal Assistants (IPAs) are increasingly present in people’s daily lives, with natural language interaction allowed by voice assistants improving the user experience. In the television ecosystem, the integration of voice assistants is also enabling the interaction with media devices in a globally more satisfying way. Additionally, the inclusion of proactive behaviours is considered to be one of the critical factors for the improvement of the user experience. To better understand this dimension of voice assistants, the present article analyses the proactivity concept and examples of voice assistants that incorporate proactive behaviours. Also, voice assistants have been analysed in the television ecosystem and it was realized a lack of systems presenting a real proactive behaviour able to assist users in a television context.
Layered drawing of undirected graphs with generalized port constraints

The aim of this research is a practical method to draw cable plans of complex machines. Such plans consist of electronic components and cables connecting specific ports of the components. Since the machines are configured for each client individually, cable plans need to be drawn automatically. The drawings must be well readable so that technicians can use them to debug the machines. In order to model plug sockets, we introduce port groups; within a group, ports can change their position (which we use to improve the aesthetics of the layout), but together the ports of a group must form a contiguous block. We approach the problem of drawing such cable plans by extending the well-known Sugiyama framework such that it incorporates ports and port groups. Since the framework assumes directed graphs, we propose several ways to orient the edges of the given undirected graph. We compare these methods experimentally, both on real-world data and synthetic data that carefully simulates real-world data. We measure the aesthetics of the resulting drawings by counting bends and crossings. Using these metrics, we experimentally compare our approach to Kieler [JVLC 2014], a library for drawing graphs in the presence of port constraints. Our method produced 10–30 % fewer crossings, while it performed equally well or slightly worse than Kieler with respect to the number of bends and the time used to compute a drawing.
Kernel interpolation-based technique for privacy protection of pluggable data in cloud computing

Recent technological revolution has been reported as the revolution of the cloud computing technology. The explosive availability of the unstructured data in the cloud has gained the attention of the researchers and the users store their data in the cloud without any right over controlling the data, causing the privacy concerns. Therefore, there is a need for the effective privacy protection techniques that assure the privacy of the user data in the cloud. Accordingly, this paper proposes a kernel interpolation-based technique for preserving the privacy of the data in the cloud. Privacy and accuracy are the two factors assuring the privacy for the data, which are afforded using the proposed technique that uses the proposed rider-cat swarm optimisation (R-CSO) algorithm for computing the kernel interpolation coefficient, which is associated with affording the privacy in the cloud. The proposed rider-cat swarm optimisation (R-CSO) algorithm is the integration of the standard cat swarm optimisation (CSO) in the standard rider optimisation algorithm (ROA). The analysis of the methods using the dataset from UCI machine learning repository reveals that the proposed method acquired the maximal accuracy and minimal database difference ratio (DBDR) of 80.552% and 15.58%, respectively.
Quantum LDPC codes with $Ω(\sqrt{n}\log^kn)$ distance, for any $k$

In this work we construct quantum LDPC codes of distance $\sqrt{n} \log^k n$ for any $k$, improving a recent result of Evra et. al. \cite{EKZ}. The work of \cite{EKZ} took advantage of the high dimensional expansion notion known as cosystolic expansion, that occurs in Ramanujan complexes. Our improvement is achieved by considering tensor product of Ramanujan complexes. The main conceptual contribution of our work is the following: a tensor product of a cosystolic expander with a complex with a linear cosystole has a linear cosystole.
Generation of High-resolution Lung Computed Tomography Images using Generative Adversarial Networks

To deal with the limiting data in training for new deep learning modules, we purpose a method to generate high-resolution medical images by implementing generative adversarial networks (GAN) models. Firstly, the boundary equilibrium generative adversarial networks model was used to generate the whole lung computed tomography images. Image inpainting was then integrated to generate the delicate details of the lung part by dividing into a coarse network and a refinement network to inpaint more completed and intricate details. With this method, we aim to increase the amount of high-resolution medical images for future applications in deep learning.
Effect of Employee Creativity on Project Performance with the Mediating Role of Knowledge Sharing and Moderating Role of Openness to Experience

Since almost last two decades employees creativity has been discussed immensely by the researchers. This study majorly focuses on the effect of employee creativity in project performance in project-based organizations of Pakistan. We have discussed that the relationship between, employees creativity and project performance depend on the knowledge sharing whereas, openness to experience moderating the correlation of employees creativity with knowledge sharing. For this purpose, data was collected from 273 employees working in project-based organizations of Pakistan. The results obtained after the data analysis, considerably shows that there is an optimistic relationship between employees creativity and project performance whereas knowledge sharing mediates this relationship with the moderating role of openness to experience. The study significantly expands to the prevailing literature by highlighting the procedure through which employees’ creativity has a major effect on the enhancement of project performance.
Pedeutology of the XXIas a positive social science

The popular in the modern timespragmatic approach, creates in teaching instrumental attitude to professional tasks execution. It means that reaching goals, on utilitarian level,leads to more effective actions. The positive social sciencestake another approach, basing on the usual human inner drive to be happy. Such being takes place when a person realizes well-being for others and for oneself, following hope, which direction is pointed out by freedom, dignity and human responsibility. The main purpose of the article is to reveal the evidence that create pedeutology of the XXI century as a science of strong teaching profession: its values, meaningfulness and beauty. It has been pointed out to agathological and arthrological aspect of pedeutology. The first one refers to doing good deeds. It is represented by optimal pupil development, which therefore should become a priority in teacher’s professional actions. In reference to the above, the instrumental and vocational teaching context has been characterized. The second aspect refers to the teacher’s moral capabilities, called the virtues. A virtue is a central knowledgeable category of positive social sciences. According to this issue, the utility of the virtue theory has been presented in for the pedeutological thought. The extraordinary attention has been put upon the virtue of hope, reasoning its inevitability in the process of upbringing in the XXI century.
Advanced attribute-based encryption protocol based on the modified secret sharing scheme

We construct a new protocol for attribute-based encryption with the use of the modification of the standard secret sharing scheme. In the suggested modification of the secret sharing scheme, only one master key for each user is required that is achieved by linearly enlarging public parameters in access formula. We then use this scheme for designing an attribute-based encryption protocol related to some access structure in terms of attributes. We demonstrate that the universe of possible attributes does not affect the resulting efficiency of the scheme. The security proofs for both constructions are provided.
An Effective Hybrid Learning Model for Real-Time Event Summarization

Real-time event summarization (RES) aims at extracting a handful of document updates from an overwhelming document stream as the real-time event summary that tracks and summarizes the evolving event of interest. It has been attracting much attention, especially with the growth of streaming applications. Despite the effectiveness of previous studies, obtaining relevant, nonredundant, and timely event summaries remains challenging in real-life applications. This study proposes an effective Hybrid learning model for RES (HRES), which attempts to resolve all three challenges (i.e., nonredundancy, relevance, and timeliness) of RES in a unified framework. The main idea is to: 1) exploit the factual background knowledge from the knowledge base (KB) to capture the informative knowledge and implicit information from the input document/query for better text matching; 2) design a memory network to memorize the input facts temporally from the historical document stream and avoid pushing redundant facts in subsequent timesteps; 3) leverage relevance prediction as an auxiliary task to strengthen the document modeling and help to extract relevant documents; and 4) consider both historical dependencies and future uncertainty of the real-time document stream by exploiting the reinforcement learning technique. Extensive experiments demonstrate that HRES has robust superiority over competitors and gains the state-of-the-art results.
High-resolution Three-dimensional Surface Imaging Microscope Based on Digital Fringe Projection Technique

Abstract The three-dimensional (3-D) micro-scale surface imaging system based on the digital fringe projection technique for the assessments of microfiber and metric screw is presented in this paper. The proposed system comprises a digital light processing (DLP) projector, a set of optical lenses, a microscope, and a charge coupled device (CCD). The digital seven-step fringe patterns from the DLP projector pass through a set of optical lenses before being focused on the target surface. A set of optical lenses is designed for adjustment and size coupling of fringe patterns. A high-resolution CCD camera is employed to picture these distorted fringe patterns. The wrapped phase map is calculated by seven-step phase-shifting calculation from these distorted fringe patterns. The unwrapping calculation with quality guided path is introduced to compute the absolute phase values. The dimensional calibration methods are used to acquire the transformation between real 3-D shape and the absolute phase value. The capability of complex surface measurement for our system is demonstrated by using ISO standard screw M1.6. The experimental results for microfiber with 3 μm diameter indicate that the spatial and vertical resolutions can reach about 3 μm in our system. The proposed system provides a fast digital imaging system to examine the surface features with high-resolution for automatic optical inspection industry.