Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition using Smart Phone Sensors

In the new age technology, there exists many smart devices, which are using human activity data in reshaping the modern world dynamics of every aspect of our life be it health trackers, smartphones, intelligent systems. One futuristic concept is the connected devices that are way more efficient, adaptive, responsive and flexible to any conditions and reacts according to the data. For some connected devices to work more efficiently, human activity data is required. This data can be used to make devices smarter and using it can be useful in solving many problems of healthcare, efficient surveillance. Our work is an effort in efficient surveillance and using deep learning models, we detect the presence of human activities in different environments and use the data to analyze better to have efficient and effective surveillance. Many different models of deep learning model are used in our work from the likes of CNN (Convolutional Neural Networks) to LSTM (Long Short-Term Memory Networks. The data collected is from sensors’ data which is present in the mobile and can make the predictions about various activities like sitting, walking, jumping and some other human activities. The prime focus here is to detect various canonical activities that are not given to the system.
Eagle View: An Abstract Evaluation of Machine Learning Algorithms based on Data Properties

In terms of Machine Learning, the majority of the data can be grouped into four categories: numerical data, category data, time-series data, and text. We use different classifiers for different data properties, such as the Supervised; Unsupervised; and Reinforcement. Each Categorises has classifier we have tested almost all machine learning methods and make analysis among them.
Jurnal Gaya Selingkung

ABSTRACKScientific journals are an effective means of publishing scientific articles for the general public. In order for scientific journals to be accepted by national and international circles, the insight aspirations and confinement styles must refer to national and international standards. Articles designed to be published in scientific journals, in addition to being written in a scientific manner, must also follow the guidelines required by the intended scientific journal. In order for the article to be sent according to criteria and accepted by the journal, at least it must fulfill three elements, namely the suitability of the field of science, the standard grammar, and the special style (cellulite style) required by an article journal to be sent.
TUGAS ADM

Administration in the literal sense, the word "administration" comes from the Latin word consisting of ad and ministrare. The word ad has the same meaning as the word to in English which means "to" or "to". And the word ministrare is the same as the word to serve or to conduct which means to serve, help and direct. In English to administer also means "to regulate, maintain and direct".Educators are professionals, designing learning, implementing learning, assessing learning outcomes, guiding training in researching community service.Administration of educators and education personnel is the overall process of educator activities which includes planning, organizing, directing, reporting, coordinating, monitoring and financing, using or utilizing available facilities, both personal, material, and spiritual, to achieve educational goals effectively and efficiently.
Federated Online Learning Based Recommendation Systems for Mobile Social Applications

With the explosion of big data, the conventional centralized service paradigm cannot meet the rapid demands of mobile social users. Social recommender systems emerge as an effective approach can boost the recommendation effectiveness by inferring user's interests and preferences. However, it is challenge to improve the accuracy of recommendation due to the diversity and complexity of big data. To improve the quality of experience (QoE) of user, in this paper, we propose a federated online learning scheme for recommendation systems to promote information sharing and improve the accuracy of recommendation. The system model for recommender systems in mobile social networks is first presented. Then, to encourage mobile clients to participant in local training, the contract-based incentive scheme is designed to increase the utilities of clients and the third party (TTP). Next, the federated online learning problem for recommendation systems in social community is then established. The federated online learning for recommender systems algorithm is proposed to derive the optimal model parameters. Finally, experiment results have been conducted to validate the efficiency of the proposed scheme.
Context-Aware Seeds for Read Mapping

Abstract Motivation: Most modern seed-and-extend NGS read mappers employ a seeding scheme that requires extracting t non-overlapping seeds in each read in order to find all valid mappings under an edit distance threshold of t . As t grows (such as in long reads with high error rate), this seeding scheme forces mappers to use more and shorter seeds, which increases the seed hits (seed frequencies) and therefore reduces the efficiency of mappers. Results: We propose a novel seeding framework, context-aware seeds (CAS). CAS guarantees finding all valid mappings but uses fewer (and longer) seeds, which reduces seed frequencies and increases efficiency of mappers. CAS achieves this improvement by attaching a confidence radius to each seed in the reference. We prove that all valid mappings can be found if the sum of confidence radii of seeds are greater than t . CAS generalizes the existing pigeonhole-principle-based seeding scheme in which this confidence radius is implicitly always 1. Moreover, we design an efficient algorithm that constructs the confidence radius database in linear time. We experiment CAS with E. coli genome and show that CAS reduces seed frequencies by up to 20.3% when compared with the state-of-the-art pigeonhole-principle-based seeding algorithm, the Optimal Seed Solver.
An Examination in IFS and IVIFS based on Various Distance Measure

There are many distance measure in Fuzzy such as Hamming, Normalized Hamming, Euclidean, Normalized Euclidean, Geometric, Normalized Geometric, Hausdroff’s etc… we discussed, the comparison between IFS and IVIFS based on various distance measure is explored. IFS is converted into IVIFS in various steps. The distance between IFS and IVIFS are also compared.
Intuitionistic Fuzzy 𝜋𝑔𝛾* Contra Continuous Mappings

This paper is devoted to the study of intuitionistic fuzzy topological spaces.In this paper, we have introduced intuitionistic fuzzy πg* contra continuous mappings.Also, we have studied some of their properties of intuitionistic fuzzy πg* contra continuous mappings in intuitionistic fuzzy topological spaces.Identified few examples for intuitionistic fuzzy πg* contra continuous mappings.Studied the cases of IF contra continuous mapping, IFS contra continuous mapping, IFP contra continuous mapping, IFcontra continuous mapping, IFγcontra continuous mapping, IFG contra continuous mapping and IFγ*G contra continuous mapping.Derived some properties of cT1/2 space.Also studied the case in gT1/2 space.Have done some analysis on the composition of two IFπgγ* contra continuous mappings.
Kawasaki Heavy Industries, Ltd.

Together with about 100 group companies in Japan and overseas, Kawasaki Heavy Industries（“KHI”） oversees the formation of a “technology corporate group”. Our technological capabilities, polished over a history that exceeds a century, send diverse products forth into wide–ranging fields that go beyond land, sea, and air, extending from the ocean depths to space. Aerospace division is active in products ranging from aircraft to satellites. The products that our rolling stock division delivers to the world include Shinkansen and New York subway cars, while our ship and offshore structure division’s products range from gas carriers and large tankers to submarines, and our energy solutions division covers the spectrum from development and manufacture of energy equipment to management systems. We are also active in wide–ranging businesses driven by diverse and high–level engineering technologies, including environmental and recycling plants, industrial plants, precision machinery, industrial robots, and infrastructure equipment. We operate our leisure and power products business that features the motorcycles known as the Kawasaki brand. Through the development of unique and broad businesses unmatched elsewhere, we will continue to create new values that solve the issues facing our customers and society.
Deep Learning Methods for System Identification of UAV

Nowadays, deep learning is the most prominent subjectin the machine learning field. With the bloom of researchers in this field, numerous novel algorithms are used to solve everyday life problems. The control systems field is one of the subjects that get many impacts of machine learning emergence. System identification of Unmanned Aerial Vehicles (UAV) is one of the control systems problems that could be solved by using deep learning methods. In this paper, Recurrent Neural Networks (RNNs) are applied toidentify the system of UAV. Three different models of Deep RNNs have been tried, and the results implied that the RNNs-1 was giving more excellent performance both on the testing MSE and RMSE with the values equal to 0.0006 and 0.0242, successively.
Decreasing Trend of Plasmodium Falciparum and Vivax Transmission in Mojo Health Center, East Shoa Oromia Region, Ethiopia: a Retrospective Study

Abstract Introduction : Malaria continues to strike hardest against the public health and economic development in Ethiopia. Its transmission tends to be highly heterogeneous within or between years, and from area to area which need understanding of the contextual diversity of malaria prevalence within each site to deliver optimal intervention according to the site specific situation of the disease. Thus, this study was aimed to determine the trend prevalence of malaria in Mojo health center, East Shoa Zone, Ethiopia. Methods : A retrospective study was conducted in Mojo health center, East Shoa zone, Ethiopia from February to March, 2021. Malaria cases and related data reported 2016-2020 were carefully reviewed from laboratory registration logbooks. The collected data was analyzed using SPSS 25 software. Results : A total of 19, 106 blood films were examined from malaria suspected patients. The overall microscopically confirmed prevalence of malaria was 4.2%. P. vivax was the predominant species accounting 76.2% of positive samples. Malaria cases declined from 259 in 2016 to 77 in 2020. The proportion of malaria was higher among males (64.8%) than females (35.2%) in all five years. Higher malaria cases was observed from the age group 15-24 years old followed by the age group of 25-34. Malaria cases were at a peak level from September-November and lowest from December-February. Conclusion : Although the declining trend of malaria prevalence was observed, malaria still remains a public health burden in the area. The high burden of malaria among reproductive age group, males, and during cultivation season reflects its impact on health and economic development. Shifting of P. falciparum to P.vivax related malaria should get an attention during prevention and control strategies for the successful progress of malaria elimination programme.
Statistical field theory of the transmission of nerve impulses

Stochastic processes leading voltage-gated ion channel dynamics on the nerve cell membrane are a sufficient condition to describe membrane conductance through statistical mechanics of disordered and complex systems. Voltage-gated ion channels in the nerve cell membrane are described by the Ising model. Stochastic circuital elements called ”Ising machines” are introduced. Action potentials are described as quasi-particles of a statistical field theory for the Ising system. The particle description of action potentials is a powerful new tool for describing the generation and propagation of nerve impulses. We thus have the opportunity to exploit another useful point of view to describe the generation and propagation of nerve impulses, especially when classical electrophysiological models break down. Moreover, the corpuscular description allows us to develop new hardware and software devices based on particle physics to study neurodegenerative and demyelinating diseases (Multiple Sclerosis), even integrated by connectomes. It is also suitable for the study of complex networks, cryptography, machine learning, quantum computing and many other applications of medical, physical and engineering interest.
Traffic Anomaly Classification by Support Vector Machine with Radial Basis Function on Chula-SSS Urban Road Network

Recognition of urban road traffic pattern is an important part of intelligent transportation systems .An enormous number of traffic data could be obtained with the develop ment of in formation techniques .This motivates the application of machine learn ing in the road traffic area, especially in traffic incident detection.Incident detection algorith m in the machine learning can be defined as a binary classification problem, where each occurrence is the traffic state on a road segment at a particular time.This paper is concerned with how to detect traffic anomaly patterns in an urban road network by using potential sensor data.In this paper, by using Simulation of Urban Mobility (SUM O) software, we have chosen to work on the Chula -Sathorn SUM O Simulator (Chula -SSS) dataset.SUMO enables users to simulate traffic networks and supports the traffic data by setting up conveniently simu lated lane area detectors.By using calibrated Chula -SSS dataset, anomaly traffic patterns have been generated and classified with the support vector mach ine algorithm with the radial basis function.The algorith m has been shown here to detect accurately of at least 87% (and 71 %) of the simulated lane-closure incidences, by rely ing on sensors fro m (i) within the incident area, and (ii) at the upstream as well as downstream areas adjacent to that incident link, respectively.
PROLIFERATION OF HEALTH MISINFORMATION ON SOCIAL MEDIA PLATFORMS: A SYSTEMATIC LITERATURE REVIEW

This paper presents a systematic literature review to provide a conceptual framework and future research directions for the identification and detection of health misinformation on social networking sites.We have found the health misinformation problem is yet to evolve and requires more exploration towards the identification of reliable techniques.We have extracted the existing literature from five research databases.The focus of this review is to explore various concepts, techniques, and datasets adopted by the prior studies.After applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines we have got 27 final papers to include in this review.The findings show that despite adopting various techniques there was a lack of research done on concepts like fake news and healthcare factchecking.Furthermore, this paper also identified significant feature and technique-based research gaps for future research directions.
MLPM: Machine Learning Package Manager

In recent years, the machine learning community has witnessed rapid growth in the development of deep learning and its application. Unlike other software that can be installed through the package manager, developing machine learning systems usually need to search for the source code or start from scratch, debug and then deploy to production. It usually costs much for small companies and research institutions to run, test, evaluate, deploy and monitor machine learning system. In this paper, we proposed a machine learning package manager aiming to assist users1) find potentially useful models,2) resolve dependencies,3) deploy as HTTP service. By using the MLPM, users are enabled to easily adopt existing and well-established machine learning algorithms and libraries to their project within few steps. MLPM also allows third-party extensions to be installed, which makes the system customizable according to users’ workflow.rpose
Policy interventions for eradication of SARS-CoV-2 by mobile-phone contact-tracing

With the recent announcement that Apple and Google will introduce a contact-tracing API to iOS and Android, and later add contact tracing functionality directly to their OS's, it seems increasingly likely that contact tracing via a smart phone will form an important part of the effort to manage the COVID-19 pandemic and prevent resurgences of the disease after an initial outbreak.However, contact-tracing models have shown that there remains a high degree of uncertainty over whether contact tracing alone will be enough to control the virus. Here, we suggest complementary policies that could be used as part of a responsive policy to increase the effectiveness of smart phone contact tracing in the event that a resurgence looks imminent.
Enhancing Asynchronous Linear Solvers through Randomization

Asynchronous iterative methods present a mechanism to improve the performance of parallel algorithms for highly parallel computational platforms by removing the overhead associated with synchronization among computing elements. This paper considers a class of asynchronous iterative linear system solvers that employ randomization to determine the component update orders, and specifically focusing on the effects of non-uniform distributions. Results show that using distributions favoring the selection of components with a larger residual may lead to a faster convergence than that when selecting uniformly. In particular, in the best case of parameter choices, average times for the normal and exponential distributions were, respectively, 13.3% and 17.3% better than the performance with a uniform distribution.
Design and Implementation of a Data Acquisition System for Gyroscopes Based on PSoC

This paper introduces a data acquisition system for a MEMS gyroscope using a PSoC development board, for use in infrared imaging guidance systems.Using a PSoC 5 as the control core, we proposed a design scheme with high quality, low cost, low power consumption, which satisfied the design requirements.We performed a detailed investigation regarding the realization process of the hardware and software of the PSoC 5. Static and dynamic experiments were designed for data analysis.It is shown that the data acquisition system has high measurement accuracy.
Introduction to the Minitrack on Reports from the Field: Knowledge and Learning Applications in Practice

Welcome to this, the 53rd Hawaii International Conference on System Sciences (HICSS) conference.This Reports from the Field Minitrack, under the Knowledge Innovation and Entrepreneurial Systems Track, proudly brings you the latest research focused on the application of innovation of knowledge management issues as reported by both researchers and practitioners alike.With the focus on application, this minitrack helps practioners and theorists alike.It is here we examine how theory informs and impacts practice as well as how practice can influence theory from the practioner perspective.
Optimal Tone-Mapping for HDR Images

This research paper proposes a unique optimal tone-mapping technique for high dynamic range (HDR) images, performing local adjustments with overlapping windows covering complete image. A local linear adjustment is applied on each window to preserve the radiance values. This problem may be treated as global optimization problems to satisfy the local restriction for every overlapping window. These Local constraints may be considered as a guidance map to suppress high contrast without losing its details. M-estimation technique may be used for solving this optimization problem. This technique may be applied to HDR images with sudden radiance changes or comparatively smooth transitions. Further, this technique may be applied to differentiate and analyzes HDR images from LDR images. Simulation results are included to support the performance gains achieved by the proposed technique.
KELAYAKAN SISTEM SALURAN DRAINASE MENINJAU SUPPLY DAN DEMAND UNTUK MENDUKUNG RENCANA KAWASAN PERKOTAAN PRINGSEWU

Pringsewu Regency is a district that is currently focusing on the preparation and maturation of the 2011-2031 Regional Spatial Plan, which includes a plan to prepare it to become an urban area. In order to have good city management, it's therefore, necessary to study the feasibility of the drainage system which later can be used as input for careful planning to support the Pringsewu Urban Area plan by paying attention to the availability and quantity of drainage channels so as not to cause a disaster problem in the future. Based on the results of the analysis that has been carried out that the capacity of the drainage channel, namely Qtotal&gt; Qsaluran, so that the channel cannot accommodate rainwater and wastewater, so to support the Pringsewu Urban Area, the drainage channel is currently not feasible to support the Pringsewu Urban Area plan when viewed from an infrastructure perspective, namely existing drainage system.&#x0D; Keywords: Drainage, Urban Area.
Supervision, One of the Main Aspects in Social Work in Georgia

Abstract This article aims to describe the origin and development of supervision in Georgia, especially in the sector of social workers whose services are focused on the needs of people in difficulty. We will also talk about how training programs and providers (supervisors) are organized today. The intervention of supervisors in social work is very recent in Georgia, but it is notable that it has already impacted in a positive way, which statistics are shown in the article. The information is important for practicing supervisors, those in training, as well as those considering training, and also for those who would simply like to know more about the subject. Currently, scientific and statistical publications on the supervision of social workers in Georgia are quite scarce. We are interested, from a comparative and international perspective, in how supervision is developing in Georgia and where it stands today. The following description presents, from an outside observation, the result of research, statistical data, and interviews carried out with social workers, as well as with the group of supervisors of The LEPL Agency for state Сare and assistance for the (statutory) victims of human trafficking of the Ministry of Internally Displaced Persons from the Occupied Territories, Labor, Health and Social Affairs of Georgia.
Voice analysis system for detection of vishing using deep learning

For many years, telecom fraud has caused significant financial harm to Indian telecommunications users. Traditional approaches for identifying telecom fraud frequently center on boycotting of faux phone numbers. Attackers, on the other hand, could merely escape discernment by modifying their phone numbers, which is fairly straightforward with VoIP (Voice over IP).To address this issue, this method detects telecommunication fraud supporting the substance of a spoken language rather than merely the caller's sign. This paper collects chronicles of telecommunication deceit, above all, from press sources and social platforms. To make datasets, our planned model utilizes machine learning techniques to look at knowledge and opt for high-quality descriptions from antecedently gathered knowledge. After that, Natural language processing is employed to draw out characteristics from the text- based data. Then, for extra telecommunication fraud detection, criteria to acknowledge identical material within the same call are formed. To spot telecommunication fraud online, the system provides an associate degree android application that will be loaded on a customer's smartphone. Once an associate degree incoming fraud decision is answered, the program will dynamically measure the call's contents to sight fraud. Our findings demonstrate that the system will effectively safeguard clients.