Reviewerly/exHarmony huggingface dataset:
Consider the “Big 10” Distribution Water Quality Challenges

Journal AWWAVolume 111, Issue 11 p. 71-72 Water Quality Matters Consider the “Big 10” Distribution Water Quality Challenges Kira Smith, Kira SmithSearch for more papers by this author Kira Smith, Kira SmithSearch for more papers by this author First published: 04 November 2019 https://doi.org/10.1002/awwa.1400Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume111, Issue11November 2019Pages 71-72 This article also appears in:Drinking Water Week 2021 RelatedInformation
KLASIFIKASI PENGENALAN BUAH MENGGUNAKAN ALGORITMA NAIVE BAIYES

Manually sorting varieties of apples result in high costs, subjectivity, boredom, and inconsistencies associated with humans. A means is needed to distinguish between types of apples and, therefore, some reliable techniques are necessary to identify varieties quickly and without damage. The purpose of conducting research is to investigate the application and performance for Naive Bayes algorithm for apple varieties. This software methodology involves image acquisition, preprocessing, segmentation and analysis classification varieties for apple. The prototype of Apple's classification system was built using the MATLAB R2017 development platform environment. The results in this study indicate that the estimated average accuracy, sensitivity, precision, and specificity are 81%, 73%, 100%, and 70%, respectively. MLP-Neural shows that performance of the Naive Bayes technique is consistent with Principal, Fuzzy Logic, and Neural analysis with 89%, 91%, 87%, and 82% respectively in terms of accuracy. This study shows that Naif Bayes has excellent potential for identifying nondestructive and accurate apple varieties.
Video-level Multi-model Fusion for Action Recognition

The approaches based on spatio-temporal features for video action recognition have emerged such as two-stream based methods and 3D convolution based methods. However, current methods suffer from the problems caused by partial observation, or restricted to single information modeling, and so on. Segment-level recognition results obtained from dense sampling can not represent the entire video and, therefore lead to partial observation. And a single model is hard to capture the complementary information on spacial, temporal and spatio-temporal information from video at the same time. Therefore, the challenge is to build the video-level representation and capture multiple information. In this paper, a video-level multi-model fusion action recognition method is proposed to solve these problems. Firstly, an efficient video-level 3D convolution model is proposed to get the global information in the video which assembling segment-level 3D convolution models. Secondly, a multi-model fusion architecture is proposed for video action recognition to capture multiple information. The spatial, temporal and spatio-temporal information are aggregate with SVM classifier. Experimental results show that this method achieves the state-of-the-art performance on the datasets of UCF-101(97.6%) without pre-training on Kinetics.
Selecting for retention: Understanding turnover prehire

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Post-collection ultrasound-audio synchronization

Speech research using lingual ultrasound often requires both the ultrasound images as well as audio from the corresponding audio speech signals, but synchronization of these signals is not always available. We propose that periods of matching rates of change in the two signals could be used to align articulatory and acoustic signals where synchronization is impossible or would otherwise benefit from verification. In this study, we analyze pre-synchronized ultrasound and audio recordings of English speakers reading words. For each recording, we calculated the articulatory change as the change in pixel brightness values over time and the acoustic change as the change in Mel Frequency Cepstral Coefficient representations of the audio, and then calculated the degree of correlation between the acoustic and articulatory change over a window shifting through the recording. Then, we deliberately offset the signals in increments of 5 ms to verify that the known synchronization results in the best correlations. We manipulated several other variables, and preliminary results suggest that shorter window lengths and analyzing correlations only during detected speech result in the most accurate alignments. Analysis is ongoing to determine whether duration of correlation (number of windows with high r-values) or overall degree of correlation (median r-values) leads to the most accurate alignments.
Efficient out-of-home activity recognition by complementing GPS data with semantic information

Smartphones have become an indispensable human device due to their increasing functionalities and decreasing prices. Their embedded sensors, including global positioning system (GPS), have opened opportunities to support human activity recognition, both indoor (in assisted living, for instance) and outdoor. This paper proposes a minimalist activity recognition model for out-of-home environments based on a smartphone. The only sensor used is GPS, whose data is enriched with semantic knowledge extracted online from the Internet, and with brief user’s profile data collected off-line. We conducted an experiment for 20 days with 22 subjects in their day to day life, with identification of 13 selected activities, of which three were performed in movement. Experimental results show that the approach has a high activity recognition performance. This demonstrates that an adequate combination of information with different levels of semantic content can produce an efficient non-invasive solution to monitoring human activity in out-of-home environments.
DESIGN OF STUDENT LABOR INFORMATION SYSTEM MOBILE APPLICATION USING IONIC FRAMEWORK

In this era, smartphone is a device that is difficult to separate from human life. This is because smartphones are not only used as a means of communication, but rather have applications that can be used to enjoy entertainment. Smartphones can also meet the needs in education, social, health, entertainment and even business, through an operating system that is almost like a computer. Universitas Advent Indonesia (UNAI) has a program for students who want to study while working. The program is known as student labor. The system used in the student labor program is still manual in which all stages, ranging from students applying for jobs in each department they want to fill the working hours still use paper. Student labor information system applications are built using hybrid mobile technology. The SDLC waterfall model is a research method used to support the process in making this student labor application. And the design of UML ensures that the system is built according to its purpose. This information system was developed using the IONIC framework. The result is an information system that can enabled departments to control student labor programs at UNAI. The application able to send push notifications that will display the latest notifications directly for each stage that can be used as a service to facilitate each student, vice chancellor, head of the department, including finance to find out every activity that takes place as program management for student labor more easily. Blackbox testing is used to validate the performance of the application and ensure that the application is built according to purpose. And it was found that the application is running well. Advice from the authors is that further research can be developed using react native applications or using another updated application. And pay more attention to the security side of data transmission.
Entrepreneurial Skills and Youth Employment in Kogi State, Nigeria: The Case of N-Power

This study aimed at investagiting the effects of entrepreneurial skills on youth employment in Kogi State, using N-Power as a case study. Survey research design was adopted. The study adopted snowball sampling technique to select N-Power beneficiaries. A well-designed questionnaire was used as a viable instrument to collect data from beneficiaries in N-Teach, N-Health, N-Tax and N-Agro. Data collected were analysed using Multiple Regression and Ordered Probit Regression Model. Finding shows that entrepreneurial skills development has significant effect on N-Power beneficiaries in Kogi State. Finding furthers shows that interpersonal relation skill, technology adoption skill, decision-making skill and risk-taking skill have significant and positive relationship with youth self-employment in Kogi State. The study concluded that the acquisition of entrepreneurial skills is a vital approach towards self-employment. The study recommended that the government and policy-makers should device special focus for interpersonal relation skill, decision-making skill, technology adoption skill and risk-taking skill of N-Power beneficiaries; with less attention to financial literacy and creative thinking skills.
Improving Privacy Through Fast Passive Wi-Fi Scanning

Traditionally, Wi-Fi networks are discovered by actively transmitting probe requests. The alternative, passive scanning, is rarely used because it is substantially slower. Unfortunately, active scanning can be abused to track users based on (physical) fingerprints of probe requests. Previous work attempted to address these issues by making active scanning more privacy-friendly. For instance, Franklin et al. proposed to make implementations more uniform (USENIX Security 2006), and Lindqvist et al. suggested to use encrypted probe requests (WiSec 2009). However, a better approach is to make passive scanning faster. This motivates vendors to use passive scanning, increasing the privacy of users. Motivated by the above insight, we improve the performance of passive scanning. We implement our proposals on Android, and show the average time needed to connect to a known network using passive scanning now matches active scanning. Additionally, we implement a new network-discovery mechanism that drastically decreases scanning times, and present a new method to fingerprint Wi-Fi radios. All combined, our results show that passive scanning is a viable and more privacy-friendly alternative to active scanning.
Private runtime verification

Complex systems require runtime monitoring of their operation, and this task can be performed by a separate monitoring process. A hacked monitor however could use the system traces it receives to attack the system or leak the information to competitors. We introduce private runtime verification, in which the monitoring process learns nothing about the traces, and yet is still able to correctly monitor them for satisfaction of temporal specifications.
Evaluation of Elazığ Province Precipitation Data by Innovative Trend Analysis

The effect of climate change that have been occurred in worldwide is felt especially in Turkey in recent years. Rainfall is the parameter most affected by climate change. Rainfalls affects the amount of water to be used for drinking, irrigation and electrical energy production by feeding the streams flow. In this study, the total monthly precipitation data of 5 meteorological observation stations in Elazığ province were investigated and the changes of these precipitations under the influence of climate change were analyzed monthly, seasonal and yearly using Innovative Trend Analysis (ITA). When the results obtained by ITA method were evaluated, it was found that rainfall measurements of selected stations were negatively affected by climate change and there was a significant decrease in precipitation when analyzed monthly, seasonal and yearly.
Parallelizable cipher of color image based on two-dimensional chaotic system

This paper aims to present a parallel implementation based color image encryption using non -linear chaotic system. The adopted chaotic system was suggested and approved in our previous work [1] which generates key streams with chaotic behavior. In this paper, pixel level permutation algorithm based on chaotic map generation is investigated and analyzed. The encryption–decryption schemes are achieved in parallel and composed of three main phases: chaotic keys generation, pixel-level permutation and bit-level diffusion phase. Both permutation and diffusion processes are achieved according to the chaotic keys. The parallel implementation of the proposed image encryption system is realized and inspired with parallel computing library offered by Matlab 2018, which equips highly performance than the pipeline ones and would be helpful to utilize in image encryption/decryption for real time application. Security and statistical analysis in addition to the main differential attacks analysis are specified to evaluate the performance of the proposed image encryption algorithm with parallel implementation. From the experimental results, the output image of the encryption task shows a higher randomness of the encrypted image which can be effectively resistant to attacker. Furthermore, the run time of encryption process is faster than other research works.
Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension.

The existing approaches to intrinsic dimension estimation usually are not reliable when the data are nonlinearly embedded in the high dimensional space. In this work, we show that the explicit accounting to geometric properties of unknown support leads to the polynomial correction to the standard maximum likelihood estimate of intrinsic dimension for flat manifolds. The proposed algorithm (GeoMLE) realizes the correction by regression of standard MLEs based on distances to nearest neighbors for different sizes of neighborhoods. Moreover, the proposed approach also efficiently handles the case of nonuniform sampling of the manifold. We perform numerous experiments on different synthetic and real-world datasets. The results show that our algorithm achieves state-of-the-art performance, while also being computationally efficient and robust to noise in the data.
Exploration and assessment of proactive use cases for an in-car voice assistant

More and more people tend to use voice assistants on their smartphones,at home or in the car. These assistants help people to find their way in a foreigncity, call their friends or add items to the shopping list – mostly in a user-initiatedmanner. Whereas there have been several scientific publications on adaptive andproactive Spoken Dialog Systems (SDSs) in the past decade (e.g., [1], [2]), mostof these primarily focused on the technical implementation, but none of them dealtwith basic research on which proactive use cases a user of the respective assistantmight actually wish for. In order to compensate for this, we explore and assesspotentially suitable proactive use cases in this work as follows. First, we conducteda creative workshop in which expert teams brainstormed, rated, selected, elaboratedand finally presented eight proactive use cases in a role play manner to internalstudy subjects. Second, according to the previous subjects’ ratings, we selectedand elaborated four primarily proactive use cases in order to present them to 20 external subjects in an interview study in a non-moving car. All of the latter usecases achieved high acceptance rates, whileProactive Parkingboth got the highestacceptance and the highest additional value score.
Derivative Manipulation For Adjusting Emphasis Density Function: A General Example Weighting Framework

Real-world large-scale datasets usually contain noisy labels and are imbalanced. Therefore, we propose derivative manipulation (DM), a novel and general example weighting approach for training robust deep models under these adverse conditions. DM has two main merits. First, loss function and example weighting are common techniques in the literature. DM reveals their connection (a loss function does example weighting) and is a replacement of both. Second, despite that a loss defines an example weighting scheme by its derivative, in the loss design, we need to consider whether it is differentiable. Instead, DM is more flexible by directly modifying the derivative so that a loss can be a non-elementary format too. Technically, DM defines an emphasis density function by a derivative magnitude function. DM is generic in that diverse weighting schemes can be derived. Extensive experiments on both vision and language tasks prove DM's effectiveness.
Unmanned aerial vehicle trajectory data fusion based on an active and passive feedback system

The surveillance data obtained from an unmanned aerial vehicle during real-time operation are very important for unmanned aerial vehicle supervision. In this study, in order to obtain more reliable and accurate unmanned aerial vehicle flight paths, we have proposed a trajectory fusion method of unmanned aerial vehicle data based on an active and passive feedback system. The active surveillance data are obtained from automatic dependent surveillance – broadcast and the passive surveillance data are derived from the unmanned aerial vehicle ground control station. The convex combination fusion algorithm is employed to fuse the two sets of data to obtain the accurate flight state and a continuous stable running track for the unmanned aerial vehicle. The simulation results showed that the fusion trajectory obtained using the convex combination fusion algorithm was smoother than the local trajectory obtained by the interacting multiple model algorithm.
Zero-Error Coding via Classical and Quantum Channels in Sensor Networks

Today’s sensor networks need robustness, security and efficiency with a high level of assurance. Error correction is an effective communicational technique that plays a critical role in maintaining robustness in informational transmission. The general way to tackle this problem is by using forward error correction (FEC) between two communication parties. However, by applying zero-error coding one can assure information fidelity while signals are transmitted in sensor networks. In this study, we investigate zero-error coding via both classical and quantum channels, which consist of n obfuscated symbols such as Shannon’s zero-error communication. As a contrast to the standard classical zero-error coding, which has a computational complexity of , a general approach is proposed herein to find zero-error codewords in the case of quantum channel. This method is based on a n-symbol obfuscation model and the matrix’s linear transformation, whose complexity dramatically decreases to . According to a comparison with classical zero-error coding, the quantum zero-error capacity of the proposed method has obvious advantages over its classical counterpart, as the zero-error capacity equals the rank of the quantum coefficient matrix. In particular, the channel capacity can reach n when the rank of coefficient matrix is full in the n-symbol multilateral obfuscation quantum channel, which cannot be reached in the classical case. Considering previous methods such as low density parity check code (LDPC), our work can provide a means of error-free communication through some typical channels. Especially in the quantum case, zero-error coding can reach both a high coding efficiency and large channel capacity, which can improve the robustness of communication in sensor networks.
Goal-Based Modeling and Analysis of Non-Functional Requirements

Non-functional goals specify a quality attribute of the functional goals for the system-to-be (e.g., cost, performance, security, and safety). However, non-functional goals are often cross-cutting and do not naturally fit within the default decomposition expressed by a functional goal model. Further, any functional mitigations that ensure the satisfaction of a non-functional goal, or occur in the event a non-functional goal is violated, are conditionally applicable to the remainder of the system-to-be. Rather than modeling non-functional goals and their associated mitigations as a part of the system-to-be goal model, we introduce a method of modeling and analyzing non-functional goals and their associated mitigation as separate models. We illustrate our approach by applying our method to model non-functional goals related to an industry-based automotive braking system and analyzing for non-functional violations.
Mineralogy of the Drazhnoye gold ore deposit, the Republic of Sakha (Yakutia)

New data on the mineral composition of the Drazhnoye gold deposit, located within the Taryn ore field, have been presented. The samples from the central and flank areas of the deposit, which characterize ore and barren zones, have been studied. Based on the detailed studies of the interrelationship and the form of mineral exhalations and their aggregates, the stages of mineral formation have been proposed, including two main stages: sediment-diagenetic and hydrothermal-metasomatic. It has been revealed that the main precipitator of early native gold was pyrrhotite, which later, with an increase in fugacity of sulfur, was almost replaced by later pyrite. At the end of hydrothermalmetasomatic stage, native gold is formed from gold-bearing solutions and is located often in quartz and quartzcarbonate veinlets as independent precipitates, less commonly associated with polymetallic minerals, pyrite and arsenopyrite. Previous researchers of the Drazhnoye deposit established its genetic relationship with hydrothermal solutions. The data obtained confirm this. According to the type of gold ore mineralization and the type of prospective deep source of solution, this field is previously attributed to a hydrothermal pluton-related gold — quartz formation.
Quantum Computation and Visualization of Carbon Single and Double Nano-Rings

This paper concentrates on computational nanoscience to better understand the properties of different materials of interest. In particular, the material graphene has many interesting properties with promising applications. This project will focus on different carbon nanostructures, including the hexagonal honeycomb lattices of a single graphene sheet. Using visualization, animation, and simulation, the thermodynamic properties and energy spectrum of the different carbon nanostructures, mainly carbon nano-ring of different geometries were investigated. The tight-binding model for interacting and non-interacting systems of bosons and fermions was implemented on Mathematica and IBM-Q's quantum computing platform QISkit for a thorough understanding of these atomic structures.