Reviewerly/exHarmony huggingface dataset:
A Vision-Based Detection and Tracking Algorithm for a Child Monitoring Robot

Accidents have been found to be one of the leading causes of both fatal and non-fatal injuries to children. Though some accidents that occur are often unavoidable, more often than not these injuries can be prevented by giving the child proper attention. The researchers intend to address certain gaps in stationary monitoring solutions by adding abilities such as an insured way of continuously monitoring the test subject and a real time notification feature to a mobile spherical robot. This research presents the software division of a technological solution to child monitoring by developing a computer vision algorithm for following and monitoring children indoors utilizing an RGB-D camera. This algorithm will work hand in hand with a hardware design of a spherical robot that utilizes microcontrollers, RFID technology and GSM system. An Android application will also be created to provide the users the means of manually overriding the spherical robot, color calibration and location indicator as a part of the robot's notification system. The detection and tracking ability of the algorithm is tested by using objects with varying characteristics. The autonomous navigation testing of the robot is performed at two controlled test setups: living room and child's playroom.
Size-free generalization bounds for convolutional neural networks

We prove bounds on the generalization error of convolutional networks. The bounds are in terms of the training loss, the number of parameters, the Lipschitz constant of the loss and the distance from the weights to the initial weights. They are independent of the number of pixels in the input, and the height and width of hidden feature maps. We present experiments with CIFAR-10, along with varying hyperparameters of a deep convolutional network, comparing our bounds with practical generalization gaps.
A Moving Object Detection Algorithm of Jitter Video

In view of the problem that camera shake cannot accurately detect moving target in natural environment, this paper proposes a moving target detection algorithm combining grey projection, background difference and continuous frame difference. The algorithm calculates the gray projection after removing the peripheral edge pixels from the image frame, and calculates the interrelation between the images according to the obtained gray projection curve, and completes the jitter sequence correction. The fusion strategy of background difference and continuous three frame difference method is proposed to enhance the motion target area. The fusion differential image was segmented using Ostu method to detect the foreground moving target. After the public dither video sequence experiment and comparison and verification with different algorithms, it can be concluded that this algorithm can accurately detect the moving target in camera dither scene, and ensure a good detection effect while the detection speed is fast and the calculation amount is small.
Facial Expression Sequence Interception Based on Feature Point Movement

Automatic and accurate Facial Expression Sequence Interception (FESI) for the face video is an enormous challenge to Facial Expression Recognition (FER). This paper proposes a framework for FESI by the relative movement between facial feature points. Firstly, Active Appearance Model (AAM) is used to mark the feature points and the most representative 24 of them are chosen. Secondly, the intraframe Euclidean distances between any two feature points are calculated and the interframe Euclidean distances variation polylines are fitted, thus the slopes of different segments in the polylines are obtained. Finally, facial expression sequence is intercepted from the face video by determining two key frames whose emotional intensities are minimum and maximum, respectively. Corresponding experiments on Beihang University (BHU) facial expression database and MMI facial expression database are conducted whose results have shown the satisfactory interception effect of the proposed method.
Boarding House Selection using SAW Method

Determination of residence in a new area known is influenced by many factors namely location, facilities, contract systems and prices. This journal determines the criteria used in selecting a residence. These criteria were analyzed using simple additive weighting (SAW) method. The results of the analysis obtained in the form of location criteria to determine the most suitable boarding house because the existence of boarding houses in the vicinity of public facilities is highly expected for migrants in a new area. With the advancement of information technology it can answer the need to find a boarding house, and will be very helpful both from the owner and tenant side. And not only that, with this internet technology, prospective tenants can also select a boarding house that suits their wishes. Planning a system that can assist in the determination of temporary housing is needed by tenants in finding housing that fits the desired criteria. With the creation of a DSS (Decision Support System), the selection of boarding houses is expected to help resolve the problem of alternative housing. Therefore a decision support system application program is needed to determine a temporary residence or boarding house. Decision support system is a computer-based system that aims to assist decision making by utilizing data and models to solve unstructured problems.
Development Status and Prospects of Graphical Password Authentication System in Korea

Security is becoming more important as society changes rapidly.In addition, today's ICT environment demands changes in existing security technologies.As a result, password authentication methods are also changing.The authentication method most often used for security is password authentication.The most-commonly used passwords are text-based.Security enhancement requires longer and more complex passwords, but long, complex, text-based passwords are hard to remember and inconvenient to use.Therefore, authentication techniques that can replace text-based passwords are required today.Graphical passwords are more difficult to steal than text-based passwords and are easier for users to remember.In recent years, researches into graphical passwords that can replace existing text-based passwords are being actively conducting in various places throughout the world.This article surveys recent research and development directions of graphical password authentication systems in Korea.For this purpose, security authentication methods using graphical passwords are categorized into technical groups and the research associated with graphical passwords performed in Korea is explored.In addition, the advantages and disadvantages of all investigated graphical password authentication methods were analyzed along with their characteristics.
Frequency-based Search-control in Dyna

Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency. Particularly, Dyna architecture, as an elegant model-based architecture integrating learning and planning, provides huge flexibility of using a model. One of the most important components in Dyna is called search-control, which refers to the process of generating state or state-action pairs from which we query the model to acquire simulated experiences. Search-control is critical to improve learning efficiency. In this work, we propose a simple and novel search-control strategy by searching high frequency region on value function. Our main intuition is built on Shannon sampling theorem from signal processing, which indicates that a high frequency signal requires more samples to reconstruct. We empirically show that a high frequency function is more difficult to approximate. This suggests a search-control strategy: we should use states in high frequency region of the value function to query the model to acquire more samples. We develop a simple strategy to locally measure the frequency of a function by gradient norm, and provide theoretical justification for this approach. We then apply our strategy to search-control in Dyna, and conduct experiments to show its property and effectiveness on benchmark domains.
Speech Watermarking for Tampering Detection Based on Modifications to LSFs

There have been serious issues concerning the protection of speech signals from malicious tampering. Digital watermarking has been paid much attention in solving this problem. This paper proposes a tampering detection approach based on speech watermarking by modifying the line spectral frequencies (LSFs). Watermarks are embedded into LSFs that derived from linear prediction (LP) analysis with dither modulation-quantization index modulation (DM-QIM). Minor modifications to LSFs introduced by quantization not only enable the watermarks to be inaudible to human auditory system but also provide the possibility of robustness against meaningful processing and fragility against tampering. We evaluated the proposed approach with objective evaluations with respect to inaudibility, robustness, and fragility. The results indicated that the proposed approach for tampering detection not only satisfied inaudibility but also provided good robustness against meaningful processing and fragility against malicious tampering.
A unified and simplified flow diagram of the production of active pharmaceutical ingredients and the finished dosage form

The article shows that all biotechnological processes consist of four stages which include the step of obtaining the biomass (micro-organisms), fitosyrya biomass (plants, lichens) and biomass from animal raw materials; obtaining the active pharmaceutical substance; obtaining the final dosage form; storage and sale of APS and SFS. The authors have proposed a unified and simplified process sequence that represents the life cycle of the production of APS and SFR, showing the potential risks. Introduction of the HACCP system at the same time to the GMP system allows the identification of CCPs and therefore to analyze the product and manage the risk only in the CCP. The authors have proposed a multi-level model of a complex of pharmaceutical quality system. It is necessary to emphasize that in all environmental conditions to obtain drugs should be carried out in strict compliance with GMP and HACCP system and under the strict control of all types of risk, and, in particular, be sure to microbiological risk.
Fault Prediction for Software System in Industrial Internet: A Deep Learning Algorithm via Effective Dimension Reduction

In recent years, as information technology develops, Industrial Internet has become a hot issue in international industry. Because of the use of networked software in Industrial Internet, many machines are software-intensive. But software is a product of human brain thinking activities, with the increase of its scale and complexity, there will inevitably be some software defects caused by human errors in the process of design and development. Nowadays, software fault diagnosis mostly relies on personal experience and lacks effective technology and methods, which seriously affects the ability of software-intensive systems. This paper mainly designed a software system fault prediction model, which can be used by software-intensive system users of Industrial Internet to quickly predict whether software failures occur or not. Different fault prediction methods based on deep learning are introduced. We propose a software fault prediction model based on locally linear embedding (LLE) algorithm and long short-term memory (LSTM) algorithm to train the model. Original data sets are from MDP dataset of NASA. We process original datasets by using LLE algorithm to reduce dimensions of datasets. After processed datasets were trained by LSTM algorithm, the prediction model can be obtained. Compared with single LSTM and principal components analysis-long short-term memory algorithm (PCA-LSTM), the results show that locally linear embedding-long short-term memory algorithm (LLE-LSTM) algorithm has a better performance than other existing algorithms in terms of prediction accuracy, precision, recall, f-measure.
Detection of Operation Type and Order for Digital Speech

Most existing speech forensic works implicitly assume the suspected speech either has or has not been processed by a specific operation. In practice, however, the operation type performed on the target speech is usually unknown to the forensic analyst, and in most cases, multiple operations may be involved in order to conceal the forgery trace. Few works have considered these issues. In this study, we propose a universal forensic algorithm that can detect four typical speech operations: pitch shifting, noise-adding, low-pass filtering, and high-pass filtering. The motivation of the proposed algorithm is based on the observation that different operations will cause different effects on Mel-frequency cepstral coefficients (MFCC). The statistical moments of MFCC are extracted as detecting features. Additionally, cepstral mean and variance normalization (CMVN), which is a computationally efficient normalization technique, is used to eliminate the impact of channel noise. Finally, an ensembled binary classifier is used to detect the type of various operations, and multiclass classifiers are adopted to identify the order of operations. The experimental results on the TIMIT and UME-ERJ datasets show that the proposed forensic features achieve good performance on the operation type and order detection. Additionally, the results demonstrate the effectiveness of the proposed algorithm in terms of robustness against the MP3 compression attack.
STUDENTS’ INTEREST IN LEARNING ENGLISH THROUGH BELAJAR BAHASA INGGRIS WITH ACO APPLICATION

The objective of the research was to find out: (1) whether or not the students are interested in learning through Belajar Bahasa Inggris with ACO Application; (2) Students’ perception in Learning English through Belajar Bahasa Inggris with ACo Application. The research was conducted by using mixed method design (Quan-qual) model. The research applied cluster random sampling by taking one class as the sample. The sample consists of 30 students of A3Accounting Class of STIE Muhammadiyah Palopo. The data were collected through questionnaire and supported by interview. The result of the research showed that the students are interested in learning English through Belajar Bahasa Inggris with ACO Application. Furthermore, the students have positive perception toward the application and the most favourable menu in the application is “chat” and “talk.”
Gesture Recognition in Leap Motion Using LDA and SVM

In this paper we propose a framework to support patients with hand injuries such as those recovering from strokes or similar illness to better perform daily tasks hence enhance their quality of life. This framework employs a low-cost “off-the-shelf” markerless sensor device called the Leap Motion controller (LM). During a rehabilitation procedure, we consider patients flexing and extending their fingers when their hands are placed above the LM. Various forms of measurement errors are expected including patients placing their hands out of reach of the LM “sweet-spot”. Experimental results show that this sweet-spot is always along the middle position of the device and within 25 cm elevation above its surface. During our evaluation, data collection was conducted using an “artist's hand” fixed above the LM when performing static gestures. This is because artist's hand gives more accurate measurements than a human hand as it can maintain a posture as long as is required. We have applied machine learning techniques such as Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). These techniques help in recognising and classifying performed gestures. In addition, these techniques can learn about measurement errors and compensate for them. Experimental results show a significant benefit when applying LDA and SVM yielding a performance accuracy above 88 %, which is far better than the baseline performance of 67.25%. Furthermore, as our approach enables more accurate measurements to be obtained, more meaningful feedback can be generated to help the patients that make use of the LM for hand rehabilitation exercises.
Community for Data Integration 2018 annual report

The USGS assesses coastal hazard vulnerability, including the severe beach erosion shown here.(Photograph by USGS) The Denali Fault, Alaska, displaced the
Blockchain-based OCF Firmware Update

As the IoT devices growth rate is increasing, various issues related to this device need attention. One of the problems is the distribution of the firmware update. Since the development of IoT devices does not stop when the manufacturer sells the devices to the distributor, retailer, and customer, it will need to keep updated. One of the reasons is to prevent cyber-attacks. The commonly used firmware update process is over-the-air (OTA). It is a centralized way and direct downloads from the manufacturer server. It is vulnerable to single-point-of-failure and can delay critical patches from being applied to vulnerable devices. The firmware update process also needs to make sure the integrity and security of the patches. Open Connectivity Foundation (OCF) is one of the organizations that are contributing to providing interoperability for IoT devices. In this paper, we proposed blockchain-based OCF firmware updates. The blockchain technology and smart contract can protect the integrity and transparency of the firmware updates. Besides, it will prevent a single-point-of-failure if the adversary attacks the manufacturer by providing peer-to-peer firmware update.
An examination of metadata practices for research data reuse: Characteristics and predictive probability of metadata elements

This study explores metadata practices in the relation to data reuse in biology. Metadata has long been viewed as a major constituent in research data management and reuse. However, the topic of whether metadata is used in a way that encourages data reuse has been understudied. The current study examined metadata elements used to describe datasets and the predictive probability of those metadata elements for data reuse under the assumption that citation frequency reflects the frequency of research data reuse. A total of 34,491 cited records from the biology category of the Clarivate Analytics Data Citation Index were analyzed using descriptive comparison and multiple regression analysis to compare usage patterns of metadata elements between data records cited more than twice and those cited only once. Of the five types of metadata elements identified and examined, metadata elements that provided descriptions about datasets and author-related information dominantly appeared across datasets, whereas DOI and ORCID identifier were scarce. Metadata related to author and funding resources were found to be positive influential factors in predicting data reuse, whereas data descriptions and identifiers appeared to have negative influences. This study contributed to a better understanding of metadata needs for data reuse.
Exploring Antimicrobial Resistance Prediction Using Post-hoc Interpretable Methods

An accurate and timely prediction of whether an infection is going to be resistant to a particular antibiotic could improve the clinical outcome of the patient as well as reduce the risk of spreading resistant microorganisms. From a data analysis perspective, four key factors are present in antimicrobial resistance prediction: the high dimensionality of the data available, the imbalance present in the datasets, the concept drift along time and the need for their acceptance and implantation by clinical staff. To date, no study has looked specifically at combining different strategies to deal with each of these four key factors. We believe interpretable prediction models are required. This study was undertaken to evaluate the impact of baseline interpretable predicting approaches using a dataset of real hospital data. In particular, we study the capacity of logistic regression, conditional trees and C5.0 rule-based models to improve the prediction when they are combined with oversampling, filtering and sliding windows.
IMPROVING THE QUALITY IN WRITING DESCRIPTIVE TEXTS OF MADRASAH ALIYAH STUDENTS THROUGH DIRECTED WRITING ACTIVITY STRATEGY

This study was intended to improve the tenth graders’ quality in writing a descriptive text at Madrasah Aliyah through the Directed Writing Activity strategy. Its design was action research consisting of 4 stages, namely planning the action, implementing and observing the action, and reflecting on the action. Its subjects were class X consisting of 31 students. There were 3 instruments used to collect the data needed, i.e. fieldnotes, portfolios, a scoring guide, and questionnaire. The criteria for success of this study were (1) all students participated in the teaching and learning process, (2) all students got score of equal to or more than 3 in each aspect of writing. The findings indicated that in Cycle 1, the study did not meet the criteria of success. Concerning the first criterion, it was only 99% students following the teaching and learning process. One student was absent from the first meeting. In connection with the second criterion, 18 students (58.06%) had achieved a score of equal to or more than 3 and the rest, 13 students (41.94%) got scores below 3. Because the first cycle did not meet the criteria of success, so the researcher carried out the second cycle. All students (100%) participated in the teaching and learning process. In addition to the second criterion, all students got score equal to even greater than 3 in each aspect of writing. From the findings, it can draw the conclusion that the directed writing activity is being able to upgrade the quality in writing descriptive texts of students.
Implementation and Performance Comparison of Partitioning Techniques in Apache Spark

Apache spark is one of the most demanded frameworks for High performance computing of Big Data. Data is growing day by day to such a large extent that the power of existing analytical tool is not sufficient. The degree of parallelism achieved directly impacts the performance of the framework. Parallelism can be achieved only when there is a proper division among the dataset. Partitioning helps in this division of work. Appropriate number of partitions and related data in partition will ensure the proper storage and faster access of data. Spark provides the inbuilt libraries and also flexibility to mould the available methods of partitioning according to convenience. In this paper we discus about the architecture of storage structure in spark, implementation of various partitioning techniques and performance comparison of those techniques.
Pengembangan Bahan Ajar Mata Kuliah Konsep Dasar Bahasa dan Sastra Indonesia Berbasis Kontekstual STKIP PGRI Lubuklinggau

This study aims to determine the design of instructional materials development, validation results, and effectiveness of the use of contextual basic concepts of indonesian language and literature in STKIP PGRI Lubuklinggau students. This research uses the R&amp;D (research and development) approach of the Borg and Gall model. The steps of development that researchers do namely, preliminary research, planning development models, validation, evaluation, and revision of the model. Furthermore, teaching material products are tested on students with the steps: 1) Portotype testing, 2) Initial input, 3) Small group testing, 4) Field testing. Based on the results of the validation of three experts regarding the teaching materials of the basic concepts of indonesian language and literature, the results obtained by 80%, the teaching materials are in good category. The results of this study also showed that contextual based indonesian conceptual language and literature teaching materials were effectively used in lectures.&#x0D; Keywords: Teaching Materials, Contextual, Basic Concepts