Reviewerly/exHarmony huggingface dataset:
Color Recognition for Rubik's Cube Robot

In this paper, we proposed three methods to solve color recognition of Rubik's cube, which includes one offline method and two online methods. Scatter balance \& extreme learning machine (SB-ELM), a offline method, is proposed to illustrate the efficiency of training based method. We also point out the conception of color drifting which indicates offline methods are always ineffectiveness and can not work well in continuous change circumstance. By contrast, dynamic weight label propagation is proposed for labeling blocks color by known center blocks color of Rubik's cube. Furthermore, weak label hierarchic propagation, another online method, is also proposed for unknown all color information but only utilizes weak label of center block in color recognition. We finally design a Rubik's cube robot and construct a dataset to illustrate the efficiency and effectiveness of our online methods and to indicate the ineffectiveness of offline method by color drifting in our dataset.
Fast CNN-Based Object Tracking Using Localization Layers and Deep Features Interpolation.

Object trackers based on Convolution Neural Network (CNN) have achieved state-of-the-art performance on recent tracking benchmarks, while they suffer from slow computational speed. The high computational load arises from the extraction of the feature maps of the candidate and training patches in every video frame. The candidate and training patches are typically placed randomly around the previous target location and the estimated target location respectively. In this paper, we propose novel schemes to speed-up the processing of the CNN-based trackers. We input the whole region-of-interest once to the CNN to eliminate the redundant computations of the random candidate patches. In addition to classifying each candidate patch as an object or background, we adapt the CNN to classify the target location inside the object patches as a coarse localization step, and we employ bilinear interpolation for the CNN feature maps as a fine localization step. Moreover, bilinear interpolation is exploited to generate CNN feature maps of the training patches without actually forwarding the training patches through the network which achieves a significant reduction of the required computations. Our tracker does not rely on offline video training. It achieves competitive performance results on the OTB benchmark with 8x speed improvements compared to the equivalent tracker.
The MOND Limit of the Inverse Square Law

Abstract This paper attempts to give a theoretical foundation for the Modified Newtonian Dynamics equations developed by M. Milgrom Ref.1. It will show that there is a cross sectional limit, below which 1/r2 asymptotically changes to 1/r. When the cross section of a distant star falls below the cross section of a particle transmitting the gravitational force, such as a graviton, as viewed from a baryon, such as a proton, then the inverse square law is no longer valid. Gravitational attraction then depends on the distance travelled by the particle and will result in the force of gravity varying as 1/r. It is only the relative geometries which limit the inverse square law. The sizes of the suns and particles involved will only result in different limits and will not change the basic outcome of 1/r.
Optimized Method for Reticulocyte Counting: Simple, Accurate, and Comparable to Flow Cytometry

Background: Reticulocytes are immature red blood cells with RNA, spending the final stages of their maturation in the peripheral blood. The number of reticulocytes in the peripheral blood is the salient evidence of the effectiveness of bone marrow to produce red blood cells. Currently, reticulocyte count is done manually or automatically in clinical laboratories. Difficulties and limitations of these approaches, including imprecision, poor reproducibility and laboriousness are clear. This study used modification in manually staining method to achieve better counting.&#x0D; Materials and Methods: The reticulocyte counts of 30 samples were obtained from patients referred to Afzalipour hospital in Kerman, Iran. The patients aged between 0-3 years old (median age= 1.4 years). Samples were investigated for reticulocyte count by three different methods, including conventional, modified, and flow cytometry methods. Modified staining method is used in the optimized method which helps operator to countg reticulocyte better than conventional method.&#x0D; Results: Analysis of the results showed a reasonable agreement. Sphericity assumption was checked by Kolmogorov-Smirnov and Mouchley analyses. Repeated measurement analysis was performed using Greenhouse-Geisser correction (F=16.6, df=1.105) according to pairwise comparisons with Bonferroni adjustment. Mean differences for flow cytometry, optimized and common methods were 3.88, 3.59, and 2.46 were significant at the 0.05 level, respectively.&#x0D; Conclusion: The proposed optimized method was simple method, highly reliable, and comparable to flow cytometry. Due to the ease of implementation, this method did not need specialized training or the use of complex devices so it is affordable.&#x0D; Key words: Blood Stains, Flow Cytometry, Reticulocyte, Reticulocyte Count, Staining and Labeling
Mystery thriller DANGEROUS CARGO now available as an audio book

The second in the fast-paced, action-packed crime series featuring undercover investigator, former Royal Marine Commando, Art Marvik released as an audio book
High-Fidelity Vector Space Models of Structured Data

Machine learning systems regularly deal with structured data in real-world applications. Unfortunately, such data has been difficult to faithfully represent in a way that most machine learning techniques would expect, i.e. as a real-valued vector of a fixed, pre-specified size. In this work, we introduce a novel approach that compiles structured data into a satisfiability problem which has in its set of solutions at least (and often only) the input data. The satisfiability problem is constructed from constraints which are generated automatically a priori from a given signature, thus trivially allowing for a bag-of-words-esque vector representation of the input to be constructed. The method is demonstrated in two areas, automated reasoning and natural language processing, where it is shown to produce vector representations of natural-language sentences and first-order logic clauses that can be precisely translated back to their original, structured input forms.
Color Image Watermarking Based on Octonion Discrete Cosine Transform

There are few watermarking algorithms for color images.In most traditional watermarking algorithms, transforms are applied to each component of the color model individually resulting in less robust experimental results.Therefore, we propose a color image watermarking algorithm which is a high-dimensional algorithm.First, Octonion Discrete Cosine Transform (ODCT) and its inverter transform (IODCT) are proven.Then, a novel color image watermarking technique based on ODCT is proposed.Experimental results show that it not only has good anti to compression ability, but also has robustness to noise, filtering and rotation attacks.
Image Enhancement Network Trained by Using HDR images

In this paper, a novel image enhancement network is proposed, where HDR images are used for generating training data for our network. Most of conventional image enhancement methods, including Retinex based methods, do not take into account restoring lost pixel values caused by clipping and quantizing. In addition, recently proposed CNN based methods still have a limited scope of application or a limited performance, due to network architectures. In contrast, the proposed method have a higher performance and a simpler network architecture than existing CNN based methods. Moreover, the proposed method enables us to restore lost pixel values. Experimental results show that the proposed method can provides higher-quality images than conventional image enhancement methods including a CNN based method, in terms of TMQI and NIQE.
Understanding the ambient Assisted Living systems: concepts, architectural trends and challenges

As a novel field of research AAL (Ambient Assisted Living) is still lacking a common understanding. The field is devoted to serve elder people and those with special disabilities to make their daily life tasks easier and controllable. Many efforts have been done to develop reliable systems capable of responding to the needs of the stakeholders in acceptable budget. The reusability, the integration and the implementation issues are the main concerns of the developers. For this purpose, this paper addresses the AAL systems due to their importance in the technological and societal fields.  We highlight the need and urge for such systems. We also define various aspects of AAL such as the field itself and the related applications. The technical view of ALL systems will be given, along with the technologies and the tools. We discuss also in this paper the AAL architectural trends and the systems’ requirements. Finally, we investigate some of the challenges facing the developments of AAL.
DEMN: Distilled-Exposition Enhanced Matching Network for Story Comprehension

This paper proposes a Distilled-Exposition Enhanced Matching Network (DEMN) for story-cloze test, which is still a challenging task in story comprehension. We divide a complete story into three narrative segments: an \textit{exposition}, a \textit{climax}, and an \textit{ending}. The model consists of three modules: input module, matching module, and distillation module. The input module provides semantic representations for the three segments and then feeds them into the other two modules. The matching module collects interaction features between the ending and the climax. The distillation module distills the crucial semantic information in the exposition and infuses it into the matching module in two different ways. We evaluate our single and ensemble model on ROCStories Corpus \cite{Mostafazadeh2016ACA}, achieving an accuracy of 80.1\% and 81.2\% on the test set respectively. The experimental results demonstrate that our DEMN model achieves a state-of-the-art performance.
$4\times4$ unextendible product basis and genuinely entangled space

We show that there are six inequivalent $4\times4$ unextendible product bases (UPBs) of size eight, when we consider only 4-qubit product vectors. We apply our results to construct Positive-Partial-Transpose entangled states of rank nine. They are at the same 4-qubit, $2\times2\times4$ and $4\times4$ states, and their ranges have product vectors. One of the six UPBs turns out to be orthogonal to an almost genuinely entangled space, in the sense that the latter does not contain $4\times4$ product vector in any bipartition of 4-qubit systems. We also show that the multipartite UPB orthogonal to a genuinely entangled space exists if and only if the $n\times n\times n$ UPB orthogonal to a genuinely entangled space exists for some $n$. These results help understand an open problem in [Phys. Rev. A 98, 012313, 2018].
The Virtual Pole: Exploring Human Responses to Fear of Heights in Immersive Virtual Environments

JVRB, 14(2017), no. 6. - Measuring how effective immersive virtual environments (IVEs) are in reproducing sensations as in similar situations in the real world is an important task for many application fields. In this paper, we present an experimental setup which we call the virtual pole, in which we evaluated human responses to fear of heights. We conducted a set of experiments in which we analyzed correlations between subjective and physiological anxiety measures as well as the participant's view direction. Our results show that the view direction plays an important role in subjective and physiological anxiety in an IVE due to the limited field of view (FOV), and that the subjective and physiological anxiety measures monotonically increase with the increasing height. In addition, we also found that participants recollected the virtual content they saw at the top height more accurately compared to that at the medium height. We discuss the results and provide guidelines for simulations aimed at evoking fear of heights responses in IVEs.
RANCANGAN TATA KELOLA PEREMAJAAN RUANG KELAS DIREKTORAT PEMBINAAN SEKOLAH DASAR

Rejuvenation classroom is a reference to the Directorate of Primary School activity conducted on an ongoing basis. So the availability, truth and certainty of information from the renovation must show current conditions in order to meet the needs of the coaching process, accountability and financial audit, as well as in the management of data rejuvenation should be integrated in one unified database. Then it is necessary to build an application that is able to manage the process of rejuvenation consistently. In the design of the governance renewal application classrooms, use agile software development methodology with extreme approach to programming process to explain each activity in each phase of the design. Stages of design and implementation is limited to the aid management process module rejuvenation classroom consisting of the proposed input, input the data verification process, the process of filing the disbursement of funds, the process of managing the data returns, and the handling of school accountability reports. With the construction of this rejuvenation governance can help meet the needs of data and information quickly and accurately. So that the information presented can be used as one of the considerations in the process of planning, policy making, monitoring, evaluation and inspection.
자유글쓰기(Free Writing)를 활용한 이공계 글쓰기 수업의 효과

This study examined the effects of free writing in writing class targeted for engineering students on their writing skills. To assess the effects, we have asked 100 engineering students from writing class to write free writing, classifying them as the test group(N=50) and the control group(N=50). We have also compared their learning effect, their writing ability, and learning satisfaction via pre and post intervention surveys. The results showed higher change in learning effect, interest in writing, learning attitude, writing ability, learning satisfaction in the test group compared to the control group. This positive change proves that free writing is a significant teaching method in writing class for engineering and science students.
A test bed for measuring UAV servo reliability

Extant literature suggests minimal research in the area of system reliability of components used in the design of these UAS (Unmanned Air Systems), thus, subjecting UAS to critical failures that may pose a safety hazard to flight operations. The purpose of the study was to critically assess the reliability of a laboratory designed UAS component test-bed operated using real-world data collected from a Boeing Scan Eagle UAS aileron servo unit via a flight data recorder. The study hypothesized that the test-bed unit's replicating a UAS aileron servo motor's reliability, in terms of a base-line measured encoder output of commanded servo positions, will not be significantly different after double and triple periods of time for continuous operating cycles. Study adds to paucity of extant research on UAS reliability and recommends further studies on commercial UAS components reliability and time to failure.
Quantum Real numbers and Measurement

The quantum mechanical measurement problem does not arise in the quantum real number approach to quantum measurements of the first kind. The attributes of individual microscopic systems in the experimental ensemble always have qr-number values so the individual systems can be followed throughout the process. The interaction with an apparatus connects the qr-number value of the quantity to be measured with the qr-number value of an attribute of the apparatus that can be locally approximated by a classical number and subsequently amplified to a recordable output.
Reengineering the library: Issues in electronic resources management

"Reengineering the library: Issues in electronic resources management." Journal of Web Librarianship, 13(1), p. 91
PENGEMBANGAN MODUL PERENCANAAN STUDI LANJUT BAGI SISWA SMP/MTs

The research is a research and development. The subjects of this research are the professional of education media, the professional of guidance and counseling, the guidance and counseling teacher or practical in the school and student. The technigue of collecting data are used with inquiri and interview the resultang from. This research of this development are (i) the description of result the level necessary guidance and counseling teachers and student about the development further study plan module indicate that the students need very much the module of the development of planning the study of continue. From inquiry result are got precentase level the necessary student in the amount 90,33%. (ii) The further study plan module for SMP/MTs student have passed validation test phase and acceptabilities by professional (the professional education media and guidance and counseling professional), and small group test by guidance and counseling teacher and students. With result for validating test by professional are got 3,19 on the average it is mean that are developed very valid. Meanwhile acceptability test (usability, feasibility, and accuracy) from the professional/expert subjects are got 3,41 on the average it is mean that very acceptable. While acceptability test (usability, feasibility, and accuracy) by the small grouf test (guidance teacher and students) obtained the average each 3,65 and 3,42 it is mean that very acceptable.
Size and Pose Identification of Bolts Based on Monocular Vision

Machine vision has extensively developed in automatic image recognition research, and the automatic identification of bolts can improve the degree of automation greatly for bolts operation. This paper provides a method to extract the outline and the geometrical shape of the bolt thereby to identify the target bolt and locate the position according to the shape and the size of a kind of common six-angle bolt. A dynamic threshold segmentation method is used to extract the bolt geometry information and set the target area as region of interests (ROI). By the sub pixel boundary calculation, Ramer algorithm for segmentation of contours, and Tukey weight function with five iterations for linear fitting, the intersection features which are the bolt corners can be obtained. Through calibration, the relative position of bolt can be located and the size of bolt can be acquired.
Vocational Guidance in School: Relevance and New Challenges

The article analyzes the results of studies of the features of the choice of profession and university by modern youth, conducted in 2018. The problems of professional self-determination of schoolchildren are revealed and new tasks of vocational guidance work at school are identifi ed. The material of the authors can be useful for creating an environment for professional self-determination, early career guidance and pre-professional training of schoolchildren in educational organizations.
Internet of Things (IoT) Saudi Arabia Healthcare Systems: State-Of-The-Art, Future Opportunities and Open Challenges

Background: Internet of Things (IoT)-based health care applications are designed to explore and exploit the latest communication technologies in a manner that connects health care providers and patients by way of added-value services such as the remote monitoring of patient health and the availability of data analytics applications for clinicians and patients alike. This study aimed to provide a qualitative analysis of the enabling technologies, applications, benefits, challenges, future opportunities, and prospects for IoT-based health care applications that have been introduced to date in collaboration with some of the hospitals and medical cities in Saudi Arabia.Method: A qualitative research design was used. Open-ended interviews were conducted with 10 informants, including information technology (IT) executives, IT directors, IT managers, and health informatics specialists, who were selected due to their knowledge of the health care systems in the medical cities of Saudi Arabia. Results: State-of-the-art IoT technologies are not yet applied in the medical cities of Riyadh, Saudi Arabia. The results of the study show the most challenges with IoT technologies that affect patient's privacy and security and how to have this technology be safe, reliable, and accessible. Moreover, the benefits of IoT lie in its use for patients, which can increase the quality of care and services, increase the patient satisfaction and patient experience, and reduce the cost of time and amount of effort required for providing the best care for patients. Furthermore, standardization of IoT facilitates communications and accessibility of care between patients and health care providers.Conclusion: There is a strong need to an infrastructure of health information systems in most of Saudi medical cities that fit with IoT. Furthermore, the attainment of higher numbers of staff with more experience in this field is warranted. However, the present research highlights that the promotion of the use of IoT in medical cities should be more focused on training the users and pushing them to increase adoption of IoT. The initiation of real projects that apply IoT in health care should also be promoted. These findings may also be useful for health care professionals when implementing future IoT health care services in Saudi medical cities for superior patient management.
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting

We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN). We achieve state-of-the-art forecasting accuracy on the benchmark CDC dataset. To improve model efficiency, we sparsify the network weights via transformed-$\ell_1$ penalty and maintain prediction accuracy at the same level with 70% of the network weights being zero.
Study on the Dynamic Higher Vocational English Evaluation System Based on “Internet +”

This paper aims to study the dynamic higher vocational English evaluation system based on "Internet +". Under the background of â€œInternet +â€, the higher vocational English evaluation system makes full use of the advantage of â€ Internet +â€, establish a link between school, students and teachers, correct dynamically the teaching methods and contents timely according to the feedback information of the evaluation, thus finally achieve individualized teaching.
Multiple Graph Adversarial Learning

Recently, Graph Convolutional Networks (GCNs) have been widely studied for graph-structured data representation and learning. However, in many real applications, data are coming with multiple graphs, and it is non-trivial to adapt GCNs to deal with data representation with multiple graph structures. One main challenge for multi-graph representation is how to exploit both structure information of each individual graph and correlation information across multiple graphs simultaneously. In this paper, we propose a novel Multiple Graph Adversarial Learning (MGAL) framework for multi-graph representation and learning. MGAL aims to learn an optimal structure-invariant and consistent representation for multiple graphs in a common subspace via a novel adversarial learning framework, which thus incorporates both structure information of intra-graph and correlation information of inter-graphs simultaneously. Based on MGAL, we then provide a unified network for semi-supervised learning task. Promising experimental results demonstrate the effectiveness of MGAL model.