Reviewerly/exHarmony huggingface dataset:
EdTech in Indonesia : Ready for Take-off?

This EdTech landscape survey provides an overview of the Indonesian startup ecosystem in EdTech, drawing upon three main sources of information: publicly available data, information collected via an online-questionnaire sent to 60 EdTech players—representing the vast majority of the main players—and 18 structured, in-depth face-to-face interviews from December 2018 through February 2019, as well as a group consultation with preliminary findings and recommendations. The findings reveal that the Indonesian EdTech sector is starting to catch up with the global frontier, and with growth of similar platforms, such as Harukaedu (a platform offering online university degrees), Ruangguru (an interactive e-learning platform for K-12 students in Indonesia) and Cakap by Squline (a tutoring platform for language learning), but overall the sector is still in its infancy. This early stage of development applies to evidence as well; there is almost no rigorous information available about the quality or effectiveness of the products and services offered in the Indonesian EdTech market, something that is true of many EdTech markets globally. Indonesian EdTech products generally aim at helping students with learning and upskilling, helping educators with student management, communication and teaching, and helping educational institutions with administration. For example, companies such as Ruangguru, Zenius and Quipper provide self-directed e-learning content, interactive learning platforms, and study tools that help students to expedite the learning process, along with interactive online services that help students with theirassignments and test preparation. Companies such as Arsa Kids, Digikids and Educa Studio develop gamebased and blended learning experiences, including interactive storybooks and educational mobile apps, to help improve early childhood educators' effectiveness.
Formal Structure: Image Schemas as Families of Theories

The previous chapters introduced some core problems for computational concept invention as well as for the theory of image schemas. Here the structure problem and the categorisation problem were mentioned. The problems capture the problems of determining which conceptual structures are image-schematic respectively which structures belong to which image schema. This chapter addresses these problems by introducing a formal method to structure image-schematic notions. The categorisation problem is approached by allowing notions of similar structure to be part of an image-schematic family that groups together similar concepts rather than having strict definitions of a particular image schema. Simultaneously, the structure problem is approached by ordering this family into a hierarchy where simpler concepts are made increasingly complex through the addition of conceptual and spatial primitives. These methods solve, to some degree, the issues regarding defining and classifying image-schematic notions for artificial intelligence research while simultaneously providing a formal method for how to structure them. As a proof of concept two image schema families are introduced: the Two-Object family and the PATH family. The first deals with spatial relationships between two objects and the latter with dynamic movement of one object. Formally these families will be represented using theory graphs. The chapter includes:
Antisemitism in the Urban Dictionary and the Responsibilities of Online Publishers

This article focuses on antisemitic and racist content in the Urban Dictionary: a global top-1000 website built upon user-generated content. It argues that the Urban Dictionary’s founding principles have directly facilitated the site’s exploitation as a platform for the dissemination of antisemitic hate speech and white supremacist ideology. These principles can be seen as typifying the free speech absolutism that became dominant within the US tech industry during the 1990s. However, the right to free expression cannot reasonably be taken to exempt internet companies from responsibility for content whose publication they facilitate. The article concludes by arguing that websites such as the Urban Dictionary are essentially publishers, and that the solution to the problem of their indulgence of bigots may be for those who do not wish to be associated with bigotry to refrain from doing business with institutions that publish content that they consider abhorrent. Keywords:  alt-right, antizionism, brand contamination, definitions, dictionaries, free speech, Urban Dictionary, user-generated content, Web 2.0
Development of Learning Media Assisted by Interactive PowerPoint in Thematic Learning at Elementary Schools

This study aimed to examine the effectiveness of group counseling through the self-instruction technique empirically based on observational learning (SIBOL) to foster a steady and great attitude in life as a student.This study used quantitative methods by "within-group or individual design" through equivalent time-series design, pre-test, posttest 1 and post-test 2 continuously with self-efficacy scale (being steady in a life), and conducted by 6 students who had low self-efficacy in the academic context.Data analysis used t-test calculations and showed results that group counseling through SIBOL techniques being more effective to improve student's self-efficacy, as evidenced by an increasing score from pre-test to post-test 2 namely 16%.
A novel incremental person re-recognition method with constant update speed

Person re-identification (Re-ID) is an important technique towards the automatic search of a person’s presence in a surveillance video or security systems. Applying incremental learning techniques to accelerate the online training speed with ever-increasing data is desired and critical for Re-ID. As an incremental learning algorithm, Incremental Kernel Null Foley-Sammon Transform (IKNFST) method significantly reduces the computational complexity while holds the accuracy. However, with ever-increasing person samples within the same category, the corresponding growth of dimensions makes it difficult to update the online model. To address the issue, we propose to maintain constant update speed by constructing Reduce Set (RS) expansions during online updating. The key idea is to firstly extract new information brought by newly-added samples and integrate it with the existing model by Incremental Kernel Principal Component Analysis (IKPCA) scheme for further Reduce Set (RS) compression. And the compressed samples and the corresponding model are then input to Kernel Null Foley-Sammon Transform (KNFST) algorithm for generating an updated model. Extensive experiments have been carried on three public datasets, including Market-1501, DukeMTMCReID and CUHK03. The results show that our proposed method beats the state-of-the-art IKNFST by a big margin.
• CONNECTEDNESS AND PATH CONNECTEDNESS ON SPACE-TIME TOPOLOGICAL MANIFOLD

W e conduct a study on R 4 which is a space- time Topological Manifold. The properties of R 4 like time-path, path- connectedness, sequential connected, weakly connected, irreversibility sequential connected with respect to time, we also study densely connectivity of R 4 .  Theexistence of homeomorphism between U ⊆ R 4 -a space time manifold and a Toplogical Manifold T(M) provides a sound mathematical base for the study  path connectedness, corona virus  in the world manifold.
RETRACTED: A method for remote sensing image denoising based on structured dictionary learning and cloud computing

With the emerging technology of remote sensing, a huge amount of remote sensing data is collected and stored in the remote sensin02222g platform, and the transmission and processing of data on the platform is extremely wasteful. It is essential to incorporate the speedy remote sensing processing services in an integrated cloud computing architecture. In order to improve the denoising ability of remote sensing image, a new structured dictionary-based method for multispectral image denoising based on cluster is proposed. This method incorporates both the locality of spatial and the correlation across spectrum of multispectral image. Remote sensing image is divided into different groups by clustering, and sparse representation coefficients of spatial and spectral and dictionary is obtained according to the dictionary learning algorithm. After threshold processing, the similar blocks are averaged and realized with multispectral remote sensing image denoising. The algorithm is applied to denoise the noisy remote sensing image of Maoergai area in the upper Minjiang which contain typical vegetation and soil is chosen as study area, simulation results show that higher peak-signal to noise ratio can be obtained as compared to other recent image denoising methods.
Direct loss minimization algorithms for Bayesian predictors

Recent work proposed direct loss minimization (DLM), which minimizes the predictive loss of a hypothetical posterior, as an alternative to variational inference, and showed that such approaches come with strong theoretical guarantees. However the use of DLM for Bayesian predictors has not been thoroughly evaluated to date. One difficulty with using DLM is the fact that, for log-loss, the objective function includes a complex term with a logarithm of an expectation (logE) that is not easily amenable to stochastic optimization with unbiased samples. The paper makes three contributions in this context. The first is a new method using product sampling for unbiased estimates of gradients (uPS) for objectives with LogE terms. The second is an analysis of biased Monte Carlo (bMC), a simple form of biased sampling of gradients for logE terms, showing that stochastic gradient descent converges despite the biased gradients. The third is an empirical evaluation of DLM in sparse Gaussian processes, including log loss for the conjugate and non-conjugate cases and square loss for regression. The results show that DLM is an effective approach which in some cases matches and in some cases significantly improves over the performance of variational inference. Results comparing the sampling methods show that uPS is potentially more sample-efficient but bMC provides a better tradeoff in terms of convergence time and computational efficiency.
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location

Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department. In this work we propose a student-teacher network via reinforcement learning to deal with this specific problem. A representation of the weights of the student network is treated as the state and is fed as an input to the teacher network. The teacher network's action is to select the most appropriate batch of data to train the student network on from a training set sorted according to entropy. By validating on three datasets, not only do we show that our approach outperforms state-of-the-art methods on tabular data and performs competitively on image recognition, but also that novel curricula are learned by the teacher network. We demonstrate experimentally that the teacher network can actively learn about the student network and guide it to achieve better performance than if trained alone.
A scale-adaptive real-time target tracking algorithm based on KCF

Kernelized Correlation Filter (KCF) algorithm has been successfully applied in object tracking. By finding the maximum confidence in the search region, the candidate target block is determined by KCF with constant size. When the scale of the target changes during its actual movement, the traditional KCF algorithms often fail to track the target. Moreover, it is difficult to judge whether the object is missing due to the lack of self-adaptive threshold regulation scheme in the KCF tracking. In order to solve these problems, this paper proposes a scale-adaptive target tracking algorithm based on KCF, which is mainly divided into the following steps. Firstly, the positive and negative samples of the nearest neighbor classifier are initialized by the selected target and its surrounding non-target areas. Secondly, the peak point of the spectral response of the current frame image is obtained by executing the KCF algorithm, which is the target center point. Thirdly, the scale change of object is obtained by calculating the ratio of the bandwidth of spectral response peak centered candidate regions and the target in the previous frame. Fourthly, the scaled candidate target is confirmed by calculating the sample similarity between it and the Nearest Neighbor Classifier (NNC). Finally, the positive and negative samples of the nearest neighbor classifier are updated with the confirmed tracking target and non-target respectively. Extensive experiments have been carried on four test video sequences. The experimental results show that our proposed method achieves a higher success rate and accuracy with less running time compared with the state-of-the- art methods.
Research and Application of Improved APRIORI Algorithm Based on Hash Technology

Apriori Algorithm is the most Classic Association Rule Mining Algorithm, which has unique advantages, but it also has some disadvantages such as high overhead. This paper first describes Apriori Algorithm, points out its shortcomings, introduces related concepts, and then proposes a method based on Hash technology and compressed combination item set technology to improve APRIORI algorithm. This paper introduces the basic idea and the concrete process of the improvement in detail, analyzes the efficiency of the improved algorithm by the experiment, and advances the application of the improved algorithm in the library personalized service.
Generating Concepts: Guiding Computational Conceptual Blending with Image Schemas

In the previous chapters, the formal aspects of representing image schemas were dealt with. Likewise, the role of image schemas in conceptualisations was investigated in the previous chapter. This chapter advances the work on concept invention by suggesting how image schemas can be integrated into conceptual blending, introduced in Chapter 1 as a theoretical framework for creativity. It includes two different approaches. The first focuses on giving image-schematic information higher priority to be inherited into the blended space. The second is to use image schemas as the foundation in the generic space. In addition to providing a series of examples of how this would look, the chapter also goes into details on how the family structure from Chapter 3 can be used during the blending to either strengthen or weaken the image-schematic structure in the input spaces, if needed. The chapter includes considerations and discussion on:
IMPACT OF EDUCATION ON ECONOMIC GROWTH. THE CASE OF RWANDA

This paper focused on the impact of education on economic growth in Rwanda. It was an attempt to explore the extent to which investment in education level by the Government of Rwanda affects its economic growth hence the output level is achieved. The government of Rwanda has year by year increased budget for education. In 2019/20, the Government of Rwanda allocated FRW 310.2 billion to the education sector, up from FRW 278.2 billion in 2018/19, reflecting an increase of 11.5 per cent in nominal terms or 10.4 per cent in real terms. The study adopted Cobb-Douglas production function with rate of returns being constant to scale where human capital is treated as an independent factor of production in the human capital augmented growth model. Data that exists from the National Institute of Statistics Rwanda for a period 1999 to 2019 reports. The results revealed that human capital plays an important role in economic growth mainly as an engine for improvement of the output level. There is compelling evidence that human capital increases productivity, suggesting that education really is productivity-enhancing rather than just a device that individuals use to signal their level of ability to the employer. The variables were significantly fit the model as it was shown by the R 2 Adjusted –higher than 55% which shows strong goodness of fit in the short run estimated model. As conclusion since there was no problem of multicollinearity the goodness of fit is achieved. The study concludes that education has positive effect on economic growth. The study recommends that the government of Rwanda should encourage the citizen to engage in education. The government of Rwanda might do this directly by supporting infrastructure, small farmers, building schools in order to resolve those problem and external aids have significant on economic growth. Keywords: Education, Human Capital, Economic Growth, Cobb-Douglas Function , Rwanda CITATION: Rwigema, P. C. (2020). Impact of education on economic growth. The case of Rwanda. The Strategic Journal of Business & Change Management , 7(2), 490 – 512.
Bookkeeping Accounting Information System Untuk Meningkatkan Kinerja Laporan Keuangan Pada Toko YK

YK store is a UKM (small and medium enterprises) whose activities are buying and selling wholesale goods such as oil, spices, noodles, flour, coconut milk, pepper, and others in moderation (wholesale). YK store manager does not have a bookkeeping application system for the process of making financial reports in accordance with financial accounting standards, and there is no support system for merchandise inventory which supports buying and selling transactions. Information systems for managing merchandise needed by companies to determine value according to transactions, for financial statements will use a journal of eternal methods to buy and sell transactions then post them to the ledger and make financial reports for all businesses. Bookkeeping information system design using microsoft visual basic 6.0, xammp as a server and mysql as a database. The results obtained from this information system can contain reports obtained in accordance with the types and financial statements for business activities
LibGuides: SPC 1017 (Merceron): Job Search

This research guide is designed to assist Poinciana Campus students in researching careers in Professor Merceron's Interpersonal Communications class. Using the tabs above to navigate, you will find research tools, MLA formatting help, and library contact
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions

This paper proposes a new framework for providing approximation guarantees of local search algorithms. Local search is a basic algorithm design technique and is widely used for various combinatorial optimization problems. To analyze local search algorithms for set function maximization, we propose a new notion called localizability of set functions, which measures how effective local improvement is. Moreover, we provide approximation guarantees of standard local search algorithms under various combinatorial constraints in terms of localizability. The main application of our framework is sparse optimization, for which we show that restricted strong concavity and restricted smoothness of the objective function imply localizability, and further develop accelerated versions of local search algorithms. We conduct experiments in sparse regression and structure learning of graphical models to confirm the practical efficiency of the proposed local search algorithms.
HUBUNGAN FAKTOR HUMAN, ORGANIZATION DAN TECHNOLOGY (HOT-FIT MODEL) DENGAN KINERJA SISTEM INFORMASI MANAJEMEN FARMASI DI RUMAH SAKIT BWT SEMARANG

Pharmacy management information system is the application of an organized system for inputting, processing and presenting data to support the information needed by users in pharmaceutical services. At Bhakti Wira Tamtama Hospital, there are problems related to pharmaceutical management information systems such as frequent system disruptions, inaccurate drug stock in applications and in warehouses, etc. So it is necessary to improve the performance of pharmaceutical management information systems by knowing the variables contributed so that the intervention can be done on these variables. Based on previous research that also used the HOT-Fit method to evaluate information systems, the purpose of this study is to knowing that there is a relation between human, organization and technology with the performance of pharmaceutical management information systems. The method used is explanatory research using cross sectional. The subjects of this study used a total population of 14 respondents. The result of this study shows that there is a relation between technology and human (p = 0.021), technology and organization (p = 0.0001), human and organization (p = 0.006) and human and information system performance (p = 0.005), while there is no relation between organization and performance pharmaceutical management information system (p = 0.530). Recommendation for hospitals need to improve exciting features include drug stock features to function properly and add new features such as editing features.
Behavior Tracking in Video Surveillance Applications: A Detailed Study

The problem of video surveillance toward behavior tracking has been well studied. The object tracking in video surveillance and behavior analysis has been used for different problems. Numerous techniques have been presented earlier for the tracking of objects and classification of behaviors. The general approaches use a background model to identify the foreground objects. Based on the foreground objects identified, the object tracking has been performed. Similarly, various approaches consider different features to perform object detection and classification. Each method produces a different result with varying accuracy in behavior tracking. This paper analyses various methods of object tracking and behavior analysis in video surveillance. A detailed survey on the methods of object tracking is performed, and a comparative study is presented in this paper.
A vehicle detection algorithm in complex traffic scenes

In complex traffic scenarios, there are many problems such as inaccurate vehicle positioning, low recognition rate, and missed detection of vehicles for instability of the traffic environment. This paper proposes an improved Faster R-CNN algorithm for accurate vehicle detection. We use feature maps of depth images to supplement vehicle details by adding a depth channel into the detection model. When training the model, we add a hard sample mining strategy. We evaluate our newly proposed approach using KITTI dataset. The experimental results show that our proposed approach has a significant improvement in recognition accuracy by 5%.
Saving Zoë: Worldline self-monitoring can better limit the spread of diseases

The emergence of the novel coronavirus COVID-19 has sparked intense interest in the use of mobile phone technology to aid in contact tracing. We demonstrate, using a simple model of infectious transmission, that voluntary offering of a richer dataset—worldline information—can reveal transmission paths that are missed through contact tracing alone. This information can be captured securely by a mobile device and kept confidential, thus allowing individuals to weigh their personal privacy concerns against the benefits to society, and make their own decisions. Worldline self-monitoring has other benefits for the adopter that are unrelated to health concerns, which could aid in widespread adoption. Because each worldline contributes to a more comprehensive model of objective reality, independent platforms can contribute information without coordination, similar to the way that scientific knowledge is constructed.