Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)

Massive open online courses (MOOC) describe platforms where users with completely different backgrounds subscribe to various courses on offer. MOOC forums and discussion boards offer learners a medium to communicate with each other and maximize their learning outcomes. However, oftentimes learners are hesitant to approach each other for different reasons (being shy, don't know the right match, etc.). In this paper, we propose a reciprocal recommender system which matches learners who are mutually interested in, and likely to communicate with each other based on their profile attributes like age, location, gender, qualification, interests, etc. We test our algorithm on data sampled using the publicly available MITx-Harvardx dataset and demonstrate that both attribute importance and reciprocity play an important role in forming the final recommendation list of learners. Our approach provides promising results for such a system to be implemented within an actual MOOC.Comment: 10 pages, accepted as full paper @ ICWL 201

Analisa Strategi Pada Perusahaan Benih Tanaman Berbasis Metode Performance Prism Dan Swot (Studi Kasus: PT. X)

Pengukuran kinerja merupakan suatu metode atau alat yang digunakan untuk mencatat dan menilai pencapaian pelaksanaan kegiatan berdasarkan meningkatkan kualitas pengambilan keputusan dan akuntabilitas. Terdapat 4 elemen pokok pengukuran kinerja antara lain, menetapkan tujuan, sasaran, dan strategi organisasi, merumuskan indikator dan ukuran kinerja, mengukur tingkat ketercapaian tujuan dan sasaran organisasi, evaluasi kinerja. Sedangkan, untuk merumuskan strategi menggunakan analisis SWOT yang merupakan gambaran secara jelas bagaimana peluang dan ancaman eksternal Perusahaan yang disesuaikan dengan kekuatan dan kelemahan internal Perusahaan. Berkaitan dengan pencapaian target penjualan, target penjualan berbagai jenis benih sebagian besar tidak tercapai dimana kondisi ini terjadi pada tahun 2012. Maka dari itu dibutuhkan metode yang dapat diterapkan dalam pengukuran tersebut untuk mengetahui sejauh mana performansi kinerja Perusahaan telah tercapai sehingga dapat merumuskan strategi unggulan yang tepat dan berdaya saing. Hasil penelitian dengan metode Performance Prism didapatkan nilai indeks sebesar 8.60 yang masuk kedalam kategori hijau, sehingga dinyatakan kinerja Perusahaan secara keseluruhan telah memenuhi performa Perusahaan yang diharapkan. Pada analisis SWOT telah didapatkan 5 strategi baru yaitu memperluas pangsa pasar, menjaga hubungan baik dengan supplier, memberikan reward, peningkatan daya tahan benih, dan menjaga hubungan baik dengan koperasi

SMARTer Discontinuation Trial Designs for Developing an Adaptive Treatment Strategy

Abstract Objective: Developing evidenced-based practices for the management of childhood psychiatric disorders requires research studies that address how to treat children during both the acute phase of the disorder and beyond. Given the selection of a medication for acute treatment, discontinuation trials are used to evaluate the effects of treatment duration (e.g., time on medication) and/or maintenance strategies following successful acute-phase treatment. Recently, sequential multiple assignment randomized trials (SMART) have been proposed for use in informing sequences of critical clinical decisions such as those mentioned. The objective of this article is to illustrate how a SMART study is related to the standard discontinuation trial design, while addressing additional clinically important questions with similar trial resources. Method: The recently completed Child/Adolescent Anxiety Multimodal Study (CAMS), a randomized trial that examined the relative efficacy of three acute-phase treatments for pediatric anxiety disorders, along with a next logical step, a standard discontinuation trial design, is used to clarify the ideas. This example is used to compare the discontinuation trial design relative to the SMART design. Results: We find that the standard discontinuation trial can be modified slightly using a SMART design to yield high-quality data that can be used to address a wider variety of questions in addition to the impact of treatment duration. We discuss how this innovative trial design is ultimately more efficient and less costly than the standard discontinuation trial, and may result in more representative comparisons between treatments. Conclusions: Mental health researchers who are interested in addressing questions concerning the effects of continued treatment (for different durations) following successful acute-phase treatment should consider SMART designs in place of discontinuation trial designs in their research. SMART designs can be used to address these and other questions concerning individualized sequences of treatment, such as the choice of a rescue treatment in case of postacute phase relapse.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98496/1/cap%2E2011%2E0073.pd

Optimal client recommendation for market makers in illiquid financial products

The process of liquidity provision in financial markets can result in prolonged exposure to illiquid instruments for market makers. In this case, where a proprietary position is not desired, pro-actively targeting the right client who is likely to be interested can be an effective means to offset this position, rather than relying on commensurate interest arising through natural demand. In this paper, we consider the inference of a client profile for the purpose of corporate bond recommendation, based on typical recorded information available to the market maker. Given a historical record of corporate bond transactions and bond meta-data, we use a topic-modelling analogy to develop a probabilistic technique for compiling a curated list of client recommendations for a particular bond that needs to be traded, ranked by probability of interest. We show that a model based on Latent Dirichlet Allocation offers promising performance to deliver relevant recommendations for sales traders.Comment: 12 pages, 3 figures, 1 tabl

Pythia: AI-assisted Code Completion System

In this paper, we propose a novel end-to-end approach for AI-assisted code completion called Pythia. It generates ranked lists of method and API recommendations which can be used by software developers at edit time. The system is currently deployed as part of Intellicode extension in Visual Studio Code IDE. Pythia exploits state-of-the-art large-scale deep learning models trained on code contexts extracted from abstract syntax trees. It is designed to work at a high throughput predicting the best matching code completions on the order of 100 $ms$. We describe the architecture of the system, perform comparisons to frequency-based approach and invocation-based Markov Chain language model, and discuss challenges serving Pythia models on lightweight client devices. The offline evaluation results obtained on 2700 Python open source software GitHub repositories show a top-5 accuracy of 92\%, surpassing the baseline models by 20\% averaged over classes, for both intra and cross-project settings.Comment: Published in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19

Combining mouse mammary gland gene expression and comparative mapping for the identification of candidate genes for QTL of milk production traits in cattle

<p>Abstract</p> <p>Background</p> <p>Many studies have found segregating quantitative trait loci (QTL) for milk production traits in different dairy cattle populations. However, even for relatively large effects with a saturated marker map the confidence interval for QTL location by linkage analysis spans tens of map units, or hundreds of genes. Combining mapping and arraying has been suggested as an approach to identify candidate genes. Thus, gene expression analysis in the mammary gland of genes positioned in the confidence interval of the QTL can bridge the gap between fine mapping and quantitative trait nucleotide (QTN) determination.</p> <p>Results</p> <p>We hybridized Affymetrix microarray (MG-U74v2), containing 12,488 murine probes, with RNA derived from mammary gland of virgin, pregnant, lactating and involuting C57BL/6J mice in a total of nine biological replicates. We combined microarray data from two additional studies that used the same design in mice with a total of 75 biological replicates. The same filtering and normalization was applied to each microarray data using GeneSpring software. Analysis of variance identified 249 differentially expressed probe sets common to the three experiments along the four developmental stages of puberty, pregnancy, lactation and involution. 212 genes were assigned to their bovine map positions through comparative mapping, and thus form a list of candidate genes for previously identified QTLs for milk production traits. A total of 82 of the genes showed mammary gland-specific expression with at least 3-fold expression over the median representing all tissues tested in GeneAtlas.</p> <p>Conclusion</p> <p>This work presents a web tool for candidate genes for QTL (cgQTL) that allows navigation between the map of bovine milk production QTL, potential candidate genes and their level of expression in mammary gland arrays and in GeneAtlas. Three out of four confirmed genes that affect QTL in livestock (<it>ABCG2</it>, <it>DGAT1</it>, <it>GDF8, IGF2</it>) were over expressed in the target organ. Thus, cgQTL can be used to determine priority of candidate genes for QTN analysis based on differential expression in the target organ.</p

A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer