Reviewerly/exHarmony huggingface dataset:
Comparison Between Suitable Priors for Additive Bayesian Networks

Additive Bayesian networks (ABN) areKratzer, Gilles types of graphical models that extend the usual Bayesian-generalised linear model to multiple dependent variables through the factorisation of the joint probability distribution of the underlying variables.Furrer, Reinhard When fitting an ABN model, the choice of the prior for the parameters is of crucial importance. If an inadequate prior—like a not sufficiently informative one—is used, data separation and data sparsity may lead to issues in the model selection process. In this work we present a simulationPittavino, Marta study to compare two weakly informative priors with a strongly informative one. For the weakly informative prior, we use a zero mean Gaussian prior with a large variance, currently implemented in the R package abn. The candidate prior belongs to the Student’s t-distribution. It is specifically designed for logistic regressions. Finally, the strongly informative prior is Gaussian with a mean equal to the true parameter value and a small variance. We compare the impact of these priors on the accuracy of the learned additive Bayesian network as function of different parameters. We create a simulation study to illustrate Lindley’s paradox based on the prior choice. We then conclude by highlighting the good performance of the informative Student’s t-prior and the limited impact of Lindley’s paradox. Finally, suggestions for further developments are provided.
RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks

We present Random Partition Relaxation (RPR), a method for strong quantization of neural networks weight to binary (+1/-1) and ternary (+1/0/-1) values. Starting from a pre-trained model, we quantize the weights and then relax random partitions of them to their continuous values for retraining before re-quantizing them and switching to another weight partition for further adaptation. We demonstrate binary and ternary-weight networks with accuracies beyond the state-of-the-art for GoogLeNet and competitive performance for ResNet-18 and ResNet-50 using an SGD-based training method that can easily be integrated into existing frameworks.
An Interactive Video Search Platform for Multi-modal Retrieval with Advanced Concepts

The previous version of our retrieval system has shown some significant results in some retrieval tasks such as Lifelog’s moment retrieval tasks. In this paper, we adapt our platform to the Video Browser Showdown’s KIS and AVS tasks and present how our system performs in video search tasks. In addition to the smart features in our retrieval system that take advantage of the provided analysis data, we enhance the data with object color detection by employing Mask R-CNN and clustering. In this version of our search system, we try to extract the location information of the entities appearing in the videos and aim to exploit the spatial relationship between these entities. We also focus on designing efficient user interaction and a high-performance way to transfer data in the system to minimize the retrieval time.
Graph Neighborhood Attentive Pooling

Network representation learning (NRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and sparse graphs. Most studies explore the structure and metadata associated with the graph using random walks and employ an unsupervised or semi-supervised learning schemes. Learning in these methods is context-free, because only a single representation per node is learned. Recently studies have argued on the sufficiency of a single representation and proposed a context-sensitive approach that proved to be highly effective in applications such as link prediction and ranking. However, most of these methods rely on additional textual features that require RNNs or CNNs to capture high-level features or rely on a community detection algorithm to identify multiple contexts of a node. In this study, without requiring additional features nor a community detection algorithm, we propose a novel context-sensitive algorithm called GAP that learns to attend on different parts of a node's neighborhood using attentive pooling networks. We show the efficacy of GAP using three real-world datasets on link prediction and node clustering tasks and compare it against 10 popular and state-of-the-art (SOTA) baselines. GAP consistently outperforms them and achieves up to ~9% and ~20% gain over the best performing methods on link prediction and clustering tasks, respectively.
TEKNIK CLUSTERING DENGAN ALGORITMA K-MEDOIDS UNTUK MENANGANI STRATEGI PROMOSI DI POLITEKNIK TEDC BANDUNG

This research was made based on promotional data that was conducted in 2015-2017. The unequal distribution of information about Politeknik TEDC Bandung can be seen in the data collected by two methods, first direct interviews to students from 2015-2017 and data taken at the time the student enrolled at Politeknik TEDC Bandung. In 2015-2017 there were several promotions carried out by Politeknik TEDC Bandung, including through Tribun Jabar newspaper, promotions to several educational institutions, installing billboards at several places, giving information about Politeknik TEDC Bandung on website, collaboration with other collages through alumni who work in industry or in schools, and last government scholarship programs. Unequal distribution of information to prospective students is one of the main problems of this research being made so that the promotions that have been carried out so far can be more efficient and effective. To be able to carry out promotions that are more effective and efficient, the researcher will process all data to produce a better promotional strategy. Data processing is carried out by grouping using the KMedoids algorithm on the data of prospective students including Name, Place and Date of Birth, Address, Religion, Telephone Number, School Origin, Selection of Study Programs and who provide information. Which data is obtained from promotion activities carried out from 2015 until 2017. After cleaning and repairing the data, a detailed and more specific dataset can be obtained for grouping the data. If necessary, there is a possibility that some data will be discarded to be more effective in testing the clustering process. Clustering results are made in the form of a chart bar which shows that the most registrants at the TEDC Polytechnic in Bandung are from West Java Province, while the City of Sumedang is the least registrant.
Pengembangan Media Box of Number Berbasis Tematik untuk Pembelajaran Matematika Permulaan Anak Usia 4-5 Tahun

Media plays to improve in achieving a learning goal because it can help teachers deliver learning material quickly. This research develops thematic-based box of number media that aims to introduce the beginning of mathematics to children aged 4-5 years so that it can delight children in learning. The resulting media include three themes, names of plants, myself, and the universe. This type of research used in this research is Research and Development (RnD). The results showed that the media box of number obtained an assessment from media experts with the criteria of feasible, namely the percentage of 92.5%, which previously earned 62.5%. Assessment by material experts get "decent" standards, with a percentage of 97.5% from 62.5% previously. While the results of the evaluation by users get the criteria "feasible" with a percentage of 95%. These results can be concluded that the thematic-based box of number media is suitable for beginning mathematics learning in children aged 4-5 years.
Position: Manual Refactoring (by Novice Programmers) Considered Harmful

In recent years, the issue of quality in introductory and end-user programming has become increasingly prominent. In particular, the poor quality of block-based programs has been shown to harm the educational effectiveness in this programming domain. As a result, there has been a growing interest in code quality and its improvement practices among researchers and educators. Refactoring-transforming code to remove quality problems while preserving semantics-is taught as a common and practical technique for improving code quality. To maximize pedagogical effectiveness, some educators advocate teaching students to refactor by hand to elucidate the mechanics behind refactoring instead of relying on tools that transform code automatically. In this position paper, we argue that this pedagogical approach is counterproductive. We advocate teaching refactoring similarly to compilation, a technique that students start applying automatically right away, but learn its inner workings only much later in the curriculum. Even professional developers are advised to avoid carrying out complex refactoring transformations by hand, as this activity leads to hard-to-trace bugs as well as wastes time and effort. To put appropriate tools for improving software quality into the hands of Scratch programmers, we created an automated refactoring infrastructure for Scratch, and we argue that such facility should become a mainstay of programming environments for blocks.
Image Sets Compression Via Patch Redundancy

In the last years, the development of compression algorithms for image collections (e.g., photo albums) has become very popular due to the enormous diffusion of digital photographs. Typically, current solutions create an image sequence from images of the photo album to make them suitable for compression using a High Performance Video Coding (HEVC) encoder. In this study, we investigated a different approach to compress a collection of similar images. Our main idea is to exploit the inter- and intra- patch redundancy to compress the entire set of images. In practice, our approach is equivalent to compress the image set with Vector Quantization (VQ) using a global codebook. Our tests show that our clusterization algorithm is effective for a large number of images.
Measuring Semantic Similarity between Words Based on Multiple Relational Information

The similarity of words extracted from the rich text relation network is the main way to calculate the semantic similarity. Complex relational information and text content in Wikipedia website, Community Question Answering and social network, provide abundant corpus for semantic similarity calculation. However, most typical research only focused on single relationship. In this paper, we propose a semantic similarity calculation model which integrates multiple relational information, and map multiple relationship to the same semantic space through learning representing matrix and semantic matrix to improve the accuracy of semantic similarity calculation. In experiments, we confirm that the semantic calculation method which integrates many kinds of relationships can improve the accuracy of semantic calculation, compared with other semantic calculation methods.
Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules

Image compression is a fundamental research field and many well-known compression standards have been developed for many decades. Recently, learned compression methods exhibit a fast development trend with promising results. However, there is still a performance gap between learned compression algorithms and reigning compression standards, especially in terms of widely used PSNR metric. In this paper, we explore the remaining redundancy of recent learned compression algorithms. We have found accurate entropy models for rate estimation largely affect the optimization of network parameters and thus affect the rate-distortion performance. Therefore, in this paper, we propose to use discretized Gaussian Mixture Likelihoods to parameterize the distributions of latent codes, which can achieve a more accurate and flexible entropy model. Besides, we take advantage of recent attention modules and incorporate them into network architecture to enhance the performance. Experimental results demonstrate our proposed method achieves a state-of-the-art performance compared to existing learned compression methods on both Kodak and high-resolution datasets. To our knowledge our approach is the first work to achieve comparable performance with latest compression standard Versatile Video Coding (VVC) regarding PSNR. More importantly, our approach generates more visually pleasant results when optimized by MS-SSIM. This project page is at this https URL https://github.com/ZhengxueCheng/Learned-Image-Compression-with-GMM-and-Attention
Security governance as a service on the cloud

Abstract Small companies need help to detect and to respond to increasing security related threats. This paper presents a cloud service that automates processes that make checks for such threats, implement mitigating procedures, and generally instructs client companies on the steps to take. For instance, a process that automates the search for leaked credentials on the Dark Web will, in the event of a leak, trigger processes that instruct the client on how to change passwords and perhaps a micro-learning process on credential management. The security governance service runs on the cloud as it needs to be managed by a security expert and because it should run on an infrastructure separated from clients. It also runs as a cloud service for economy of scale: the processes it runs can service many clients simultaneously, since many threats are common to all. We also examine how the service may be used to prove to independent auditors (e.g., cyber-insurance agents) that a company is taking the necessary steps to implement its security obligations.
EDUCATION AND CULTURE OF THINKING: RELEVANT METHODOLOGICAL DISCOURSE

The article reveals the methodological potential of using the third type of the education paradigm, which fully includes culture of innovative thinking. In the authors’ opinion, improving the quality of analytical data inevitably requires shifting accents in the mechanisms of thinking. The paper identifies the mutual influence of different factors on the formation of modern specialists’ culture of thinking as a means to enhance their professional and educational level. Rational approaches towards transformational pedagogical influences are offered.
owards a quantum-inspired proof for IP = PSPACE

We explore quantum-inspired interactive proof systems where the prover is limited. Namely, we improve on a result by \cite{AG17} showing a quantum-inspired interactive protocol ($\IP$) for $PreciseBQP$ where the prover is only assumed to be a $\PreciseBQP$ machine, and show that the result can be strengthened to show an $\IP$ for $\NP^{\PP}$ with a prover which is only assumed to be an $\NP^{\PP}$ machine - which was not known before. We also show how the protocol can be used to directly verify $\QMA$ computations, thus connecting the sum-check protocol by \cite{AAV13} with the result of \cite{AG17,LFKN90}. Our results shed light on a quantum-inspired proof for $\IP=\PSPACE$, as $\PreciseQMA$ captures the full $\PSPACE$ power.
Sketch distance-based clustering of chromosomes for large genome database compression

The rapid development of Next-Generation Sequencing technologies enables sequencing genomes with low cost. The dramatically increasing amount of sequencing data raised crucial needs for efficient compression algorithms. Reference-based compression algorithms have exhibited outstanding performance on compressing single genomes. However, for the more challenging and more useful problem of compressing a large collection of n genomes, straightforward application of these reference-based algorithms suffers a series of issues such as difficult reference selection and remarkable performance variation.We propose an efficient clustering-based reference selection algorithm for reference-based compression within separate clusters of the n genomes. This method clusters the genomes into subsets of highly similar genomes using MinHash sketch distance, and uses the centroid sequence of each cluster as the reference genome for an outstanding reference-based compression of the remaining genomes in each cluster. A final reference is then selected from these reference genomes for the compression of the remaining reference genomes. Our method significantly improved the performance of the-state-of-art compression algorithms on large-scale human and rice genome databases containing thousands of genome sequences. The compression ratio gain can reach up to 20-30% in most cases for the datasets from NCBI, the 1000 Human Genomes Project and the 3000 Rice Genomes Project. The best improvement boosts the performance from 351.74 compression folds to 443.51 folds.The compression ratio of reference-based compression on large scale genome datasets can be improved via reference selection by applying appropriate data preprocessing and clustering methods. Our algorithm provides an efficient way to compress large genome database.
Optimal Two-Qubit Circuits for Universal Fault-Tolerant Quantum Computation

We study two-qubit circuits over the Clifford+CS gate set, which consists of the Clifford gates together with the controlled-phase gate CS=diag(1,1,1,i). The Clifford+CS gate set is universal for quantum computation and its elements can be implemented fault-tolerantly in most error-correcting schemes through magic state distillation. Since non-Clifford gates are typically more expensive to perform in a fault-tolerant manner, it is often desirable to construct circuits that use few CS gates. In the present paper, we introduce an efficient and optimal synthesis algorithm for two-qubit Clifford+CS operators. Our algorithm inputs a Clifford+CS operator U and outputs a Clifford+CS circuit for U, which uses the least possible number of CS gates. Because the algorithm is deterministic, the circuit it associates to a Clifford+CS operator can be viewed as a normal form for that operator. We give an explicit description of these normal forms and use this description to derive a worst-case lower bound of 5log(1/epsilon)+O(1) on the number of CS gates required to epsilon-approximate elements of SU(4). Our work leverages a wide variety of mathematical tools that may find further applications in the study of fault-tolerant quantum circuits.
SISTEM INFORMASI PEMASARAN PRODUK DESA BERBASIS WEB

Batu Mekar Village is one of the villages in West Lombok Regency in Lingsar District. Batu Mekar village has many products in the form of handicrafts such as bags, lamp decorations made from ketak or agricultural and plantation products such as coffee, palm sugar and others. The problem that exists in Batu Mekar Village is the low sales price of handicraft products because the product is only left at a souvenir shop. The decrease in the number of sales was not due to a decrease in the number of tourists who ended up on the island of Lombok due to the earthquake. In addition, the limited promotion media. This research was developed using the Rapid Application Development method. This research resulted in an online sales information system as a medium for handicraft sales, so that the public could directly sell their products without going through intermediaries as well as promotional media so that handicraft products can be sold directly and can be recognized by the wider community. The developed application can be used as a promotional medium as well as a means of selling Batu Mekar village handicraft products.
Agile Approach as a Universal Remedy for the Usual Failure in the Development of Software Projects

Background: Agile is not a methodology neither can it be considered as a peculiar way of developing any software also it is neither a framework nor a process. Agile is a mindset or a collection of beliefs that can be used by the teams for taking the decisions while working on any software development. Agile mindset adopted for the development of software has gained attention of the researchers and industries across the world because otherwise the software project would turn out to be uncertain and very turbid. The universal remedy for the usual failure of the software project development is the agility. Actually saying agile is nothing novice instead it is a meta model based on best practices from the preceding models like waterfall, iterative, incremental and rapid application development method. Objective: The objective of this paper is to highlight various points of comparison between them and conclude that failing fast is failing cheap in case of agile for software development. Methods: So, we can say that an agile always existed but not in a structured and a formal manner. The two main methodologies of agile as an umbrella term are XP and scrum. Results: Thus in this paper we have discussed about scrum as a major methodology and also how various scrum roles contribute towards making teams self-organized to reduce the usual rate of failure of development projects. Conclusion: The success rate of the software applications developed through the agile concept is three times than that of the traditional waterfall method and also the percentage of cost and time overruns is much lower.
Anonymous Remote Voting System

In this paper we describe the cryptographic protocol of anonymous remote voting, which reliability is provided by proof and justification of its properties. This protocol was used in the real anonymous voting system developed in Institute of Computational Technologies SB RAS. In addition to the system operation protocol we describe algorithm of creating an anonymous data transfer channel and also show the possibility of applying blockchain technology to this system. The results of this work demonstrate the advantage of such systems over standard voting methods which are highly dependent on human factor.
Ares: Inferring Error Specifications through Static Analysis

Misuse of APIs happens frequently due to misunderstanding of API semantics and lack of documentation. An important category of API-related defects is the error handling defects, which may result in security and reliability flaws. These defects can be detected with the help of static program analysis, provided that error specifications are known. The error specification of an API function indicates how the function can fail. Writing error specifications manually is time-consuming and tedious. Therefore, automatic inferring the error specification from API usage code is preferred. In this paper, we present Ares, a tool for automatic inferring error specifications for C code through static analysis. We employ multiple heuristics to identify error handling blocks and infer error specifications by analyzing the corresponding condition logic. Ares is evaluated on 19 real world projects, and the results reveal that Ares outperforms the state-of-the-art tool APEx by 37% in precision. Ares can also identify more error specifications than APEx. Moreover, the specifications inferred from Ares help find dozens of API-related bugs in well-known projects such as OpenSSL, among them 10 bugs are confirmed by developers. Video: https://youtu.be/nf1QnFAmu8Q. Repository: https://github.com/lc3412/Ares.
Ancilla-assisted schemes are beneficial for Gaussian state phase estimation

We study interferometry with Gaussian states and show that an ancilla-assisted scheme outperforms coherent state interferometry for all levels of loss. We also compare the ancilla-assisted scheme to other interferometric schemes involving squeezing, and show that it has the most advantage in the high-loss, high photon-number regime. In fact, in the presence of high loss, it outperforms many other strategies proposed to date. We find the optimal measurement observable for each scheme discussed. We also find that, with the appropriate measurement, the achievable precision of the proposal by Caves [Phys. Rev. D 23, 1693 (1981)] can be improved upon, and is less vulnerable to losses than previously thought.