Reviewerly/exHarmony huggingface dataset:
Subspace-based aggregation for enhancing utility, information measures, and cluster identification in privacy preserved data mining on high-dimensional continuous data

Clustering is a data mining technique that has been effectively used in the last few decades for knowledge extraction. Privacy is a major problem while releasing data for clustering and therefore privacy-preserving data mining (PPDM) algorithms have been developed. Aggregation is a popular PPDM technique that has been used. However, in the last few years, certain applications require that data mining be performed on high-dimensional data. The present privacy preservation techniques perform aggregation in a univariate manner along each dimension. This affects the utility measures, information measures, and especially retention of original clusters. This paper proposes a new technique called as subspace-based aggregation (SBA). SBA categorizes the dimensions into dense and non-dense subspaces based on the density of points. Aggregation is performed separately for dense and non-dense subspaces. This approach helps to maximize utility measures, information measures, and retention of clusters. SBA is run on high-dimensional continuous datasets from UCI Machine Learning repository. SBA is compared with related work methods such as SINGLE, SIMPLE, MDAV, and PPPCA. SBA provides an improvement of 66% in utility, 400% in cluster identification, 5% in co-variance, and standard deviation.
Bottleneck Potentials in Markov Random Fields

We consider general discrete Markov Random Fields(MRFs) with additional bottleneck potentials which penalize the maximum (instead of the sum) over local potential value taken by the MRF-assignment. Bottleneck potentials or analogous constructions have been considered in (i) combinatorial optimization (e.g. bottleneck shortest path problem, the minimum bottleneck spanning tree problem, bottleneck function minimization in greedoids), (ii) inverse problems with $L_{\infty}$-norm regularization and (iii) valued constraint satisfaction on the $(\min,\max)$-pre-semirings. Bottleneck potentials for general discrete MRFs are a natural generalization of the above direction of modeling work to Maximum-A-Posteriori (MAP) inference in MRFs. To this end we propose MRFs whose objective consists of two parts: terms that factorize according to (i) $(\min,+)$, i.e. potentials as in plain MRFs, and (ii) $(\min,\max)$, i.e. bottleneck potentials. To solve the ensuing inference problem, we propose high-quality relaxations and efficient algorithms for solving them. We empirically show efficacy of our approach on large scale seismic horizon tracking problems.
ACCOLÉ: A Collaborative Platform of Error Annotation for Aligned Corpora

This article presents a platform, named ACCOLE, for the collaborative annotation of translation errors. ACCOLE offers a range of services that allow simplified management of corpora and error typologies, annotation of effective errors, collaborative discussion during annotation, and finally different kinds of search in corpora. ACCOLE allows the annotation of translation errors according to built-in error typologies, Vilar's typology or DQF-MQM or uploaded ones, on several annotated corpora of different texts, translated by different Statistical or Neural MT systems. It also help to process the annotated corpora created in order to look for typical error models and patterns, related to a specific MT system. ACCOLE currently provides 15 corpora, 19 projects of 134,273 source words and 114,511 target words, and 23,525 annotations. Eventually, we will implement the semi-automatic propagation of found patterns on other corpora to compare the behaviour of the MT systems on different domains, thus providing to the community a wide range of error-annotated bilingual parallel corpora.
Smart Contract Interactions in Coq

We present a model/executable specification of smart contract execution in Coq. Our formalization allows for inter-contract communication and generalizes existing work by allowing modelling of both depth-first execution blockchains (like Ethereum) and breadth-first execution blockchains (like Tezos). We represent smart contracts programs in Coq's functional language Gallina, enabling easier reasoning about functional correctness of concrete contracts than other approaches. In particular we develop a Congress contract in this style. This contract -- a simplified version of the infamous DAO -- is interesting because of its very dynamic communication pattern with other contracts. We give a high-level partial specification of the Congress's behavior, related to reentrancy, and prove that the Congress satisfies it for all possible smart contract execution orders.
Adjacency Cloud-Oriented Storage Overlay Topology Using Self-organizing M-Way Tree

This paper proposes a self-organizing and scalableKumar, Ajay storageBawa, Seema approach named adjacency COS overlay topology (ACOT). This topology is based on balanced multi-way tree organization. ACOT removes the traditional static and centralized storage management and adopts the dynamic and highly scalable data accessibility for the cloud-oriented storage system. A Hadoop test bed has been created for experimental review and result in analysis on various aspects like dynamicity, scalability, effectiveness, and bandwidth evaluation of proposed topology. Our experiments have proven that the ACOT topology offers the dynamic and highly scalable for cloud-oriented storage systems.
Hyper: Distributed Cloud Processing for Large-Scale Deep Learning Tasks

Training and deploying deep learning models in real-world applications require processing large amounts of data. This is a challenging task when the amount of data grows to a hundred terabytes, or even, petabyte-scale. We introduce a hybrid distributed cloud framework with a unified view to multiple clouds and an on-premise infrastructure for processing tasks using both CPU and GPU compute instances at scale. The system implements a distributed file system and failure-tolerant task processing scheduler, independent of the language and Deep Learning framework used. It allows to utilize unstable cheap resources on the cloud to significantly reduce costs. We demonstrate the scalability of the framework on running pre-processing, distributed training, hyperparameter search and large-scale inference tasks utilizing 10,000 CPU cores and 300 GPU instances with overall processing power of 30 petaflops.
Big Data Clustering and Hadoop Distributed File System Architecture

Due to advancement in the technological world, there is a great surge in data. The main sources of generating such a large amount of data are social websites, internet sites etc. The large data files are combined together to create a big data architecture. Managing the data file in such a large volume is not easy. Therefore, modern techniques are developed to manage bulk data. To arrange and utilize such big data, Hadoop Distributed File System (HDFS) architecture from Hadoop was presented in the early stage of 2015. This architecture is used when traditional methods are insufficient to manage the data. In this paper, a novel clustering algorithm is implemented to manage a large amount of data. The concepts and frames of Big Data are studied. A novel algorithm is developed using the K means and cosine-based similarity clustering in this paper. The developed clustering algorithm is evaluated using the precision and recall parameters. The prominent results are obtained which successfully manages the big data issue.
Experimental Analysis of OpenStack Effect on Host Resources Utilization

Cloud computing is one of the frontier technologies, which over the last decade has gained a widespread commercial and educational user base. OpenStack is one of the popular open-source cloud management platforms for establishing a private or public Infrastructure-as-a-Service (IAAS) cloud. Although OpenStack started with very few core modules, it now houses nearly 38 modules and is quite complex. Such a complex software bundle is bound to have an impact on the underlying hardware utilization of the host system. This paper analyzes the effect of OpenStack on the host machine’s hardware. For this purpose, an extensive empirical evaluation has been done on different types of hardware, different virtualization levels and with different flavors of operating systems comparing the CPU utilization, and memory consumption. OpenStack was deployed using Devstack on a single node. From the results it is evident that standalone machine with Ubuntu server operating system is the least affected by OpenStack and thereby has more available resources for computation of user workloads.
Online Replanning in Belief Space for Partially Observable Task and Motion Problems.

To solve multi-step manipulation tasks in the real world, an autonomous robot must take actions to observe its environment and react to unexpected observations. This may require opening a drawer to observe its contents or moving an object out of the way to examine the space behind it. Upon receiving a new observation, the robot must update its belief about the world and compute a new plan of action. In this work, we present an online planning and execution system for robots faced with these challenges. We perform deterministic cost-sensitive planning in the space of hybrid belief states to select likely-to-succeed observation actions and continuous control actions. After execution and observation, we replan using our new state estimate. We initially enforce that planner reuses the structure of the unexecuted tail of the last plan. This both improves planning efficiency and ensures that the overall policy does not undo its progress towards achieving the goal. Our approach is able to efficiently solve partially observable problems both in simulation and in a real-world kitchen.
Preventing Posterior Collapse in Sequence VAEs with Pooling

Variational Autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic properties from local regularities of natural language. Practically, however, VAEs with autoregressive decoders often suffer from posterior collapse, a phenomenon where the model learns to ignore the latent variables, causing the sequence VAE to degenerate into a language model. Previous works attempt to solve this problem with complex architectural changes or costly optimization schemes. In this paper, we argue that posterior collapse is caused in part by the encoder network failing to capture the input variabilities. We verify this hypothesis empirically and propose a straightforward fix using pooling. This simple technique effectively prevents posterior collapse, allowing the model to achieve significantly better data log-likelihood than standard sequence VAEs. Compared to the previous SOTA on preventing posterior collapse, we are able to achieve comparable performances while being significantly faster.
Mass Formula for Self-Dual Codes over Galois Rings GR(p3, r)

Let p be an odd prime and r a positive integer. Let GR(p3, r) be the Galois ring of characteristic p3 and cardinality p3r. In this paper, we investigate the self-dual codes over GR(p3, r) and give a method to construct self-dual codes over this ring. We establish a mass formula for self-dual codes over GR(p3, r) and classify self-dual codes over GR(p3, 2) of length 4 for p = 3, 5.
Earth Observation Data Mining: A Use Case for Forest Monitoring

The increased number of free and open satellite images has led to new applications of these data. Among them is the systematic classification of land cover/use types based on patterns of settlements or agriculture recorded by satellite imagers, in particular, the identification and quantification of temporal changes. In this paper, we will present guidelines and practical examples of how to obtain reliable image patch classification results based on data mining techniques for detecting possible changes that can appear within a data set. Here, we will focus on a scenario, namely forest monitoring using Earth observation Synthetic Aperture Radar data acquired by Sentinel-1, and multispectral data acquired by Sentinel-2.
DialogAct2Vec: Towards End-to-End Dialogue Agent by Multi-Task Representation Learning.

In end-to-end dialogue modeling and agent learning, it is important to (1) effectively learn knowledge from data, and (2) fully utilize heterogeneous information, e.g., dialogue act flow and utterances. However, the majority of existing methods cannot simultaneously satisfy the two conditions. For example, rule definition and data labeling during system design take too much manual work, and sequence-to-sequence methods only model one-side utterance information. In this paper, we propose a novel joint end-to-end model by multi-task representation learning, which can capture the knowledge from heterogeneous information through automatically learning knowledgeable low-dimensional embeddings from data, named with DialogAct2Vec. The model requires little manual work for intervention in system design and we find that the multi-task learning can greatly improve the effectiveness of representation learning. Extensive experiments on a public dataset for restaurant reservation show that the proposed method leads to significant improvements against the state-of-the-art baselines on both the act prediction task and utterance prediction task.
Fast and Accurate Continuous User Authentication by Fusion of Instance-based, Free-text Keystroke Dynamics

Keystroke dynamics study the way in which users input text via their keyboards, which is unique to each individual, and can form a component of a behavioral biometric system to improve existing account security. Keystroke dynamics systems on free-text data use n-graphs that measure the timing between consecutive keystrokes to distinguish between users. Many algorithms require 500, 1,000, or more keystrokes to achieve EERs of below 10%. In this paper, we propose an instance-based graph comparison algorithm to reduce the number of keystrokes required to authenticate users. Commonly used features such as monographs and digraphs are investigated. Feature importance is determined and used to construct a fused classifier. Detection error tradeoff (DET) curves are produced with different numbers of keystrokes. The fused classifier outperforms the state-of-the-art with EERs of 7.9%, 5.7%, 3.4%, and 2.7% for test samples of 50, 100, 200, and 500 keystrokes.
PENANGANAN DATA MISSING VALUE PADA KUALITAS PRODUKSI JAGUNG DENGAN MENGGUNAKAN METODE K-NN IMPUTATION PADA ALGORITMA C4.5

Corn is a staple crop for Indonesian people because most of his life is from the agriculture sector. To increase the productivity of corn, another thing to be aware of is looking at the quality of the corn products. Through empirical observation and observation, research explores and extracts data through the concept of data mining so that neglected data becomes useful. Thus determining the quality of corn production is an important task to help the farmers in determining the classification process. Missing value is a problem in maintaining a quality data. Missing value can be caused by several things, one of which is caused by an error at the time of data entry. Missing value will be a problem when the amount of data in large quantities, so it is very influential in the survey results. Therefore on this research proposed K-NN imputation method to handle missing value data. The results showed the accuracy of the C 4.5 algorithm classification process on the corn production dataset that experienced a missing value accuracy value of 92.90%. Whereas if done with special handling using the method K-NN imputation on the handling process missing value best value at k = 5 of 94.50% with this that the proposed method increases significantly.
Deep Supervised Summarization: Algorithm and Application to Learning Instructions

We address the problem of finding representative points of datasets by learning from multiple datasets and their ground-truth summaries. We develop a supervised subset selection framework, based on the facility location utility function, which learns to map datasets to their ground-truth representatives. To do so, we propose to learn representations of data so that the input of transformed data to the facility location recovers their ground-truth representatives. Given the NP-hardness of the utility function, we consider its convex relaxation based on sparse representation and investigate conditions under which the solution of the convex optimization recovers ground-truth representatives of each dataset. We design a loss function whose minimization over the parameters of the data representation network leads to satisfying the theoretical conditions, hence guaranteeing recovering ground-truth summaries. Given the non-convexity of the loss function, we develop an efficient learning scheme that alternates between representation learning by minimizing our proposed loss given the current assignments of points to ground-truth representatives and updating assignments given the current data representation. By experiments on the problem of learning key-steps (subactivities) of instructional videos, we show that our proposed framework improves the state-of-the-art supervised subset selection algorithms.
Mushroom Images Identification Using Orde 1 Statistics Feature Extraction with Artificial Neural Network Classification Technique

Abstract There are many kinds of mushrooms difficult to identified manually. Because of that, a certain system that can be used to identify mushrooms is needed. One feature that artificial intelligence has is image identification. One image that can be identified mushroom image. Mushroom image identification can contribute to artificial intelligence technology development. Computer-based mushroom image identification can be done by conducting a segmentation process that converts the original image to a grayscale image. The mushroom image pattern characteristics are selected and separated using a feature extraction process. Mushrooms feature extraction conducted by using orde 1 statistics. Feature extraction results are classified using the Artificial Neural Network method with the Backpropagation Algorithm. Classification process carried out by training and testing with neurons variations 5, 10, 15 and 20, while hidden layers are 0.1, 0.3, 0.5, 0.7, and 0.9 with 10,000 times iteration. 30 images that are consist of 15 images for training data and 15 images for test data. From research can be seen that mushroom image identification using orde 1 statistics features extraction with artificial neuron network has the best result with 93% accuracy on neuron 20. Mushroom’s image identification system that is developed can be implemented in other applications.
Perfectly Secure Oblivious RAM with Sublinear Bandwidth Overhead

Oblivious RAM (ORAM) has established itself as a fundamental cryptographic building block. Understanding which bandwidth overheads are possible under which assumptions has been the topic of a vast amount of previous works. In this work, we focus on perfectly secure ORAM and we present the first construction with sublinear bandwidth overhead in the worst-case. All prior constructions with perfect security require linear communication overhead in the worst-case and only achieve sublinear bandwidth overheads in the amortized sense. We present a fundamentally new approach for constructing ORAM and our results significantly advance our understanding of what is possible with perfect security. Our main construction, Lookahead ORAM, is perfectly secure, has a worst-case bandwidth overhead of , and a total storage cost of on the server-side, where N is the maximum number of stored data elements. In terms of concrete server-side storage costs, our construction has the smallest storage overhead among all perfectly and statistically secure ORAMs and is only a factor 3 worse than the most storage efficient computationally secure ORAM. Assuming a client-side position map, our construction is the first, among all ORAMs with worst-case sublinear overhead, that allows for a online bandwidth overhead without server-side computation. Along the way, we construct a conceptually extremely simple statistically secure ORAM with a worst-case bandwidth overhead of , which may be of independent interest.
Modified Capsule Network for Object Classification

The recognition of images in complex scenes is essential to intelligent unmanned systems. The CapsNet performs well on MNIST datasets with overlapping numbers, but it has too many parameters on real scene datasets. In this paper, we proposes three methods to reduce its excessive parameters: (1) proposing the CapsNetPr network, in which the shallow feature extraction network is introduced, to reduce the data dimension of the input capsule layer. (2) utilizing the method of decomposing the transformation matrix to reduce space consumption and time consumption. (3) sharing the transformation matrix on the same location to reduce the number of matrices in the low-level capsule layers. The study successfully reduces the number of parameters of the capsule network and accelerates training and testing at the same time, which is of great value to the promotion and use of the capsule network.
Towards Joint Multiply Semantics Hashing for Visual Search

With the rapid growth of visual data on the web, deep hashing has shown enormous potential in preserving semantic similarity for visual search. Currently, most of the existing hashing methods employ pairwise or triplet-wise constraint to obtain the semantic similarity or relatively similarity among binary codes. However, some potential semantic context cannot be fully exploited, resulting in a suboptimal visual search. In this paper, we propose a novel deep hashing method, termed Joint Multiply Semantics Hashing (JMSH), to learn discriminative yet compact binary codes. In our approach, We jointly learn multiply semantic information to perform feature learning and hash coding. To be specific, the semantic information includes the pairwise semantic similarity between binary codes, the pointwise binary codes semantics and the pointwise visual feature semantics. Meanwhile, three different loss functions are designed to train the JMSH model. Extensive experiments show that the proposed JMSH yields state-of-the-art retrieval performance on representative image retrieval benchmarks.
Design and Implementation of Movie Recommendation System Based on Naive Bayes

Abstract Today, with the rapid development of the Internet industry, the era of big data has arrived. What follows is that the surge in data volume has brought great challenges to people’s rapid and timely screening of useful information. Based on this highly realistic problem, the recommendation system that solves the user’s individual needs is born. In this paper, based on the theory of Naive Bayes, the MovieLens data set is used for testing. The user’s scoring data are used for similar analysis to generate the user’s similarity matrix, and the target user can be personalized. Compared with other algorithms, this paper has a good recommendation effect.