Reviewerly/exHarmony huggingface dataset:
Burnt Pancake Problem: New Lower Bounds on the Diameter and New Experimental Optimality Ratios

For the burnt pancake problem, we provide new values of g(-IN), new hard positions and new experimental optimality ratios.
Using hashing and lexicographic order for Frequent Itemsets Mining on data streams

Frequent Itemsets Mining is a Data Mining technique that has been employed to extract useful knowledge from datasets and, more recently, also from data streams. Data streams are unbounded and infinite flows of data arriving at high rates which cannot be stored for off-line processing; therefore, proposed algorithms for Frequent Itemsets Mining approaches from datasets cannot be used straightforwardly for Frequent Itemsets Mining from data streams. Frequent Itemsets Mining is a compute intensive task, hence developing custom hardware-based architectures to speed up this process is an active research topic. This paper introduces an algorithm for a hardware-based Frequent Itemsets Mining on data streams that uses the top-k frequent 1-itemsets detection as preprocessing. The received transactions are handled using hash functions, and the lexicographic order of items is used for obtaining frequent itemsets. The proposed algorithm is focused on discovering frequent itemsets in data streams composed of short transactions in large alphabets. Experimental results demonstrate that the proposed algorithm outperforms the processing time of the state-of-the-art algorithms used as the baseline.
Experiencing the AI emergence in Indian retail – Early adopters approach

The usage of Artificial Intelligence (AI) technique under retail industry will bring glorious outcome and flourishing benefits for both the retailers and the distinguished customers.The multiple platforms of AI usage in retail arena are discussed under two different cluster classified as online and offline, based on the terms of execution of retail activity.The present research was conducted with the objectives of evaluating the contribution of quality, customer relationship management and big data in designing futuristic retail model and analyzing the intention of retailers and shoppers in experiencing the emergence of AI.Disproportionate multistage judgement sampling method was employed.A sample of 610 shoppers from four different capital regions of southern part of states in India was considered for the statistical analysis.Data was collected during the first quarter period of 2018.Descriptive research design was used to describe the intention of shoppers towards the emergence of AI in the Indian retail sector.The usages of AI technologies in online and offline retail are grouped separately and its effect on building the quality, customer relationship management and big data was evolved.Finally, its impact on the retailers intention and customers delight was studied through Structural Equation Modeling with testing of appropriate hypothesis.
A Hybrid Command Governor Scheme for Rotary Wings Unmanned Aerial Vehicles

In this paper, we develop an obstacle avoidance control scheme for autonomous aerial vehicles. The strategy is based on command governor (CG) ideas that are here extended to take into account nonconvex constraints typically arising in path planning obstacle avoidance problems. As one of its main merits, the collision avoidance task is accomplished by exploiting convex inner approximations of the obstacle-free region and an ad hoc constraints switching procedure which significantly reduces the online computations pertaining to the CG design. Experimental results on the quadrotor Qball-X4 show the applicability and effectiveness of the proposed approach.
The Role of Work Engagement in Agile Software Development: Investigating Job Demands and Job Resources

Agile software development projects still show a high failure rate.Despite a growing amount of research, underlying reasons for project performance currently remain rare.Drawing on the job demandsresources theory, we propose a theoretical model of work engagement in agile software development teams.Using structural equation modeling, we found that agile practices diminish job demands (perceived workload and role ambiguity) and support job resources (perceived meaningfulness and job autonomy).Job resources have been found to be positively related to work engagement in agile software development teams.Our research contributes to the limited empirical understanding on work engagement in agile software development.For practitioners, our model provides tools to effectively manage team members' work engagement.
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets

Data augmentation is rapidly gaining attention in machine learning. Synthetic data can be generated by simple transformations or through the data distribution. In the latter case, the main challenge is to estimate the label associated to new synthetic patterns. This paper studies the effect of generating synthetic data by convex combination of patterns and the use of these as unsupervised information in a semi-supervised learning framework with support vector machines, avoiding thus the need to label synthetic examples. We perform experiments on a total of 53 binary classification datasets. Our results show that this type of data over-sampling supports the well-known cluster assumption in semi-supervised learning, showing outstanding results for small high-dimensional datasets and imbalanced learning problems.
Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

Predicting properties of nodes in a graph is an important problem with applications in a variety of domains. Graph-based Semi Supervised Learning (SSL) methods aim to address this problem by labeling a small subset of the nodes as seeds, and then utilizing the graph structure to predict label scores for the rest of the nodes in the graph. Recently, Graph Convolutional Networks (GCNs) have achieved impressive performance on the graph-based SSL task. In addition to label scores, it is also desirable to have confidence scores associated with them. Unfortunately, confidence estimation in the context of GCN has not been previously explored. We fill this important gap in this paper and propose ConfGCN, which estimates labels scores along with their confidences jointly in GCN-based setting. ConfGCN uses these estimated confidences to determine the influence of one node on another during neighborhood aggregation, thereby acquiring anisotropic capabilities. Through extensive analysis and experiments on standard benchmarks, we find that ConfGCN is able to outperform state-of-the-art baselines. We have made ConfGCN’s source code available to encourage reproducible research.
Introducing the geostationary environment monitoring spectrometer (Erratum)

The <i>Journal of Applied Remote Sensing</i> (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications.
Design, Validation, and Application of a Questionnaire on Media Education for Teachers in Training

This article details the process of design, validation and application (N = 501) of an ad-hoc questionnaire that measures Peruvian teachers’ knowledge, attitudes, and reasoning in training on media education. With regards to the method, several psychometric validation techniques were applied and the results were analyzed using descriptive statistics. The results showed a high level of internal coherence and confidence in the construct. Limited knowledge of the subject was found in the participants, without observing significant differences between the variables, and a very positive assessment of its importance and urgency. To conclude, media education is transversal to different classifications and interest in it is homogeneous throughout the teaching staff in training.
Partial Multi-View Clustering Based on Sparse Embedding Framework

In this paper, we propose a novel partial multi-view clustering method based on the sparse embedding framework, which can handle incomplete view data well and obtain good clustering performance.Most real-world datasets are often comprised of different views, which provide complementary information for each other for clustering or classification tasks.Existing multi-view clustering methods assume that each example appears in all views or all examples contained at least one view.But each view of real data is often subject to various degrees of damage resulting in partial examples.There are some works on partial view clustering, in which almost all are based on standard non-negative matrix factorization (NMF) to solve the problem that did not consider the sparseness degree of coefficient and unsatisfactory basic matrices learned by NMF.Besides, most existing clustering methods only carry out dimensionality reduction before learning the models, which is unable to make full use of the discriminative information in raw data.Thus, partial multi-view clustering based on sparse embedding framework is proposed, in which dimensionality reduction and dictionary learning are taken into consideration simultaneously to learn better sparse coefficients and dictionaries.The proposed method preserves as much useful information as possible in original space by constraining projection matrix to be orthogonal and imposes Fisher discrimination analysis on dictionaries rather than sparse coefficients, which makes the learned dictionaries more discriminating and promotes sparsity of coefficients.Finally, the synthetic dataset, the extended Yale B dataset, the MNISIT Handwritten Digit dataset and the large dataset-Caltech101 are employed to perform clustering tasks, and the experimental results show that the proposed method achieved a better clustering performance than the other state-of-the-art clustering methods.
Spatio-Temporal Features in Action Recognition Using 3D Skeletal Joints

Robust action recognition methods lie at the cornerstone of Ambient Assisted Living (AAL) systems employing optical devices. Using 3D skeleton joints extracted from depth images taken with time-of-flight (ToF) cameras has been a popular solution for accomplishing these tasks. Though seemingly scarce in terms of information availability compared to its RGB or depth image counterparts, the skeletal representation has proven to be effective in the task of action recognition. This paper explores different interpretations of both the spatial and the temporal dimensions of a sequence of frames describing an action. We show that rather intuitive approaches, often borrowed from other computer vision tasks, can improve accuracy. We report results based on these modifications and propose an architecture that uses temporal convolutions with results comparable to the state of the art.
Cryptanalysis of a code-based one-time signature

In 2012, Lyubashevsky introduced a new framework for building lattice-based signature schemes without resorting to any trapdoor [such as Gentry C, Peikert C, Vaikuntanathan V, in: Ladner and Dwork (eds) 40th ACM STOC, ACM Press, Victoria, pp. 197–206, 2008 or Hoffstein J, Pipher J, Silverman JH in: Pfitzmann (ed) EUROCRYPT 2001. LNCS, vol. 2045, pp 211–228, Springer, Heidelberg, 2001]. The idea is to sample a set of short lattice elements and construct the public key as a Short Integer Solution (SIS for short) instance. Signatures are obtained using a small subset sum of the secret key, hidden by a (large) Gaussian mask. (Information leakage is dealt with using rejection sampling.) Recently, Persichetti proposed an efficient adaptation of this framework to coding theory (Persichetti E in Cryptography 2(4):30, 2018). In this paper, we show that this adaptation cannot be secure, even for one-time signatures (OTS), due to an inherent difference between bounds in Hamming and Euclidean metrics. The attack consists in rewriting a signature as a noisy syndrome decoding problem, which can be handled efficiently using the extended bit flipping decoding algorithm. We illustrate our results by breaking Persichetti’s OTS scheme built upon this approach (Persichetti 2018): using a single signature, we recover the secret (signing) key in about the same amount of time as required for a couple of signature verifications.
Escaping Saddle Points with Adaptive Gradient Methods.

Adaptive methods such as Adam and RMSProp are widely used in deep learning but are not well understood. In this paper, we seek a crisp, clean and precise characterization of their behavior in nonconvex settings. To this end, we first provide a novel view of adaptive methods as preconditioned SGD, where the preconditioner is estimated in an online manner. By studying the preconditioner on its own, we elucidate its purpose: it rescales the stochastic gradient noise to be isotropic near stationary points, which helps escape saddle points. Furthermore, we show that adaptive methods can efficiently estimate the aforementioned preconditioner. By gluing together these two components, we provide the first (to our knowledge) second-order convergence result for any adaptive method. The key insight from our analysis is that, compared to SGD, adaptive methods escape saddle points faster, and can converge faster overall to second-order stationary points.
Converting Text to Features

In this chapter, we are going to cover basic to advanced feature engineering (text to features) methods. By the end of this chapter, you will be comfortable with following recipes:
PENERAPAN METODE BRAINSTORMING DENGAN BANTUAN MEDIA GAMBAR GRAFIS UNTUK MENINGKATKAN HASIL BELAJAR PKn SISWA KELAS V SD NEGERI 009 PULAU KECAMATAN BANGKINANG KABUPATEN KAMPAR

ABSTRACTThis research was conducted issuing the low learning outcomes on Civics subject by the fifth grader students of SD Negeri 009 Pulau Bangkinang District, Kampar Regency with an average score of 63.40. It was seen that out of 16 students, there were only 7 students who passed the test (43.75%), while the other 9 students (56.25%) did not. The result was established according to the KKM (minumum score) determined by the school which was 75. The students' low performance might occur because of several reasons: (a) the way the presentation of the material delivered by the teacher did not really involve the students activity; (b) the teacher did not use learning media in explaining the learning material; and (c) the teacher applied an approach, model, or method that was less attractive to the students. This study aims at improving the students' achievement in Civics by using brainstorming methods with the help of graphic image media. The research is a classroom action research (CAR) consisting of three cycles with research subjects of grade V students of SDN 009 Pulau Bangkinang District, Kampar Regency. The results showed improvements in each cycle. The score increased from an average score of 62.50 to 69.06 in the first cycle. In the second cycle, the score increased to 75.93 and in the third cycle increased to 80.94. The increase of the score that occurred in the baseline score of Daily Test I was 10.49%, then 21.49% in the Daily Test II and 29.50% in the Daily Test III. In other words, the brainstorming method with the help of graphic image media can improve the students learning outcomes in Civics subject.
Binary medical image compression using the volumetric run-length approach

Image compression has become an inevitable tool along with the advancing medical data acquisition and telemedicine systems. The run-length encoding (RLE), one of the most effective and practical lossless compression techniques, is widely used in two-dimensional space with common scanning forms such as zigzag and linear. In this study, an algorithm which takes advantage of the potential simplicity of the run-length algorithm is devised in a volumetric approach for three-dimensional (3D) binary medical data. The proposed algorithm, namely 3D-RLE, being different from the two-dimensional approach that utilizes only intra-slice correlations, is designed to compress binary volumetric data by employing also the inter-slice correlation between the voxels. Furthermore, it is extended to several scanning forms such as Hilbert and perimeter to determine an optimal scanning procedure coherent with the morphology of the segmented organ in data. The algorithm is employed on four datasets for a comprehensive assessment. Numerical simulation results demonstrated that the performance of the algorithm is 1:30 better than those of the state-of-the-art techniques, on average.
A robust outlier control framework for classification designed with family of homotopy loss function

We propose a new homotopy loss, where practitioners can tune the parameter to derive different loss functions such as l1-norm loss, logarithmic loss, Geman–Reynolds loss, Geman–McClure loss and correntropy-based loss. Moreover, we illustrate that the proposed loss satisfies Fisher consistency, and we analyze the robustness of the proposed homotopy loss from different perspectives: M-estimation and adversarial perturbations. Then, we represent a new evaluation standard to measure robustness and demonstrate its upper bound to ensure the validity of this measure. Applied the proposed homotopy loss to least square support vector machine (LSSVM) and the extreme learning machine (ELM), two robust models are presented to enhance the robustness. Furthermore, re-weighted least square algorithm is used to solve the problems, and the resulting algorithms converge globally. In addition, the proposed methods are implemented on various datasets with different levels of noise. Compared with traditional methods, experiment results on real-world datasets show that the proposed methods have superior anti-interference ability to outliers in most cases.
Formal Verification of Random Forests in Safety-Critical Applications

Recent advances in machine learning and artificial intelligence are now being applied in safety-critical autonomous systems where software defects may cause severe harm to humans and the environment. Design organizations in these domains are currently unable to provide convincing arguments that systems using complex software implemented using machine learning algorithms are safe and correct. In this paper, we present an efficient method to extract equivalence classes from decision trees and random forests, and to formally verify that their input/output mappings comply with requirements. We implement the method in our tool VoRF (Verifier of Random Forests), and evaluate its scalability on two case studies found in the literature. We demonstrate that our method is practical for random forests trained on low-dimensional data with up to 25 decision trees, each with a tree depth of 20. Our work also demonstrates the limitations of the method with high-dimensional data and touches upon the trade-off between large number of trees and time taken for verification.
Deep Feature Ranking for Person Re-Identification

Person re-identification plays a critical part in many surveillance applications.Due to complicated illumination environments and various viewpoints, it is still a challenging problem to extract robust features.To solve this issue, we propose a novel deep feature ranking scheme.Our main contribution is to rank achieved deep features, which are obtained by classic deep learning model, and set the sort order number as our feature vector, named as ordinal deep features (ODFs).Person re-identification results are acquired by ranking person candidates by measuring distance based on ODFs.Since applying for rank orders rather than original feature values, our method achieves robust results, especially under the situation of viewpoints shift.Comprehensive experiments are carried out to demonstrate the significance of the proposed feature.Meanwhile, comparative experiments are applied over the publicly available dataset, our method achieves promising performance and outperforms the state of the art methods.Moreover, we applied the proposed feature in the scenario of image classification and discussed the effectiveness.
Click Fraud Detection and Prevention System for Ad Networks

Click fraud detection consists of identifying the intention behind received clicks, given only technical data and context information. Reviewing concepts involved in click fraud practices and related work, a system that detects and prevents this type of fraud is proposed and implemented. The system is based and implemented on an ad network, one of the 3 main agents in the online ad environment, and for its validation, 3 servers were used, representing the publisher, the ad network with the system implemented and the announcer, and a bot that attempts to commit a click fraud.
Revisiting Wedge Sampling for Triangle Counting

Triangle counts of massive graphs can provide important information regarding the structure of the networks these graphs model. Exact triangle counting can be expensive and thus researchers have proposed a number of approximation approaches. The state-of-the-art triangle count approximation techniques depend on wedge (two-path) sampling. In this paper we offer a mechanism to significantly improve wedge sampling for triangle counting. We shrink the sampling space by eliminating wedges that are less likely to participate in triangles. Experiments over large-scale real-world graphs show that proposed mechanism provides five- to a few hundred-folds sampling space reduction. When compared against the state-of-the-art approaches, it requires as low as ~ 100 × less sampling to provide the same accuracy, or makes as low as ~ 8 × less error when used with the same sampling ratio.
Size and scope of the tobacco trade on the darkweb

Globally, tobacco is responsible for the death of approximately 6 million individuals per year. In reaction, governments have enacted tobacco control policies that cope with the harmful effects of the drug. However, these policies have also created geographic price disparities and increased the effort needed to purchase legal tobacco products. The result was the creation of a new illicit tobacco market. While tobacco smuggling continues through conventional avenues, a new generation of traffickers have turned to a different source for the illicit distribution of tobacco – the internet, specifically the darkweb. The purpose of this paper is to describe the size and scope of illicit trafficking of tobacco on cryptomarkets, and the diverse types of tobacco-related products available on the cryptomarkets. Our analysis will provide guidance for policymakers, researchers, and all interested in the illicit trade of tobacco.