Reviewerly/exHarmony huggingface dataset:
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up

We analyse the learning performance of Distributed Gradient Descent in the context of multi-agent decentralised non-parametric regression with the square loss function when i.i.d. samples are assigned to agents. We show that if agents hold sufficiently many samples with respect to the network size, then Distributed Gradient Descent achieves optimal statistical rates with a number of iterations that scales, up to a threshold, with the inverse of the spectral gap of the gossip matrix divided by the number of samples owned by each agent raised to a problem-dependent power. The presence of the threshold comes from statistics. It encodes the existence of a big data regime where the number of required iterations does not depend on the network topology. In this regime, Distributed Gradient Descent achieves optimal statistical rates with the same order of iterations as gradient descent run with all the samples in the network. Provided the communication delay is sufficiently small, the distributed protocol yields a linear speed-up in runtime compared to the single-machine protocol. This is in contrast to decentralised optimisation algorithms that do not exploit statistics and only yield a linear speed-up in graphs where the spectral gap is bounded away from zero. Our results exploit the statistical concentration of quantities held by agents and shed new light on the interplay between statistics and communication in decentralised methods. Bounds are given in the standard non-parametric setting with source/capacity assumptions.
McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds

A crucial assumption in most statistical learning theory is that samples are independently and identically distributed (i.i.d.). However, for many real applications, the i.i.d. assumption does not hold. We consider learning problems in which examples are dependent and their dependency relation is characterized by a graph. To establish algorithm-dependent generalization theory for learning with non-i.i.d. data, we first prove novel McDiarmid-type concentration inequalities for Lipschitz functions of graph-dependent random variables. We show that concentration relies on the forest complexity of the graph, which characterizes the strength of the dependency. We demonstrate that for many types of dependent data, the forest complexity is small and thus implies good concentration. Based on our new inequalities we are able to build stability bounds for learning from graph-dependent data.
Augmented Reality Teaching Aid for Electromagnetic Induction for Middle School Students

Understanding electromagnetic induction, whose study is part of the middle school curriculum in Japan, is difficult for students. Thus, this study developed an augmented reality (AR) teaching aid for electromagnetic induction for middle school students. Magnetic lines are illustrated in three dimensions as an AR using conventional tablet PCs. Lessons were conducted with 127 middle school students, and the changes in their explanation of the principle of electromagnetic induction were investigated before and after using the teaching aid. The investigation revealed that only 3% of the students could explain scientifically the principle of electromagnetic induction before using the teaching aid; however, 63% of them were able to provide a scientific explanation after the AR teaching aid was applied.
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

We consider distributed optimization under communication constraints for training deep learning models. We propose a new algorithm, whose parameter updates rely on two forces: a regular gradient step, and a corrective direction dictated by the currently best-performing worker (leader). Our method differs from the parameter-averaging scheme EASGD in a number of ways: (i) our objective formulation does not change the location of stationary points compared to the original optimization problem; (ii) we avoid convergence decelerations caused by pulling local workers descending to different local minima to each other (i.e. to the average of their parameters); (iii) our update by design breaks the curse of symmetry (the phenomenon of being trapped in poorly generalizing sub-optimal solutions in symmetric non-convex landscapes); and (iv) our approach is more communication efficient since it broadcasts only parameters of the leader rather than all workers. We provide theoretical analysis of the batch version of the proposed algorithm, which we call Leader Gradient Descent (LGD), and its stochastic variant (LSGD). Finally, we implement an asynchronous version of our algorithm and extend it to the multi-leader setting, where we form groups of workers, each represented by its own local leader (the best performer in a group), and update each worker with a corrective direction comprised of two attractive forces: one to the local, and one to the global leader (the best performer among all workers). The multi-leader setting is well-aligned with current hardware architecture, where local workers forming a group lie within a single computational node and different groups correspond to different nodes. For training convolutional neural networks, we empirically demonstrate that our approach compares favorably to state-of-the-art baselines.
Detecting online expressional anomie and its evolutions in social media

Purpose Expressional anomie (e.g. obscene words) can hinder communications and even obstruct improvements of national literacy. Meanwhile, the borderless and rapid transmission of the internet has exacerbated the influences. Hence, the purpose of this paper is detecting online anomic expression automatically and analyzing dynamic evolution processes of expressional anomie, so as to reveal multidimensional status of expressional anomie. Design/methodology/approach This paper conducted expressional anomie analysis via fine-grained microblog mining. Specifically, anomic microblogs and their anomic types were identified via a supervised classification method. Then, the evolutions of expressional anomie were analyzed, and impacts of users’ characteristics on the evolution process were mined. Finally, expressional anomie characteristics and evolution trends were obtained. Findings Empirical results on microblogs indicate that more effective and diversified measures need to be used to address the current large-scale anomie in expression. Moreover, measures should be tailored to individuals and local conditions. Originality/value To the best of the authors’ knowledge, it is the first research to mine evolutions of expressional anomie automatically in social media. It may discover more continuous and universal rules of expressional anomie, so as to optimize the online expression environment.
Bilingual lexical extraction based on word alignment for improving corpus search

Purpose This paper aims to describe the structure of an aligned Serbian-German literary corpus (SrpNemKor) contained in a digital library Bibliša. The goal of the research was to create a benchmark Serbian-German annotated corpus searchable with various query expansions. Design/methodology/approach The presented research is particularly focused on the enhancement of bilingual search queries in a full-text search of aligned SrpNemKor collection. The enhancement is based on using existing lexical resources such as Serbian morphological electronic dictionaries and the bilingual lexical database Termi. Findings For the purpose of this research, the lexical database Termi is enriched with a bilingual list of German-Serbian translated pairs of lexical units. The list of correct translation pairs was extracted from SrpNemKor, evaluated and integrated into Termi. Also, Serbian morphological e-dictionaries are updated with new entries extracted from the Serbian part of the corpus. Originality/value A bilingual search of SrpNemKor in Bibliša is available within the user-friendly platform. The enriched database Termi enables semantic enhancement and refinement of user’s search query based on synonyms both in Serbian and German at a very high level. Serbian morphological e-dictionaries facilitate the morphological expansion of search queries in Serbian, thereby enabling the analysis of concepts and concept structures by identifying terms assigned to the concept, and by establishing relations between terms in Serbian and German which makes Bibliša a valuable Web tool that can support research and analysis of SrpNemKor.
A Combined Clustering Algorithm Based on ESynC Algorithm and a Merging Judgement Process of Micro-Clusters

The first synchronization clustering (SynC) algorithm based on an extensive Kuromato model was presented in 2010. In 2017, an effective synchronization clustering (ESynC) algorithm, inspired by SynC algorithm and a linear version of Vicsek model, was proposed. When facing complex data distributions, ESynC algorithm may regard an irregular and whole cluster as some micro-clusters. To conquer this shortcoming, a combined clustering algorithm based on ESynC algorithm and a merging judgement process of micro-clusters (CESynC) is presented. CESynC algorithm first uses ESynC algorithm to detect clusters or micro-clusters, then merges those conjoint micro-clusters by using a merging judgement process. For some datasets that ESynC and SynC cannot detect correct clusters, CESynC can capture natural clusters. From the simulation experiments, we observe that CESynC can get better (or the same) clustering results than (or as) that of ESynC in many cases. We also observe that the clustering results of CESynC and ESynC are often better than that of SynC. Therefore, we can say CESynC can often obtain better clustering quality than ESynC and SynC in some kinds of datasets. Further comparison experiments with some classical clustering algorithms demonstrate the clustering effect of CESynC.
Analysis of Various Credit Card Fraud Detection Techniques

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Recent evaluation on Content Based Image Retrieval

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
A Survey on Cloud Security Issues

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Effectiveness of Cyber Security Awareness Program for young children: A Case Study in UAE

Children are an important element of the community; they represent the hope of a bright future. The safety of our children is priceless. Nowadays children are using the internet on a daily basis for many reasons; playing online games, social media or even for doing their school works. The internet takes a huge part of their times and unfortunately, most of these children are not aware of online risks and how they should respond properly to online risks. Moreover, children may have unlimited access to the internet without any control or supervision. The cybercrimes are increasing year after a year and the government of UAE considers the safety of the citizens as one of the most priorities to achieve. As the safety of our children is a priority, the Ministry of Education in UAE provided a cyber security awareness program for children in order to raise their security awareness level about online risks and online best practices. The objective of this research is to investigate the effectiveness of the cyber security awareness program offered by the Ministry of Education in UAE to students aged 8 to 10 years old. This research paper methodology based on qualitative methods for collecting data in which data were collected from interviews conducted with both the trainers of the program and students who attended the program. It was found that the children may expose to different online risks and the topics included in the cyber security awareness program were effective and aligned along with possible online risks children may expose to. Both the trainers and the students agreed on the efficiency of the cyber security awareness program and students believed that the awareness program influenced their online behavior. Although the interviews provided valuable findings, the real effectiveness of the cyber security awareness program hard to be measured as it depends on how students will behave online.
The stochastic linear combination of Dirichlet distributions

Generalizing [Homei H., and S. Nadarajah. 2018. On products and mixed sums of gamma and beta random variables motivated by availability. Methodology and Computing in Applied Probability 20:799–810] stochastic linear combinations of some random vectors are studied where the distribution of the random vectors and the joint distribution of their coefficients have Dirichlet distributions. A method is provided for calculating the distribution of these combinations which has been studied before by some authors. Our main result is a generalization of some existing results with a simpler proof.
A Survey of Travel Recommender System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Type-2 Fuzzy Logic Approach for Overloaded Hosts in Consolidation of Virtual Machines in Cloud Computing

Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing.Determining when it is best to relocate VMs from an overloaded host is one aspect of dynamic VM consolidation that directly influences the resource utilization and Quality of Service (QoS) offered by the system.This paper presents a new proposal with a Type-2 Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation.Results in a simulated cloud computing environment show improvements in energy efficiency of 15.64% in the Median Absolute Deviation (MAD), 15.43% in the Inter Quartile Range (IQR), and 9.19% in the Local Regression (LR).
Addressing the Big Data Multi-class Imbalance Problem with Oversampling and Deep Learning Neural Networks

The class imbalance problem is a challenging situation in machine learning but also it appears frequently in recent Big Data applications. The most studied techniques to deal with the class imbalance problem have been Random Over Sampling (ROS), Random Under Sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE), especially in two-class scenarios. However, in the Big Data scale, multi-class imbalance scenarios have not extensively studied yet, and only a few investigations have been performed. In this work, the effectiveness of ROS and SMOTE techniques is analyzed in the Big data multi-class imbalance context. The KDD99 dataset, which is a popular multi-class imbalanced big data set, was used to probe these oversampling techniques, prior to the application of a Deep Learning Multi-Layer Perceptron. Results show that ROS and SMOTE are not always enough to improve the classifier performance in the minority classes. However, they slightly increase the overall performance of the classifier in comparison to the unsampled data.
The Online Harms White Paper: comparing the UK and German approaches to regulation

The internet has revolutionised our ability to communicate and connect across historic social, political and geographic divides. Where previously gatekeepers mitigated and negotiated access to mass media platforms, today potentially anyone – and any content – can reach millions of users in an instant. This development bears great opportunities for the democratisation of expression and the diversification of public discourse but has likewise broadened the impact of harm caused online. This raises the question how platforms and services can be regulated effectively to combat online harms without jeopardising free and open discourse. The paper explores the Online Harms White Paper published by the UK Government earlier this year and compares its regulatory approach with the infamous German Network Enforcement Law.
Multi-Tasks Discovery Method Based on the Concept Network for Data Mining

Business and data understanding, that aims to identify multiple mining tasks, is the most primary phase in planning a practical data mining project. However, traditional tasks determination problem could only be solved by experienced analysts, which suffers from high communication cost and low efficiency. In this paper, we study the automatic task discovery method following the problem solving theory. First, we establish the concept network (CN) model to represent human knowledge and experience in the problem solving process. Then, we propose and demonstrate the structure of two major mining tasks (clustering and classification) in a CN. Finally, a data mining tasks discovery method (DMTD) is put forward, followed by two analysis subject evaluation algorithms. Experiment results illustrate that the DMTD is able to discover all the potential mining tasks from a predefined concept network, filtered by the important or interesting analysis subjects. Moreover, these tasks defined by the DMTD are proven to be available and valuable by fifty published papers.
Over-parameterization as a Catalyst for Better Generalization of Deep ReLU network.

We consider a deep ReLU / Leaky ReLU student network trained from the output of a fixed teacher network of the same depth, with Stochastic Gradient Descent (SGD). The student network is \emph{over-realized}: at each layer $l$, the number $n_l$ of student nodes is more than that ($m_l$) of teacher. Under mild conditions on dataset and teacher network, we prove that when the gradient is small at every data sample, each teacher node is \emph{specialized} by at least one student node \emph{at the lowest layer}. For two-layer network, such specialization can be achieved by training on any dataset of \emph{polynomial} size $\mathcal{O}( K^{5/2} d^3 \epsilon^{-1})$. until the gradient magnitude drops to $\mathcal{O}(\epsilon/K^{3/2}\sqrt{d})$. Here $d$ is the input dimension, $K = m_1 + n_1$ is the total number of neurons in the lowest layer of teacher and student. Note that we require a specific form of data augmentation and the sample complexity includes the additional data generated from augmentation. To our best knowledge, we are the first to give polynomial sample complexity for student specialization of training two-layer (Leaky) ReLU networks with finite depth and width in teacher-student setting, and finite complexity for the lowest layer specialization in multi-layer case, without parametric assumption of the input (like Gaussian). Our theory suggests that teacher nodes with large fan-out weights get specialized first when the gradient is still large, while others are specialized with small gradient, which suggests inductive bias in training. This shapes the stage of training as empirically observed in multiple previous works. Experiments on synthetic and CIFAR10 verify our findings. The code is released in this https URL.
Traffic Monitoring on City Roads Using UAVs

Unmanned Aerial Vehicles (UAVs) based systems are a suitable solution for monitoring, more particularly for traffic monitoring. The mobility, the low cost, and the broad view range of UAVs make them an attractive solution for traffic monitoring of city roads. UAVs are used to collect and send information about vehicles and unusual events to a traffic processing center, for traffic regulation. Existing UAVs based systems use only one UAV with a fixed trajectory. In this paper, we are using multiple cooperative UAVs to monitor the road traffic. This approach is based on adaptive UAVs trajectories, adjusted by moving points in UAVs fields of view. We introduced a learning phase to search for events locations with a frequent occurrence and to place UAVs above those locations. Our approach allows the detection of a lot of events and permits the reduction of UAVs energy consumption.