Reviewerly/exHarmony huggingface dataset:
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning

Federated learning has emerged as a promising approach for collaborative and privacy-preserving learning. Participants in a federated learning process cooperatively train a model by exchanging model parameters instead of the actual training data, which they might want to keep private. However, parameter interaction and the resulting model still might disclose information about the training data used. To address these privacy concerns, several approaches have been proposed based on differential privacy and secure multiparty computation (SMC), among others. They often result in large communication overhead and slow training time. In this paper, we propose HybridAlpha, an approach for privacy-preserving federated learning employing an SMC protocol based on functional encryption. This protocol is simple, efficient and resilient to participants dropping out. We evaluate our approach regarding the training time and data volume exchanged using a federated learning process to train a CNN on the MNIST data set. Evaluation against existing crypto-based SMC solutions shows that HybridAlpha can reduce the training time by 68% and data transfer volume by 92% on average while providing the same model performance and privacy guarantees as the existing solutions.
On the Error Resistance of Hinge-Loss Minimization

Commonly used classification algorithms in machine learning, such as support vector machines, minimize a convex surrogate loss on training examples. In practice, these algorithms are surprisingly robust to errors in the training data. In this work, we identify a set of conditions on the data under which such surrogate loss minimization algorithms provably learn the correct classifier. This allows us to establish, in a unified framework, the robustness of these algorithms under various models on data as well as error. In particular, we show that if the data is linearly classifiable with a slightly non-trivial margin (i.e. a margin at least $C/\sqrt{d}$ for $d$-dimensional unit vectors), and the class-conditional distributions are near isotropic and logconcave, then surrogate loss minimization has negligible error on the uncorrupted data even when a constant fraction of examples are adversarially mislabeled.
Influence-Augmented Online Planning for Complex Environments

How can we plan efficiently in real time to control an agent in a complex environment that may involve many other agents? While existing sample-based planners have enjoyed empirical success in large POMDPs, their performance heavily relies on a fast simulator. However, real-world scenarios are complex in nature and their simulators are often computationally demanding, which severely limits the performance of online planners. In this work, we propose influence-augmented online planning, a principled method to transform a factored simulator of the entire environment into a local simulator that samples only the state variables that are most relevant to the observation and reward of the planning agent and captures the incoming influence from the rest of the environment using machine learning methods. Our main experimental results show that planning on this less accurate but much faster local simulator with POMCP leads to higher real-time planning performance than planning on the simulator that models the entire environment.
Taming Discrete Integration via the Boon of Dimensionality

Discrete integration is a fundamental problem in computer science that concerns the computation of discrete sums over exponentially large sets. Despite intense interest from researchers for over three decades, the design of scalable techniques for computing estimates with rigorous guarantees for discrete integration remains the holy grail. The key contribution of this work addresses this scalability challenge via an efficient reduction of discrete integration to model counting. The proposed reduction is achieved via a significant increase in the dimensionality that, contrary to conventional wisdom, leads to solving an instance of the relatively simpler problem of model counting. Building on the promising approach proposed by Chakraborty et al, our work overcomes the key weakness of their approach: a restriction to dyadic weights. We augment our proposed reduction, called DeWeight, with a state of the art efficient approximate model counter and perform detailed empirical analysis over benchmarks arising from neural network verification domains, an emerging application area of critical importance. DeWeight, to the best of our knowledge, is the first technique to compute estimates with provable guarantees for this class of benchmarks.
Impossibility Results for Grammar-Compressed Linear Algebra

To handle vast amounts of data, it is natural and popular to compress vectors and matrices. When we compress a vector from size $N$ down to size $n \ll N$, it certainly makes it easier to store and transmit efficiently, but does it also make it easier to process? In this paper we consider lossless compression schemes, and ask if we can run our computations on the compressed data as efficiently as if the original data was that small. That is, if an operation has time complexity $T(\rm{inputsize})$, can we perform it on the compressed representation in time $T(n)$ rather than $T(N)$? We consider the most basic linear algebra operations: inner product, matrix-vector multiplication, and matrix multiplication. In particular, given two compressed vectors, can we compute their inner product in time $O(n)$? Or perhaps we must decompress first and then multiply, spending $\Omega(N)$ time? The answer depends on the compression scheme. While for simple ones such as Run-Length-Encoding (RLE) the inner product can be done in $O(n)$ time, we prove that this is impossible for compressions from a richer class: essentially $n^2$ or even larger runtimes are needed in the worst case (under complexity assumptions). This is the class of grammar-compressions containing most popular methods such as the Lempel-Ziv family. These schemes are more compressing than the simple RLE, but alas, we prove that performing computations on them is much harder.
Deep Dictionary Learning for Inpainting

Sparse coding techniques have shown to perform well in solving the conventional inverse imaging problems like inpainting and denoising. In the recent past, the performance of deep learning architectures in solving these inverse imaging problems have exceeded the conventional approaches several times. The only limitation of these architectures is the requirement of large volumes of training data. Deep dictionary learning (DDL) is an emerging approach and has been shown to solve some important classification problems in the scenarios where there is a scarcity of training data. DDL framework effectively combines the advantages of sparse coding and deep learning. In this paper, DDL framework is adapted to solve the inverse imaging problem with specific focus on imaging inpainting. An alternating minimization (AM) approach is proposed to derive the dictionaries and their corresponding sparse coefficients at each level of the DDL framework. The aim of this work is to show that the multilevel dictionaries can be leveraged to derive the sparse representations without compromising on the restoration quality of streaked multispectral images. Inspite of being a conventional machine learning based technique, we show that the performance of our approach is better to the state-of-the-art deep learning approaches for multispectral image inpainting.
Sulle varianti d’autore

Abstract The aim of this paper is to explore the nature of author variants in the humanistic age, starting from a survey of the variants added by Petrarch to his unfinished work Africa during the revision of the text around the mid-fourteenth century. In particular, two new definitions of variant, which were presented in a previous paper, are here outlined and discussed, that is, ‘active variants’ and ‘working variants’. Furthermore, the so-called Scevola Mariotti’s norm in relation to the author variants’ authenticity is reviewed.
Novel Structure for E-business Through Dynamic Web Page Visualization

Now-a-days when everything is going digitalized, internet and web plays a vital role in everyone’s life. When one has to ask something or has any online task to perform, one has to use internet to access relevant web-pages throughout. These web-pages are mainly designed for large screen terminals. But due to mobility, handy and economic reasons most of the persons are using small screen terminals (SST) like mobile phone, palmtop, pagers, tablet computers and many more. Reading a web page which is actually designed for large screen terminal on a small screen is time consuming and cumbersome task because there are many irrelevant content parts which are to be scrolled or there are advertisements, etc. Here main concern is e-business users. To overcome such issues the source code of a web page is organized in tree data-structure. In this paper we are arranging each and every main heading as a root node and all the content of this heading as a child node of the logical structure. Using this structure, we regenerate a web-page automatically according to SST size. Background: DOM and VIPS algorithms are the main background techniques which are supporting the current research. Objective: To restructure a web page in a more user friendly and content presenting format. Method Backtracking: Method Backtracking: Results: web page heading queue generation. Conclusion: Concept of logical structure supports every SST.
Computationally Efficient Super-Resolution Approach for Real-World Images

Most of the existing single image super-resolution (SISR) methods are trained and evaluated on synthetic datasets in which the low-resolution (LR) images are synthesized with simple and uniform bicubic degradation. Those methods perform better on a synthesized testing dataset but fail to obtain better super-resolution (SR) results on real-world images. However, by stacking more convolution layer, the SR performance can be improved. But, such techniques increase the number of training parameters and hence offer heavy computational burden on resources which make them unsuitable for real-world applications. To solve this problem, we propose a computationally efficient SR approach called real-world super-resolution network (RSRN) to super-resolve the real-world images. In RSRN, we propose a novel residual block called densely connected parallel residual block (DPRB) which helps to extract more complex features of LR observations. To prove the effectiveness of the proposed RSRN method, we train the proposed model on real-world images as well as on synthetic dataset. We compare the SR performance of the proposed method with that of other existing SISR methods and observe that the proposed RSRN method obtains better SR performance with more high-frequency details than that of the recently proposed state-of-the-art SISR methods with significantly less number of training parameters.
FAIR: Fair Adversarial Instance Re-weighting.

With growing awareness of societal impact of artificial intelligence, fairness has become an important aspect of machine learning algorithms. The issue is that human biases towards certain groups of population, defined by sensitive features like race and gender, are introduced to the training data through data collection and labeling. Two important directions of fairness ensuring research have focused on (i) instance weighting in order to decrease the impact of more biased instances and (ii) adversarial training in order to construct data representations informative of the target variable, but uninformative of the sensitive attributes. In this paper we propose a Fair Adversarial Instance Re-weighting (FAIR) method, which uses adversarial training to learn instance weighting function that ensures fair predictions. Merging the two paradigms, it inherits desirable properties from both -- interpretability of reweighting and end-to-end trainability of adversarial training. We propose four different variants of the method and, among other things, demonstrate how the method can be cast in a fully probabilistic framework. Additionally, theoretical analysis of FAIR models' properties have been studied extensively. We compare FAIR models to 7 other related and state-of-the-art models and demonstrate that FAIR is able to achieve a better trade-off between accuracy and unfairness. To the best of our knowledge, this is the first model that merges reweighting and adversarial approaches by means of a weighting function that can provide interpretable information about fairness of individual instances.
WITHDRAWN: Image recognition of sports training system based on FPGA and Markov model

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Investigation on Intelligent Recognition System of Instrument Based on Multi-step Convolution Neural Network

Digital instruments are widely used in industrial control, traffic, equipment displays and other fields because of the intuitive characteristic of their test data. Aiming at the character recognition scene of digital display Vernier caliper, this paper creatively proposes an intelligent instrument recognition system based on multi-step convolution neural network (CNN). Firstly, the image smples are collected from the Vernier caliper test site, and their resolution and size are normalized. Then the CNN model was established to train the image smples and extract the features. The digital display region in the image smples were extracted according to the image features, and the numbers in the Vernier caliper were cut out. Finally, using the MINIST datas set of Vernier caliper is established, and the CNN model is used to recognize it. The test results show that the overall recognition rate of the proposed CNN model is more than 95%, and has good robustness and generalization ability.
Probabilistic Active Meta-Learning

Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics due to the wear and tear. To address this problem, meta-learning algorithms use prior experience about tasks to learn new, related tasks efficiently. Typically, a set of training tasks is assumed given or randomly chosen. However, this setting does not take into account the sequential nature that naturally arises when training a model from scratch in real-life: how do we collect a set of training tasks in a data-efficient manner? In this work, we introduce task selection based on prior experience into a meta-learning algorithm by conceptualizing the learner and the active meta-learning setting using a probabilistic latent variable model. We provide empirical evidence that our approach improves data-efficiency when compared to strong baselines on simulated robotic experiments.
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs

Graph neural networks have shown superior performance in a wide range of applications providing a powerful representation of graph-structured data. Recent works show that the representation can be further improved by auxiliary tasks. However, the auxiliary tasks for heterogeneous graphs, which contain rich semantic information with various types of nodes and edges, have less explored in the literature. In this paper, to learn graph neural networks on heterogeneous graphs we propose a novel self-supervised auxiliary learning method using meta-paths, which are composite relations of multiple edge types. Our proposed method is learning to learn a primary task by predicting meta-paths as auxiliary tasks. This can be viewed as a type of meta-learning. The proposed method can identify an effective combination of auxiliary tasks and automatically balance them to improve the primary task. Our methods can be applied to any graph neural networks in a plug-in manner without manual labeling or additional data. The experiments demonstrate that the proposed method consistently improves the performance of link prediction and node classification on heterogeneous graphs.
Configuring the Dev Environment

Now that the initial introduction to MonoGame is out of the way, we are ready to begin coding right? Not quite – we need to set up our development environment first in Visual Studio. Thankfully, over the years, this has become a much more streamlined process and will not take much time. In addition, configuring Linux for MonoGame development will be reviewed; however, as mentioned in Chapter 1, Windows will be used for all screenshots and included sample code.
Anytime mining of sequential discriminative patterns in labeled sequences

It is extremely useful to exploit labeled datasets not only to learn models and perform predictive analytics but also to improve our understanding of a domain and its available targeted classes. The subgroup discovery task has been considered for more than two decades. It concerns the discovery of patterns covering sets of objects having interesting properties, e.g., they characterize or discriminate a given target class. Though many subgroup discovery algorithms have been proposed for both transactional and numerical data, discovering subgroups within labeled sequential data has been much less studied. First, we propose an anytime algorithm SeqScout that discovers interesting subgroups w.r.t. a chosen quality measure. This is a sampling algorithm that mines discriminant sequential patterns using a multi-armed bandit model. For a given budget, it finds a collection of local optima in the search space of descriptions and thus, subgroups. It requires a light configuration and is independent from the quality measure used for pattern scoring. We also introduce a second anytime algorithm MCTSExtent that pushes further the idea of a better trade-off between exploration and exploitation of a sampling strategy over the search space. To the best of our knowledge, this is the first time that the Monte Carlo Tree Search framework is exploited in a sequential data mining setting. We have conducted a thorough and comprehensive evaluation of our algorithms on several datasets to illustrate their added value, and we discuss their qualitative and quantitative results.
Reinforcement Learning with Feedback Graphs

We study episodic reinforcement learning in Markov decision processes when the agent receives additional feedback per step in the form of several transition observations. Such additional observations are available in a range of tasks through extended sensors or prior knowledge about the environment (e.g., when certain actions yield similar outcome). We formalize this setting using a feedback graph over state-action pairs and show that model-based algorithms can leverage the additional feedback for more sample-efficient learning. We give a regret bound that, ignoring logarithmic factors and lower-order terms, depends only on the size of the maximum acyclic subgraph of the feedback graph, in contrast with a polynomial dependency on the number of states and actions in the absence of a feedback graph. Finally, we highlight challenges when leveraging a small dominating set of the feedback graph as compared to the bandit setting and propose a new algorithm that can use knowledge of such a dominating set for more sample-efficient learning of a near-optimal policy.
Sentimental Analysis using Capsule Network with Gravitational Search Algorithm

Day by day the recent development of communication and the data on the web is increasing tremendously. Moreover, the use of social media among people to express their opinion has greatly increased. Therefore, analyzing this textual data using sentimental analysis techniques can be very helpful in capturing and categorizing people’s opinions. This work aims to propose an algorithm which is combination of Capsule Network (CN) with Gravitational Search Algorithm (GSA) to analyze people’s sentiments from twitter data. In text data mining, CN works to an excessive extent for sentiment analysis compared with other models. The performance of the proposed approach is studied using existing benchmark datasets and COVID-19 twitter posts. The results showed that the proposed approach could automatically classify the sentiments with high performance. It works better compared to other algorithms and results also encourage further research.
The Use of Human-Centered AI to Augment the Health of Older Adults

The research studies the impact of Human-Centred Artificial Intelligence (HAI) to augment the health of older adults. The researchers are especially interested in looking at the impact of the decrease in the cognitive effort for users and an increase in the naturalness of Human-Machine interaction. A mobile application that uses Natural Language Processing to enable senior citizens to interpret doctors’ handwriting is used as the mode of experimental study. The application consists of an optical character recognition program that leverages a Recurrent Neural Network and a data set of thousands of handwritten prescriptions. The interpreted information is linked to a personal calendar that automatically sets reminders to take the medicines. Voice is used as a medium of interaction with older adults to evaluate the role of naturalness in Human-Machine interaction. Initial results showcase a reduction in the amount of cognitive effort required to comprehend prescriptions and taking timely medication, while simultaneously making the elderly more aware and self-sufficient.
Automatic Identification of Obstacles and Crack Sensing Scheme in Rail Tracking System

Abstract In this paper, we have established into the identification of cracks and obstacles in rail tracking system. This system comprises of GSM modem, ultrasonic sensor, proximity sensor, PIC microcontroller to bring into the operation the crack detection and obstacle detection. The GSM modem contributes in transmission of railway geometric parameter of crack detection to the control room. Ultrasonic and Proximity sensors are used to detect the obstacles and crack in the track. If the cracks or obstacles are detected, the receiver sends the information to the microcontroller. The microcontroller manages the information from the sensor. The information sends to the control room or server when cracks or obstacles are detected through GSM. To store the data, about the cracking and obstacles detection, we use DBMS, which is interface with microcontrollers. Keywords: Ultrasonic sensor; Proximity sensor; GSM modem; PIC Microcontroller; DBMS. Cite this Article S. Ramachandran, G. Kaviya, S. Keerthana, S. Sivaranjani. Automatic Identification of Obstacles and Crack Sensing Scheme in Rail Tracking System. Journal of Network Security . 2019; 7(2): 6–10p.