Reviewerly/exHarmony huggingface dataset:
Long Distance Relationships Without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling

In sequence learning tasks such as language modelling, Recurrent Neural Networks must learn relationships between input features separated by time. State of the art models such as LSTM and Transformer are trained by backpropagation of losses into prior hidden states and inputs held in memory. This allows gradients to travel through time from present to past and effectively learn with perfect hindsight, but at a significant memory cost. In this paper we show that it is possible to train high performance recurrent networks using information that is local in time, and thereby achieve a significantly reduced memory footprint. We describe a predictive autoencoder called bRSM featuring recurrent connections, sparse activations, and a boosting rule for improved cell utilization. The architecture demonstrates near optimal performance on a non-deterministic (stochastic) partially-observable sequence learning task consisting of high-Markov-order sequences of MNIST digits. We find that this model learns these sequences faster and more completely than an LSTM, and offer several possible explanations why the LSTM architecture might struggle with the partially observable sequence structure in this task. We also apply our model to a next word prediction task on the Penn Treebank (PTB) dataset. We show that a ‘flattened’ RSM network, when paired with a modern semantic word embedding and the addition of boosting, achieves 103.5 PPL (a 20-point improvement over the best N-gram models), beating ordinary RNNs trained with BPTT and approaching the scores of early LSTM implementations. This work provides encouraging evidence that strong results on challenging tasks such as language modeling may be possible using less memory intensive and more biologically-plausible training regimes.
Canal-LASSO: A sparse noise-resilient online linear regression model

Least absolute shrinkage and selection operator (LASSO) is one of the most commonly used methods for shrinkage estimation and variable selection. Robust variable selection methods via penalized regression, such as least absolute deviation LASSO (LAD-LASSO), etc., have gained growing attention in works of literature. However those penalized regression procedures are still sensitive to noisy data. Furthermore, “concept drift” makes learning from streaming data fundamentally different from the traditional batch learning. Focusing on the shrinkage estimation and variable selection tasks on noisy streaming data, this paper presents a noise-resilient online learning regression model, i.e. canal-LASSO. Comparing with the LASSO and LAD-LASSO, canal-LASSO is resistant to noisy data in both explanatory variables and response variables. Extensive simulation studies demonstrate satisfactory sparseness and noise-resilient performances of canal-LASSO.
Design and Development of Centralized Squid Proxy Management System

Commercial proxy solutions are not always cost-effective and easy to implement. Due to their closed nature, they are often difficult to customize and also have security concerns. Open source solutions serve better in such situations. Squid is one such popular open source web proxy server. A large organization with multiple categories of users, networks and implementation constraints cannot fulfill all its security requirements with a single proxy server thus requiring multi server deployment. Manual management of multiple proxy servers is cumbersome and requires technically strong operator. Webmin, an open source squid proxy management solution makes the process automatic for a single server but requires a separate instance for each server in a multi-server scenario which leads to management complexity. A centralized management solution is thus required. Configuration Management tools like Chef, Puppet etc though provide centralized management features but these are suited for generic tasks and require effort for tool setup, learning tool's language, writing scripts etc and therefore do not serve the purpose. In this paper, we present a solution for centralized squid proxy management, named CSPMS (Centralized Squid Proxy Management System) which does auto-installation of proxy server as well as provides a web based interface for framing policies and deploying them across remote servers. It has features like group based policy management, cloning, proxy parameter tuning, basic and advanced modes of access control, remote policy deployment, periodic policy checks and re-deployment, backup and restore etc which makes multi-server management fast and easy with reduced human intervention.
Crvos: Clue Refining Network For Video Object Segmentation

The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attention due to their superior performances. However, most of them have complex intermediate networks which generate strong specifiers to be robust against challenging scenarios, and this is quite inefficient when dealing with relatively simple scenarios. To solve this problem, we propose a real-time network, Clue Refining Network for Video Object Segmentation (CRVOS), that does not have any intermediate network to efficiently deal with these scenarios. In this work, we propose a simple specifier, referred to as the Clue, which consists of the previous frame’s coarse mask and coordinates information. We also propose a novel refine module which shows the better performance compared with the general ones by using a deconvolution layer instead of a bilinear upsampling layer. Our proposed method shows the fastest speed among the existing methods with a competitive accuracy. On DAVIS 2016 validation set, our method achieves 63.5 fps and $\mathcal{J} \& \mathcal{F}$ score of 81.6%.
Safe Reinforcement Learning via Probabilistic Timed Computation Tree Logic

Reinforcement learning aims to discover an optimal policy that maximizes reward based on the feedback signal. Although the method succeeds in numerous systems, it may not apply to safe-critical systems due to the absence of safety protection mechanism. Besides, the agent is unable to model the environment accurately if getting biased observation. We present a safe algorithm called Safe Control with Supervisor (SCS) for addressing the limitation. If the model is accurate, the supervisor monitors the system and repairs the action of the agent at runtime, which guides the system to obey the specification described by probabilistic timed Computation Tree Logic (ptCTL). If not, the supervisor would maximize the probability of satisfying a given task specification. We validate our method through experiments of adaptive cruise control under uncertainty.
PRUCARS: improved association rule-based social recommender systems using overlapping community detection

With the increase of information on the web and online users' activities, to find the appropriate information at the right time and discover items that customers are interested in among the available choices become difficult challenges. Recommender Systems have been introduced to overcome this problem by offering potentially relevant elements and providing users with personalized recommendations. Although several social-based recommendation techniques have been proposed in the literature, applying the community detection techniques based on previous transactions of users can improve the precision of the algorithms and better handle the data sparsity and cold-start problem. In this paper, we propose an improved association rule mining process based on Rule Power Factor to generate potent rules and enhance the accuracy of recommendation. The performance of the algorithm is analyzed on transactional datasets of MovieLens. The experimental results show that our method outperforms several state-of-the-art recommendation methods with increased precision, accuracy and minimum Mean Average Error values.
Satellite-observed changes of surface spectral reflectances due to solar farming and the implication for radiation budget

Abstract Increased deployment of solar farms in the last decade is coincident with continued monitoring of surface spectral reflectances by the moderate resolution imaging spectroradiometers (MODIS) aboard NASA Aqua satellite. By analyzing the surface spectral reflectances retrieved at seven MODIS shortwave bands, this study quantifies the changes of surface spectral reflectances caused by solar farms commissioned in the southwestern US. Our case study using one solar farm indicates a 20%–25% reduction of surface reflectance over the seven MODIS bands due to the solar panel installation. Radiative transfer calculation shows that such a reduction in surface spectral reflectance leads to a ∼23% decrease in the upward shortwave broadband flux at the surface and a ∼14%–18% decrease in the clear-sky reflected shortwave flux at the top of atmosphere (TOA). Though the MODIS surface reflectance retrievals can be affected by other factors such as aerosol or thin-cirrus contaminations, five out of six solar farm sites analyzed here show unambiguously detectable changes of surface reflectances due to solar panel installations. The brighter the surface is at a given MODIS band before the solar-farm construction, the larger the spectral reflectance reduction tends to be. If all the bright deserts, which occupies about 4% of the global surface, were covered with the solar panels similar to what has been studied here, the estimated instantaneous TOA shortwave radiative forcing would be no more than 1.1 W m −2 .
Analysis of achievable distances of BB84 and KMB09 QKD protocols

Quantum key distribution (QKD) is a proven secured way to transmit shared secret keys using quantum particles. Any adversarial attempt to intercept and eavesdrop secret key results in generating errors alerting the legitimate users. Since QKD is constrained by quantum mechanics principles, the practical transmission of the key at a greater distance is an issue. In this paper, we discover and analyze the key factors associated with transmission media, hardware components and protocol implementation of the QKD system that causes hindrance in distance range. Practical implementation of BB84 and KMB09 protocols is discussed to determine the achievable distance given current technology. We find that by using ultra low loss fiber, short-pulse laser and superconducting nanowire single photon detector the maximum achievable distance for both of the quantum protocols is 250[Formula: see text]km.
Similarity Measure for Product Attribute Estimation

Representing products as a combination of properties that capture the essence of consumer sentiment is critical for companies that strive to understand consumer behavior. A catalogue of products described in terms of their attributes could offer companies a wide range of benefits; from improving existing products or developing new ones, to improving the quality of site search and offering better item recommendations to users. In this paper, we propose a method that encodes products as a sequence of attributes, each of which represents a different dimension of the consumer perception. In the proposed method, first, a base product set with known attribute values is built based on consumers' perceptions. Then, new product attribute vectors are estimated using product similarity. The proposed method also incorporates a new similarity measure that is based on purchase behavior and which is suitable for estimating product attribute vector distances. Because it takes into account the magnitude of the individual components of the vectors under comparison, the proposed method is free from the limitations of conventional similarity measures. The results of experiments conducted using real-world data indicate that the proposed method has superior performance compared to conventional approaches in terms of mean absolute error (MAE) and root mean squared error (RMSE).
Classification of Ancient Handwritten Tamil Characters on Palm Leaf Inscription Using Modified Adaptive Backpropagation Neural Network with GLCM Features

The core aspiration of this proposed work is to classify Tamil characters inscribed in the palm leaf manuscript using an Artificial Neural Network. Tamil palm leaf manuscript characters in the form of images were processed and segmented using contour-based convex hull bounding box segmentation. The segmented characters were transformed into two forms: Binary Coded Value and the Gray-Level Co-occurrence Matrix (GLCM) feature. The features extracted from the segmented characters were trained by the proposed method of the Modified Adaptive Backpropagation Network (MABPN) algorithm with Shannon activation function. Weight initialization plays an important role in the Backpropagation Neural Network, and hence Nguyen-Widrow weight initialization was introduced to initialize the weights instead of random weight initialization in the proposed method. The models evaluated are MABPN with Shannon activation function using Nguyen-Widrow weight initialization in two forms of input: Binary Coded Value and GLCM feature extracted values. The proposed method with GLCM features as input gave a promising result over binary coded transform.
Adaptive Initialization for Recurrent Photonic Networks using Sigmoidal Activations

Photonic Deep Learning (DL) accelerators are among the most promising approaches for providing fast and energy efficient neural network implementations for several applications. However, photonic accelerators require using different activation functions compared to those typically used in DL. This renders the training process especially difficult to tune, often requiring several trials just for selecting the appropriate initialization hyper-parameters for the network. This process becomes even more difficult for recurrent networks, where exploding gradient phenomena can further destabilize the training process. In this paper, we propose an adaptive data-driven initialization approach for recurrent photonic neural networks. The proposed method is activation-agnostic, while it takes into account the actual distribution of the data used to train the network, overcoming a number of significant limitations of existing approaches. The proposed method is simple and easy to implement, yet it leads to significant improvements in the performance of DL models, as it was experimentally demonstrated using two large-scale challenging time-series datasets.
Minority Oversampling Using Sensitivity

The Synthetic Minority Oversampling Technique (SMOTE) is effective to handle imbalance classification problems. However, the random candidate selection of SMOTE may lead to severe overlap between classes and introduce new noise factors. Many variants of SMOTE have been proposed to relieve these problems by generating new examples in safe regions. Most of these methods generate new examples with existing minority examples without considering the negative impact that class imbalance have brought on these examples. In this paper, we handle the imbalance classification using Bayes' decision rule and propose a novel oversampling method, the Minority Oversampling using Sensitivity (MOSS). Candidates for new example generations are selected considering their sensitivity with respect to class imbalance. New examples are then generated by interpolating the candidate and one of its adjacent examples. Experiments on 30 datasets confirm the superiority of the MOSS against one baseline method and seven oversampling methods.
Speeding Up Sensor Data Encryption with a Common Key Cryptosystem combined with Fully Homomorphic Encryption on Smartphones

The following topics are dealt with: Internet of Things; learning (artificial intelligence); cloud computing; Internet; diseases; feature extraction; pattern classification; wireless sensor networks; health care; cryptography.
A Novel Anti-Collusion Audio Fingerprinting Scheme Based on Fourier Coefficients Reversing

Most anti-collusion audio fingerprinting schemes are aiming at finding colluders from the illegal redistributed audio copies. However, the loss caused by the redistributed versions is inevitable. In this letter, a novel fingerprinting scheme is proposed to eliminate the motivation of collusion attack. The audio signal is transformed to the frequency domain by the Fourier transform, and the coefficients in frequency domain are reversed in different degrees according to the fingerprint sequence. Different from other fingerprinting schemes, the coefficients of the host media are excessively modified by the proposed method in order to reduce the quality of the colluded version significantly, but the imperceptibility is well preserved. Experiments show that the colluded audio cannot be reused because of the poor quality. In addition, the proposed method can also resist other common attacks. Various kinds of copyright risks and losses caused by the illegal redistribution are effectively avoided, which is significant for protecting the copyright of audio.
An analogue of Ruzsa's conjecture for polynomials over finite fields

In 1971, Ruzsa conjectured that if f:N→Z with f(n+k)≡f(n) mod k for every n,k∈N and f(n)=O(θn) with θ<e then f is a polynomial. In this paper, we investigate the analogous problem for the ring of polynomials over a finite field using the Polynomial Method in combinatorics.
Kalman Filter Employment in Image Processing

The Kalman filter is a classical algorithm of estimation and control theory. Its use in image processing is not very well known as it is not its typical application area. The paper deals with the presentation and demonstration of selected possibilities of using the Kalman filter in image processing. Particular attention is paid to problems of image noise filtering and blurred image restoration. The contribution presents the reduced update Kalman filter algorithm, that can be used to solve both the tasks. The construction of the image model, which is the necessary first step prior to the application of the algorithm itself, is briefly mentioned too. The described procedures are then implemented in the MATLAB software and the results are presented and discussed in the paper.
Adaptive Directional Path Planner for Real-Time, Energy-Efficient, Robust Navigation of Mobile Robots

Autonomous navigation through unknown and complex environments is a fundamental capability that is essential in almost all robotic applications. Optimal robot path planning is critical to enable efficient navigation. Path planning is a complex, compute and memory intensive task. Traditional methods employ either graph based search methods or sample based methods to implement path planning, which are sub-optimal and compute/memory-intensive. To this end, an Adaptive Directional Planner (ADP) algorithm is devised to achieve real-time, energy-efficient, memory-optimized, robust local path planning for enabling efficient autonomous navigation of mobile robots. The ADP algorithm ensures that the paths are optimal and kinematically-feasible. Further, the proposed algorithm is tested with different challenging scenarios verifying the functionality and robustness. The ADP algorithm implementation results demonstrate 40- 60X less number of nodes and 40 - 50X less execution time compared to the standard TP-RRT schemes, without compromising on accuracy. Finally, the algorithm has also been implemented as an accelerator for non-holonomic, multi-shape, small form factor mobile robots to provide a silicon solution with high performance and low memory footprint (28KB).
Kinematic Structures Estimation on the RGB-D Images

In this paper, we propose a system which detects and estimates the kinematic structures of objects in the indoor environment. We are interested in specific types of objects like doors, sliding doors, and drawers which are common in the human environment and very important taking into account the full autonomy of mobile robots. We assume that the mobile robot is equipped with an RGB-D camera. We utilize a Convolutional Neural Network-based (CNN-based) object detector to locate the articulated objects on the input image created from a pair of RGB-D images. Taking into account strong prior knowledge about the articulated object, we detect the segments on the image which belong to the articulated object. Then, the optimization-based procedure finds the 3D pose and configuration of the joint detected on the scene. We train and verify the method on the images from the Kinect sensor. The performance of the proposed method shows that we can estimate articulated objects in the indoor environment using typical sensors available on the mobile robot.
Model Uncertainty for Unsupervised Domain Adaptation

The key principle of unsupervised domain adaptation is to minimize the divergence between source and target domain. Many recent methods follow this principle to learn domaininvariant features. They train task-specific classifiers to maximize the divergence and feature extractors to minimize the divergence in an adversarial way. However, this strategy often limits their performance. In this paper, we present a novel method that learns feature representations that minimize the domain divergence. We show that model uncertainty is a useful surrogate for the domain divergence. Our domain adaptation method based on model uncertainty (MUDA) employs Bayesian approach and provides an efficient way of evaluating model uncertainty loss using Monte Carlo dropout sampling. Experimental results on the image classification benchmarks show that our method is superior or comparable to state-of-the-art methods.
Diamond Accountability Model for Blockchain-enabled Cyber-Physical Systems

Blockchain has the capacity to transform the industries disruptively as it presents new features like smart contracts, tokenization of content, eliminating counterfeit products, supply chain improvement, digital twins, and end-to-end security. This letter presents a Diamond Accountability Model (DAM) where a public or private authority is included in the blockchain transactions providing non-repudiation of digital transactions, holistic security and effective governance. The proposed technique aims to present a decentralized solution for multi-agent applications where many partnering organizations have to collaborate without suffering from the security, accountability, maintenance, scalability, and integrity problems over distributed cyber-physical systems (CPS). The proposed scheme positions authorities also in the chain that enables additional accountability and trust. The scheme proposes a verification mechanism where the authorized organizations are also included in the verification of transactions over the blockchain. In order to elucidate, a conceptual use case is presented where at least two partnering organizations collaborate with each other within a decentralized but also authorized blockchain-enabled scheme.
Inspecting Data Using Natural Language Queries

In this paper, we discuss a simple architecture for supporting the inspection of a generic dataset using natural language queries. We show how to integrate modern Artificial Intelligence libraries in the system and how to derive chart visualization out of the user’s intent. The result is a lightweight architecture for supporting such natural language queries in web-based visualization tools. Finally, we report on the user evaluation of the interface, showing a good acceptance and effectiveness of the proposed approach.