Reviewerly/exHarmony huggingface dataset:
Adaptive Channel Equalization using Teaching Learning based Optimization

In digital communication channel the transmitted signal might get dispersive due to which the information may not conveyed same as transmitted. Additive noise and inter-symbol interference (ISI) cause dispersion of signal. The ISI can be minimised if the channel is precisely known. In practice, however, we seldom have prior knowledge of the channel characteristics though there is unavoidable problem of imprecision that arises in physical implementations of the filters. To compensate the intrinsic residual distortion equalization used. In this paper equalization of channel coefficients is done through evolutionary adaptive algorithms. Conventional differential evolution (DE) and particle swarm optimization (PSO) are used for equalizing second order channel. Later a new optimization technique called teaching learning based optimization (TLBO) is used which provides a better solution. In this paper a comparative study of discussed optimization techniques for different scenarios are provided.
Arbitrary-time error suppression for Markovian adiabatic quantum computing using stabilizer subspace codes

Adiabatic quantum computing (AQC) can be protected against thermal excitations via an encoding into error detecting codes, supplemented with an energy penalty formed from a sum of commuting Hamiltonian terms. Earlier work showed that it is possible to suppress the initial thermally induced excitation out of the encoded ground state, in the case of local Markovian environments, by using an energy penalty strength that grows only logarithmically in the system size, at a fixed temperature. The question of whether this result applies beyond the initial time was left open. Here we answer this in the affirmative. We show that thermal excitations out of the encoded ground state can be suppressed at arbitrary times under the additional assumption that the total evolution time is polynomial in the system size. Thus, computational problems that can be solved efficiently using AQC in a closed system setting, can still be solved efficiently subject to coupling to a thermal environment. Our construction uses stabilizer subspace codes, which require at least $4$-local interactions to achieve this result.
Multi-view Spectral Clustering via Multi-view Weighted Consensus and Matrix-Decomposition Based Discretization

In recent years, multi-view clustering has been widely used in many areas. As an important category of multi-view clustering, multi-view spectral clustering has recently shown promising advantages in partitioning clusters of arbitrary shapes. Despite significant success, there are still two challenging issues in multi-view spectral clustering, i.e., (i) how to learn a similarity matrix for multiple weighted views and (ii) how to learn a robust discrete clustering result from the (continuous) eigenvector domain. To simultaneously tackle these two issues, this paper proposes a unified spectral clustering approach based on multi-view weighted consensus and matrix-decomposition based discretization. In particular, a multi-view consensus similarity matrix is first learned with the different views weighted w.r.t. their confidence. Then the eigen-decomposition is performed on the similarity matrix and a set of c eigenvectors are obtained. From the eigenvectors, we first learn a continuous cluster label and then discretize it to build the final clustering label, which avoids the potential instability of the conventional k-means discretization. Extensive experiments have been conducted on multiple multi-view datasets to validate the superiority of our proposed approach.
Bi-objective Traffic Optimization in Geo-distributed Data Flows

Recently, there have been several proposals in the area of geo-distributed big data processing. In this work, we aim to address a limitation of the existing solutions, namely to optimize task allocation across geographically distributed data centers, in a way that both the total traffic and the running time of the whole processing in complex multi-stage flows are targeted. Apart from proposing concrete efficient solutions for this combinatorial problem, we advocate to take a critical stand on the broadly spread claim that transferring distributed data to a single or fewer places is too costly. In our proposal, we judiciously reduce the participation of some data centers in the flow execution, and we show that, in a wide range of settings, this yields significant benefits. We show that a stochastic solution is superior to a fast greedy one, at the expense of optimization time of up to a few minutes. Compared to a state-of-the-art solution, we manage to decrease total traffic by 44% and running time by 37% on average. In several cases, the improvements can reach 1-2 orders of magnitude. Moreover, we provide evidence that simple heuristics are inferior. Our experimental evaluation comprises both extensive simulations and real runs in Spark.
Compiler-Based Techniques to Secure Cryptographic Embedded Software Against Side-Channel Attacks

Side-channel attacks are a concrete and practical threat to the security of computing systems, ranging from high performance platforms to embedded devices.In this work, we will provide a brief systematization of the current existing approaches to analyze the side-channel vulnerability of an implementation, or automatically implement countermeasures, relying on methodologies typical of compiler systems.We will dedicate a spotlight to a significant progress in the countermeasures techniques which is represented by the application of dynamic compilation techniques to prevent a side-channel attacker from devising a model of the attacked application.We conclude the work highlighting promising research directions in this field.
µqSim: Enabling Accurate and Scalable Simulation for Interactive Microservices

Current cloud services are moving away from monolithic designs and towards graphs of many loosely-coupled, single-concerned microservices. Microservices have several advantages, including speeding up development and deployment, allowing specialization of the software infrastructure, and helping with debugging and error isolation. At the same time they introduce several hardware and software challenges. Given that most of the performance and efficiency implications of microservices happen at scales larger than what is available outside production deployments, studying such effects requires designing the right simulation infrastructures. We present j);qSim, a scalable and validated queueing network simulator designed specifically for interactive microser-vices. μqSim provides detailed intra- and inter-microservice models that allow it to faithfully reproduce the behavior of complex, many-tier applications. μqSim is also modular, allowing reuse of individual models across microservices and end-to-end applications. We have validated μqSim both against simple and more complex microservices graphs, and have shown that it accurately captures performance in terms of throughput and tail latency. Finally, we use μqSim to model the tail at scale effects of request fanout, and the performance impact of power management in latency -sensitive microservices.
Character Recognition in Historical Handwritten Documents – A Survey

Digitization and Recognition of handwritten documents have become popular with the growth of advances in Computing. Even after the introduction of new technologies, handwritten documents continue to be the means of communication and recording information in various fields. Handwritten Character Recognition (HCR) is a subject of research for decades. Many HCR techniques have been developed for applications where documents are written by hand like bank cheques, mail addresses, legal documents, medical prescriptions etc. and have achieved satisfactory performance. But active research is still going on in character recognition from historical documents. HCR becomes more challenging for Ancient Scripts due to the presence of various kinds of noise, low intensity, discoloration etc. Vast amount of knowledge in the field of literature, architecture, history and civilization, economic factors, medicine etc. have been inscribed and preserved in historical documents in various forms. Even though several techniques have been developed for HCR for Ancient Scripts, promising results have not been achieved. The paper reviews some of the major works carried out in HCR for Ancient handwritten documents.
Estimating Touch Force with Barometric Pressure Sensors

Finger pressure offers a new dimension for touch interaction, where input is defined by its spatial position and orthogonal force. However, the limited availability and complexity of integrated force-sensing hardware in mobile devices is a barrier to exploring this design space. This paper presents a synthesis of two features in recent mobile devices - a barometric sensor (pressure altimeter) and ingress protection - to sense a user's touch force. When a user applies force to a device's display, it flexes inward and causes an increase in atmospheric pressure within the sealed chassis. This increase in pressure can be sensed by the device's internal barometer. However, this change is uncontrolled and requires a calibration model to map atmospheric pressure to touch force. This paper derives such a model and demonstrates its viability on four commercially-available devices (including two with dedicated force sensors). The results show this method is sensitive to forces of less than 1 N, and is comparable to dedicated force sensors.
Optimal provisioning and scheduling of analytics as a service in cloud computing

Abstract We propose an analytic request‐centric multiobjective provisioning and scheduling method based on hybrid particle swarm optimization, called analytics‐as‐a‐service (AaaS) provisioning and resource scheduling (APARS) system, to optimally provision analytics service and schedule cloud resources by taking into account of user specified constraints. Analytics‐as‐a‐service provisioning and resource scheduling is employed to maximize accuracy of the analytic service and minimize the execution time and cost of the analytic request by combining multiobjectives with the constraint of minimal deadline and budget. Provisioning AaaS and scheduling cloud resources for analytic request is challenging as the analytic request requires scaling up and down of cloud resources dynamically. Thus, this domain invites a rigid system that can provide optimal analytics using cloud services for specified quality of service parameters. Our proposed system can handle diverse set of analytic requests with the goal of minimizing execution time, cost, critical performance, load balancing level and maximizing resource utilization, AaaS utilization, and success rate. The proposed APARS system is tested and evaluated under different use cases and compared with other metaheuristic algorithms, and we found that the results to be convincing our claims. The experimentation is carried out for various scenarios and the proposed APARS is evaluated with other metaheuristic algorithm. Simulation results validate the APARS system and show that the proposed method performs better than other existing methods with various use cases.
Capped $l_1$ -Norm Sparse Representation Method for Graph Clustering

As one of the most popular clustering techniques, graph clustering has attracted many researchers in the field of machine learning and data mining.Generally speaking, graph clustering partitions the data points into different categories according to their pairwise similarities.Therefore, the clustering performance is largely determined by the quality of the similarity graph.The similarity graph is usually constructed based on the data points' distances.However, the data structure may be corrupted by outliers.To deal with the outliers, we propose a capped l 1 -norm sparse representation method (CSR) in this paper.The main contribution of this paper is threefold: 1) a similarity graph with clear cluster structure is learned by employing sparse representation with proper constraints; (2) the capped l 1 -norm loss is utilized to remove the outliers, which ensures the graph quality; and 3) an iterative algorithm is developed to optimize the proposed non-convex problem.The extensive experiments on real-world datasets show the superiority of the proposed method over the state-of-the-art, and demonstrate its robustness to the outliers.
Chapter 9. The evolution of a legal genre

This chapter provides a diachronic corpus-based analysis of a vitally important, yet currently very under-researched, legal genre: the patent specification. The empirical focus of the analysis is on changes in the rhetorical move structure of the patent specification genre over the first 150 years of its existence, as represented by a corpus of British patent specification texts ranging from 1711 to 1860. The methodology of the study involves reducing each exemplar text in the corpus to a simple letter code string representing its rhetorical move structure, and then submitting the resulting list of code strings to two kinds of computational analysis: one focusing on the frequency and distribution of individual rhetorical moves over the period of the study, and the other focusing on the frequency and distribution of the different move sequence types attested in the corpus. It is argued that the findings of the study are consistent with, and may thus be interpreted as providing empirical support for, generalized evolutionary theoretical models of genre change.
Learning Fine-Grained Patient Similarity with Dynamic Bayesian Network Embedded RNNs

The adoption of Electronic Health Records (EHRs) enables comprehensive analysis for robust clinical decision-making in the rapidly changing environment. Therefore, using historical and similar patient records, we investigate how to utilize EHRs to provide effective and timely treatments and diagnoses for them under the circumstances that our patients are likely to respond to the therapy. In this paper, We propose a novel framework that embeds the Markov decision process into the multivariate time series analysis to research the meaningful distance among patients in Intensive Care Units (ICU). Specifically, we develop a novel deep learning model TDBNN that employs Triplet architecture, Dynamic Bayesian Network (DBN), and Recurrent Neural Network (RNN). Causal correlations among medical events are firstly obtained by the conditional dependencies in DBN, and to transmit this kind of correlations over time as temporal features, conditional dependencies in DBN are used to construct extra connections among RNN units. With specially-designed connections, the RNN is further utilized as fundamental components of the Triplet architecture to study the fine-grained similarities among patients. The proposed method has been applied to a real-world ICU dataset MIMIC-III. The experimental results between our approach and several existing baselines demonstrate that the proposed approach outperforms those methods and provides a promising direction for the research on clinical decision support.
Phase Correlation Decomposition: The Impact of Illumination Variation for Robust Subpixel Remotely Sensed Image Matching

Illumination variation is one of the major problems in multitemporal earth observation (EO) image matching. Despite much research has been focused on illumination invariant image matching, subpixel image matching under large illumination variation without prior knowledge is still a challenge. This paper proposes a phase correlation decomposition (PCD) theory model in order to analyze the joint effects of zenith and azimuth angles of the lighting source. A novel stepwise least-squares fitting-based PC (SLSF-PC) is proposed to accurately calculate the subpixel image shift by a stepwise function in the frequency domain. Our mathematical investigation is validated by image alignment and stereo dense matching experiments using simulated terrain shading images representing four different landscapes and a multi-illumination remotely sensed image data set containing eight different scenes under seasonal and daily illumination variation. Image matching experiments demonstrate the superior performance of the proposed SLSF-PC compared to the state-of-the-art image matching algorithms, such as speeded up robust features (SURF), mutual information (MI), and normalized cross correlation (NCC). Even under great illumination angle change, the proposed SLSF-PC is able to achieve 0.1 subpixel matching accuracy on average while other methods fail to find the correspondence.
Mobile Devices based Eavesdropping of Handwriting

When filling out privacy-related forms in public places such as hospitals or clinics, people usually are not aware that the sound of their handwriting leaks personal information. In this paper, we explore the possibility of eavesdropping on handwriting via nearby mobile devices based on audio signal processing and machine learning. By presenting a proof-of-concept system, WritingHacker, we show the usage of mobile devices to collect the sound of victims' handwriting, and to extract handwriting-specific features for machine learning based analysis. WritingHacker focuses on the situation where the victim's handwriting follows certain print style. An attacker can keep a mobile device, such as a common smartphone, touching the desk used by the victim to record the audio signals of handwriting. Then, the system can provide a word-level estimate for the content of the handwriting. To reduce the impacts of various writing habits and writing locations, the system utilizes the methods of letter clustering, dictionary filtering and letter time length based offsetting. Moreover, if the relative position between the device and the handwriting is known, a hand motion tracking method can be further applied to enhance the system's performance. Our prototype system's experimental results show that the accuracy of word recognition reaches around 70 - 80 percent under certain conditions, which reveals the danger of privacy leakage through the sound of handwriting.
Investigation of Activation Functions for Generalized Learning Vector Quantization

An appropriate choice of the activation function plays an important role for the performance of (deep) multilayer perceptrons (MLP) in classification and regression learning. Usually, these activations are applied to all perceptron units in the network. A powerful alternative to MLPs are the prototype-based classification learning methods like (generalized) learning vector quantization (GLVQ). These models also deal with activation functions but here they are applied to the so-called classifier function instead. In the paper we investigate whether successful candidates of activation functions for MLP also perform well for GLVQ. For this purpose we show that the GLVQ classifier function can also be interpreted as a generalized perceptron.
Forecasting explosion repose intervals with a non-parametric Bayesian survival model: Application to Sakura-jima volcano, Japan

Forecasting the repose between eruptions at a volcano is a key goal of volcanology for emergency planning and preparedness. Previous studies have used the statistical distribution of prior repose intervals to estimate the probability of a certain repose interval occurring in the future, and to offer insights into the underlying physical processes that govern eruption frequency. However, distributions are only decipherable after the eruption, when a full dataset is available, or not at all in the case of an incomplete time-series. Thus there is value in using an approach that does not assume an underlying distribution in forecasting likely repose intervals, and that can make use of additional information that may be related to the duration of repose. The use of a non-parametric survival model is novel in volcanology, as the size of eruption records is typically insufficient. Here, we apply a non-parametric Bayesian grouped time Markov Chain Monte Carlo (MCMC) survival model to the extensive 58-year eruption record (1956 to 2013) of Vulcanian explosions at Sakura-jima volcano, Japan. The model allows for the use of multiple observed and recorded data sets, such as plume height or seismic amplitude, even if some of the information is incomplete. Thus any relationships between explosion variables and subsequent or prior repose interval can be investigated. The model was successfully able to forecast future repose intervals for Sakura-jima using information about the prior plume height, plume colour and repose durations. For plume height, smaller plumes are followed by shorter repose intervals. This provides one of the first statistical models that uses plume height to quantitatively forecast explosion frequency.
Obstacle Avoidance of Mobile Robot using Fuzzy Logic and Hybrid Obstacle Avoidance Algorithm

The road accidents due to traffic problems and human erroneous driving are the major challenges for researches. The self-driving car or mobile robot is the solution to avoid such mishaps. In this paper, an attempted has been made to develop obstacle avoidance algorithms for bicycle vehicle model of mobile robots. The hybrid obstacle avoidance algorithm is proposed on the merits of line, wall following and tangent bug algorithm. The trajectory generated from the hybrid obstacle avoidance algorithm is fed into the overwhelming controller of the bicycle vehicle model of mobile robot for avoiding obstacles. Then, the fuzzy logic (FL) based obstacle avoidance controller is proposed. Twenty-three set of rules are proposed for fuzzy logic approach. Both the obstacle avoidance algorithms are implemented on the bicycle vehicle model of the mobile robot. The dynamic model of the mobile robot is developed using bond graph theory and is converted into Simulink block using S-function directly from the library of SYMBOLS Shakti software. The vehicle model is equipped with three ultrasonic sensors to measure the distance from the obstacles. Three input membership functions and one output membership function are considered in fuzzy logic controller. During avoiding two static obstacles and reaching the target, the comparison of obstacle free paths traced by both obstacle avoidance algorithms is done in this paper.
Analysis and Implementation of ECC Algorithm in Lightweight Device

Cryptography is a process that converts ordinary text into cipher text and vice-versa. It is not only protecting the data from theft, but can also be used for user authentication. The large number of slices increases the size of devices. In order to reduce the number of slices, lightweight algorithms are used. Achieving high speed and less memory space are the key issues in lightweight devices using conventional cryptographic algorithms. Hence Elliptic Curve Cryptographic Algorithm (ECC) is chosen, as its key size is very less and secure compared to conventional cryptographic algorithms. Elliptic-curve cryptography is a lightweight public-key cryptographic algorithm as it requires smaller keys compared to private-key cryptographic algorithms. In this paper, an ECC algorithm over Galois Field GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) for lightweight systems is proposed where the main focus is on reduction in area and the simulation and synthesis results of 8-bit ECC crypto processor are discussed. Therefore, a compact design for ECC crypto processor applicable to lightweight devices can be developed.