Reviewerly/exHarmony huggingface dataset:
Towards Resource-Efficient Classifiers for Always-On Monitoring

Emerging applications such as natural user interfaces or smart homes create a rising interest in electronic devices that have always-on sensing and monitoring capabilities. As these devices typically have limited computational resources and require battery powered operation, the challenge lies in the development of processing and classification methods that can operate under extremely scarce resource conditions. To address this challenge, we propose a two-layered computational model which enables an enhanced trade-off between computational cost and classification accuracy: The bottom layer consists of a selection of state-of-the-art classifiers, each having a different computational cost to generate the required features and to evaluate the classifier itself. For the top layer, we propose to use a Dynamic Bayesian network which allows to not only reason about the output of the various bottom-layer classifiers, but also to take into account additional information from the past to determine the present state. Furthermore, we introduce the use of the Same-Decision Probability to reason about the added value of the bottom-layer classifiers and selectively activate their computations to dynamically exploit the computational cost versus classification accuracy trade-off space. We validate our methods on the real-world SINS database, where domestic activities are recorded with an accoustic sensor network, as well as the Human Activity Recognition (HAR) benchmark dataset.
Study of performance of a ‘second‐generation wavelet video encoder with a scalable rate’

The purpose of data compression, in general, is to encode information using fewer bits than the original representation. In video coding, the goal is to reduce the bitrate of a video while preserving an acceptable quality for visual perception. In this study, the authors describe and study the performance of a proposed video coding framework that utilises a second-generation wavelet transform specifically Spline 5/3. This framework allows for unequal error protection and has a better performance than many well know video coding systems especially for low bitrate applications. The proposed framework allows for bit rate control according to users need both at the encoder end and the decoder end, which is great for networks with variable conditions like wireless IP networks since the noise can reduce the available bandwidth and the number of users can vary from time to time.
A Multi-Stage Fuzzy Model for Assessing Applicants for Faculty Positions in Universities

Assessing applicants for faculty positions in universities involves many issues. Each issue may involve a judgment based on uncertain or imprecise data. The uncertainty in data may exist in the interpretation made by the evaluator. This issue might lead to improper decision making. Modeling such a system using fuzzy logic will provide a more efficient model for handling imprecision. This article presents a fuzzy system for modeling the assessment of applicants for employment at academic universities. This system will utilize a multi-stage fuzzy model for measuring and evaluating the applicants. Utilizing fuzzy logic for applicants' evaluation will help administrators in choosing the best candidates for faculty positions. The fuzzy system was developed using jFuzzyLogic Java library. The reliability of the proposed system was proved by evaluating real-world case studies to prove its effectiveness to mimic human judgment. Moreover, the developed system has been evaluated by comparing it with a traditional mathematical method to prove the credibility and fairness of the proposed fuzzy system.
Research on Image Smoothing Diffusion Model With Gradient and Curvature Features

In this paper, two image smoothing models are proposed for the visual inspection of highdensity flexible IC package substrates with strict requirements on line width and line distance which are applied to the de-noising of high-density flexible IC package substrate images.First of all, the two models proposed in this paper combines the level set curvature feature of the image with gradient threshold, using more abundant second-order differential information as the detection factor to remove the noise in the image.Second, the theoretical analysis shows that the de-noised image obtained by the two models proposed can retain more detailed texture information and edge information of the original image.What is more, the experimental analysis shows that the proposed models have the highest structural similarity and peak signal-to-noise ratio, and have a relatively high edge-preserving index and the lowest mean squared error compared with other models.In particular, the de-noised image through Model 1 has the highest structural similarity and peak signal-to-noise ratio, as well as the lowest mean squared error.The de-noised image through Model 2 has a relatively high edge retention index.The methods proposed in this paper can effectively remove the noise of the image of the high-density flexible IC package substrate and can retain the original details and edges information of the image.
Quantum gates by resonantly driving many-body eigenstates, with a focus on Polychronakos’ model

Accurate, nontrivial quantum operations on many qubits are experimentally challenging. As opposed to the standard approach of compiling larger unitaries into sequences of 2-qubit gates, we propose a protocol on Hamiltonian control fields which implements highly selective multi-qubit gates in a strongly-coupled many-body quantum system. We exploit the selectiveness of resonant driving to exchange only 2 out of $2^N$ eigenstates of some background Hamiltonian, and discuss a basis transformation, the eigengate, that makes this operation relevant to the computational basis. The latter has a second use as a Hahn echo which undoes the dynamical phases due to the background Hamiltonian. We find that the error of such protocols scales favourably with the gate time as $t^{-2}$, but the protocol becomes inefficient with a growing number of qubits N. The framework is numerically tested in the context of a spin chain model first described by Polychronakos, for which we show that an earlier solution method naturally gives rise to an eigengate. Our techniques could be of independent interest for the theory of driven many-body systems.
New method for simultaneous moderate bias correction and image segmentation

This study proposes a new method for simultaneous image segmentation and moderate bias correction. Though many methods are proposed to deal with the image intensity inhomogeneity, some problems still exist and have influenced the segmentation results a lot. In this study, a new model is proposed for image segmentation and correction based on the multiplicative intrinsic component optimization (MICO) model. First, the new model in the level set formulation for gray images has been presented and the split Bregman method for fast minimization has been applied. The proposed model is tested with lots of magnetic resonance images and some medical colour images with promising results. Experimental results show that the proposed model can simultaneously segment images and correct bias field moderately. In the experimental part for gray images, a qualitative comparison between the proposed model and the MICO model in both segmentation and bias-correction results is made. Besides, the proposed model with the Chan-Vese model and the illumination and reflectance estimation model in the experimental part for colour images are compared. Moreover, the proposed model can segment nature colour images successfully. It is clear that the proposed model has a good performance on many characteristics such as accuracy, efficiency, and robustness.
On Nonidentical Discrete-Time Hyperchaotic Systems Synchronization: Towards Secure Medical Image Transmission

In this work, the synchronization of nonidentical coupled discrete time hyperchaotic systems is mainly investigated. Based on observer design, synchronization-sufficient conditions, using Borne and Gentina criterion for stability study and Benrejeb arrow form matrix for system description, are proposed and used to build an asymmetric cryptosystem in order to secure medical image transmission. The difference between original images and their corresponding encrypted or decrypted images was measured using mean square error, peak signal-to-noise ratio and structural similarity index measure. The effectiveness of the proposed encryption and decryption processes is considered against brute force attack and statistical attacks including key space analysis, histogram analysis, correlation analysis, correlation analysis of adjacent pixels and information entropy analysis.
Keystroke Dynamics in E-Learning and Online Exams

In the internet era, the online examination has become an integral component of online processing and online learning. Student assessment in the online education system is submitted remotely without any face-to-face recognition and interaction. However, student authentication is the significant challenge in online education and examination. This chapter aims to examine various authentication systems, potential threats, and solutions to student authentication in the online examinations and learning. In this chapter, a keystroke-based authentication system is discussed for online examinations. Keystroke-based authentication does not require any additional investments as compared to the other existing authentication approaches such as face recognition, iris recognition, fingerprint, and so on.
Multicriteria-Based Ranking Framework for Measuring Performance of Cloud Service Providers

Ranking of QoS provides significant facts for choosing best services offered by the cloud service providers from a group of service candidates who are functionally equivalent. Currently available frameworks must have the capacity to recommend service providers that are chosen probably by the clients. In maximum number of frameworks, rating score is used for representing the degree of preference. Customers who availed the services previously will give the ratings on a group of attributes. This input from the customers is given to traditional collaborative filtering algorithms for giving rating for the services which are not rated. We propose a multicriteria-based QoS ranking prediction framework, which guides the customers to choose the best service provider based on requirements of the customers and preferences for various services offered by the cloud by considering the benefits of past customers service usage and experiences. The framework proposed by us does not require any further real-world service problems from the intended cloud users for predicting QoS ranking.
RDF Stores for Enhanced Living Environments: An Overview

Handling large knowledge bases of information from different domains such as the World Wide Web is a complex problem addressed in the Resource Description Framework (RDF) by adding semantic meaning to the data itself. The amount of linked data has brought with it a number of specialized databases that are capable of storing and processing RDF data, called RDF stores. We explore the RDF store landscape with the aim of finding an RDF store that sufficiently meets the storage needs of an enhanced living environment, more concretely the requirements of a Smart Space platform aimed at running on a cluster set up of low-power hardware that can be run locally entirely at home with the purpose of logging data for a reactive assistive system involving, e.g., activity recognition or domotics. We present a literature analysis of RDF stores and identify promising candidates for implementation of consumer Smart Spaces. Based on the insights provided with our study, we conclude by suggesting different relevant aspects of RDF storage systems that need to be considered in Ambient Assisted Living environments and a comparison of available solutions.
Automatic Identification of Acute Lymphoblastic Leukemia on Blood Cell An image Using Geometric Features

Acute Lymphoblastic Leukemia (ALL) is a human blood cancer which causes most deaths among other types of blood cancer. It commonly occurs in children and teenagers. ALL is recognised when there is a presence of lymphoblast cell in human blood. Haematologists diagnose ALL using visual microscopic observation from a human periphery blood sample. The inspection takes a long time and is limited to the subjective experience of the haematologist, and therefore, causes differences in the diagnostic results among haematologists. In this study, the researchers proposed automatic ALL Identification system to solve the problem. The method used an image processing technique that analysed a human blood cells morphology. The system identified the differences between normal white blood cells and lymphoblast cells by considering the shape and the size of the nucleus and also the cytoplasm. There are 33 microscopic an image data of a human blood cell that were used in this research. Geometric features used in the study were an area, perimeter, eccentricity, equivDiameter, solidity, roundness and circularity of a human blood the nucleus and cytoplasm and also the ratio of area and the perimeter of the cells and the nuclei. Firstly, the nucleus and cytoplasm were extracted from the other component of a human blood cell using Gram-Schmidt as an orthogonalisation-based segmentation method and then using Otsu threshold. Support Vector Machine was then used to classify whether the white blood cells were lymphoblasts or normal cells. Lastly, ALL identification was evaluated by the parameter of sensitivity and misclassification rate metrics using k-fold cross-validation.
Hybrid fragments of Halpern–Shoham logic and their expressive power

Halpern and Shoham modal logic of time intervals (HS in short) is an elegant and highly influential propositional interval-based modal logic. Its sub-propositional fragments, that is fragments obtained by restricting use of propositional connectives, and their hybrid extensions with nominals and satisfaction operators have been recently studied and successfully applied in real-world use cases. Detailed investigation of their decidability and computational complexity has been conducted, however, there has been significantly less research on their expressive power. In this paper we make a step towards filling this gap. In particular, we (1) compare classes of frames definable in full HS and in its hybrid extension, and (2) determine in which sub-propositional HS-fragments we can express the difference operator, nominals, and satisfaction operators. The obtained results enable us to classify HS, its sub-propositional fragments, and their hybrid extensions according to their expressive power.
A Graphical Computational Tool for Computerized Ventricular Extraction in Magnetic Resonance Cardiac Imaging

In this research, a graphical computational tool for segmenting the ventricles (both left ventricle and right ventricle) using images that are taken from cardiac MRI has been developed and tested. The purpose of this research is to develop a tool to aid cardiologists in the extraction of clinically relevant medical information such as ejection fraction and stroke volume from the patient’s cardiac MRI images. The tool has been developed to allow the user to load any cardiac MRI image and performs segmentation upon the click of a button. Moreover, along with all other above-mentioned features, it will provide a cardiac disease prediction framework for extracting clinically relevant medical information and clinical parameters from the patient’s cardiac MRI images and for assisting cardiologists and cardiac researchers for creating patient-specific personalized cardiac treatment plans based on the extracted cardiac parameters such as left ventricular ejection fraction and stroke volume.
Variational Bayes for Mixture Models with Censored Data

In this paper, we propose a variational Bayesian algorithm for mixture models that can deal with censored data, which is the data under the situation that the exact value is known only when the value is within a certain range and otherwise only partial information is available. The proposed algorithm can be applied to any mixture model whose component distribution belongs to exponential family; it is a natural generalization of the variational Bayes that deals with “standard” samples whose values are known. We confirm the effectiveness of the proposed algorithm by experiments on synthetic and real world data.
Friendly, Humorous, Incompetent? On the Influence of Emoticons on Interpersonal Perception in the Workplace

Competence, humor and friendliness are good qualities to demonstrate in the workplace.We know that facial expressions provide recipients of a message with information about the senders-conveying that they possess such qualities.However, we only have limited knowledge of whether emoticons, facial expression surrogates in computer-mediated communications, do this in a similar way.Based on the four-ear model of communication and using a factorial survey, we examined how happy emoticons affect recipients' perceptions of senders' competence, humor and friendliness in the context of workplace emails.Our findings suggest that emoticon usage does not influence recipients' perceptions of senders' competence, but does influence the perception of their humor and friendliness.These findings hold practical implications: Senders can use happy emoticons to convey beneficial information at the self-revelation level of a message.Indeed, happy emoticons can make senders seem humorous and friendly to others, and does not make them seem incompetent.
Evolutionary computing based hybrid bisecting clustering algorithm for multidimensional data

The emerging technologies and data centric applications have been becoming an integral part of business intelligence, decision process and numerous daily activities. To enable efficient pattern classification and data analysis, clustering has emerged as a potential mechanism that classifies data elements based on respective feature homogeneity. Although K-Means clustering has exhibited appreciable performance for data clustering, it suffers to enable optimal classification with high dimensional data sets. Numerous optimization efforts including genetic algorithm (GA) based clustering also require further optimization to avoid local minima issues. In this paper, an improved Canonical GA based Bisecting K-Means algorithm (CGABC) has been developed. The proposed model incorporates min-max normalization based feature normalization of the high dimensional data sets, which is followed by T-Test analysis that significantly reduces data dimensions based on feature similarity of the data elements. The fitness value has been assigned based on inter-cluster (heterogeneous distance) and within-cluster (homogeneous distance) distances. To enable optimal features and process parameter selection, particularly cluster centers information, the conventional GA has been modified by applying multistage reproduction process, enhanced crossover and mutation. By incorporating the optimized cluster center information the Bisecting K-Means clustering has been performed, which has exhibited optimal solution for highly accurate and efficient clustering with high dimensional data sets.
DAPA

Matching between two information objects is the core of many different information retrieval (IR) applications including Web search, question answering, and recommendation. Recently, deep learning methods have yielded immense success in speech recognition, computer vision, and natural language processing, significantly advancing state-of-the-art of these areas. In the IR community, deep learning has also attracted much attention, and researchers have proposed a large number of deep matching models to tackle the matching problem for different IR applications. Despite the fact that deep matching models have gained significant progress in these areas, there are still many challenges to be addressed when applying these models to real IR scenarios. In this workshop, we focus on the applicability of deep matching models to practical applications. We aim to discuss the issues of applying deep matching models to production systems, as well as to shed some light on the fundamental characteristics of different matching tasks in IR. website : https://wsdm2019-dapa.github.io/index.html
Convolutional Neural Network for Larger JPEG Images Steganalysis

This paper proposes an effective steganalytic scheme based on CNN in order to detect steganography on larger JPEG images. Most of the CNN schemes were designed very deep to achieve high accuracy, resulting in inability to train large size images due to the limitation of GPUs’ memory. Most existing network architectures use small images of 256 $$\times $$ 256 or 512 $$\times $$ 512 pixels as their detection objects which are far from meeting the needs of practical applications. Meanwhile, the resizing operation on stegos will make the slight noise signal caused by steganography become difficult to detect. In our proposed network architecture, we try to solve the problem by compressing the depth of network structure. And in order to reduce the data dimension, we apply a histogram layer to transform the feature maps to feature vectors before the fully connected layer. We test our network on images of size 512 $$\times $$ 512, 1024 $$\times $$ 1024 and 2048 $$\times $$ 2048. For different application scenes, we take two methods to generate large samples. The result demonstrates that the proposed scheme can make directly training the steganalysis detectors on large images feasible.
Proposed RCFS-CARS Framework with Noise Detection and Correction

Context-Aware Recommender Systems generate more relevant recommendations by adapting them to the specific contextual situation and have become one of the most active research areas in the recommender systems. However, there remains a key issue as how contextual information can be used to create intelligent and useful recommender systems. Use of too many context variables increases dimensionality that leads to loss of accuracy while few context variables fail to bring contextual effects in recommendations. To assist the development and use of context-aware capabilities, we propose a framework, RCFS-CARS that uses influential contexts, make their sets with appropriate relaxation to be applied on different parts of the algorithm. We also propose a strategy to detect the noisy ratings in the datasets and fix them to refine the recommendation results. The experimental results on two datasets reveal that the noise detection and correction process in RCFS-CARS method is much superior and effective in context-aware recommendation scenarios.
A Hybrid Searchable Encryption Scheme for Cloud Computing

With the advances of technology, large amount of data is generated daily, which makes the traditional systems not to be able to handle such large quantities of data. A natural upgrade is to move to the cloud. In the last few years this was a trend adopted by an increasing number of organizations. Even an individual user uses the cloud in form of software as a service, for example, Google Drive, Google Docs, Microsoft OneDrive, Amazon Elastic Cloud etc. Also, a new trend is that smartphone manufacturers to provide a limited cloud storage in their own cloud for every user, storage used for data back-up or data storage. Extending the idea, an organization has much more data than an individual user and moving them to the cloud could be an advantage. But an organization or even an individual have sensitive documents stored on cloud and cloud computing brings many security challenges. A response to these challenges is searchable encryption, which allows searching for encrypted keywords over encrypted data on a server. In this paper, we propose a searchable encryption scheme, based on word suffix tree, in which we use two different algorithms to encrypt the keywords and documents.