Reviewerly/exHarmony huggingface dataset:
Spoken Arabic Digits Recognition Using Deep Learning

Speech recognition has undergone tremendous advancement over the past 50 years. Deep Neural Network (DNN) is one of the most popular methods for speech analysis thanks to its ability to minimize error rate for optimization problems. This research proposes an Arabic digits speech recognition model utilizing Recurrent Neural Network (RNN). The speech recognition model select the finest speech signal representation by feature extraction of Mel-Frequency Cepstrum Coefficients (MFCCs) after having been processed for noise reduction and digits separation. Extracted features from speech of digit are fed into a network with Long Short-Term Memory (LSTM) cells. The LSTM cells have the capability to solve problems associated with temporal dependencies requiring long-term learning and solve the vanishing gradient problems associated with RNN. A dataset of 1040 samples of spoken Arabic digits from different dialects are used in this study where 840 samples used to train the network and another 200 samples are used for testing purpose. The model training is carried out using a computing system with Graphics Processing Unit (GPU). The LSTM model learning parameters is tuned for optimization purpose achieving a higher accuracy of 94% during model training. The testing results of the tuned parameters model shows that the LSTM model can achieve 69% in accuracy when recognizing spoken Arabic digits. The model has the highest accuracy, i.e. 80%, when recognizing the digit zero.
ProMo: A Probabilistic Model for Dynamic Load-Balanced Scheduling of Data Flows in Cloud Systems

An important issue in cloud computing is the balanced flow of big data centers, which usually transfer huge amounts of data. Thus, it is crucial to achieve dynamic, load-balanced data flow distributions that can take into account the possible change of states in the network. A number of scheduling techniques for achieving load balancing have therefore been proposed. To the best of my knowledge, there is no tool that can be used independently for different algorithms, in order to model the proposed system (network topology, linking and scheduling algorithm) and use its own probability-based parameters to test it for good balancing and scheduling performance. In this paper, a new, Probabilistic Model (ProMo) for data flows is proposed, which can be used independently with a number of techniques to test the most important parameters that determine good load balancing and scheduling performance in the network. In this work, ProMo is only used for testing with two well-known dynamic data flow scheduling schemes, and the experimental results verify the fact that it is indeed suitable for testing the performance of load-balanced scheduling algorithms.
Rise of Anonymous Cryptocurrencies: Brief Introduction

Bitcoin cannot provide enough anonymity for its users and, thus, people started to worry about a possible traceability in their cryptocurrency transactions. More and more people get into the crypto-ecosphere while privacy concerns are paramount. In this paper, five well-known cryptocurrencies that claim they provide anonymity are analyzed to see how, if at all, they achieve anonymity. We then examine the considered cryptocurrencies: Dash, Monero, Verge, PIVX, and Zcash.
Automatic Bone Marrow Cellularity Estimation in H&amp;E Stained Whole Slide Images

Abstract Bone marrow cellularity is an important measure in diagnostic hematopathology. Currently, the gold standard for bone marrow cellularity estimation is manual inspection of hematoxylin and eosin stained whole slide images (H&amp;E WSI) by hematopathologists. However, these assessments are subjective and subject to interobserver and intraobserver variability. This may be reduced by using a computer‐assisted estimate of bone marrow cellularity. The aim of this study was to develop a fully automated algorithm to estimate bone marrow cellularity in H&amp;E WSI stains using bone marrow segmentation. Data consisted of eight bone marrow H&amp;E WSIs extracted from eight subjects. An algorithm was developed to estimate the bone marrow cellularity consisting of biopsy segmentation, tissue classification, and bone marrow segmentation. Segmentations of the red and yellow bone marrow (YBM) were used to estimate the bone marrow cellularity within the WSI H&amp;E stains. The DICE coefficient between automatic tissue segmentations and ground truth segmentations conducted by an experienced hematopathologist were used for validation. Furthermore, the agreement between the automatic and two manual cellularity estimates was assessed using Bland–Altman plots and intraclass correlation coefficients (ICC). The validation of the bone marrow segmentation demonstrated an average DICE of 0.901 and 0.920 for the red and YBM, respectively. A mean cellularity estimate difference of −0.552 and − 7.816 was obtained between the automatic cellularity estimates and two manual cellularity estimates, respectively. An ICC of 0.980 (95%CI: 0.925–0.995, P ‐value: 5.51 × 10 −7 ) was obtained between the automatic and manual cellularity estimates based on manual annotations. The study demonstrated that it was possible to obtain bone marrow cellularity estimates with a good agreement with bone marrow cellularity estimates obtained from an experienced hematopathologist. © 2019 International Society for Advancement of Cytometry
An expectation operator for belief functions in the Dempster–Shafer theory

The main contribution of this paper is a new definition of expected value of belief functions in the Dempster–Shafer (D–S) theory of evidence. Our definition shares many of the properties of the expectation operator in probability theory. Also, for Bayesian belief functions, our definition provides the same expected value as the probabilistic expectation operator. A traditional method of computing expected of real-valued functions is to first transform a D–S belief function to a corresponding probability mass function, and then use the expectation operator for probability mass functions. Transforming a belief function to a probability function involves loss of information. Our expectation operator works directly with D–S belief functions. Another definition is using Choquet integration, which assumes belief functions are credal sets, i.e. convex sets of probability mass functions. Credal sets semantics are incompatible with Dempster's combination rule, the center-piece of the D–S theory. In general, our definition provides different expected values than, e.g. if we use probabilistic expectation using the pignistic transform or the plausibility transform of a belief function. Using our definition of expectation, we provide new definitions of variance, covariance, correlation, and other higher moments and describe their properties.
Improving Web Page Access Prediction using Web Usage Mining and Web Content Mining

Many times web users face the problem of information overload because there is huge collection of resources on the web. So there is a need to identify the web users' behaviors on web sites using web mining for improving the user's experiences on these sites. Prediction of the web page that might be visited by the web user is very important because this knowledge can be used for pre-fetching of web pages, recommendation or personalization of the web page for that user or group of users. By only considering the URLs of visited pages, it is not possible to capture the intention of the user so content of the web pages is also needed to capture the intention of the user by using the semantics which is present in the web pages. For current user, the next web page that might be visited by the user is predicted by searching the most similar cluster and then searching the most similar sessions in that cluster. It's not possible to store or manage all previous sessions and prediction time is increased due to comparison with all the sessions if the cluster has many sessions. This work proposes the strategies for discarding the old sessions for storing new sessions. It chooses the sessions which are going to be kept for prediction so that the prediction time is reduced with improved accuracy.
On Equivalence of Cascade Connections of Two Nonlinear Feedback Shift Registers

Abstract Grain is a hardware-oriented finalist in the eSTREAM Stream Cipher Project. As a particular Galois nonlinear feedback shift register (NFSR), cascade connection of two NFSRs has been used as the main building block in the Grain family of stream ciphers. Two NFSRs are said to be equivalent if their sets of output sequences are equal. Finding properties of equivalent cascade connections of two NFSRs is useful to the design of the Grain family of stream ciphers. This paper first gives some properties of feedback functions between equivalent cascade connections of two NFSRs. It then shows that a cascade connection of two NFSRs and its equivalent Galois NFSR have isomorphic state diagrams if they have the same stage number. Finally, the paper reveals that for any given cascade connection of an $m$-stage NFSR1 into an $n$-stage NFSR2, there is only another one equivalent cascade connection of an $m$-stage NFSR3 into an $n$-stage NFSR4; moreover, the feedback functions of NFSR1 and NFSR3 are dual complementary, and the feedback functions of NFSR2 and NFSR4 are complementary. As an application of this property, the paper shows that the existing Grain family of stream ciphers have used the ones with lower cost of hardware implementations between their own two equivalent cascade connections, confirming their good design criteria.
Semantic Guided Deep Unsupervised Image Segmentation

Image segmentation is an important step in many image processing tasks. Inspired by the success of deep learning techniques in image processing tasks, a number of deep supervised image segmentation algorithms have been proposed. However, availability of sufficient labeled training data is not plausible in many application domains. Some application domains are even constrained by the shortage of unlabeled data. Considering such scenarios, we propose a semantic guided unsupervised Convolutional Neural Network (CNN) based approach for image segmentation that does not need any labeled training data and can work on single image input. It uses a pre-trained network to extract mid-level deep features that capture the semantics of the input image. Extracted deep features are further fed to trainable convolutional layers. Segmentation labels are obtained using argmax classification of the final layer and further spatial refinement. Obtained segmentation labels and the weights of the trainable convolutional layers are jointly optimized in iterations in a mechanism that the deep network learns to assign spatially neighboring pixels and pixels of similar feature to the same label. After training, the input image is processed through the same network to obtain the labels that are further refined by a segment score based refinement mechanism. Experimental results show that our method obtains satisfactory results inspite of being unsupervised.
Emergent Leadership Detection Across Datasets

Automatic detection of emergent leaders in small groups from nonverbal behaviour is a growing research topic in social signal processing but existing methods were evaluated on single datasets – an unrealistic assumption for real-world applications in which systems are required to also work in settings unseen at training time. It therefore remains unclear whether current methods for emergent leadership detection generalise to similar but new settings and to which extent. To overcome this limitation, we are the first to study a cross-dataset evaluation setting for the emergent leadership detection task. We provide evaluations for within- and cross-dataset prediction using two current datasets (PAVIS and MPIIGroupInteraction), as well as an investigation on the robustness of commonly used feature channels and online prediction in the cross-dataset setting. Our evaluations show that using pose and eye contact based features, cross-dataset prediction is possible with an accuracy of 0.68, as such providing another important piece of the puzzle towards real-world emergent leadership detection.
Ambiguity in Cloud Security with Malware-Injection Attack

In this modern way of living everything needs to be preserved , cloud computing is use to store online data which can be accessed from anywhere in the world. Data and processes can be processed online without the need for software or a local client. Cloud computing is a totally dependent Internet technology in which customer data is stored and stored in a cloud database provider such as Google, Amazon, Microsoft, and so on. In this paper, I have discussed how it can be attacked and what will be thee impact. I have shown different malware attacks for cloud and its impact. Cloud needs to be protected as it is essential for all. So I have provided some of the solutions for these attacks and then what can be the conclusion.
First-order Feature Extraction Methods for Image Texture and Melanoma Skin Cancer Detection

Skin cancer is a disease characterized by the growth of uncontrolled skin cells, which can damage surrounding tissue and spread to other body parts. The purpose of this study was to facilitate early recognition of skin cancer by applying the first-order extraction method using 6 parameters i.e. contrast, variance, standard deviation, kurtosis, mean and smoothness, for feature extraction based on texture to obtain a good level of accuracy and classification methods using Multilayer Perceptron Neural Network (MLP NN). The results of diagnostic identification consist of 2 outputs, i.e. melanoma and not melanoma. From the research, accuracy measurements were obtained through 4 sets of test images using melanoma and non-melanoma images and the results showed that the lowest level of accuracy was 81.81% and the highest level of accuracy was 85.71% so that the overall accuracy rate is 83.86%.
CMIB: Unsupervised Image Object Categorization in Multiple Visual Contexts

Object categorization in images is fundamental to various industrial areas, such as automated visual inspection, fast image retrieval, and intelligent surveillance. Most existing methods treat visual features (e.g., scale-invariant feature transform) as content information of the objects, while regarding image tags as their contextual information. However, the image tags can hardly be acquired in completely unsupervised settings, especially when the image volume is too large to be marked. In this article, we propose a novel contextual multivariate information bottleneck (CMIB) method to conduct unsupervised image object categorization in multiple visual contexts. Unlike using manual contexts, the CMIB method first automatically generates a set of high-level basic clusterings by multiple global features, which are unprecedentedly defined as visual contexts since they can provide overall information about the target images. Then, the idea of the data compression procedure for object category discovery is proposed, in which the content and multiple visual contexts are maximally preserved through a “bottleneck.” Specifically, two Bayesian networks are initially built to characterize the relationship between data compression and information preservation. Finally, a novel sequential information-theoretic optimization is proposed to ensure the convergence of the CMIB objective function. Experimental results on seven real-world benchmark image datasets demonstrate that the CMIB method achieves better performance than the state-of-the-art baselines.
Optimising Faceted Secure Multi-Execution

Language-Based Information Flow Control (IFC) provides strong security guarantees for untrusted code, but often suffers from a non-negligible rate of false alarms. Multi-execution based techniques promise to provide security guarantees without raising any false alarms. However, all known multi-execution approaches introduce extraneous performance overheads which are rarely studied. In this work, we lay down the foundations for optimisation techniques aimed at reducing these overheads to a managable level, thus helping to make multi-execution more practical. We characterise our optimisations as data-and control-oriented. Data-oriented optimisations reduce storage overheads- which also helps to remove unnecessary repeated computations. In contrast, computation-oriented optimisations rely on program annotations in order to reduce needless computation. These annotations motivate the need for a new, stronger, theoretical notion of transparency- i.e., a stronger notion for characterising the lack of false alarms. To show the efficacy of our optimisation techniques, we apply them to two case-studies: a secure (faceted) database and a chat server written in a multi-execution based IFC framework. Our case-studies clearly show that our optimisations significantly reduce the storage and computational overhead, sometimes from exponential to polynomial order. All of our formal results are accompanied by mechanised proofs in Agda.
Obstacle Detection and Obstacle Avoidance Algorithm based on 2-D RPLiDAR

Obstacle Detection and Obstacle Avoidance are vital technologies for autonomous navigation of vehicles. A number of sensor technologies have been used before to perform obstacle detection and obstacle avoidance. One kind of such sensor is a 2- D lidar sensor renowned for its accuracy in measuring distances. This research involves usage of low cost 2-D lidar system from Slamtech for obstacle detection and avoidance. This paper proposes an efficient algorithm for the implementation of obstacle detection and avoidance using 2-D lidar. A method is proposed as to how to get the data from the lidar sensor and also an algorithm is designed to get the information of obstacles by filtering, clustering technique. A systematic approach is used to avoid obstacles on the principle of minimum cost function. The efficacy of the algorithm implemented is verified using MATLAB simulation platform.
Multi-stakeholder Personalized Learning with Preference Corrections

Recommender systems (RS) have served as an effective technology-enhanced learning technique in the learning area. Traditional RS only consider the preferences of the end user who is the receiver the recommendations. Multi-stakeholder recommender systems (MSRS) are recently proposed to balance the needs of multiple stakeholders in the recommender systems. For example, the utility of the learning materials from the perspective of parents, instructors and even publishers may be also important in addition to the students' preferences in the area of educational learning. In this paper, we propose and exploit utility-based MSRS for personalized learning. Particularly, we develop our methods for preference corrections, in order to address the issue that instructors and students may have different perceptions on multiple aspects of the course projects. Our experimental results based on an educational data demonstrate the effectiveness of our proposed solutions.
A Brief Introduction to Blockchain Economics

We introduce economic research on blockchains and its recent advances. In particular, we highlight unifying concepts on blockchain as decentralized consensus and its core benefits, (ii) equilibrium characterizations and allegedly irreducible tensions among consensus formation, decentralization, and scalability, (iii) major issues including network security, over-concentration, energy consumption and sustainability, adoption, multi-party computation and encryption, smart contracting, and information distribution and aggregation, and (iv) future directions concerning blockchains and their applications such as informational and agency issues, as well as game-theoretical and mechanism-design approaches to blockchain protocols.
Investigating TYPE constraint for frequent pattern mining

AbstractAbstractFrequent pattern mining is one of the hottest fields of research in data mining. It has proven quite valuable for a variety of pattern analyses. However, a major limitation of existing or traditional frequent pattern mining techniques is the extraction of a huge number of patterns, which complicates the analysis performed by the users. To reduce these overwhelming patterns; a refined pattern mining technique that extracts only useful and to-the-point patterns for the users, is required. Finding such patterns of user’s interest from large databases is a challenging research issue, which can be mitigated via constraint-based data mining. In this scenario, a pattern mining technique that incorporates specific interests (constraints) of the user could result in a substantial reduction in the number of patterns generated. In this paper, a similar technique has been proposed and investigated. In this regard, firstly all the well-defined and categorized constraints are surveyed. From the survey, it is concluded that an important constraint “type” is referred to in the literature but not categorized based on its characteristics. One of the contributions of this paper is categorization of this important constraint “type”. Further, in this paper, the “type” constraint is divided into four modes for the sake of characterization and algorithms to employ the “type” constraints are also proposed. Moreover, the amalgamated constraints are proposed and characterized. Finally; comparative study of the proposed work with the existing techniques is presented and it is found that the proposed technique is promising in terms of reduced computational complexity.Subject Classification: 35B36Keywords: Pattern matchingData miningKnowledge discoveryFrequent pattern analysis
Selective Deep Convolutional Neural Network for Low Cost Distorted Image Classification

Neural networks trained using images with a certain type of distortion should be better at classifying test images with the same type of distortion than generally-trained neural networks, given other factors being equal. Based on this observation, an ensemble of convolutional neural networks (CNNs) trained with different types and degrees of distortions is used. However, instead of simply classifying test images of unknown distortion types with the entire ensemble of CNNs, an extra tiny CNN is specifically trained to distinguish between the different types and degrees of distortions. Then, only the dedicated CNN for that specific type and degree of distortion, as determined by the tiny CNN, is activated and used to classify a possibly distorted test image. This proposed architecture, referred to as a selective deep convolutional neural network (DCNN), is implemented and found to result in high accuracy with low hardware costs. Detailed simulations with realistic image distortion scenarios using three popular datasets show that memory, MAC operations, and energy savings of up to 93.68%, 93.61%, and 91.92%, respectively, can be achieved with almost no reduction in image classification accuracy. The proposed selective DCNN scores up to $2.18\times $ higher than the state-of-the-art DCNN model when evaluated using NetScore, a comprehensive metric that considers both CNN performance and hardware cost. In addition, it is shown that even higher hardware cost reduction can be achieved when selective DCNN is combined with previously proposed model compression techniques. Finally, experiments conducted with extended types and degrees of image distortion show that selective DCNN is highly scalable.