Reviewerly/exHarmony huggingface dataset:
Malaria detection using deep residual networks with mobile microscopy

Automatic segmentation of erythrocytes in microscopic blood smear phone images is a critical step to visualize and identify malaria using machine learning technologies. However, it still remains a challenging problem due to the scarcity of experts, low image qualities, slow manual and inefficient quality of diagnosis. To handle these issues to some extent, we proposed an effective multi-magnification deep residual neural network (MM-ResNet), where we fully automatically classify the microscopic blood smear images as either infected/ non-infected at multiple magnifications. We have experimentally evaluated our approach by using it to train more efficient variants of different compact deep convolutional neural networks (CNN), evaluated on phone datasets. The MM-ResNet end-to-end framework shows similar or superior accuracy than the baseline architectures, as measured by GPU timings on the publicly available microscopic blood smear phone images. This approach is the first application of a MM-ResNet for malaria-infected erythrocyte identification in microscopic blood smear images.
GraphABCD: Scaling Out Graph Analytics with Asynchronous Block Coordinate Descent

It is of vital importance to efficiently process large graphs for many data-intensive applications. As a result, a large collection of graph analytic frameworks has been proposed to improve the per-iteration performance on a single kind of computation resource. However, heavy coordination and synchronization overhead make it hard to scale out graph analytic frameworks from single platform to heterogeneous platforms. Furthermore, increasing the convergence rate, i.e. reducing the number of iterations, which is equally vital for improving the overall performance of iterative graph algorithms, receives much less attention. In this paper, we introduce the Block Coordinate Descent (BCD) view of graph algorithms and propose an asynchronous heterogeneous graph analytic framework, GraphABCD, using the BCD view. The BCD view offers key insights and trade-offs on achieving high convergence rate of iterative graph algorithms. GraphABCD features fast convergence under the algorithm design options suggested by BCD. GraphABCD offers algorithm and architectural supports for asynchronous execution, without undermining its fast convergence properties. With minimum synchronization overhead, GraphABCD is able to scale out to heterogeneous and distributed accelerators efficiently. To demonstrate GraphABCD, we prototype its whole system on Intel HARPv2 CPU-FPGA heterogeneous platform. Evaluations on HARPv2 show that GraphABCD achieves geo-mean speedups of 4.8x and 2.0x over GraphMat, a state-of-the-art framework in terms of convergence rate and execution time, respectively.
End-to-end face parsing via interlinked convolutional neural networks

Face parsing is an important computer vision task that requires accurate pixel segmentation of facial parts (such as eyes, nose, mouth, etc.), providing a basis for further face analysis, modification, and other applications. Interlinked Convolutional Neural Networks (iCNN) was proved to be an effective two-stage model for face parsing. However, the original iCNN was trained separately in two stages, limiting its performance. To solve this problem, we introduce a simple, end-to-end face parsing framework: STN-aided iCNN(STN-iCNN), which extends the iCNN by adding a Spatial Transformer Network (STN) between the two isolated stages. The STN-iCNN uses the STN to provide a trainable connection to the original two-stage iCNN pipeline, making end-to-end joint training possible. Moreover, as a by-product, STN also provides more precise cropped parts than the original cropper. Due to these two advantages, our approach significantly improves the accuracy of the original model. Our model achieved competitive performance on the Helen Dataset, the standard face parsing dataset. It also achieved superior performance on CelebAMask-HQ dataset, proving its good generalization. Our code has been released at https://github.com/aod321/STN-iCNN .
Development of e-module using flip pdf professional on temperature and heat material

Abstract Research and development are intended to determine the feasibility and the responses of students and educators to the development of e-modules using flip pdf professional on temperature and heat material. The method used was adopted from Borg &amp; Gall development model. The validation product of e-modules using flip pdf professional as learning media have fulfilled very good criteria with a percentage of material expert evaluations of 92,08%, media experts of 89,1%, and religious experts of 90%. While the responses of students and educators obtained very good criteria with percentage for small group trials of 88.15%, field trials of 88.03%, and educator response tests of 85.96%. Based on the results, the development of e-module using flip pdf professional can be used as a learning media.
Communication-Efficient (Client-Aided) Secure Two-Party Protocols and Its Application

Secure multi-party computation (MPC) allows a set of parties to compute a function jointly while keeping their inputs private. Compared with the MPC based on garbled circuits, some recent research results show that MPC based on secret sharing (SS) works at a very high speed. Moreover, SS-based MPC can be easily vectorized and achieve higher throughput. In SS-based MPC, however, we need many communication rounds for computing concrete protocols like equality check, less-than comparison, etc. This property is not suited for large-latency environments like the Internet (or WAN). In this paper, we construct semi-honest secure communication-efficient two-party protocols. The core technique is Beaver triple extension, which is a new tool for treating multi-fan-in gates, and we also show how to use it efficiently. We mainly focus on reducing the number of communication rounds, and our protocols also succeed in reducing the number of communication bits (in most cases). As an example, we propose a less-than comparison protocol (under practical parameters) with three communication rounds. Moreover, the number of communication bits is also $$38.4\%$$ fewer. As a result, total online execution time is $$56.1\%$$ shorter than the previous work adopting the same settings. Although the computation costs of our protocols are more expensive than those of previous work, we confirm via experiments that such a disadvantage has small effects on the whole online performance in the typical WAN environments.
Investigating Reference Dependence Effects on User Search Interaction and Satisfaction

How users think, behave, and make decisions when interacting with information retrieval (IR) systems is a fundamental research problem in the area of Interactive IR. There is substantial evidence from behavioral economics and decision sciences demonstrating that in the context of decision-making under uncertainty, the carriers of value behind actions are gains and losses defined relative to a reference point, rather than the absolute final outcomes. This Reference Dependence Effect as a systematic cognitive bias was largely ignored by most formal interaction models built upon a series of unrealistic assumptions of user rationality. To address this gap, our work seeks to 1) understand the effects of reference points on search behavior and satisfaction at both query and session levels; 2) apply the knowledge of reference dependence in predicting users' search decisions and variations in level of satisfaction. Based on our experiments on three datasets collected from 1840 task-based search sessions (5225 query segments), we found that: 1) users' search satisfaction and many aspects of search behaviors and decisions are significantly associated with relative gains, losses and the associated reference points; 2) users' judgments of session-level satisfaction are significantly affected by peak and end reference moments; 3) compared to final-outcome-based baselines, models employing gain- and loss-based features often achieve significantly better performances in predicting search decisions and user satisfaction. The adaptation of behavioral economics perspective enables us to keep taking advantage of the collision of interdisciplinary insights in advancing IR research and also increase the explanatory power of formal search models by providing them with a more realistic behavioral and psychological foundation.
Generating Target Image-Label Pairs for Unsupervised Domain Adaptation

Deep learning demonstrates its impressive success across various machine learning problems. However, its performance often suffers in the case where the training and test data sets follow different distributions, due to the domain shift. Most current domain adaptation methods minimize the discrepancy between the source and target domains by enforcing the alignment of their marginal distributions without considering the class-level matching. Consequently, data from different classes may become close together after mapping. To address this issue, we propose an unsupervised domain adaptation method by generating image-label pairs in the target domain, in which the model is augmented with the generated target pairs and achieve class-level transfer. Specifically, we integrate generative adversarial networks (GAN) into the model predictor, where the generator fed with labels aims to produce corresponding target domain images with a well-designed semantic loss. Meanwhile, compared to previous methods which focus on discrepancy reduction across domains, i.e., image to image translation, our model focuses on semantic preservation during image generation. Our model is straightforward yet effective for unsupervised domain adaptation problems. Without any labels in the target domain in all the experiments, we demonstrate the validity of our approach by presenting the plausible generated target image-label pairs. In addition, our proposed method achieves the best or comparable performance on multiple unsupervised domain adaptation datasets which include image classification and semantic segmentation.
A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer.It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard.This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. A multi-center study, composed of 1785 women whose “for presentation” mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.
Cluster validity index for irregular clustering results

Different clustering algorithms with different parameter settings can produce various partitions on the input data. Without the priori knowledge, it is difficult for users to select the proper clustering algorithm and the parameters for the specific data in advance. Therefore, the cluster validity index (CVI) is crucial to help select the best partition that fits the underlying structure of the data. However, most existing CVIs (including some recent ones that designed for complex partitions) have strong assumptions. They only work well for partitions where clusters are spherically distributed, with similar sizes and densities, and with large separation distances. In complicated situations where irregular clustering results (i.e., clustering results having clusters in arbitrary shapes, different sizes and densities, and with small separation distances) exist, they usually fail to find the best fitting partition. Focusing on the insufficiencies of the existing CVIs (designed for hard clustering), this paper presents a new index that helps to find the best partition produced by hard clustering algorithms when irregular clustering results exist. The proposed index uses the density changes inside a cluster, and the density changes from the inner to the inter-cluster regions of the cluster to evaluate its quality. Experiments are implemented on both real and synthetic datasets. 11 other CVIs including some well known as well as recently proposed ones are also tested for comparison. Experimental results are given to demonstrate the effectiveness of the new index.
MuLTReNets: Multilingual text recognition networks for simultaneous script identification and handwriting recognition

Multilingual handwritten text recognition is often accomplished in two cascaded steps: script identification and handwriting recognition. However, this scheme is not optimal due to error accumulation. To perform simultaneous script identification and handwriting recognition, in this paper, we propose a new framework named multilingual text recognition networks (MuLTReNets). Specifically, the system has four major modules: feature extractor, script identifier, handwriting recognizer and auto-weighter. The feature extractor integrates both spatial and temporal knowledge to encode text images into features shared by the script identifier and recognizer. The script identifier predicts script category from a variable-length sequence incorporating an auto-weighter for balancing different scripts, while the handwriting recognizer adopts long-short term memory (LSTM) and Connectionist Temporal Classification (CTC) to accomplish sequence decoding. Via multi-task learning, the proposed framework can benefit both two multilingual recognition schemes: unified recognition with merged alphabet (MuLTReNetV1) and cascaded script identification-single script recognition with joint training (MuLTReNetV2). We evaluated the performance of the proposed method on handwritten text databases of five languages, which are English, French, Kannada, Urdu, and Bangla. Experimental results demonstrate that our method performs superiorly for both script identification and handwriting recognition. The accuracy of script identification reaches 99.9%. While in handwriting recognition, the proposed system not only outperforms cascade systems but also surpasses systems particularly designed for specific scripts.
Accelerated First-Order Optimization Algorithms for Machine Learning

Numerical optimization serves as one of the pillars of machine learning. To meet the demands of big data applications, lots of efforts have been put on designing theoretically and practically fast algorithms. This article provides a comprehensive survey on accelerated first-order algorithms with a focus on stochastic algorithms. Specifically, this article starts with reviewing the basic accelerated algorithms on deterministic convex optimization, then concentrates on their extensions to stochastic convex optimization, and at last introduces some recent developments on acceleration for nonconvex optimization.
Hybrid Consensus Algorithm Based on Modified Proof-of-Probability and DPoS

As the core of blockchain technology, the consensus algorithm plays an important role in determining the security, data consistency, and efficiency of blockchain systems. The existing mainstream consensus algorithm is experiencing difficulties satisfying the needs of efficiency, security, and decentralization in real-world scenarios. This paper proposes a hybrid consensus algorithm based on modified Proof-of-Probability and Delegated Proof-of-Stake. In this method, the work of block generation and validation is, respectively, completed by the nodes using the modified Proof-of-Probability consensus algorithm and Delegated Proof-of-Stake consensus algorithm. When a transaction occurs, the system sends several target hash values to the whole network. Each modified Proof-of-Probability node has a different sorting algorithm, so they have different mining priorities. Every time a hash is decrypted by a modified Proof-of-Probability node, the modulo operation is done to the value of nonce, which is then compared with the expected value given by the supernode selected by the Delegated Proof-of-Stake nodes. If they are not the same, the Proof-of-Probability node enters the waiting time and the other Proof-of-Probability nodes continue to mine. By adopting two consensus algorithms, the malicious nodes must control more than 51% of the nodes that adopt the two consensus algorithms, at the same time, to effectively attack the system, that is, they must have more than 51% of the computing power and more than 51% of the tokens. This not only increases the cost of malicious attacks, but also reduces waste of computing power. In addition, the efficiency of the DPoS algorithm makes up for the deficiency of the PoP algorithm in system efficiency, and the mining behavior based on probability in the PoP algorithm also significantly weakens the ability of supernodes in the DPoS algorithm to conduct monopoly behavior or other malicious behaviors. In a word, the combination of the two algorithms makes the system perform better in terms of security, system efficiency, and decentralization.
A novel term weighting scheme for text classification: TF-MONO

The effective representation of the relationship between the documents and their contents is crucial to increase classification performance of text documents in the text classification. Term weighting is a preprocess aiming to represent text documents better in Vector Space by assigning proper weights to terms. Since the calculation of the appropriate weight values directly affects performance of the text classification, in the literature, term weighting is still one of the important sub-research areas of text classification. In this study, we propose a novel term weighting (MONO) strategy which can use the non-occurrence information of terms more effectively than existing term weighting approaches in the literature. The proposed weighting strategy also performs intra-class document scaling to supply better representations of distinguishing capabilities of terms occurring in the different quantity of documents in the same quantity of class. Based on the MONO weighting strategy, two novel supervised term weighting schemes called TF-MONO and SRTF-MONO were proposed for text classification. The proposed schemes were tested with two different classifiers such as SVM and KNN on 3 different datasets named Reuters-21578, 20-Newsgroups, and WebKB. The classification performances of the proposed schemes were compared with 5 different existing term weighting schemes in the literature named TF-IDF, TF-IDF-ICF, TF-RF, TF-IDF-ICSDF, and TF-IGM. The results obtained from 7 different schemes show that SRTF-MONO generally outperformed other schemes for all three datasets. Moreover, TF-MONO has promised both Micro-F1 and Macro-F1 results compared to other five benchmark term weighting methods especially on the Reuters-21578 and 20-Newsgroups datasets.
Hyper-heuristic local search for combinatorial optimisation problems

Local search algorithms have been successfully used for many combinatorial optimisation problems. The choice of the most suitable local search algorithm is, however, a challenging task as their performance is highly dependent on the problem characteristic. In addition, most of these algorithms require users to select appropriate internal neighbourhood structures to obtain desirable performance. No single local search algorithm can consistently perform well with a fixed setting, for different types of problems or even different instances of the same problem. To address this issue, we propose a hyper-heuristic framework which incorporates multiple local search algorithms and a pool of neighbourhood structures. This framework is novel in three respects. Firstly, a two-stage hyper-heuristic structure is designed to control the selection of a local search algorithm and its internal operators. Secondly, we propose an adaptive ranking mechanism to choose the most appropriate neighbourhood structures for the current local search algorithm. The proposed mechanism uses the entropy to evaluates the contribution of the local search in terms of quality and diversity. It adaptively adjusts the pool of candidate neighbourhood structures. Thirdly, we use a population of solutions within the proposed framework to effectively navigate different areas in the solutions search space and share solutions with local search algorithms. To ensure different solutions is allocated in different regions of the search space, we propose a distance-based strategy for population updating process that allowing solutions to share local search algorithms. We have evaluated the performance of the proposed framework using two challenging optimisation problems: Multi-Capacity Bin Packing benchmark instances and Google Machine Reassignment benchmark instances. The results show the effectiveness of the proposed framework, which outperformed state-of-the-art algorithms on several problem instances.
Hand Gesture Recognition by a MMG-Based Wearable Device

A novel wearable human machine interface based on mechanomyogram (MMG) signals was presented in this study. A three-axis accelerometer was fixed to a customized watch strap to measure the MMG signals that were generated by the end of the extensor digitorum muscle. Eight gaming gestures, including clapping, index figure flicking, finger snapping, coin flipping, shooting, wrist extension, wrist flexion and fist-making, were identified in real time. This study extracted the features from both the time signals and the coefficients of the wavelet packet decomposition (WPD), and sequential forward selection (SFS) was used to identify the significant features to improve the classification accuracy and reduce the processing time. The performances of the classifiers such as the k-nearest neighbors (KNN), the support vector machine (SVM), linear discriminant analysis (LDA), and deep neural network (DNN) were compared. After testing the system on 35 subjects aged from 16 to 55 years old, the proposed system has advantages with respect to its convenient portability, stable signal acquisition, low power consumption, and high classification accuracy.