Reviewerly/exHarmony huggingface dataset:
An Enhanced Feature Selection Approach based on Mutual Information for Breast Cancer Diagnosis

Breast cancer is the most feared disease in the female population. Early detection plays an important role to improve prognosis. Mammography is the best examination for the detection of breast cancer. However, in some cases, reading mammograms is difficult for radiologists. For this reason, several researches have been conducted to develop Computer Aided Diagnosis tools (CAD) for this disease which aims to interpret mammography images. This paper investigates a new CAD system based on Transductive scheme and Mutual Information for breast abnormalities diagnosis. In the proposed method, a feature vector contains a combination of two features extraction method: Grey Level Co-occurrence Matrix and local Binary Pattern. In the next step, a novel scheme combining Mutual Information and Correlation-based feature selection was applied for selecting the most relevant features. Finally, the classification was achieved using a Transductive Support Vector Machine classifier. The effectiveness of the proposed CAD is examined on the DDSM dataset using classification accuracy, recall and precision. Experimental results demonstrate that the proposed CAD system is clinically significant and can be used to classify the abnormalities of the breast.
Research on Robust Visual Tracker Based on Multi-Cue Correlation Particle Filters

For the problem of robust visual tracking in various complex tracking scenarios, a multi-cue correlation particle filter (CPF for short) visual tracker supervised by population convergence is proposed. By combining the advantages of particle filter and correlation filter, the CPF tracker gains better robustness, computational efficiency and stability for visual tracking. Meanwhile, to solve the problem of sample diversity in traditional CPF tracker, a genetic operating algorithm supervised by population convergence is proposed and introduced to the resampling process of CPF. Then considering that a single kind of feature weakens the tracking efficiency and robustness of our tracker, we propose to combine different types of features including Harris feature, HOG feature and SIFT feature based on fuzzy control theory to form a multi-cue CPF tracker (SPC-MCCPF for short). Multiple experiments on the OTB2015 and VOT2018 datasets prove that our tracker is quite effective in dealing with various challenging tracking problems.
A Fully Quantitative Scheme With Fine-grained Tuning Method For Lightweight CNN Acceleration

Based on the incremental network quantization (INQ) algorithm, this paper proposed a new quantization method with fine-grained tuning (named FT-INQ) to quantify convolutional weight of lightweight Convolutional Neural Network (CNN). The key idea of FT-INQ is to employ extra 2-bit tuning bits to provide more dense value space for the quantization process, so that the accuracy of floating-point CNN model can be well maintained. According to FT-INQ, the floating-point multiplication can be simplified to the sum of two shifting operations. Compared with original INQ algorithm, testing results shows that FT-INQ achieves 23% and 34% reduction on accuracy loss for MobilenetV1&V2, along with 2.5x improvements on training speed.
A Rule-Based Language and Verification Framework of Dynamic Service Composition

The emergence of BPML (Business Process Modeling Language) has favored the development of languages for the composition of services. Process-oriented approaches produce imperative languages, which are rigid to change at run-time because they focus on how the processes should be built. Despite the fact that semantics is introduced in languages to increase their flexibility, dynamism is limited to find services that have disappeared or become defective. They do not offer the possibility to adapt the composite service to execution. Although rules-based languages were introduced, they remain very much dependent on the BPML which is the underlying technology. This article proposes the specification of a rule-based declarative language for the composition of services. It consists of the syntactic categories which make up the concepts of the language and a formal description of the operational semantics that highlights the dynamism, the flexibility and the adaptability of the language thus defined. This paper also presents a verification framework made of a formal aspect and a toolset. The verification framework translates service specifications into Promela for model checking. Then, a validation framework is proposed that translates the verified specifications to the operational system. Finally, a case study is presented.
PFAGAN: An Aesthetics-Conditional GAN for Generating Photographic Fine Art

In this work we consider the problem of generating aesthetically pleasing photography, sometimes termed photographic fine art (PFA). We cast this problem as a generative modeling task and use a conditional GAN framework. Recent works have shown that conditioning based on semantic information is beneficial for improving photo-realism. In this work we propose a novel GAN architecture which is able to generate photo-realistic images with a specified aesthetic quality by conditioning on both semantic and aesthetic information. To condition the generator, we propose a modified conditional batch normalization layer. To condition the discriminator, we use a joint probabilistic model of semantics and aesthetics to estimate the compatibility between an image (either real or generated) and the conditioning variable. We show quantitatively and qualitatively that our model, called PFAGAN, is able to generate images conditioned on semantic categories and aesthetic scores.
H-Rank: A keywords extraction method from web pages using POS tags

We present a new keywords extraction method that applies the semantic similarity among the frequent words on the web page along with the distribution of POS tags. We apply hierarchical clustering to cluster the semantically similar words that have more coverage of the content of the web page. Our method shows better performance than CL-Rank and other existing methodologies.
Enhanced Ontology Matching for Big Data Integration

Abstract ontology matching (OM) is a critical process for many disciplines. It aims at identifying the semantic correspondences among different ontologies that are merged for data integration. Unfortunately, OM still faces challenges, especially, in the big data integration (BDI) area. The high degree of semantic heterogeneity problem prevents the integration of relevant data and increased with large-scale ontologies of BDI. The quality of OM still needs more improvements to cope with BDI applications. So, this paper proposes a semantic OM approach called semantic matcher. It achieves the goals of semantic heterogeneity resolving and the quality improvement. It exploits the semantic similarity based on the word embedding model. The word embedding model provides efficient distribution semantic representation of domain words as vectors based on their context. The applicability of the proposed semantic matcher is evaluated through an experiment. The conference and anatomy gold standard datasets are evaluating the experimental results. Accuracy evaluates the quality through the precision, recall, and F-measure measures. Based on the experimental results evaluation, the proposed semantic matcher is promising and efficient in the semantic OM for BDI.
ICDAR 2019 Time-Quality Binarization Competition

The ICDAR 2019 Time-Quality Binarization Competition assessed the performance of seventeen new together with thirty previously published binarization algorithms. The quality of the resulting two-tone image and the execution time were assessed. Comparisons were on both in "real-world" and synthetic scanned images, and in documents photographed with four models of widely used portable phones. Most of the submitted algorithms employed machine learning techniques and performed best on the most complex images. Traditional algorithms provided very good results at a fraction of the time.
Measuring System Entropy with a Deep Recurrent Neural Network Model

In this paper, a methodology for assessing the unpredictability of systems with memory was developed. The proposed approach consists in approximating the probability distribution exhibited by the response of a system, understood as a stochastic process, with a deep recurrent neural network; such networks offer increased forecasting capability by exploiting an accumulative register of previous system states. Once the probability distribution is computed, the uncertainty or entropy of the underlying process is measured. This measure determines the degree of regularity in the system, and identifies how atypical the system dynamics are. The proposed model was validated by identifying industrial gas turbine engine faults from recorded sensor data.
Design of AYUSH: A Blockchain-Based Health Record Management System

We are living in a world where data is considered to be the next fuel; also, we know how much value the data is. Considering this over the health records on which a patient deals with whenever he/she goes to a hospital, the present scenario lags in securing the patients’ health record management system in order to provide more transparency over patients’ past health data. If the same was available, then data sharing between hospitals would have been much easier. If the hospital gets to know the past health history like the amount of drugs the patient is consuming as part of medication, then the doctor could make a stern decision over the disease and could also satisfy the patient with a better treatment over his symptoms. The basic problem to be dealt with these would be the privacy consideration of the patient when he/she is sharing the data. In order to tackle that problem, we are using a patient-centric health record management system under the distributed network architecture. In this paper, we are proposing a solution that makes use of the emerging blockchain (permissioned) technology to achieve this goal.
The Evolution of Cloud Computing and Its Contribution with Big Data Analytics

In recent years, Big Data Analytics is used for the specification of a large amount of data to uncover or extract the information such as the hidden data. This allows the user or developer to utilize the large chunk or data in order to extract the correlations and patterns which may be present inside the large unstructured chunk of data. The term, Big Data was initially coined in the mid-1990s due to the seemingly increased volumes of data which were often stored in hardware devices, up until we obtained a permanent solution to the excess amount of storage space we know today as Cloud Computing. Cloud Computing, known as the phenomenon which involves mass storage of data and information among a certain entity which hosts no hardware, i.e. stored in an imaginary circumstance termed as ‘cloud’, has deemed beneficial to the Technology surrounding us and enabled an immense advantage when it comes to artificial storage. Over the years, there have been various technological improvements and studies surrounding this phenomenon in which this Technology is deemed to be present among almost every hardware component in the near future. In this paper we are going to view some of the advancements and achievements this technology has brought in our rapidly growing technological world.
Detecting Scam in Online Job Vacancy Using Behavioral Features Extraction

To design a qualified training program so that the competence of job seekers can be in line with the industrial needs, Training Need Analysis (TNA) should be conducted before establish a training program. But unfortunately there have been many fraudulent job ads by individuals on behalf of the company. In order to overcome the problem, an employment scam detection is required. This research proposed an employment scam detection using of behavorial context based features to determine whether a job advertisement is legitimate or fraudulent.
Estimation of Locally Relevant Subspace in High-dimensional Data

High-dimensional data is becoming more and more available due to the advent of big data and IoT. Having more dimensions makes data analysis cumbersome increasing the sparsity of data points due to the problem called “curse of dimensionality“. To address this problem, global dimensionality reduction techniques are used; however, these techniques are ineffective in revealing hidden outliers from the high-dimensional space. This is due to the behaviour of outliers being hidden in the subspace where they belong; hence, a locally relevant subspace is needed to reveal the hidden outliers. In this paper, we present a technique that identifies a locally relevant subspace and associated low-dimensional subspaces by deriving a final correlation score. To verify the effectiveness of the technique in determining the generalised locally relevant subspace, we evaluate the results with a benchmark data set. Our comparative analysis shows that the technique derived the locally relevant subspace that consists of relevant dimensions presented in benchmark data set.
A 307-fps 351.7-GOPs/W Deep Learning FPGA Accelerator for Real-Time Scene Text Recognition

FPGA-based deep learning accelerator has become important for high throughput and low power inference at edges. In this paper, we have developed a computing-in-memory (CIM) accelerator using the binary SegNet (BSEG) for real-time scene text recognition (STR) at edges. The accelerator can perform highly efficient pixel-wise character classification under CIM architecture with massive bit-level parallelism as well as optimized pipeline for low latency at critical path. The BSEG is obtained during training with a small model size of 2.1MB as well as a high classification accuracy over 90% on ICDAR-03 and ICDAR-13 datasets. The RTL-level realized FPGA-accelerator can process the STR with an energy-efficiency of 351.7 GOPs/W and a throughput of 307 fps for processing one frame of 128×32 pixels in latency of 3.875 ms.
Visual Analysis of Structure Formation in Cosmic Evolution

The IEEE SciVis 2019 Contest targets the visual analysis of structure formation in the cosmic evolution of the universe from when the universe was five million years old up to now. In our submission, we analyze high-dimensional data to get an overview, then investigate the impact of Active Galactic Nuclei (AGNs) using various visualization techniques, for instance, an adapted filament filtering method for detailed analysis and particle flow in the vicinity of filaments. Based on feedback from domain scientists on these initial visualizations, we also analyzed X-ray emissions and star formation areas. The conversion of star-forming gas to stars and the resulting increasing molecular weight of the particles could be observed.
EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees

Given feature-based data, how can we accurately classify individual input and interpret the result of it? Ensemble models are often the best choice in terms of accuracy when dealing with feature-based datasets. However, interpreting the decision made by the ensemble model for individual input seems intractable. On the other hand, decision trees, although being prone to overfit, are considered as the most interpretable in terms of being able to trace the decision process of individual input. In this work, we propose Ensemble to Distilled Tree (EDiT), a novel distilling method that generates compact soft decision trees from ensemble models. EDiT exploits the interpretability of a tree-based structure by removing redundant branches and learning sparse weights, while enhancing accuracy by distilling the knowledge of ensemble models such as random forests (RF). Our experiments on eight datasets show that EDiT reduces the number of parameters of an RF by 6.4 to 498.4 times with a minor loss of classification accuracy.
An SoC-FPGA-Based Micro UGV with Localization and Motion Planning

This paper presents a micro UGVs (Unmanned Ground Vehicles) using an SoC FPGA with self-localization and motion planning developed for the FPGA Design Competition. The purpose of this competition is to achieve external state recognition and vehicle control required for automatic driving with low power consumption and high performance using an FPGA. We adopt Xilinx Zynq UltraScale+ MPSoC and Xilinx Artix-7 for autonomous vehicles. The Zynq and Artix-7 are used for processing that requires high computational costs and processing that controls peripherals such as DC motors, respectively. An autonomous driving system is constructed with a layer structure from abstracted route planning to physical controlling. In the self-localization layer, high-precision estimation is performed by sensor fusion of landmark observations, wheel odometry, and inertial odometry using particle filter. In the path planning layer, a path is planned by Informed-RRT*, and in the path tracking layer, the vehicle is controlled to track the path by Pure Pursuit. A platform for implementing the autonomous driving system can be built with small amount of resources in the FPGAs. Our FPGA implementation of self-localization and motion planning are currently under development.
Key Frames Extraction Using Spline Curve Fitting for Online Video Summarization

Video summarization methods produce a video abstraction that permits users to obtain an informative video frames with minimum storage space and in less time. The keyframes is significantly characterize the salient contents of the video. This paper presents a design of video summarization framework for Internet videos to provide a quick way to realize, browse and review its contents. The main objective is to determine, extract and collect the most informative frames in the acquired video to formulate a summary video related to the original video. We have suggested a suitable approach for shot boundary detection along video frames. The sudden change benchmark between successive frames has calculated based on spline curve representation of frame data points inspired by capturing the visual difference. As well as, a selection and integration of the key frames from each shot is specified through clustering the higher differences between shot frames, in order to tackle the redundant frames issue and generate the video summary. From the experimental results, the proposed video summary approach is capable of capturing an informative content of video shots and preventing redundancy frames with minimum requirements in terms of storage space.
Enhancing the Performance of Telugu Named Entity Recognition Using Gazetteer Features

Named entity recognition (NER) is a fundamental step for many natural language processing tasks and hence enhancing the performance of NER models is always appreciated. With limited resources being available, NER for South-East Asian languages like Telugu is quite a challenging problem. This paper attempts to improve the NER performance for Telugu using gazetteer-related features, which are automatically generated using Wikipedia pages. We make use of these gazetteer features along with other well-known features like contextual, word-level, and corpus features to build NER models. NER models are developed using three well-known classifiers—conditional random field (CRF), support vector machine (SVM), and margin infused relaxed algorithms (MIRA). The gazetteer features are shown to improve the performance, and theMIRA-based NER model fared better than its counterparts SVM and CRF.
A Blockchain-based trust model for crowd environments.

Nowadays, social media has become an important platform for people to share information and opinions. More and more businesses owners and individual users rely on information shared on social media and contributed by the crowd for product promotion, decision making, etc. However, it is difficult to evaluate the trustworthiness of such information effectively as the open environment allows all people with different backgrounds and expertise to contribute. Furthermore, it gives chances for malicious users or astroturfers who pursue profits by giving fake reviews or plausible answers, and those who want to build up their business's reputation without improving quality of good or service. For solving this challenging problem, the paper considers trust as a key element and define it in the expert-based context. Then we propose a Blockchain-based trust model for information sharing in crowd environments. The proposed model uses a weighted consensus mechanism to infer the trustworthiness of shared information basing on reviews from the crowd and defines the accuracy of an agent as the ratio of his winning reviews to his total reviews. The model can reward agents effectively equivalent to his winning reviews. Experimental results show that the proposed model has higher accuracy in defining the trustworthiness of information and obtains better Collective Intelligence than existing models.
Kilroy Was Here: The First Step Towards Explainability of Neural Networks in Profiled Side-Channel Analysis

While several works have explored the application of deep learning for efficient profiled side-channel analysis, explainability, or, in other words, what neural networks learn remains a rather untouched topic. As a first step, this paper explores the Singular Vector Canonical Correlation Analysis (SVCCA) tool to interpret what neural networks learn while training on different side-channel datasets, by concentrating on deep layers of the network. Information from SVCCA can help, to an extent, with several practical problems in a profiled side-channel analysis like portability issue and criteria to choose a number of layers/neurons to fight portability, provide insight on the correct size of training dataset and detect deceptive conditions like over-specialization of networks.
Virtual Quality control using bidirectional LSTM networks and gradient boosting

Quality control is usually performed at the end of production lines. This postponed quality control, in case of deviation of the process values from desired quantities can lead to late detection of batches of defective products. A prediction model allows a virtual online quality control. Observation of any unfavourable trend or degradation in the predicted quality can enable preventive measures. There are many cases where quality control is performed manually and less often. This leads to limited data which is challenging for training any machine learning system used for purpose of prediction.In this paper the goal is prediction of quality measure with respect to the past history of all relevant process values such as sensor readings. We present a machine learning model based on bidirectional Long Short-term Memory (LSTM) recurrent neural networks and gradient boosting. This model is applied to a set of real world data from microfluidic chip production plant and we show that despite limited amount of training data compared to the large input space, it is capable of predicting the relevant quality values, and especially their basic trends over time including drifting phases and changing behaviors.
Deep Neural Network Based Sentence Boundary Detection and End Marker Suggestion for Social Media Text

For processing any natural language processing application, the knowledge of structure of sentence including its boundaries plays a vital role. Incorrect sentence boundary may lead to wrong outputs and hence decreasing the performance of NLP systems. Detecting sentence boundaries in code mixed social media text is not an easy task. People generally omits the boundary markers and use punctuation for other stylistic tasks. We propose a deep neural network approach for sentence boundary marking as well as suggesting appropriate punctuation mark in code mixed social media text. We experimented with single layer bidirectional and two layer bidirectional models. Both word sequence and character sequence are experimented. Bidirectional model using character sequence out performs all other models for sentence boundary detection as well as end marker suggestion.