Reviewerly/exHarmony huggingface dataset:
TBSAC: Token-Based Secured Access Control for Cloud Data

Growing digital world has led to enormous data. With the growing data, its availability and processing have become a tedious task which is rescued by cloud computing. The cloud computing and data integration pose critical security concern to protect the data from unauthorized access. The current literature suggests various data access control schemes and models for cloud, but all provide permanent access to the resources, i.e., once a user is authenticated, either he is required or is not to be authenticated or authorized for subsequent requests. The data industry nowadays works on auto-expiry access tokens with traditional access control mechanisms which the current literature lacks. This paper contains a framework for data privacy using contextual information, attribute-based encryption, time-based assured data deletion, JSON Web Token (JWT) for token-based authorization, challenge response-based authentication, policy and context update with hidden policy for attribute-based encryption.
Neo-Davidsonian-Based Event Class Semantic Representation

Event class is an abstract event that represents a set of events with some common features. There are inherent relations among event classes. Event classes and their relations are the important parts of event knowledge base. We gave a novel framework to represent event class semantic by marriage Neo-Davidsonian event semantic and 6-element event class model. The framework treated the predicate of event class as unary predicate with event class argument ec only. It connected the predicate and the other elements of event class with connecting symbol ⁁. And the framework can’t only represent the taxonomic relations among event classes, but also respectively represent non-taxonomic relations via the connecting symbols ⊄, ⊳, →, ∥ and ⊒. The representation examples indicate the novel framework can represent event classes semantic and different kinds of relations among event classes.
Identifying concealed messages using authorship attribution

Letter-based concealments are often used by criminals to exchange secret messages without attracting the attention of law enforcement. This paper presents a method of distinguishing an author’s writing style when employing a concealment from the author’s writing style when writing naturally.
Wheelchair Mobility Aid Through the Adaptation of Electric Scooters

This paper presents a design proposal for the use of an electric scooter to propel a conventional wheelchair. This adaptation will transform a wheelchair into a “handbike” using a scooter-chair hitch. The system allows access to these devices to a greater number of people with mobility assistance needs, mainly in the urban environment, thanks to the use of low-cost mobility devices. Keeping this is mind, the design of a scooter-chair hitch that respects the chair integrity is presented. The new set will allow the electric scooter to pull the chair analogously to the way modern handbikes do. The electric mechanism of the scooter together with the hitch mechanism has been designed under the premises of usefulness without complex ergonomic conditions, simplicity, efficiency, cost, and accessibility to parts, mechanisms and materials.
Implications of Decentralized Autonomous Organization on Political Campaign Finance Regulations

The Federal Election Commission (FEC) is the regulating authority over the monies that U.S. citizens are allowed to give to political candidates and parties. However, preexisting loopholes in FEC regulation allow for contributions to be made in situations that do not require donors to disclose their identity, a term coined as “dark money”. The increase of dark money in campaign financing and the ability of political spending to influence voter perspectives and decisions puts the credibility of the political finance system at stake. Additionally, with the introduction of blockchain, decentralized autonomous organizations (DAOs) and smart contracts to political financing these technologies collectively can serve as a new vehicle for bad actors to use. In this paper, the effects of blockchain and smart contracts on political finance are examined through four use cases to demonstrate how these technologies can extend anonymous, foreign interference in political campaign financing and further cripple public trust in it.
Integrating Action-aware Features for Saliency Prediction via Weakly Supervised Learning

Deep learning has been widely studied for saliency prediction. Despite the great performance improvement introduced by deep saliency models, some high-level concepts that contribute to the saliency prediction, such as text, objects of gaze and action, locations of motion, and expected locations of people, have not been explicitly considered. This paper investigates the objects of action and motion, and proposes to use action-aware features to compensate deep saliency models. The action-aware features are generated via weakly supervised learning using an extra action classification network trained with existing image based action datasets. Then a feature fusion module is developed to integrate the action-aware features for saliency prediction. Experiments show that the proposed saliency model with the action-aware features achieves better performance on three public benchmark datasets. More experiments are further conducted to analyze the effectiveness of the action-aware features in saliency prediction. To the best of our knowledge, this study is the first attempt on explicitly integrating objects of action and motion concept into deep saliency models.
Ensemble Framework for Big Data Stream Mining

The rapid development of industry enterprises, the large amount of data generated by these originalities and the exponential growth of industrial business website are the causes that lead to different types of big data and data stream problem. There are many stream data mining algorithms for classification and clustering with their specific properties and significance key features. Ensemble classifiers help to improve the best predictive performance results among these up-to-date algorithms. In ensemble methods, different kinds of classifiers and clusters are trained rather than training single classifier. Their prediction machine learning results are combined to a voting schedule. This paper presented a framework for stream data mining by taking the benefits of assembling technology based on miss classification stream data. Experiments are carried out with real world data streams. The experimental performance results are compared with the modern popular ensemble techniques such as Boosting and Bagging. The increasing in accuracy rate and the reducing in classification time can be seen from the test results.
Sentence-Final Prosody Analysis of Japanese Communicative Speech Based on the Command-Response Model

Aiming at communicative speech synthesis, we analyzed sentence-final prosody characteristics through subjective impression on constituting lexicons. Since Japanese sentence-final particles and postpositionals are expected to be employed to generate communicative prosody showing speaker's intention and attitudes, we designed 52 single-phrase utterances showing different strength of the speaker's impressions about judgment. These impressions were quantified in Semantic Differential (SD) scales. F0 contour characteristics were analyzed by using the command-response model. To cope with sentence final F0 characteristics, an additional accent command was introduced for F0 rise and drop of sentence-final particles. The analysis showed systematic communicative prosody control by the accent command reflecting effect of judgment impressions which can be obtained from constituting lexicons. These results indicate possibility of sentence-final prosody control using impression obtained from lexicons constituting output sentences.
Adversarial Machine Learning for Text

In this tutorial, we investigate the history, evolution and latest research topics in the area of adversarial machine learning for text data. Both classical attacks on spam filters and more recent attacks on deep learning models for text classification problems will be discussed. We then discuss proposed and potential defenses against these attacks. We conclude with some directions for future research.
Pointer-Generator Abstractive Text Summarization Model with Part of Speech Features

The typical sequence-to-sequence with attention mechanism models have achieved good results in the task of abstractive text summarization. However, this kind of models always have some shortcomings: they have out-of-vocabulary (OOV) problems, sometimes may repeat themselves and are always of low quality. In order to solve these problems, we propose a pointer-generator text summarization model with part of speech features. First, we use the word vector and prat of speech vector as the input of the model, and then improve the quality of generated abstracts by combining convolutional neural network (CNN) and bi-directional LSTM. Second, we use pointer-generator network to control whether generating or copying words to solve the problem of OOV. Finally, we use coverage mechanism to monitor the abstract we have generated to avoid duplication problems. Compared with the classic pointer-generator network, the ROUGE scores of our model have greatly improved and the performance on LCSTS dataset is better than the state-of-the-art model at present.
TNN based Adaptive Learning Algorithms for Precise ECG Signal Processing

Most of the cardiac arrhythmias are interpreted by observing the abnormal changes in the QRS complex, P and T waves of the noise free ECG signal. But, the recorded ECG signal is contaminated with the other noise sources present in the surrounding environment. These noises within the ECG signal hide the useful information and may mislead to improper diagnosis. This gives the primary importance to de-noise and obtain an interference free ECG signal for accurate diagnosis and proper treatment of arrhythmias. Wavelet shrinkage methods which are widely used de-noising approaches, employ conventional universal threshold value. The main limitation of shrinkage methods is the high universal threshold value due to which noise as well as signal coefficients are suppressed and thus unable to provide optimal solution for de-noising. To overcome this, wavelet transform based Thresholding Neural Network (TNN) is used for adaptive noise reduction which provides a better de-noising procedure by finding the optimal threshold value. In this work, two methodologies are proposed. In the first method Gauss Newton algorithm is implemented in TNN and in the second Newton Raphson algorithm is implemented to obtain the optimal threshold value. The performance of the proposed algorithms are studied using SNR and MSE parameters and the investigated results illustrate that Newton Raphson method is computationally efficient compared to Gauss Newton method.
Building Speaker Identification Dataset for Noisy Conditions

Speech signal processing plays a crucial role in any speech-related system whether Automatic Speech Recognition or Speaker Recognition or Speech Synthesis or something else. Burmese language can be considered as an under resource language due to its linguistic resource availability. For building Burmese speaker identification system, the sufficient amount of speech data collection is a very challenging task in a short time. In order to get higher data size, this paper analyzes that the getting higher duration of speech data actually combining with various noises encountering in our surroundings. For increased noisy state speech data, we also used the voice activity detection (VAD) technique to acquire only the speaker specific information. For feature extraction, we used MFCC, Filter Banks and PLP techniques. The experiments were developed with i-vector methods on GMM-UBM together with PLDA and presented the performance of different data set in the form of EER with two models trained on clean and noisy data to prove that the developed speaker identification system is noise robust.
Water Motion Safety Recognition and Monitoring System Based on the Wireless Signal Transmission

Abstract Motion process monitoring is the core component of motion state analysis system and motion safety assurance system. For the water sport, implementation of monitoring is directly related to the safety and security of sportsmen, which is more significant. This paper presents a kind of underwater sports safety monitoring system based on radar idea. The system consists of four parts: video acquisition, image processing, data sample comparison and signal transmission. Reasonable cameras are used to capture the whole swimming pool, and then the motion information parameters of the swimmers are obtained by image segmentation and tracking, and compared with the standard drowning behavior parameters, so as to determine whether the swimmers are drowning or not. If it is judged that a person may be drowning, the system will send out a corresponding rescue signal, which will quickly alert and notify rescuers through the wireless signal transmission system, so as to rescue those who may be drowning, so as to enhance the safety of people’s movement in the water. The drowning behavior is identified by the established drowning feature data sample set, which avoids the occurrence of invisible drowning events that are difficult to detect. By using the high-speed DSP, the data processing and alarm system’s timely response can be realized, which greatly shortens the rescue time, directly improves the survival probability of drowning and reduces the physiological damage. The design of the monitoring system will eventually help the lifeguards to complete the task of underwater movement safety.
Speech Demodulation-based Techniques for Replay and Presentation Attack Detection

Spoofing is one of the threats that bypass the voice biometrics and gains the access to the system. In particular, Automatic Speaker Verification (ASV) system is vulnerable to various kinds of spoofing attacks. This paper is an extension of our earlier work, the combination of different speech demodulation techniques, such as Hilbert Transform (HT), Energy Separation Algorithm (ESA), and its Variable length version (VESA) is investigated for replay Spoof Speech Detection (SSD) task. In particular, the feature sets are developed using Instantaneous Amplitude and Instantaneous Frequency (IA-IF) components of narrowband filtered speech signals obtained from linearly-spaced Gabor filterbank. We observed relative effectiveness of these demodulation techniques on two spoof speech databases, i.e., BTAS 2016 and ASVspoof 2017 version 2.0 challenge database that focus on the presentation and replay attacks, respectively. The results obtained from different demodulation techniques gave comparable results on both databases showing small variations in % Equal Error Rate (EER). For VESA, we found that with Dependency Index (DI) = 2 gave relatively better performance compared to the other DI on both the databases for SSD task. All the demodulation technique-based feature sets gave lower % EER than their baseline system for both the databases.
The self- upgrading mobile application for the automatic malaria detection

The UN World Health Organization (WHO) set an ambitious vision of malaria control which includes reducing malaria case incidence by 90% by 2030. Many tools and approaches have been considered to enable the progress toward malaria vision. The use of portable smartphones and machine learning (ML) software is a promising one among them. Recently, many ML models have been proposed for malaria detection. From the interview with health workers in the field, we realized ML model should be continuously improved to provide higher accuracy and/or more capability to cover practical issues found in a real setting such as malaria mosquitoes with drug resistance. In this paper, we propose the mobile application for malaria detection which upgrades ML model on its own without depending on internet connection. Unavailability of internet connection is commonly observed in malaria epidemic countries. We also learned that ML model should be not only accurate but also resource-efficient. This motivated us to set up performance metrics for ML model. Based on the metrics, we chose the optimal ML model of Resnet-50. While most of the prior art ML models were optimized in terms of accuracy only, the optimal model of our choice satisfies both accuracy and resource efficiency. With adopting the model, we architect self-upgrading malaria-detection application and it is validated using ATAM (Architecture Tradeoff Analysis Method) to ensure the application works in the resource-constrained setting as desired. Lastly, we develop the prototype application and show it diagnoses malaria parasites as expected. To collect more blood samples and feedback from prospect users, we plan to do testing at local clinics in India and Myanmar.
EAGLE: Exploiting Essential Address in Both Weight and Activation to Accelerate CNN Computing

Efficient quantization techniques can compress Convolutional Neural Networks (CNNs) with less bit-width while maintaining the accuracy on large extent. However, the quantized CNN models hardly boost the computation performance of CNN accelerators with the conventional bit-parallel Multiply-Accumulate (MAC) operations. Previous works proposed a shifting-based bit-serial operation, which can be called as Shift-Accumulate (SAC) operation, to take advantage of the reduced bit-width. However, it is also found that there are many invalid computations in both MAC and SAC operation, caused by zero bits in activations and weights, which are not optimized. To fully exploit the computations in CNN models, we proposed a Essen-tial Address only GAC based Low-latency Efficient (EAGLE) architecture that can further accelerate the CNN computation through bypassing zero bits computation in the activations and weights. An essential address is adopted to encode the nonzero bits in activations and weights in this architecture. Furthermore, to support the essential address-only computations, Generate-Accumulate (GAC), an operation which produces partial sums with essential addresses, is implemented. The architecture is implemented with a TSMC 28nm CMOS technology. Based on the results, if scaled in a 65nm technology, the EAGLE only requires 63.6% area and 43.1% power consumption compare to that of the Pragmatic. The EAGLE reaches an average speedup of $ 2.08\times$ and $ 1.43\times$ on six CNN models over the Stripe and Pragmatic at a similar frequency, respectively. It also improves energy efficiency by $ 3.69\times$ on average over the DaDianNao baseline.
Partial-Equivalence: A Semantic Strategy in Service Composition and Modeling

Semantic technology has a significant contribution to the service composition. Services coming from providers can be classified with semantic annotations and placed in the specific ontology classes, which accelerates the understanding procedure for machines to quickly decompose coming requests and locate classes accurately for service composition. However, semantic annotations for services in traditional logic may ignore inner connections between classes, which will reduce the amount of potential solutions and chances to advance the performance of the composition. Here we present our Partial-Equivalence logic in service composition, we propose an algorithm to detect the inner connections between ontology classes and enrich the annotation. Then we update the model of the workflow and operate a comparison on the performance of our Partial Equivalent compositon against traditional composition. Our experimental results show that the Partial-Equivalent logic not only offers more solutions to the request, but also simplifies the workflow and advance the performance of solutions. We anticipate that our Partial-Equivalent logic may enrich the tradition logic of semantic annotations and have broad applications in optimizing service composition.
A Study on Segmentation and Evaluation of Leukocyte Section

Clinical image assessment plays an imperative part in the assessment of the illness in humans. Hence, in most of the disease cases, a suitable procedure is followed to record the bio-medical image in a controlled environment, which further can be evaluated by an experienced doctor or a suitable Computed-Assisted-Diagnosis (CAD) system to confirm the severity of disease; for planning suitable treatment process. In this work, a morphological operation based CAD system is developed to examine the leukocyte segment from the RGB-scale blood smear image. The presentation of developed CAD is evaluated with the other leukocyte examination procedures existing in the literature. The outcome of this work substantiate that, proposed approach is fast and efficient in extracting the leukocyte section from the blood-smear picture with improved accuracy. In this work, the validation of the proposed technique is experimentally authorized with the benchmark LISC database and the results obtained with the proposed study offers an overall accuracy of > 97.96% compared to the related existing techniques.
Recommendation of Multimodal User Authentication for Wireless Sensor Networks

This paper proposes a Multimodal User Authentication System for Wireless Sensor Networks (WSN). User Authentication is important for customized services and restricted access control in Wireless Sensor Networks. Considering the need, the proposed Enhanced Multimodal Biometric System shall combine two biometric traits to provide authentication for accessing the information from Sensor nodes. The user will be granted access by the Enhanced Authentication System to the user for accessing sensitive information from Wireless Sensor Networks.
A Comparative Discussion on Various Modern Video Retrieval Strategies

In the recent past, wide ranges of video retrieval processes were presented by different researchers. In order to boost the ease of access of video clip, keen applications, which have item removal, video purchasing, video clip healing and also fast perusing are performed with foundation demonstrating and also recovery summation. That is, both vital outline video reading and also explicit product recuperation reading can be performed on the very first video clip. Secondly, the techniques based on material matching for video access are evaluated. In view of these actualities, we provide a query by the criterion video clip recovery method, in this paper. We present a computation for comparability collaborating, to find the neighbourhood video clip set-ups with various sizes. In addition, the computation can find the similitude between an inquiry video clip and an item of another video clip group. Amid this treatment, a certainty price will be taken into consideration that makes the inexact similitude coordinating possible. The access methods based on info concept are additionally reviewed. In the recent years, a substantial arrangement of video documents is made and quickly sight, and also sound growth makes a brand-new examination aware preparing the world. Our proposed technique is in view of detailed theory. These structures consist of three essential components which integrate: shot border detection, ordered video clip synopsis, and submit and recover target video clip.
Topological Labelling of Scene using Background/Foreground Separation and Epipolar Geometry

The robust Principal Component Analysis (rPCA) efficiently separates an image into the foreground and background regions. The stixels provide middle-level expression of a scene using vertical columnar-superpixels of pixels with same depth computed from a pair of stereo image. Combining the classification of pixels by rPCA and depth map, topological labelling of pixels of each frame in an image sequence is achieved. The algorithm constructs static stixels and moving boxes of an image sequence from background and foreground regions, respectively. The algorithm also estimates free-space for motion planning from background regions as a collection of horizontal columnar-superpixels parallel to the epipolar lines.