Reviewerly/exHarmony huggingface dataset:
Subject Independent Human Activity Recognition with Foot IMU Data

Human activity recognition is a very active research on pervasive computing and mobile health application. Many human activity systems based on inertial measurement unit (IMU) sensor data were proposed in the past few years. These systems mainly use IMU sensor placed on he torso and limbs to collect data and utilize supervised machine learning algorithms on sensor data. One main issue of these systems is that wearing multiple on-body IMU sensors may bring inconvenience to users' daily life. The other issue of these exiting methods is that an activity recognition model that is trained on a specific subject does not work well when being applied to predict another subject's activities since IMU activity data always carry information that is specific to the human subject who conducts the activities. In our work, inspired by the principle of domain adaption, we proposed a new deep-learning activity recognition model based on an adversarial network which can remove the subject-specific information within the IMU activity data and extract subject-independent features shared by the data collected on different subjects. We also for the first time use data collected from insole based IMU sensors on 8 participants for 5 common activities to build a new real world human activity dataset which can minimize the inconvenience for users to wear. We conducted experiments with our new real-world dataset. Results show that our subject independent activity recognition model outperforms state-of-art supervised learning techniques and eliminates the effects of individual differences between subjects successfully. The average recognition accuracy under the leave-one-out (L1O) condition achieves 99.0% which is higher than the performance of traditional human activity recognition system based on CNNs.
Underwater Image Enhancement Using SWT Based Image Fusion and Colour Correction

Underwater pictures are corrupted because of assimilation and dispersing of light. Due to these effects may change the visibility of the underwater objects. Several methods are developed to restore and enhance the underwater degraded images. This paper briefly presents a method that is based on SWT image fusion, colour correction and White balance technique removes the colour cast through colour correction estimator and preserves edges through the sharpening filter, a fusion process is introduced to embrace the images.
Effective Gaps Are Not Effective: Quasipolynomial Classical Simulation of Obstructed Stoquastic Hamiltonians

All known examples suggesting an exponential separation between classical simulation algorithms and stoquastic adiabatic quantum computing (StoqAQC) exploit symmetries that constrain adiabatic dynamics to effective, symmetric subspaces. The symmetries produce large effective eigenvalue gaps, which in turn make adiabatic computation efficient. We present a classical algorithm to subexponentially sample from an effective subspace of any k-local stoquastic Hamiltonian H, without a priori knowledge of its symmetries (or near symmetries). Our algorithm maps any k-local Hamiltonian to a graph G=(V,E) with |V|=O(poly(n)), where n is the number of qubits. Given the well-known result of Babai [Graph isomorphism in quasipolynomial time, in Proceedings of the Forty-Eighth Annual ACM Symposium on Theory of Computing (2016), pp. 684-697], we exploit graph isomorphism to study the automorphisms of G and arrive at an algorithm quasipolynomial in |V| for producing samples from effective subspace eigenstates of H. Our results rule out exponential separations between StoqAQC and classical computation that arise from hidden symmetries in k-local Hamiltonians. Our graph representation of H is not limited to stoquastic Hamiltonians and may rule out corresponding obstructions in nonstoquastic cases, or be useful in studying additional properties of k-local Hamiltonians.
Artistic multi-script identification at character level with extreme learning machine

In this work, a novel problem, namely artistic multi-script identification at character level has been addressed. Two types of documents: real/ natural and synthetic have been used for dataset preparation. After binarizing using Otsu's global thresholding algorithm, a semi-automatic segmentation technique has been applied for character separation. Some well-known texture based features have been considered from the segmented images and further, they have been converted into lower dimensional space by applying principal component analysis. Those final feature set are classified using an Extreme Learning based classifier and performance are compared with traditional machine learning techniques and other features. Observing the inherent complexity of the multi-script character level datasets, an encouraging outcome has been obtained.
Lock-Free Parallelization for Variance-Reduced Stochastic Gradient Descent on Streaming Data

Stochastic Gradient Descent (SGD) is an iterative algorithm for fitting a model to the training dataset in machine learning problems. With low computation cost, SGD is especially suited for learning from large datasets. However, the variance of SGD tends to be high because it uses only a single data point to determine the update direction at each iteration of gradient descent, rather than all available training data points. Recent research has proposed variance-reduced variants of SGD by incorporating a correction term to approximate full-data gradients. However, it is difficult to parallelize such variants with high performance and accuracy, especially on streaming data. As parallelization is a crucial requirement for large-scale applications, this article focuses on the parallel setting in a multicore machine and presents LFS-STRSAGA, a lock-free approach to parallelizing variance-reduced SGD on streaming data. LFS-STRSAGA embraces a lock-free data structure to process the arrival of streaming data in parallel, and asynchronously maintains the essential information to approximate full-data gradients with low cost. Both our theoretical and empirical results show that LFS-STRSAGA matches the accuracy of the state-of-the-art variance-reduced SGD on streaming data under sparsity assumption (common in machine learning problems), and that LFS-STRSAGA reduces the model update time by over 98 percent.
Performance Analysis of Multiple Classifiers using different Term Weighting Schemes for Sentiment Analysis

Information sharing and review platforms has generated large volumes of opinionated data which is usually in unstructured form. With the help of Sentiment Analysis, this data can be transformed into structured data which can be useful for commercial applications such as product reviews and feedback, marketing analysis, etc. The purpose of this work is to analyzes the performance of three classifiers(SVM, Naive Bayes, and Logistic Regression) with respect to providing positive or negative sentiment for three different scenarios(Movie Reviews, Election Opinions, and Food Reviews). The three classifiers are compared using fixed set of preprocessing steps and four different weighting schemes(Term frequency inverse document frequency (TFIDF), Term frequency inverse class frequency (TFICF), Mutual Information (MI), and X <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> statistic (CHI)). The controlled experimental results showed that Logistic Regression classifier performs better in terms of overall accuracy when MI is used as weighting scheme.
Prediction of mortality in patients with cardiovascular disease using data mining methods

Healthcare information systems store a huge amount of patient data, so the trend of the use of data mining in healthcare is on the rise. Heart and blood vessel diseases are a leading cause of mortality both worldwide and here in Bosnia and Herzegovina, and prevention, surveillance and treatment are of great public health importance. Based on data on patients with cardiovascular disease, collected from 2011 to 2017 at Mostar Hospital, models for mortality prediction using techniques for data tree mining, neural network and logistic regression are presented. The aim of this research is to compare the effectiveness of these methods in modeling the effectiveness of predicting mortality in patients with cardiovascular disease.
Text Mining and Support Vector Machine for Sentiment Analysis of Tourist Reviews in Bangkalan Regency

Abstract Tripadvisor is a travel site that offers reviews of hotels, flights, restaurants and tourist attractions. Reviews from tourists are indispensable for developing tourism, but the number of comments will complicate the owner to analyze the important aspects of the review so that the reviews should be beneficial to develop spot, overlooked or unreadable. This research aims to facilitate the owner of tourist places in the Bangkalan regency to classify negative opinion, positive opinion, and to know the target opinion using techniques sentiment analysis. The initial stage of the sentiment process analysis on this research is Web scrapping on the TripAdvisor site, the purpose of this stage to collect user review data. The review Data obtained will then be classified using the Support Vector Machine (SVM) method. Further review data classification results will be processed using the Text Mining method, to find the target opinion that is considered important in the review. Based on the research that has been done, obtained the accuracy of the classification process with SVM method of 70.22% for Indonesian-language reviews. A post-publication change was made to this article on 20 Apr 2020 as the previously published article was a duplicate.
Utilization of Postural Transitions in Sensor-based Human Activity Recognition

Sensor-based human activity recognition (HAR) has gained tremendous attention due to numerous applications that aim to monitor the movement and behaviour of humans. However, the occurrence of transitions between activities gives rise to many problems in HAR as they can affect the performance of the recognition system by causing fluctuations in the prediction. This paper proposes an HAR system using groups of similar postural transitions, discrete wavelet transform (DWT) and bidirectional long short-term memory (BiLSTM) to deal with the postural transitions, thereby improving the accuracy of the system. In this system, the transitions which have similar patterns are grouped into same groups, after that the essential features are extracted by using DWT before being fed into the BiLSTM network for activity classification task. Our experiment results indicate that the proposed model achieves competitive performance compared to a non-transition model.
DEX: A DApp for the Decentralized Marketplace

It is not an overstatement to say the true value of blockchain technology lies in its ability to use a decentralized model of interaction at the protocol level.
Bangla Compound Character Recognition by Combining Deep Convolutional Neural Network with Bidirectional Long Short-Term Memory

Recognition of Bangla handwritten compound characters has a significant role in Bangla language in order to develop a complete Bangla OCR. It is a challenging task owing to its high variation in individual writing style and structural resemblance between characters. This paper proposed a novel approach to recognize Bangla handwritten compound characters by combining deep convolutional neural network with Bidirectional long short-term memory (CNN-BiLSTM). The efficacy of the proposed model is evaluated on test set of compound character dataset CMATERdb 3.1.3.3, which consists of 171 distinct character classes. The model has gained 98.50% recognition accuracy which is significantly better than current state-of-the-art techniques on this dataset.
Combination of Local Binary Pattern and Face Geometric Features for Gender Classification from Face Images

In the recent time bioinformatics take wide field in image processing and computer vision. Gender classification is essentially the task of identifying the person gender based on the facial image. Currently the gender classification by facial images becomes very popular due to the current visual instruments. There are different algorithms of gender classification, and each algorithm has a different approach to extract the facial feature from the input image and perform the classification. However, the single type face feature cannot be enough to represent the detailed in facial images. In this paper, we propose a new approach which consists in combining the local binary patterns (LBP) and the face geometric features to classify gender from the face images. The Histogram equalization is used to adjust the contrast of the input image. For encoding the gray level pixel, the LBP is used as a binary quantization, then the face GLCMs are used to extract the geometric structure of the face image. For gender classification, the Support Vector Machine is used as the classifier. The face images from AT&T face dataset is used to perform the experiments. The experimental results show that the application of both LBP, and the GLCMs features improves the performance the classification of gender in face images.
Voices of youth in reconceptualising and repositioning the role of mobile learning for sustainable development

This qualitative study explores youth’ perception of opportunities for mobile learning (M-Learning) as a means to achieve Sustainable Development Goals (SDGs). Data were collected through the qualitative case study of a M-Learning project called the 8th e-ICON World Contest hosted by Korean Ministry of Education and the Hawaii Department of Education. Drawing on youth’ insights and their knowledge as a basis for exploring the potential of M-Learning, the study recommends three areas for the integration of M-Learning with a view to achieving SDGs as follows: (1) M-Learning for improving access to education; (2) M-Learning for improving the quality of education; (3) M-Learning for building and scaling up collective partnership. The study also confirms that youth could play an active role in utilising opportunities of M-Learning to address the current educational development agenda and suggests how M-Learning can be conceptualized as a development intervention.
Medical Image Encryption Into Smart Healthcare IOT System

The security and privacy of medical data are highly required when it involved in connecting with smart and intelligent sensor devices in the hospital ecosystem. The Internet of things is mutually integrated with healthcare to produce effective and smart working system but it still needs better security mechanism which can protect patient's privacy. This paper is focusing to protect medical data by using image encryption techniques. In which we are applying three rounds of high-speed scrambling as well as pixel adaptive diffusion concept for shuffling random neighboring pixels. In addition, implementing pixel adaptive diffusion our mechanism is used modulo arithmetic operation. Simulations and experiments demonstrate that the encryption mechanism employing modulo arithmetic operation have comparatively higher levels of security. It can adapt better than several traditional as well as state-of - the-art image encryption.
Handwriting Detection and Recognition Improvements Based on Hidden Markov Model and Deep Learning

The online handwriting detection and recognition has become an important research in ... area. An individual's writing can be easily forged and disguised in various ways including freehand simulation, tracing and image transfer, making genuine handwriting recognition a challenging task. With the advent of various online handwriting recognition systems developed, but for English characters recognition these still lack the simplicity and accuracy. While identification approaches were successfully reported, good forgeries are able to outsmart the existing tools. Existing flaws in recognition systems led to more research works in automatic detection and recognition works via computer techniques, feature extraction, classification accuracy comparison, performance evaluation and pattern recognition. To realize simpler and efficient English character recognition, we develop a handwriting detection and recognition system based on the Kohonen Network and deep learning. The system consists of interfaces for the online handwritten character was featured in matrix form of sizes 5x7 pixel and 35x33 pixels represented with binary values. Identifying all occupied character strokes in the series of binary string recognizes the full character. The recognition performance was compared between 35 pixels and 1155 pixels environment, evaluated in terms of accuracy, and consistency. An experiment was conducted with 25 online handwritten input data of straight stroke (' `V', ' `X', ' `Y') and curve stroke (' `C', ' `O', ' `S') characters collected from 25 participants. Findings show an overall improvement of 31 % recognition accuracy of using 35x33 pixels against the 5x7 pixels. Handwriting characters featured in 35x33 pixels outperformed the 5x7 pixels accuracy by 37.49% on straight stroke characters and 24.52 % on curve stroke.
KBC: Multiple Key Generation using Key Block Chaining

Cryptography is an important aspect of securing communications, however, the encryption scheme may be known to the attacker(s). Hence, the key can be rewarded as a crucial part of any cryptographic algorithm. With the symmetric key, researchers have developed chaining concepts where the output of one operation becomes an input to the next one. Such concept of modes of operation (Cipher Block Chaining) is integrated in this article to generate multiple key blocks. The generated keys can be provided to an individual block of plaintext for any encryption technique as per the requirements. The proposed Key Block Chaining (KBC) approach can also be referred to as a key derivation approach. The basic concepts of Advanced Encryption Standard and Message Authentication Code have also been incorporated in the proposed model to introduce randomness, and thereby, ensuring security as well as robustness. To assess the randomness of the sequence of generated keys, KBC is implemented and tested using the National Institute of Standards and Technology statistical test suite and reasonable randomness has been successfully achieved. Possible variations and key points of the proposed approach along with statistical analysis and significance of our work have been discussed.
Privacy Preserving Data Mining Using Non-Negative Matrix Factorization and Singular Value Decomposition

With the increasing technologies, people share information time to time from different places. The rapid growing improved technologies and improved data mining algorithms make it easy for adversary to disclose sensitive information. So there is always a second thought while individuals share their personal information. That's why privacy protection is an important consideration at every stage of data mining process. Privacy Preserving Data Mining (PPDM) maintains data utility and protects privacy at the same time. In this paper, real world datasets are perturbed by using combined Non-Negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD) method. The utility of perturbed datasets is analyzed with respect to query accuracy. The result of query accuracy implies that the combined method needs to be improved or more efficient methods need to be introduced.
Identifying Spam Patterns in SMS using Genetic Programming Approach

SMS spam, also known as mobile spam, has become a prevalent and an ever growing issue due to the availability of bulk SMS services at nominal costs. These spam messages may not only be commercial but also pose a great deal of financial threats to the users. To fight against SMS spam, a variety of solutions have been proposed including content-based filtering, semantic indexing, machine learning classifiers, etc. However, in this regard evolutionary algorithms have not been utilized. Since the nature of SMS is contemporary, the representation of text messages keep evolving with the help of slangs, symbols, misspelled words, abbreviations and acronyms. Hence, such a solution is required which can accommodate these changes, also keeping the length of SMS in consideration. The model proposed in this paper generates regular expressions as individuals of population, using Genetic Programming Approach. These regular expressions so generated are used for the classification purpose. The application of Genetic Programming in the domain of SMS spam filtering has not been explored widely. It is able to eliminate False Positive errors, thus saving legitimate messages from being misclassified. The performance tends to improve with higher number of generations. Performance and confusion matrix for different number of generations are tabulated.
A Proposed Secure Mobile Money Transfer System for SME in Bangladesh: An Industry 4.0 Perspective

Nowadays, the fourth industrial revolution (Industry 4.0) sets new goals for production and impacts on industrial values. Adopting Industry 4.0 in Small and Medium Enterprises (SMEs) provides new paradigms for industrial management to enhance the business policies and outcomes. Payments in SMEs primarily follow the existing banking system in Bangladesh. Again, though the mobile money transferring system is a convenient and easy way, it required to be highly secure and reliable. Thus, an automated and secured mobile money transfer can be introduced towards adopting the Industry 4.0 in SMEs in Bangladesh. Therefore, the objective of this article is to propose a money transfer system to automate and enhance the security of payment process for SMEs in Bangladesh. To attain this objective, a conceptual framework from an Industry 4.0 perspective along with required algorithm is proposed here. The proposed system includes iris verification technique to authenticate a user uniquely and consistently over the time. The theoretical justification and expected benefits are also discussed.
A bit toggling approach for AMBTC tamper detection scheme with high image fidelity

The existing tamper detection schemes for absolute moment block truncation coding (AMBTC) compressed images are able to detect the tampering. However, the marked image qualities of these schemes can be enhanced, and their authentication methods may fail to detect some special tampering. We propose a secure AMBTC tamper detection scheme that preserves high image fidelity with excellent detectability. In the proposed approach, a bit in bitmaps of AMBTC codes is sequentially toggled to generate a set of authentication codes. The one that causes the least distortion is embedded into the quantization levels with the guidance of a key-generated reference table (RT). Without the correct key, the same reference table cannot be constructed. Therefore, the proposed method is able to detect various kinds of malicious tampering, including those special tampering techniques designed for RT-based authentication schemes. The proposed method not only offers better image quality, but also provides an excellent and satisfactory detectability as compared with previous works.