Reviewerly/exHarmony huggingface dataset:
A CNN Model for SMS Spam Detection

A convolutional neural network (CNN) model is proposed for detection of Chinese short message service (SMS) spams. The Chinese Wikipedia corpus is used to train the word2vec model. On the basis of the training result, we conduct word segmentation on the sample short messages, convert the words into word vectors, and construct word-vector matrices of sentences as the input of the convolutional neural network. In the convolutional neural network, we first obtain a convolutional layer by performing convolution on the input layer, and then obtain a pooling layer by pooling (downsampling) the convolution layer; at last, we concatenate all pooling features on the pooling layer into a long vector which is used as the input of the Softmax classifier to detect SMS spams. This study also discusses the influence of hyperparameters on the CNN model and identifies the optimal combination of hyperparameters.
A New Document Image Quality Assessment Method Based on Hast Derivations

With the rapid emergence of new technologies, a voluminous number of images including document images is generated every day. Considering the volume of data and complexity of processes, manual analysis, annotation, recognition, classification, and retrieval, of such document images is impossible. To automatically deal with such processes, many document image analysis applications exist in the literature and many of them are currently in place in different organisation and institutes. The performance of those applications are directly affected by the quality of document images. Therefore, a document image quality assessment (DIQA) method is of primary need to allow users capture, compress and forward good quality (readable) document images to various information systems, such as online business and insurance, for further processing. To assess the quality of document images, this paper proposes a new full-reference DIQA method using first followed by second order Hast derivations. A similarity map is then created using second order Hast derivation maps obtained by employing Hast filters on both reference and distorted images. An average pooling is then employed to obtain a quality score for the distorted document image. To evaluate the proposed method, two different datasets were used. Both datasets are composed of images with the mean human opinion scores (MHOS) considered as ground truth. The results obtained from the proposed DIQA method are superior to the results reported in the literature.
Symmetry-Like Relation of Relative Entropy Measure of Quantum Coherence

Quantum coherence is an important physical resource in quantum information science, and also as one of the most fundamental and striking features in quantum physics. To quantify coherence, two proper measures were introduced in the literature, the one is the relative entropy of coherence C r ( ρ ) = S ( ρ diag ) - S ( ρ ) and the other is the ℓ 1 -norm of coherence C ℓ 1 ( ρ ) = ∑ i ≠ j | ρ i j | . In this paper, we obtain a symmetry-like relation of relative entropy measure C r ( ρ A 1 A 2 ⋯ A n ) of coherence for an n-partite quantum states ρ A 1 A 2 ⋯ A n , which gives lower and upper bounds for C r ( ρ ) . As application of our inequalities, we conclude that when each reduced states ρ A i is pure, ρ A 1 ⋯ A n is incoherent if and only if the reduced states ρ A i and tr A i ρ A 1 ⋯ A n ( i = 1 , 2 , … , n ) are all incoherent. Meanwhile, we discuss the conjecture that C r ( ρ ) ≤ C ℓ 1 ( ρ ) for any state ρ , which was proved to be valid for any mixed qubit state and any pure state, and open for a general state. We observe that every mixture η of a state ρ satisfying the conjecture with any incoherent state σ also satisfies the conjecture. We also observe that when the von Neumann entropy is defined by the natural logarithm ln instead of log 2 , the reduced relative entropy measure of coherence C ¯ r ( ρ ) = - ρ diag ln ρ diag + ρ ln ρ satisfies the inequality C ¯ r ( ρ ) ≤ C ℓ 1 ( ρ ) for any state ρ .
Overview of Machine Learners in Classifying of Speech Signals

Machine learning (ML) remains a buzzword during the last few decades due to the requirement of a huge amount of data for adequate processing, the continuously surfacing of better innovative and efficient algorithms, and the advent of powerful computers with enormous computation power. The ML algorithms are mostly based on data mining, clustering, classification, and regression approaches for efficient utilization. Many vivid application domains in the field of speech and image signal processing, market forecast, biomedical signal processing, robotics, trend analysis of data, banking and finance sectors, etc. benefits from such techniques. Among these modules, the classification of speech and speaker identification has been a predominant area of research as it has been alone medium of communication via phone. This has made the author to provide an overview of a few state-of-art ML algorithms, their advantages and limitations, including the advancement to enhance the application domain in this field.
Performance of Computer-Aided Diagnosis in Ultrasonography for Detection of Breast Lesions Less and More Than 2 cm: Prospective Comparative Study

Background Computer-aided diagnosis (CAD) is used as an aid tool by radiologists on breast lesion diagnosis in ultrasonography. Previous studies demonstrated that CAD can improve the diagnosis performance of radiologists. However, the optimal use of CAD on breast lesions according to size (below or above 2 cm) has not been assessed. Objective The aim of this study was to compare the performance of different radiologists using CAD to detect breast tumors less and more than 2 cm in size. Methods We prospectively enrolled 261 consecutive patients (mean age 43 years; age range 17-70 years), including 398 lesions (148 lesions&gt;2 cm, 79 malignant and 69 benign; 250 lesions≤2 cm, 71 malignant and 179 benign) with breast mass as the prominent symptom. One novice radiologist with 1 year of ultrasonography experience and one experienced radiologist with 5 years of ultrasonography experience were each assigned to read the ultrasonography images without CAD, and then again at a second reading while applying the CAD S-Detect. We then compared the diagnostic performance of the readers in the two readings (without and combined with CAD) with breast imaging. The McNemar test for paired data was used for statistical analysis. Results For the novice reader, the area under the receiver operating characteristic curve (AUC) improved from 0.74 (95% CI 0.67-0.82) from the without-CAD mode to 0.88 (95% CI 0.83-0.93; P&lt;.001) at the combined-CAD mode in lesions≤2 cm. For the experienced reader, the AUC improved from 0.84 (95% CI 0.77-0.90) to 0.90 (95% CI 0.86-0.94; P=.002). In lesions&gt;2 cm, the AUC moderately decreased from 0.81 to 0.80 (novice reader) and from 0.90 to 0.82 (experienced reader). The sensitivity of the novice and experienced reader in lesions≤2 cm improved from 61.97% and 73.23% at the without-CAD mode to 90.14% and 97.18% (both P&lt;.001) at the combined-CAD mode, respectively. Conclusions S-Detect is a feasible diagnostic tool that can improve the sensitivity for both novice and experienced readers, while also improving the negative predictive value and AUC for lesions≤2 cm, demonstrating important application value in the clinical diagnosis of breast cancer. Trial Registration Chinese Clinical Trial Registry ChiCTR1800019649; http://www.chictr.org.cn/showprojen.aspx?proj=33094
Fuzzy model for evaluating the results of online learning

Abstract The article is devoted to the development of a fuzzy model for an integrated assessment of the results of mastering the contents of the curriculum of an online course. It is determined that the mathematical apparatus of fuzzy logic and fuzzy inference is an integral part of the decision support system for the intelligent management of individual educational trajectories in an online learning environment. It is described how the presented model can be used for decision making under conditions of uncertainty and fuzzy given initial data circulating in an online environment. The technique of integrated assessment of the results of online learning using the application package “Fuzzy Logic Toolbox” for MATLAB is shown. The calculation results are presented using an example of a real online course. The proposed model can be used to modernize the system of objective assessment of educational achievements in online courses and introduce the intellectual management of individual educational trajectories into the practice of online learning.
A Multitier Reinforcement Learning Model for a Cooperative Multiagent System

In multiagent cooperative systems with value-based reinforcement learning, agents learn how to complete the task by an optimal policy learned through value-policy improvement iterations. But how to design a policy that avoids cooperation dilemmas and comes to a common consensus between agents is an important issue. A method that improves the coordination ability of agents in cooperative systems by assessing the cooperative tendency and increases the collective payoff by candidate policy is proposed in this article. The method learns the cooperative rules by recording the cooperation probabilities for agents in a multitier reinforcement learning model. The candidate action sets are selected through the candidate policy which considers the payoff of the coalition. Then, the optimal strategy is selected through the Nash bargaining solution (NBS) from these candidate action sets. The method is tested using two cooperative tasks. The results show that the proposed algorithm, which addresses the instability and ambiguity in a win or learning fast policy hill-climbing (WoLF-PHC) and requires significantly less memory space than the NBS, is more stable and more efficient than other methods.
Fisher-Yates and fuzzy Sugeno in game for children with special needs

As a country that has its language, English is an international language that needs to be mastered. Until now, the mastery of English in Indonesian on an international scale is in a low category. Learning English should be taught to children from an early age. For children with special needs, special learning methods are needed so that the material is conveyed. Educational games can be used as an interesting learning media. In this study, an English educational game was created that had the concepts of a quiz, rearrange, and matching. Fisher-Yates algorithm was applied to randomize the questions so that the questions that came out varied. Fuzzy Sugeno algorithm is also applied to the scoring calculation, with input variables of time, value, and the number of stars obtained. The system test outcomes show that the application of the Fisher-Yates algorithm was successful because every question that came out was randomized. The application of the Fuzzy Sugeno algorithm happened also successful because of the high degree of accuracy. Besides, the use of games shows there is an increase in student understanding as evidenced by the acquisition of grades. The results of the average value in doing the test is from 80.41 to 88.3 after playing the game.
Sign Language Translation System for Railway Station Announcements

The research aims at developing a system for hearing impaired citizens at public platforms such as railway stations, banks and bus stands, where in information transfer is crucial. Information at these platforms are generally conveyed either through text displays or audio announcements and the main drawback of conventional information broadcast is that they are not perceived by deaf people due to their inability to read or hear. In this research work, a generic sign language converter has been implemented for railway announcements in India. The corpus generation algorithm that provides the data of root words has been implemented thoroughly to provide legitimate database. For the text translation, effective and efficient use of phrase-based technique combined with rule-based translation technique has been used that yields optimum speed output thereby reducing redundancies and thus time for translation. The animated avatar has Inverse Kinematic(IK) solver tools and other rigged joints and chains which aids in movement of body parts similar to human. The sentence is translated into ASL gloss and the Web UI provides an interface which is easy to operate, it displays video of an avatar which conveys information using hand signs.
Examining Student Response to Virtual Reality in Education and Training

Virtual reality (VR) allows users to experience a virtual world in 3D by wearing a headset. This headset includes a screen and lenses to produce this effect. This study uses VR in a training context to test if the quality of preparation for laboratories in engineering education can be improved. A virtual version of a second-year engineering lab was created to teach students the fundamental concepts involved, such that they are more prepared for the real lab. Unity was used to create the application, Blender was used to model the equipment, and Substance Painter was used to texture the models. Engineering students were tested before and after training, and their learning improvement measured. These students were split into two sets; the first used the VR, the second used the traditional briefing sheet. All students also answered a questionnaire to reflect their feelings on their given system of work. Students consistently showed a better improvement and therefore understanding of the lab when using VR. It was also seen that students gave more positive answers for the VR than for the briefing sheet in the questionnaire.
Weighted Direct Matching Points for User Stability Model in Multiple Domains: A Proposal for On-Line Signature Verification

On-line signature verification involves the use of many different features or domains. The most stable domains for a signer are analysed in this paper. For this purpose, stable domains are calculated with the weighted Direct Matching Points (ωDMP), which is a relaxed version of the classical DMP technique. In addition to the direct coupling, ωDMP also considers the information contained in the 1:N couplings from the Dynamic Time Warping algorithm. Using the ωDMP technique, state-of-the-art verification results are obtained, showing the capacity to outperform previous DMP techniques to calculate the local stability model of signers.
Extending Persuasive System Design Frameworks: An Exploratory Study

Many designers, developers, and stakeholders in information systems design attempt to create persuasive technologies, although many times without a formal support from persuasive design approaches. In addition, research on design methodology is one of the most challenging issues in the field of persuasive technology. This paper intends to discuss and expand the persuasive design frameworks regarding four key aspects: considering the socially-awareness in the process of designing persuasive systems; inclusion of persuasive principles not only for web and mobile applications but also for pervasive applications; the extension of ethical aspects in the design of persuasive information systems; and the inclusion stakeholder behavior to influence achieve a target behavior. We conclude this paper with prospections for frameworks to persuasive systems design and future lines of works.
Balancing the Trade-Off Between Capacity and Imperceptibility for Least Significant Bit Audio Steganography Method: A New Parameter

Data is vulnerable to unauthorized interception communication hence steganography is used to protect it. However, there is a trade-off between capacity and imperceptibility features in audio steganography. Based on the existing study, number of bit embedded per sample and the ratio of size secret message to the number of cover audio sample affects the quality of stego-file but lack of focus was given on reducing the trade-off problem. An evaluation study on 6 Least Significant Bit (LSB) embedding methods was carried out to capture the performance of the stego-file based on Peak Signal to Noise Ratio (PSNR). The selected cover audio files were 44.1 kHz mono with 16-bit depth. Different size of data was embedded at 30 different initial embedding points. Based on that study, this paper proposes a new parameter to improve the imperceptibility feature while maintaining the capacity feature. A new parameter which is the initial embedding point is proposed to maximize imperceptibility given a capacity value. The experimental result shows that there is an effect by selecting the initial embedding point towards imperceptibility features of each LSB method. However, the significant effect was only recorded by the embedding method at the 4th level and higher levels of LSB. In conclusion, the selection on the initial embedding point could improve the imperceptibility feature while maintaining the capacity feature at the higher levels of LSB embedding. However, the selection on initial embedding point will become completely insignificant if bigger size of data is embedded and become completely insignificant at 0.05 standard deviation, as the embedded data increases.
Human Activity Recognition using Deep Neural Network

The smartphone has become quite ubiquitous and an indispensable part of our lives in the modern day. It has many sensors which capture several minute details pertaining to our activities. So, it is but inevitable that human desire creeps in to augment and improve one's own actions by studying such behaviour captured through the instrumentalities of the smart-phone. In this context, study of data on human activities captured through accelerometer and gyroscope get primal significance. In this paper, we have attempted to apply machine learning and deep learning techniques on a publicly available dataset. Initially, classification algorithms like K-Nearest Neighbours and Random Forest are applied. The classification accuracies observed are 90.46% and 92.97% respectively. Using benchmark feature selection and dimensionality reduction techniques does not improve the model accuracies to a large extent - with reported accuracies of 91.48% and 92.56% respectively. However, on employing deep neural network techniques, an accuracy of 97.32% is achieved, which indicates suitability of deep learning techniques over traditional machine learning techniques for the task of human activity recognition using mobile sensor data.
Transformer Models for Question Answering at BioASQ 2019

We describe our experiments in building a system to tackle task B of the BioASQ 2019 challenge on semantic question answering. We built separate systems to handle the five different types of questions in the dataset. We explored using transformer-based models using both ELMo, BERT and BioBERT. For the yesno questions, the results of our submissions using BERT ranked first in batches 3 and 4, while second best in batch 5.
An approach to detect the accident in VANETs using acoustic signal

Event detection using acoustic signal pattern recognition is a fascinating research area among researchers. This paper is based on algorithm development to detect vehicle accidents based on acoustic signals generated during vehicle accidents. Two types of acoustic signals have been taken for event (accident) detection. First is the sound of a vehicle collision, skidding of wheels, rollover of the vehicle, and second is distress and panic call of human. According to the world health organization (WHO), 1,250,00-man died globally, and 238,562-man died in India due to vehicle accidents. One of the significant reasons is expanding quantities of vehicles with the increasing population. As indicated by an overview, 79.02 million autos sold worldwide in 2017, and 81.6 million are anticipated in 2018. Accidents on the streets are also increasing, along with traffic. Many lives are lost because of accidents on the road. A genuine street accident happens regularly, and 16 persons die. That’s why a solution to prevent all life in a vehicle accident is required. This paper describes the solution by developing a robust algorithm that can be implemented on the microprocessor. The algorithms and processes of the result are described in the paper. The testing and results show the algorithm is robust, and efficiency is 94.7% for collision, with 88.7% recognition for a human distress call.
Explaining Supervised Learning Models: A Preliminary Study on Binary Classifiers

The reach of artificial intelligence continues to grow, particularly with the expansion of machine learning techniques that capitalize on increased computing power. Such systems could have tremendous benefits by providing predictions and suggestions. However, they are limited by the fact that they offer incomplete explanations of their predictions to human decision makers. The objective of this work was to summarize general information that could help users make judgments about whether a system is trustworthy and whether the system’s training “makes sense.” A preliminary study was summarized to show the importance of iterative design and testing for visualizing explanations.
Adversarial Feature Enhancing Network for End-to-End Handwritten Paragraph Recognition

To date, offline handwriting paragraph recognition systems either separately crop text line images and recognize them or perform implicit line segmentation by integrating complicated multi-dimensional long short-term memory (MDLSTM) with an attention mechanism. The former abovementioned approachs could lead to sub-optimal performances while the latter is very time-consuming. In this paper, a fast end-to-end system, called adversarial feature enhancing network (AFEN), is proposed for offline handwritten paragraph recognition. The proposed AFEN system comprises five components: a shared feature extractor for robust feature learning, a text detection branch for text box proposal; RoIRotate for oriented feature region extraction, an adversarial feature learning network for joint feature learning of text detection and recognition branch, and a text recognition branch for text transcription. Experiments on two popular handwritten paragraph recognition benchmarks, namely IAM and Rimes are used to verify the efficacy of the proposed AFEN system. The proposed approach yields impressive results compared to previously proposed systems in the literature.
Estimating Correlation Coefficients for Quantum Radar and Noise Radar: A Simulation Study

For target detection, quantum two-mode squeezing (QTMS) radars and noise radars require estimation of a covariance matrix. A scalar function of the covariance matrix is then used for deciding the presence or absence of a target. In this paper, estimation of the covariance matrix is carried out by minimizing the Frobenius norm between the sample covariance matrix and the theoretically expected form of the matrix. Two scalars, a normalized correlation coefficient and an unnormalized cross-correlation, are used for detecting the target. Their performances are compared and found to be the same. Probability distributions are then fit to the histograms of these estimated correlations. Using these fitted distributions, expressions are obtained for receiver operating characteristic (ROC) curves that predict the performance of these functions in the task of deciding whether a target is present or not. This work is a first step toward understanding and modeling target detection performance of a QTMS radar or noise radar when using correlation-related measures as detector functions.