Reviewerly/exHarmony huggingface dataset:
Myeloma Cell Detection in Bone Marrow Aspiration Using Microscopic Images

Multiple myeloma is a haematological cancer that occurs in plasma cells. The increase in the myeloma cells induces the reduction in all the blood cells. This paper presents the computer-aided methodology for multiple myeloma detection and diagnosis. Convolutional neural network is employed in order to detect multiple myeloma while using microscopic images of blood. Pre- trained and fine- tuned AlexNet is used for diagnosis of myeloma. Microscopic images are classified into normal and blast using AlexNet as feature extractor and SVM for classification. Proposed methodology is compared with state-of-the art in literature and the presented technique outperforms the previous methodologies.
Selfie‐Help: The Multimodal Appeal of Instagram Poetry

The Journal of Popular CultureVolume 52, Issue 2 p. 296-314 Article Selfie-Help: The Multimodal Appeal of Instagram Poetry Lili Pâquet, Lili Pâquet orcid.org/0000-0001-5300-1689 Search for more papers by this author Lili Pâquet, Lili Pâquet orcid.org/0000-0001-5300-1689 Search for more papers by this author First published: 15 April 2019 https://doi.org/10.1111/jpcu.12780Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume52, Issue2April 2019Pages 296-314 RelatedInformation
An assistive model for visually impaired people using YOLO and MTCNN

Visually impaired people face difficulties in safe and independent movement which deprive them from regular professional and social activities in both indoors and outdoors. Similarly they have distressin identification of surrounding environment fundamentals. This paper presents a model to detect brightness and major colors in real-time image by using RGB method by means of an external camera and then identification of fundamental objects as well as facial recognition from personal dataset. For the Object identification and Facial Recognition, YOLO Algorithm and MTCNN Networking are used, respectively. The software support is achieved by using OpenCV libraries of Python as well as implementing machine learning process. The major processor used for our model, Raspberry Pi scans and detects the facial edges via Pi camera and objects in the image are captured and recognized using mobile camera. Image recognition results are transferred to the blind users by means of text-to-speech library. The device portability is achieved by using a battery. The object detection process achieved 6-7 FPS processing with an accuracy rate of 63-80%. The face identification process achieved 80-100% accuracy.
Relevance of a Toll-Free Call Service Using an Interactive Voice Server to Strengthen Health System Governance and Responsiveness in Burkina Faso

In Africa, health systems are poorly accessible, inequitable, and unresponsive. People rarely have either the confidence or the opportunity to express their opinions. In Burkina Faso, there is a political will to improve governance and responsiveness to create a more relevant and equitable health system. Given their development in Africa, information and communication technologies (ICTs) offer opportunities in this area.This article presents the results of an evaluation of a toll-free call service coupled with an interactive voice server (TF-IVS) tested in Ouagadougou, Burkina Faso, to assess its relevance for improving health systems governance. The approach consisted of a 2-phased action research project to test 2 technologies: recorded messages and touch keypad. Using a concurrent mixed approach, we assessed the technological, social, and instrumental relevance of the service.The call service is available everywhere, 24 hours per day, seven days per week. The equipment and its physical location were not adequately protected against technological hazards. Of the 278 days of operation, 49 were non-functional. In 8 months, there were 13 877 calls, which demonstrated the popularity of ICTs and the ease of access to telephone networks and mobile technologies. The TF-IVS was free, anonymous, and multilingual, which fostered the expression of public opinion. However, cultural context (religion, ethnic culture) and fear of reprisals may have had a negative influence. In the end, questions remained regarding people's capacity to use this innovative service. In the first trial, 49% of callers recorded their message and in the second, 48%. Touch key technology appeared more relevant for automated and real-time data collection and analysis, but there was no comprehensive strategy for translating the information collected into a response from healthcare actors or the government.This study showed the relevance and feasibility of implementing a TF-IVS to strengthen health system responsiveness in one of the world's poorest countries. Public opinion expressed through data collected in real-time is helpful for improving system responsiveness to meet care needs and enhance equity. However, the strategy for developing this tool must take into account the implementation context and the activities needed to influence the mechanisms of social responsibility (eg, information provision, citizen action, and state response).
Temporal Constraints in Human--Building Interaction

Human--building interaction (HBI) is converging the fields of architecture and interaction design, leading to new and interesting tensions in perspectives and methodological approaches. One such tension is related to temporal constraints. Architecture and interaction design typically produce outcomes with very different lifetime expectancies and, predominantly, use methods with very different pace. As an example, fast, iterative approaches of contemporary interaction design, consisting of frequent updates and redesigns, contrasts with much slower, plan-driven and long-term vision driven approaches within architecture. One question emerging from this tension is how to meaningfully combine perspectives and approaches. One suggestion, among others, has been that interaction design methods such as participatory design can be used to heighten the involvement of inhabitants and other stakeholders in continuous adaptations of the buildings they inhabit. While an interesting proposal, we believe that methodological considerations only partly address the complexity of the tension at play from the different lifetime expectancies of buildings and interactive computer systems. Unfolding this complexity further, we therefore propose a framework of temporal constraints at three levels of abstraction: (1) rationale, (2) method, and (3) outcome. Inspired by previous work, we discuss temporal constraints in HBI at these levels. We argue that designing for HBI requires an understanding of temporally constrained design conventions that apply meaningfully to both the short term and long term.
NgramSPD: Exploring optimal n-gram model for sentiment polarity detection in different languages

Due to the rapid growth of web platforms such as blogs, discussion forums, peer-to-peer networks, and various other types of social media, Sentiment Polarity Detection (SPD) (classifying texts by “positive” or “negative” orientation) has become more important and challenging task in recent years. T here is a growing need for management and study of SPD not only in English, but also in other languages. The key reason for using Machine Learning (ML) for SPD lies in engineering a representative set of features. This paper explores different (byte, character and word) n-gram based text representation models in order to determine the most valuable model for the representation of text documents in various languages, which can be used successfully by ML classification techniques for solving SPD task. Proposed n-gram models were used in conjunction with k Nearest Neighbourhood (kNN), Support Vector Machine (SVM) and Maximum Entropy (MaxEnt) algorithms to determine opinion polarity of the proposed movie reviews. The effectiveness and language independence of the proposed n-gram models were demonstrated in experiments performed on seven publicly available movie review benchmarks in Arabic, Czech, English, French, Spanish,Turkish, and Serbian being the authors’ mother tongue. Formal evaluation has confirmed that the proposed byte and character n-gram models outperform word n-gram model, and in conjunction with the presented MaxEnt algorithm outperform other ML supervised techniques used with more complex document representation approaches. In some cases (Arabic, Czech, French, Serbian and Turkish), signficant improvements over the baselines have been achieved. Despite their simplicity and broad applicability, byte and character n-grams have been shown to be able to capture information on different levels – lexical and syntactic.
Single-Image Depth Inference Using Generative Adversarial Networks

Depth has been a valuable piece of information for perception tasks such as robot grasping, obstacle avoidance, and navigation, which are essential tasks for developing smart homes and smart cities. However, not all applications have the luxury of using depth sensors or multiple cameras to obtain depth information. In this paper, we tackle the problem of estimating the per-pixel depths from a single image. Inspired by the recent works on generative neural network models, we formulate the task of depth estimation as a generative task where we synthesize an image of the depth map from a single Red, Green, and Blue (RGB) input image. We propose a novel generative adversarial network that has an encoder-decoder type generator with residual transposed convolution blocks trained with an adversarial loss. Quantitative and qualitative experimental results demonstrate the effectiveness of our approach over several depth estimation works.
Comprehensive Introduction to Fully Homomorphic Encryption for Dynamic Feedback Controller via LWE-Based Cryptosystem

The cryptosystem based on the Learning-with-Errors (LWE) problem is considered as a post-quantum cryptosystem, because it is not based on the factoring problem with large primes which is believed to be easily solved by a quantum computer. Moreover, the LWE-based cryptosystem allows fully homomorphic arithmetics so that two encrypted variables can be added and multiplied without decrypting them. This chapter provides a comprehensive introduction to the LWE-based cryptosystem with examples. A key to the security of the LWE-based cryptosystem is the injection of random errors in the ciphertexts, which however hinders unlimited recursive operation of homomorphic arithmetics on ciphertexts due to the growth of the error. We show how this limitation can be overcome for dynamic feedback controllers that guarantee stability of the closed-loop system when the system matrix of the controller consists of integers. Finally, we illustrate through MATLAB codes how the LWE-based cryptosystem can be customized to build a secure feedback control system. This chapter is written for the control engineers who do not have background on cryptosystems.
Super-Resolution for Practical Automated Plant Disease Diagnosis System

Automated plant diagnosis using images taken from a distance is often insufficient in resolution and degrades diagnostic accuracy since the important external characteristics of symptoms are lost. In this paper, we first propose an effective preprocessing method for improving the performance of automated plant disease diagnosis systems using super-resolution techniques. We investigate the efficiency of two different super-resolution methods by comparing the disease diagnostic performance on the practical original high-resolution, low-resolution, and super-resolved cucumber images. Our method generates super-resolved images that look very close to natural images with 4 × upscaling factors and is capable of recovering the lost detailed symptoms, largely boosting the diagnostic performance. Our model improves the disease classification accuracy by 26.9% over the bicubic interpolation method of 65.6% and shows a small gap (3% lower) between the original result of 95.5%.
Multi-scale Vehicle Re-identification Using Self-adapting Label Smoothing Regularization

Vehicle re-identification (re-id) plays an important role in intelligent surveillance. Since difference vehicle models may have similar appearances, together with the problem of image scale variations, the vehicle re-id remains long-term challenging. We present a novel multi-scale vehicle re-id framework using self-adapting label smoothing regularization (SLSR). It integrates the appearance information from multi-scale images to alleviate the influence of scale changes caused by perspectives. To enhance the generalization ability in feature representations, we design the self-adapting label smoothing regulation in semi-supervised training process. It dynamically assigns labels to fake images to realize data augmentation. We validate the effectiveness of our proposed framework on popular VeRi and VehicleID datasets. Extensive experimental results demonstrate that our method outperforms most state-of-the-art methods on both datasets. Especially, we exceeds the latest method by 3.81% in mAP and 5.32% in rank-1 on VeRi dataset.
Applying the Single Responsibility Principle in Industry

Refactoring is a prevalent technique that can be applied for improving software structural quality. Refactorings can be applied at different levels of granularity to resolve 'bad smells' that can be identified in various artifacts (e.g., methods, classes, packages). A fundamental software engineering principle that can be applied at various levels of granularity is the Single Responsibility Principle (SRP), whose violation leads to the creation of lengthy, complex and non-cohesive artifacts; incurring smells like Long Method, God Class, and Large Package. Such artifacts, apart from being large in size tend to implement more than one functionalities, leading to decreased cohesion, and increased coupling. In this paper, we study the effect of applying refactorings that lead to conformance to the SRP, at all three levels of granularity to identify possible differences between them. To study these differences, we performed an industrial case study on two large-scale software systems (more than 1,500 classes). Since SRP is by definition related to modularity, as a success measure for the refactoring we use coupling and cohesion metrics. The results of the study can prove beneficial for both researchers and practitioners, since various implications can be drawn.
Multi-objective clustering: a kernel based approach using Differential Evolution

A multi-objective algorithm is always favoured over a single objective algorithm as it considers different aspects of a dataset in the form of various objectives. In this article, a multi-objective clustering algorithm has been proposed based on Differential Evolution. Here, three objectives have been considered to handle different complex datasets. In addition to this, a kernel function is hybridised with the objectives to evaluate the data on a hyperspace for reducing the impact of nonlinearity on cluster formation. Moreover, to get the best compromised solution from the Pareto front an effective fuzzy concept has been followed. Five metaheuristic approaches have been taken into consideration for performance comparison. These methodologies have been applied to twelve datasets and the result reveals the efficacy of the proposed model in data clustering.
Speech Augmentation Using Wavenet in Speech Recognition

Data augmentation is crucial to improving the performance of deep neural networks by helping the model avoid overfitting and improve its generalization. In automatic speech recognition, previous work proposed several approaches to augment data by performing speed perturbation or spectral transformation. Since data augmented in this manner has similar acoustic representations as the original data, it has limited advantage in improving generalization of the acoustic model. In order to avoid generating data with limited diversity, we propose a voice conversion approach using a generative model (WaveNet), which generates a new utterance by transforming an utterance to a given target voice. Our method synthesizes speech with diverse pitch patterns by minimizing the use of acoustic features. With the Wall Street Journal dataset, we verify that our method led to better generalization compared to other data augmentation techniques such as speed perturbation and WORLD-based voice conversion. In addition, when combined with the speed perturbation technique, the two methods complement each other to further improve performance of the acoustic model.
A Survey on Human Activity Recognition Based on Temporal Signals of Portable Inertial Sensors

In recent years, automatic human activity recognition has drawn much attention. On one hand, this is due to the rapid proliferation and cost degradation of a wide variety of sensing hardware, which resulted in the tremendous explosion of activity data. On the other hand there are urgent growing and pressing demands from many application areas such as: in-home health monitoring especially for the elderly, smart cities, safe driving by monitoring and predicting driver's behavior, healthcare applications, entertainment, assessment of therapy, performance evaluation in sports, etc. In this paper, we introduce a detailed survey on multiple human activity recognition (HAR) systems which use portable inertial sensors (Accelerometer, Magnetometer, and Gyro), where the sensor's produced temporal signals are used for modeling and recognition of different human activities based on various machine learning techniques.
Normal-to-Lombard adaptation of speech synthesis using long short-term memory recurrent neural networks

In this article, three adaptation methods are compared based on how well they change the speaking style of a neural network based text-to-speech (TTS) voice. The speaking style conversion adopted here is from normal to Lombard speech. The selected adaptation methods are: auxiliary features (AF), learning hidden unit contribution (LHUC), and fine-tuning (FT). Furthermore, four state-of-the-art TTS vocoders are compared in the same context. The evaluated vocoders are: GlottHMM, GlottDNN, STRAIGHT, and pulse model in log-domain (PML). Objective and subjective evaluations were conducted to study the performance of both the adaptation methods and the vocoders. In the subjective evaluations, speaking style similarity and speech intelligibility were assessed. In addition to acoustic model adaptation, phoneme durations were also adapted from normal to Lombard with the FT adaptation method. In objective evaluations and speaking style similarity tests, we found that the FT method outperformed the other two adaptation methods. In speech intelligibility tests, we found that there were no significant differences between vocoders although the PML vocoder showed slightly better performance compared to the three other vocoders.
Research on Improving Prediction Accuracy of Sports Performance by Using Glowworm Algorithm to Optimize Neural Network

In order to improve the accuracy of sports performance prediction and solve the shortcomings of low precision and slow speed of current sports performance prediction model, this paper proposes a prediction model based on glowworm optimization neural network.Firstly, the training samples and test samples of the neural network are generated by pretreatment of the sports performance; secondly, the connection weights and thresholds of the BP neural network are determined by using the glowworm optimization algorithm, and the prediction model of the sports performance is established by learning the training samples; finally, the prediction effect is tested by specific simulation experiments.The results show that the glowworm optimized neural network improves the prediction accuracy of sports performance, and solves the limitations of other sports performance prediction models.The prediction results are more reliable, which can provide scientific decision-making basis and valuable information for sports training.
Prediction of maximum oil-yield from almond seed in a chemical industry: A novel type-2 fuzzy logic approach

Almond (Termanilia catappa) is one of the most popular tree nuts on a world-wide basis due to its huge benefits in human health. Since it is not readily available in every country, research and investigations are still going on this particular. Therefore, here we have developed a unique model using type-2 fuzzy logic controller (T2FLC) approach to predict the optimum process conditions for the extraction of oil at maximum from almond seeds. Here, we have taken pressure, temperature, heating time and moisture content as input parameters and oil yield is taken as an output parameter to optimize the process. Though hundred percent recovery of oil by extraction of almond seeds is never possible, but controlling the process parameters at suitable condition, oil yield may vary within the range of 40%–45%. Using these four input and one output parameters, four Mamdani fuzzy inference systems are formed depending on the different membership functions of the variables. In T2FLC assists to trace inputs and outputs in a well-organized manner for building the inferences train so that various types of oil yield and its assessment can be predicted during extraction. Finally, a statistical analysis has been done using type-2 fuzzy data set to improve the control of process parameters that can be easily determined in the type-2 fuzzy prediction model to get high yield.
Clustering the Unknown - The Youtube Case

Recent stringent end-user security and privacy requirements caused the dramatic rise of encrypted video streams in which YouTube encrypted traffic is one of the most prevalent. Regardless of their encrypted nature, metadata derived from such traffic flows can be utilized to identify the title of a video, thus enabling the classification of video streams into a single video title using a given video title set. Nonetheless, scenarios where no video title set is present and a supervised approach is not feasible, are both frequent and challenging. In this paper we go beyond previous studies and demonstrate the feasibility of clustering unknown video streams into subgroups although no information is available about the title name. We address this problem by exploring Natural Language Processing (NLP) formulations and Word2vec techniques to compose a novel statistical feature in order to further cluster unknown video streams. Through our experimental results over real datasets we demonstrate that our methodology is capable to cluster 72 video titles out of 100 video titles from a dataset of 10,000 video streams. Thus, we argue that the proposed methodology could sufficiently contribute to the newly rising and demanding domain of encrypted Internet traffic classification.
Performance Comparison of Linear Congruent Method and Fisher-Yates Shuffle for Data Randomization

Linear Congruent Method (LCM) and Fisher-Yates Shuffle Algorithms are widely known and used in various fields and needs. Both of these algorithms are used because of their advantage and are used for data randomization needs. There have been many studies using these algorithms, but it is not known which algorithm performance is better. In this study, performance comparison testing was carried out in the form of speed testing based on the amount of randomized data. The amount of randomized data varies from 10 to 25,000 data. The tests performed are displayed in tables and graphs. The results obtained, Fisher-Yates algorithm 11.768% faster than LCM.