Reviewerly/exHarmony huggingface dataset:
ANFIS Construction With Sparse Data via Group Rule Interpolation

A major assumption for constructing an effective adaptive-network-based fuzzy inference system (ANFIS) is that sufficient training data are available. However, in many real-world applications, this assumption may not hold, thereby requiring alternative approaches. In light of this observation, this article focuses on automated construction of ANFISs in an effort to enhance the potential of the Takagi-Sugeno fuzzy regression models for situations where only limited training data are available. In particular, the proposed approach works by interpolating a group of fuzzy rules in a certain given domain with the assistance of existing ANFISs in its neighboring domains. The construction process involves a number of computational mechanisms, including a rule dictionary which is created by extracting the rules from the existing ANFISs; a group of rules which are interpolated by exploiting the local linear embedding algorithm to build an intermediate ANFIS; and a fine-tuning method which refines the resulting intermediate ANFIS. The experimental evaluation on both synthetic and real-world datasets is reported, demonstrating that when facing the data shortage situations, the proposed approach helps significantly improve the performance of the original ANFIS modeling mechanism.
Cyberbullying Detection by Using Artificial Neural Network Models

Cyberbullying is a rising problem in worldwide and also Turkey is suffering from this problem dramatically. According to researches, the rate of children that are victims of cyberbullying actions in Turkey was tripled in the last seven years and increased to 20%. On the other hand, considering literature there are only a few studies about the detection of cyberbullying in Turkish contents. Thus, we designed eight different artificial neural network models to detect cyberbullying in Turkish social media contents automatically in our study. These models along with various text mining techniques were experimented in the dataset that includes 3000 Turkish tweets. According to the evaluation results, the most successful model for predicting cyberbullying content is YSA2 with 91% F-measure score. Additionally, YSA2 gives better performance than the experimented machine learning classifiers in our previous study.
Machine Learning Models for Software Cost Estimation

Software cost estimation is a critical task in software projects development. It assists project managers and software engineers to plan and manage their resources. However, developing an accurate cost estimation model for a software project is a challenging process. This paper builds a software cost estimation model using machine learning approach. Different machine learning algorithms are applied to two public datasets to predict the software cost in the early stages. Results show that machine learning methods can be used to predict software cost with a high accuracy rate.
Probabilistic encryption &amp; how to play mental poker keeping secret all partial information

research-article Share on Probabilistic encryption & how to play mental poker keeping secret all partial information Authors: Shafi Goldwasser University of California University of CaliforniaView Profile , Silvio Micali University of California University of CaliforniaView Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 173–201https://doi.org/10.1145/3335741.3335749Published:04 October 2019Publication History 10citation140DownloadsMetricsTotal Citations10Total Downloads140Last 12 Months12Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A new path planning method based on concave polygon convex decomposition and artificial bee colony algorithm

Free space algorithms are kind of graphics-based methods for path planning. With previously known map information, graphics-based methods have high computational efficiency in providing a feasible path. However, the existing free space algorithms do not guarantee the global optimality because they always search in one connected domain but not all the possible connected domains. To overcome this drawback, this article presents an improved free space algorithm based on map decomposition with multiple connected domains and artificial bee colony algorithm. First, a decomposition algorithm of single-connected concave polygon is introduced based on the principle of concave polygon convex decomposition. Any map without obstacle is taken as single-connected concave polygon (the convex polygon map can be seen as already decomposed and will not be discussed here). Single concave polygon can be decomposed into convex polygons by connecting concave points with their visible vertex. Second, decomposition algorithm for multi-connected concave polygon (any map with obstacles) is designed. It can be converted into single-connected concave polygon by excluding obstacles using virtual links. The map can be decomposed into several convex polygons which form multiple connected domains. Third, artificial bee colony algorithm is used to search the optimal path in all the connected domains so as to avoid falling into the local minimum. Numerical simulations and comparisons with existing free space algorithm and rapidly exploring random tree star algorithm are carried out to evaluate the performance of the proposed method. The results show that this method is able to find the optimal path with high computational efficiency and accuracy. It has advantages especially for complex maps. Furthermore, parameter sensitivity analysis is provided and the suggested values for parameters are given.
Microseismic monitoring using a fibre-optic Distributed Acoustic Sensor (DAS) array

We present a case study demonstrating the use of an “L”-shaped downhole fibre-optic array to monitor microseismicity. We use a relatively simple method to detect events from continuous waveform data, and develop a workflow for manual event location. Locations are defined with a cylindrical coordinate system, with the horizontal axis of the DAS cable being the axis of symmetry. Events are located using three manual “picks”, constraining (1) the zero-offset “broadside” channel to the event (2) the P-S wave arrival time difference at the broadside channel, and (3) the angle, ? of the event from the array. Because the one-component DAS array is unable to record P-wave energy on the broadside channel, the P-wave pick is made indirectly by ensuring that the modeled P- and S-wave moveout curves match the observed data. The ? angle requires that signal is observed on the vertical part of the array, in our case this is possible because an engineered fiber, rather than standard telecommunications fiber, provided a significant reduction in the noise level. Because only three picks need to be made, our manual approach is significantly more efficient than equivalent manual processing of downhole geophone data, where picks for P- and S-waves must be made for each receiver. We find that the located events define a tight cluster around the injection interval, indicating that this approach provides relatively precise and accurate event locations. A surface microseismic array was also used at this site, which detected significantly fewer events, the locations of which had significantly greater scatter than the DAS array locations. We conclude by examining some other aspects of the DAS microseismic data, including the presence of multiple events within very short time windows, and the presence of converted phases that appear to represent scattering of energy from the hydraulic fractures themselves.
Smart Shopping System with RFID Technology Based on Internet of Things

&lt;p&gt;The Internet of Things (IoT) incorporates most extensive number of innovations and imagines a mixture of things or questions around us that, through one of a kind tending to plans and standard correspondence conventions, consume the ability to connect through one another's also collaborate by their neighbors in the direction of attain regular purposes. The misuse of such a standard has been broadly explored in Internet-related investigates. As a result, a few plans have been suggested that utilization informal organizations to hunt Internet assets, to course activity, or to choose powerful strategies for substance dissemination. The essential understood this knowledge remains persistent locality everywhere us as a diverse carrier of things or articles – i.e. Radio-Frequency identification (RFID) sensors, labels, cellular telephones, actuators, and more – that finished novel inclining to ideas, consume the ability to subordinate through one another besides chip in through their neighbors to realize normal objectives of shopping situation in business sector. Web of Things (IoT) overpowered standard imperfection for structure code, which backing to shopping in business sector or undertaking gathering of most prominent concern and examinant.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
Time-memory trade-off in Toom-Cook multiplication: an application to module-lattice based cryptography

Since the introduction of the ring-learning with errors problem, the number theoretic transform (NTT) based polynomial multiplication algorithm has been studied extensively. Due to its faster quasilinear time complexity, it has been the preferred choice of cryptographers to realize ring-learning with errors cryptographic schemes. Compared to NTT, Toom-Cook or Karatsuba based polynomial multiplication algorithms, though being known for a long time, still have a fledgling presence in the context of post-quantum cryptography.In this work, we observe that the pre- and post-processing steps in Toom-Cook based multiplications can be expressed as linear transformations. Based on this observation we propose two novel techniques that can increase the efficiency of Toom-Cook based polynomial multiplications. Evaluation is reduced by a factor of 2, and we call this method precomputation, and interpolation is reduced from quadratic to linear, and we call this method lazy interpolation.As a practical application, we applied our algorithms to the Saber post-quantum key-encapsulation mechanism. We discuss in detail the various implementation aspects of applying our algorithms to Saber. We show that our algorithm can improve the efficiency of the computationally costly matrix-vector multiplication by 12−37% compared to previous methods on their respective platforms. Secondly, we propose different methods to reduce the memory footprint of Saber for Cortex-M4 microcontrollers. Our implementation shows between 2.6 and 5.7 KB reduction in the memory usage with respect to the smallest implementation in the literature.
LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning

Gradient-based distributed learning in parameter server (PS) computing architectures is subject to random delays due to straggling worker nodes and to possible communication bottlenecks between PS and workers. Solutions have been recently proposed to separately address these impairments based on the ideas of gradient coding (GC), worker grouping, and adaptive worker selection. This article provides a unified analysis of these techniques in terms of wall-clock time, communication, and computation complexity measures. Furthermore, in order to combine the benefits of GC and grouping in terms of robustness to stragglers with the communication and computation load gains of adaptive selection, novel strategies, named lazily aggregated GC (LAGC) and grouped-LAG (G-LAG), are introduced. Analysis and results show that G-LAG provides the best wall-clock time and communication performance while maintaining a low computational cost, for two representative distributions of the computing times of the worker nodes.
Spatial alignment facilitates visual comparison.

Humans have a uniquely sophisticated ability to see past superficial features and to understand the relational structure of the world around us. This ability often requires that we compare structures, finding commonalities and differences across visual depictions that are arranged in space, such as maps, graphs, or diagrams. Although such visual comparison of relational structures is ubiquitous in classrooms, textbooks, and news media, surprisingly little is known about how to facilitate this process. Here we suggest a new principle of spatial alignment, whereby visual comparison is substantially more efficient when visuals are placed perpendicular to their structural axes, such that the matching components of the visuals are in direct alignment. In four experiments, this direct alignment led to faster and more accurate comparison than other placements of the same patterns. We discuss the spatial alignment principle in connection to broader work on relational comparison and describe its implications for design and instruction. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Feature-Temporal Semi-Supervised Extreme Learning Machine for Robotic Terrain Classification

Robotic terrain classification refers to the ability that a field robot could identify the traversing terrains autonomously under as little human supervision as possible. Such a task could be achieved by semi-supervised learning which works in the premise of smoothness assumption in the feature space. However, we found that the feature smoothness assumption cannot be fully satisfied (i.e., there is no apparent low-density region in the feature space) in the robotic terrain classification, which motivates us to propose the feature-temporal semi-supervised extreme learning machine (FT-S2ELM). With introducing the feature-temporal similarity matrix, the accuracy of the classifier trained by semi-supervised learning increases significantly. Furthermore, considering the uncertainty in determining the smoothness degree (i.e., the free parameters of similarity matrix), we introduce an automatic approach to find the optimal graph Laplacian, thus increasing the safety. The proposed method is verified experimentally on the data gathered by a micro tracked robot.
Learning Feature Fusion in Deep Learning-Based Object Detector

Object detection in real images is a challenging problem in computer vision. Despite several advancements in detection and recognition techniques, robust and accurate localization of interesting objects in images from real-life scenarios remains unsolved because of the difficulties posed by intraclass and interclass variations, occlusion, lightning, and scale changes at different levels. In this work, we present an object detection framework by learning-based fusion of handcrafted features with deep features. Deep features characterize different regions of interest in a testing image with a rich set of statistical features. Our hypothesis is to reinforce these features with handcrafted features by learning the optimal fusion during network training. Our detection framework is based on the recent version of YOLO object detection architecture. Experimental evaluation on PASCAL-VOC and MS-COCO datasets achieved the detection rate increase of 11.4% and 1.9% on the mAP scale in comparison with the YOLO version-3 detector (Redmon and Farhadi 2018). An important step in the proposed learning-based feature fusion strategy is to correctly identify the layer feeding in new features. The present work shows a qualitative approach to identify the best layer for fusion and design steps for feeding in the additional feature sets in convolutional network-based detectors.
Voxento: A Prototype Voice-controlled Interactive Search Engine for Lifelogs

In this paper, we describe an interactive voice-based retrieval system for lifelogs which has been developed to participate in the third Lifelog Search Challenge LSC'20, at ACM ICMR'20. Based on a standard text-based retrieval methodology, the novelty of Voxento is in the interactive voice facility that allows a user to interact with a personal lifelog using simple voice commands. Voxento was developed as an initial prototype of a pervasive computing system that can be deployed for wearable technologies such as Google Glass. The version of Voxento described in this paper has been optimised for use with a desktop computer in order to be competitive at the LSC'20 challenge.
Deep Learning Techniques for Implementation of Chatbots

Chatbots are software programs that interact with clients using natural languages. The motto of the researchers was to know if chatbots can able to fool the clients that they were real humans. To develop a chatbot that can pass the Turing test, plenty of effort done with the introduction of the ELIZA chatbot in the year 1966. Various approaches for the development of chatbots and different technologies in the creation of chatbots developed because of those efforts. NLTK is a module in python which can able to perform Natural Language Processing. It is used to analyse the input in the form of speech and generate responses that are similar to humans. Nowadays there is a lot of demand for virtual assistants such as Siri, Cortana, Google Assistant and Alexa, and speech-based search engines. Nowadays Chatbots are gaining massive demand mainly in the business sector for automating client service and also for reducing efforts of humans. Chatbots typically used for information acquisition in dialogue systems. To perfectly imitate a human response, a chatbot should examine the query asked by a client correctly and design an appropriate response. In this study we compare and discuss the different technologies used in the chatbots and also address the design and implementation of a chatbot system.
RETRACTED ARTICLE: Criminal tendency detection from facial images and the gender bias effect

Abstract Explosive performance and memory space growth in computing machines, along with recent specialization of deep learning models have radically boosted the role of images in semantic pattern recognition. In the same way that a textual post on social media reveals individual characteristics of its author, facial images may manifest some personality traits. This work is the first milestone in our attempt to infer personality traits from facial images. With this ultimate goal in mind, here we explore a new level of image understanding, inferring criminal tendency from facial images via deep learning. In particular, two deep learning models, including a standard feedforward neural network (SNN) and a convolutional neural network (CNN) are applied to discriminate criminal and non-criminal facial images. Confusion matrix and training and test accuracies are reported for both models, using tenfold cross-validation on a set of 10,000 facial images. The CNN was more consistent than the SNN in learning to reach its best test accuracy, which was 8% higher than the SNN’s test accuracy. Next, to explore the classifier’s hypothetical bias due to gender, we controlled for gender by applying only male facial images. No meaningful discrepancies in classification accuracies or learning consistencies were observed, suggesting little to no gender bias in the classifier. Finally, dissecting and visualizing convolutional layers in CNN showed that the shape of the face, eyebrows, top of the eye, pupils, nostrils, and lips are taken advantage of by CNN in order to classify the two sets of images.
Ultrasound Image Despeckling and detection of Breast Cancer using Deep CNN

Breast Cancer is a common type of cancer diagnosed and it is a leading cause of death amongst the female population worldwide. Ultrasound imaging is the preferred method used by hospitals for detection of breast cancer, due to the fact that it is much safer that other imaging modalities. However, Ultrasound images are contaminated with noise that is non-Gaussian and multiplicative referred to as speckles. Currently, medical technicians and physicians do diagnosis of breast cancer by manually inspecting the ultrasound images, which makes the process time consuming and costly. This may be considered as an issue which prevents the early detection of breast cancer. Hence, an early diagnosis of breast cancer can be beneficial in not only prescribing medical procedure that inhibits the cancer from spreading but also in minimizing the fatality rate. Due to the Speckles (noise) in ultrasound, automatic detection and diagnosis is an extremely difficult task. In this paper, a Convolutional Neural Network (CNN) has been proposed for Despeckling (Denoising) the ultrasound images and afterwards another CNN model is proposed for the classification of the ultrasound images into benign and malignant classes. The proposed models are tested on a Mendeley Breast Ultrasound dataset. Experimental results indicate that a classification accuracy of 99.89% is achieved through the proposed model and that the proposed model(s) outperform other methods in proposed in recent studies.
Performance Measurement of Various Hybridized Kernels for Noise Normalization and Enhancement in High-Resolution MR Images

In this article, a focus is laid on the hybridization of various noise removal kernels that are used in the normalization of the noise in the medical MR images which is acquainted into the images during the processes of image rendering caused due to inappropriate calibration of the equipment and poor illumination of the subject. In the process of statistical study of various kernels that include Otsu-based Adaptive Weighted Bilateral Kernel (AWBK), Adaptive Contourlet Transform (ACT), Adaptive Fuzzy Hexagonal Weighted Mean (AFHWM) Kernel, and Adaptive Multiscale Data Condensation Kernel (AMDC), the experimentation is carried over images that are corrupted at distinct noise levels. During the recovery of the noisy image, the performances of the various included approaches have been evaluated and presented in this article. Upon practical implementation, it is observed that each of those hybridized kernels outperformed the type of noise on which they are experimented. The mean computational time of each kernel is also been presented in the results.
Facial Expression Recognition Based on Graph Neural Network

Facial expressions are one of the most powerful, natural and immediate means for human being to present their emotions and intensions. In this paper, we present a novel method for fully automatic facial expression recognition. The facial landmarks are detected for characterizing facial expressions. A graph convolutional neural network is proposed for feature extraction and facial expression recognition classification. The experiments were performed on the three facial expression databases. The result shows that the proposed FER method can achieve good recognition accuracy up to 95.85% using the proposed method.
Faster Search Algorithm for Speckle Tracking in Ultrasound Images

Block matching techniques have been studied exhaustively for motion estimation in Ultrasound (US) images. Exhaustive Search (ES) is the most commonly used search algorithm for block matching in US images. However, ES can be computationally expensive and slow. In this paper, a faster search algorithm called the Adaptive Rood Pattern Search (ARPS) is adopted to US images along with subpixel matching to reduce the computational cost and enhance block matching. Both ES and ARPS were applied in the context of block matching based 2D speckle tracking and were compared using Number of Computations per Frame (NCF), Computational Time per Frame (CTF) and Root Mean Squared Error (RMSE) as metrics. Our simulations and experimental results proved that ARPS outperformed ES by a substantial margin. Adaptation of this technique could help improve the performance of real-time motion estimation drastically.