Reviewerly/exHarmony huggingface dataset:
The Usability Argument for Refinement Typed Genetic Programming

The performance of Evolutionary Algorithms is frequently hindered by arbitrarily large search spaces. In order to overcome this challenge, domain-specific knowledge is often used to restrict the representation or evaluation of candidate solutions to the problem at hand. Due to the diversity of problems and the unpredictable performance impact, the encoding of domain-specific knowledge is a frequent problem in the implementation of evolutionary algorithms. We propose the use of Refinement Typed Genetic Programming, an enhanced hybrid of Strongly Typed Genetic Programming (STGP) and Grammar-Guided Genetic Programming (GGGP) that features an advanced type system with polymorphism and dependent and refined types. We argue that this approach is more usable for describing common problems in machine learning, optimisation and program synthesis, due to the familiarity of the language (when compared to GGGP) and the use of a unifying language to express the representation, the phenotype translation, the evaluation function and the context in which programs are executed.
Preliminary Notions of Arguments from Commonsense Knowledge

The field of Computational Argumentation is well-tailored to approach commonsense reasoning, due to its ability to model contradictory information. In this paper, we present preliminary work on how an argumentation framework can explicitly model commonsense knowledge, both at a logically structured and at an abstract level. We discuss the correlation with current research and present interesting future directions.
An improved age invariant face recognition using data augmentation

In spite of the significant advancement in face recognition expertise, accurately recognizing the face of the same individual across different ages still remains an open research question. Face aging causes intra-subject variations (such as geometric changes during childhood adolescence, wrinkles and saggy skin in old age) which negatively affects the accuracy of face recognition systems. Over the years, researchers have devised different techniques to improve the accuracy of age invariant face recognition (AIFR) systems. In this paper, the face and gesture recognition network (FG-NET) aging dataset was adopted to enable the benchmarking of experimental results. The FG-Net dataset was augmented by adding four different types of noises at the preprocessing phase in order to improve the trait aging face features extraction and the training model used at the classification stages, thus addressing the problem of few available training aging for face recognition dataset. The developed model was an adaptation of a pre-trained convolution neural network architecture (Inception-ResNet-v2) which is a very robust noise. The proposed model on testing achieved a 99.94% recognition accuracy, a mean square error of 0.0158 and a mean absolute error of 0.0637. The results obtained are significant improvements in comparison with related works.
Autoepistemic answer set programming

Defined by Gelfond in 1991, epistemic specifications constitute an extension of Answer Set Programming (ASP) that introduces subjective literals. A subjective literal allows checking whether some regular literal is true in all (or in some of) the answer sets of the program, that are further collected in a set called world view. One epistemic program may yield several world views but, under the original semantics, some of them resulted from self-supported derivations. During the last eight years, several alternative approaches have been proposed to get rid of these self-supported world views. Unfortunately, their success could only be measured by studying their behaviour on a set of common examples in the literature, since no formal property of “self-supportedness” had been defined. To fill this gap, we extend in this paper the idea of unfounded set from standard logic programming to the epistemic case. We define when a world view is founded with respect to some program. Accordingly, we define the foundedness property for an arbitrary semantics, so it holds when its world views are always founded. Using counterexamples, we explain that the previous approaches violate foundedness, and proceed to propose a new semantics based on a combination of Moore's Autoepistemic Logic and Pearce's Equilibrium Logic. This combination paves the way for the development of an autoepistemic extension of ASP. The main result proves that this new semantics precisely captures the set of world views of the original semantics that are founded.
Hackers vs. Security: Attack-Defence Trees as Asynchronous Multi-agent Systems

Attack-Defence Trees (ADTrees) are a well-suited formalism to assess possible attacks to systems and the efficiency of counter-measures. This paper extends the available ADTree constructs with reactive patterns that cover further security scenarios, and equips all constructs with attributes such as time and cost to allow for quantitative analyses. We model ADTrees as (an extension of) Asynchronous Multi-Agents Systems: EAMAS. The ADTree–EAMAS transformation allows us to quantify the impact of different agents configurations on metrics such as attack time. Using EAMAS also permits parametric verification: we derive constraints for property satisfaction, e.g. the maximum time a defence can take to block an attack. Our approach is exercised on several case studies using the Uppaal and IMITATOR tools. We developed the open-source tool adt2amas implementing our transformation.
Natural Language Processing based New Approach to Design Factoid Question Answering System

The field of text mining which deals with the providing of answers to the questions of the users is also one of the hot topics for researchers. The difficulty seen in the proper answering of the questions needs to be resolved. The large variety of questions fails in the QA system. In this paper, Natural Language Processing (NLP) has been used which deals with the processing of the data that comes in any form like text, video, image, or audio. This NLP comes under the field of artificial intelligence (AI), which is used in the field of question answering (QA) system. Here proposed work for designing a system that works for factoid QA which will answer the questions that are asked by the users. Lexical Chain and Keyword analysis are used in our system for the answering of questions from a given set of articles. The reasoning system is used for the validity of the answering. The experiment here is done with the SQUAD dataset. In our experiment, the accuracy obtained for the passage retrieval using TFIDF is 69.69%. The overall average of the correct prediction of the answer is 69.93%.
TextOG: A Recommendation Model for Rating Prediction Based on Heterogeneous Fusion of Review Data

It is beneficial to use user review as a preference expression because they contain information that is not in the interaction record. However, most current research on recommendation systems only models the explicit records of users and items. It does not mine more personalized information from the review texts generated simultaneously with the interaction records. In this article, we proposed a heterogeneous fusion recommendation model for extracting fine-grained product attributes and user behavior from the review texts. The model we proposed is called TextOG. In the first half of the model, we used two blocks to learn user reviews and item reviews, one of which is dedicated to learning user behavior using reviews written by users, and the other block determines product attributes from reviews written for products. In the second half of the model, we connected the latent factors learned by users and items to perform spatial convolution on the graph. That way, implicit features can perform complex interactions in non-Euclidean spaces. We conducted experiments on a large review data set, and the results show that TextOG performs better than the baseline recommendation methods on various datasets.
Data Anonymization for Privacy Protection in Fog-Enhanced Smart Homes

Smart homes are revolutionizing lives of their residents in multiple ways. Benefits range from bringing convenience in day to day life, optimization of decision processes to use of their data for analytics by businesses. However, privacy threats are inevitable when individual data is shared publicly. This paper presents architecture of a fog enhanced smart home environment for preserving privacy of individuals when data from their smart homes is shared with third parties. A k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> data anonymization technique is employed for preventing privacy breaches of individuals. The proposed architecture and technique are evaluated on a real world data set and results indicate their effectiveness in privacy preservation.
EFFICIENT MEDICAL IMAGE COMPRESSION BASED ON INTEGER WAVELET TRANSFORM

Every day the amount of medical image generated grows rapidly so there is a need for efficient image compression for storing and managing the medical images. In this work, a suitable image codec is proposed for medical images. During the past few years, frequency domain analyzes such as Discrete Cosine Transform and Discrete Wavelet Transform (DWT) have been widely used in the field of image compression due to their well localized property of its coefficients in both space and frequency domain. This research work also deals with image compression based on frequency domain transformation. As the medical images are vital for diagnosis, they require lossless compression to store them. However, the coefficients of DWT are real numbers; lossless compression cannot be achieved. The Lifting Wavelet Transform (LWT) is used in proposed system to overcome limitation of DWT.
Study of Security Issues in Platform-as-a-Service (PaaS) Cloud Model

Security is one of the key considerations when businesses look to adopt cloud models like Platform as a Service (PaaS). The main concern comes from the fact that business losses total control of deployed applications and data, leaving that trust in the hands of the Cloud Service Providers (CSP). Although CSPs put some level of security controls in place, it is ultimately the responsibility of the client to verify the presence of the controls and verify the suitability of their security needs. It is also the user's responsibility to ascertain compliance with these offerings to appropriate standards before patronage. This paper reviewed various security issues inherent in the PaaS cloud model, classified them according to the essential cloud characteristics and finally recommended high-level solutions to the identified security issues. The result of the review and recommendations provided can serve as a reference for organizations and individual users when deciding security needs and service level objectives for PaaS based deployments.
Subtask analysis of process data through a predictive model

Abstract Response process data collected from human–computer interactive items contain detailed information about respondents' behavioural patterns and cognitive processes. Such data are valuable sources for analysing respondents' problem‐solving strategies. However, the irregular data format and the complex structure make standard statistical tools difficult to apply. This article develops a computationally efficient method for exploratory analysis of such process data. The new approach segments a lengthy individual process into a sequence of short subprocesses to achieve complexity reduction, easy clustering and meaningful interpretation. Each subprocess is considered a subtask. The segmentation is based on sequential action predictability using a parsimonious predictive model combined with the Shannon entropy. Simulation studies are conducted to assess the performance of the new method. We use a case study of PIAAC 2012 to demonstrate how exploratory analysis for process data can be carried out with the new approach.
A ‘Complete Blind’ No-Reference Stereoscopic Image Quality Assessment Algorithm

We propose a complete blind no-reference (NR) image quality assessment algorithm for assessing the perceptual quality of natural stereoscopic (S3D) images. Towards this end, we have generated an intermediate image from the left and right views, and hypothesize that the perceived quality of the S3D view close to that cyclopean image. We perform multi-steerable decomposition on cyclopean images and we compute the naturalness image quality evaluator (NIQE) score [1] and entropy score from each subband. Finally, the primitive quality scores of steerable subbands are pooled to obtain the overall perceptual quality score of an S3D image. The proposed algorithm is evaluated on the LIVE Phase I [2] and LIVE Phase II [3] stereoscopic image datasets and demonstrates its robust performance on both the datasets and across distortions. The proposed algorithm, which is a `complete blind' model (neither requires pristine S3D images nor requires training on human opinion scores), is called the Multi-Orient NIQE based 3D image quality evaluator (MO-NIQE).
IN ALGORITHMS WE TRUST: MAGICAL THINKING, SUPERINTELLIGENT AI AND QUANTUM COMPUTING

Abstract This article analyzes current attitudes toward artificial intelligence (AI) and quantum computing and argues that they represent a modern‐day form of magical thinking. It proposes that AI and quantum computing are thus excellent examples of the ways that traditional distinctions between religion, science, and magic fail to account for the vibrancy and energy that surround modern technologies.
Convolutional Neural Network

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.
Hardware and Power-Efficient Compression Technique Based on Discrete Tchebichef Transform for Neural Recording Microsystems

In this paper a new compression technique based on the discrete Tchebichef transform is presented. To comply with strict on-implant hardware implementation requirements, such as low power dissipation and small silicon area consumption, the discrete Tchebichef transform is modified and truncated. An algorithm is proposed to generate approximate transform matrices capable of truncation without suffering from destructive energy leakage among the coefficients. This is achieved by preserving orthogonality of the basis functions that convey majority portion of the signal energy. Based on the presented algorithm, a new truncated transformation matrix is proposed, which reduces the hardware complexity by up to 74% compared to that of the original transform. Hardware implementation of the proposed neural signal compression technique is prototyped using standard digital hardware. With pre-recorded neural signals as the input, compression rate of 26.15 is achieved while the root-mean-square of error is kept as low as 1.1%.Clinical Relevance- This paper proposes a technique for data compression in high-density neural recording brain implants, along with a power- and area-efficient hardware implementation. From among clinical applications of such implants one can point to neuro-prostheses, and brain-machine interfaces for therapeutic purposes.
Deep Learning-Based Estimation of Non-Specific Uptake in Amyloid-PET Images from Structural MRI for Improved Quantification of Amyloid Load in Alzheimer's Disease

Methods like PET imaging which use radiotracers that bind to amyloid-β (Aβ) - plaques that accumulate and lead to Alzheimer's disease (AD) - are crucial to detect AD early. However, current semi-quantitative methods that quantify Aβ using the Standardized Uptake Values Ratio (SUVr) cannot distinguish between the specific binding of radiotracers to Aβ and non-specific binding to other targets. In this paper, we propose a novel method to predict non-specific binding from MR images using deep learning by developing a novel conditional Generative Adversarial Network (cGAN) to improve Aβ quantification. PET images with low amyloid load were selected to represent non-specific uptake of the Aβ [11C]PiB radiotracer. Paired MR and PET images with low amyloid load were used for training, with the MRI image as the network input and the PET image as the training label. The cGAN was trained to learn the mapping between the MR image and the non-specific PET image. The generated non-specific PET images were compared to the real PET scans, achieving a mean SUVr difference of 1.90% in the gray matter. The small SUVr difference indicated that we have successfully developed cGAN for predicting the non-specific uptake of PET and that deep learning can be used to estimate the non-specific binding of Aβ PET radiotracer from MR images.
A CNN Accelerator on FPGA with a Flexible Structure

Most of the existing convolutional neural networks (CNNs) are based on PC software, which cannot meet the real-time, low power and miniaturization requirements of the systems. In this paper, a CNN accelerator with flexible structure based on Field-Programmable Gate Array (FPGA) is proposed to achieve recognition of MNIST handwritten numeric characters. The system adopts deep pipeline processing and optimizes inter-layer and intra-layer parallelism from two levels of coarse and fine granularity. In view of the similarity of convolution structure, this design adopts structured circuit, which can easily expand the number of layers and neurons. The classification throughput and inter-layer data throughput capability can be improved by rationally organizing the internal memory resources of the FPGA. Compared with the general CPU, it achieves 3 times acceleration at 50MHz frequency, while the power consumption is only 2% of the CPU. Finally performance and power consumption are compared with other accelerators by VGG16.
A Combined Fuzzy C-Means and Level Set Method for Automatic DCE-MRI Kidney Segmentation Using Both Population-Based and Patient-Specific Shape Statistics

Kidney segmentation from Dynamic Contrast Enhanced Magnetic Resonance Images (DCE-MRI) is a fundamental step for the early detection of transplanted kidney function. This paper presents an accurate and automatic DCE-MRI kidney segmentation method which combines fuzzy c-means (FCM) algorithm and geometric deformable model (level set) method. In order to precisely extract the kidney from its background, the evolution of the level set contour in the proposed method is controlled by the fuzzy memberships of the pixels and both population-based and patient-specific shape model. The FCM algorithm is used to initially divide the input image into kidney and background clusters. The obtained fuzzy clustering membership is used to define the initial contour of the level set method. For segmenting the kidney of a specific patient, a number of high contrast time-point images are segmented constraining the evolution of the level set contour by the population-based shape model constructed from different subjects. As more images are segmented, the patient-specific shape model is built from the obtained segmentation results and gradually used to guide the evolution of the level set contour. The performance of the proposed method is evaluated on 40 subjects. Experimental results demonstrate the efficiency, consistency, and accuracy of the proposed method especially for low contrast images.
A Survey on Methodologies for Handling Imbalance Problem in Multiclass Classification

An imbalanced class distribution introduces the difficulty in classification tasks and exists in various real-world applications. Imbalanced class distribution happens when minority class has fewer instances than the majority class which has a large number of instances. The misclassification problem classifies the minority class into wrong class as compared to the majority class. Much work and solution are proposed for binary or two-class imbalance problem whereas the class imbalanced datasets that contain more than two-class labels with varying degree of imbalance have been received limited attention. Other than smaller representation of instances, factors like noise, overlapping classes, multidimensional classes, and data instances with multiminority and multimajority classes may affect the predicting rate of traditional classifiers on minority instances. Several solutions have been proposed for the multiclass imbalance problem that includes data sampling techniques, class decomposition techniques, cost-sensitive ensemble methods, etc. This paper presents a detailed survey of the approaches to handle multiclass imbalance problem.
Prediction of non-stationary response functions using a Bayesian composite Gaussian process

The modeling and prediction of functions that can exhibit non-stationarity characteristics is important in many applications; for example, this is often the case for simulator output. One approach to predict a function with unknown stationarity properties is to model it as a draw from a flexible stochastic process that can produce stationary or non-stationary realizations. One such model is the composite Gaussian process (CGP) which expresses the large-scale (global) trends of the output and the small-scale (local) adjustments to the global trend as independent Gaussian processes; an extension of the CGP model can produce realizations with non-constant variance by allowing the variance of the local process to vary over the input space. A new, Bayesian extension of a global-trend plus local-trend model is proposed that also allows measurement errors. In contrast to the original CGP model, the new Bayesian CGP model introduces a weight function to allow the total process variability to be apportioned between the large- and small-scale processes. The proposed prior distributions ensure that the fitted global mean is smoother than the local deviations, a feature built into the CGP model. The log of the process variance for the Bayesian CGP is modeled as a Gaussian process to provide a flexible mechanism for handling variance functions that vary across the input space. A Markov chain Monte Carlo algorithm is proposed that provides posterior estimates of the parameters for the Bayesian CGP. It also yields predictions of the output and quantifies uncertainty about the predictions. The method is illustrated using both analytic and real-data examples.
Cryptanalysis of RSA: Integer Prime Factorization Using Genetic Algorithms

In recent years, researchers have been exploring alternative methods to solving Integer Prime Factorization, the decomposition of an integer into its prime factors. This has direct application to cryptanalysis of RSA, as one means of breaking such a cryptosystem requires factorization of a large number that is the product of two prime numbers. This paper applies three different genetic algorithms to solve this issue, utilizing mathematical knowledge concerning distribution of primes to improve the algorithms. The best of the three genetic algorithms has a chromosome that represents m in the equation prime = 6 m ± 1, and is able to factor a number of up to 22 decimal digits. This is a significantly larger number than the largest factored by comparable methods in earlier work. This leads to the conclusion that approaches such as genetic algorithms are a promising avenue of research into the problem of integer factorization.