Reviewerly/exHarmony huggingface dataset:
Designing Lightweight Feature Descriptor Networks with Depthwise Separable Convolution

Extracting feature points and their descriptors from images is one of the fundamental techniques in computer vision with many applications such as geometric fitting and camera calibration, and for this task several deep learning models have been proposed. However, existing feature descriptor networks have been developed with the intention of improving the accuracy, and consideration for practical networks that can run on embedded devices has somewhat been deferred. Therefore, the objective of this study is to devise light feature descriptor networks. To this end, we employ lightweight convolution operations that have been developed for image classification networks (e.g. SqueezeNet and MobileNet) for the purpose of replacing the normal convolution operators in the state-of-the-art feature descriptor network, RF-Net. Experimental results show that the model size of the detector can be reduced by up to 80% compared to that of the original size with only a 11% degradation at worst performance in our final lightweight detector model for image matching tasks. Our study indicates that the modern convolution techniques originally proposed for small image classification models can be effectively extended to designing tiny models for the feature descriptor extraction and matching portions in deep local feature learning networks.
Breast Image Segmentation for evaluation of Cancer Disease

Breast cancer is one of dangerous diseases and difficult to cure. It is observed that early detection of malignancy can help in the diagnosis of the disease and patient can be saved. For the detection of breast cancer, breast images will be enhanced using a Fuzzy logic and possibility distribution algorithm and then segmented to produce images with region of interest, in which just cancer shape appears in the image for detecting and estimating disease status. This paper proposes a statistic method based on the gray level of pixels in the image through histograms of two breast image sets to classify two cases of cancer and normal one. Simulation results on breast image sets will show that the proposed method is effective and it can be developed for detection of benign and malignant tumors in artificial intelligent systems.
Text Detection Design Based on Deep Neural Network

Text in images provides rich and accurate high-level semantic information, which is of great significance for many potential applications such as scene understanding, image and video retrieval. As a necessary means of understanding scene text, text detection has been widely concerned in the field of computer vision in recent years. However, due to the diversity of text scale and the uncertainty of image quality, text detection in natural scenes is still a challenging problem. Inspired by the successful application of deep neural network in target detection task, this paper proposes a text detection method based on multi-level feature extraction combined with deep learning technology. The multi-level feature extraction designed by this method can capture the details of the text, which is conducive to enhance the robustness of the algorithm to small-scale text and low-resolution images. In addition, the method is end-to-end, and can output text detection results directly. This paper evaluates the method on several standard datasets, and the experimental results prove the effectiveness of the proposed method.
Special Session: Impact of Noise on Quantum Algorithms in Noisy Intermediate-Scale Quantum Systems

A major challenge in realizing efficient and powerful quantum algorithms is quantum noise. Quantum noise itself is a sophisticated topic that is not seen in the classical domain. In this paper, we explore the impact of noise on quantum algorithms in Noisy Intermediate-Scale Quantum (NISQ) systems. This paper first introduces the origins of quantum noise. Next, it proposes a common treatment to simplify the view of quantum noise. Finally, it presents a case study to investigate the impact of noise on quantum Fourier transform algorithm.
Methodology for Analysis of Gm-C Filters based on Statistical, Fuzzy Logic and Machine Learning Approach

In the paper a new approach for analysis of Gm-C filters is presented that is suitable for automation of some engineering tasks and integration in CAD tools. The proposed methodology includes data gathering through simulation and circuit mathematical description, utilization of statistical experimental design technique, fuzzy logic method and machine learning algorithm. It is verified through analysis of a low pass Gm-C second order Butterworth filter and creation of several models: high level behavioral VHDL-AMS model of Gm-C active filter, statistical and fuzzy logic based model of inference and machine learning analytical model.
Enhancing Neural Recommender Models through Domain-Specific Concordance

Recommender models trained on historical observational data alone can be brittle when domain experts subject them to counterfactual evaluation. In many domains, experts can articulate common, high-level mappings or rules between categories of inputs (user's history) and categories of outputs (preferred recommendations). One challenge is to determine how to train recommender models to adhere to these rules. In this work, we introduce the goal of domain-specific concordance: the expectation that a recommender model follow a set of expert-defined categorical rules. We propose a regularization-based approach that optimizes for robustness on rule-based input perturbations. To test the effectiveness of this method, we apply it in a medication recommender model over diagnosis-medicine categories, and in movie and music recommender models, on rules over categories based on movie tags and song genres. We demonstrate that we can increase the category-based robustness distance by up to 126% without degrading accuracy, but rather increasing it by up to 12% compared to baseline models in the popular MIMIC-III, MovieLens-20M and Last.fm Million Song datasets.
Inferring Temporal Parametric L-systems Using Cartesian Genetic Programming

Lindenmayer Systems (L-systems) are formal grammars that use rewriting rules to replace, in parallel, every symbol in a string with a replacement string. By iterating, a sequence of strings is produced whose symbols can model temporal processes by interpreting them as simulation instructions. Among the types of L-systems, parametric L-systems are considered useful for simulating mechanisms that change based on different influences as the parameters change. Typically, L-systems are found by taking precise measurements and using existing knowledge, which can be addressed by automatic inference. This paper presents the Plant Model Inference Tool for Parametric L-systems (PMIT-PARAM) that can automatically learn parametric L-systems from a sequence of strings generated, where at least one parameter represents time. PMIT-PARAM is evaluated on a test suite of 20 known parametric L-systems, and is found to be able to infer the correct rewriting rules for the 18 L-systems containing only non-erasing productions; however, it can find appropriate parametric equations for all 20 of the L-systems. Inferring L-systems algorithmically not only can automatically learn models and simulations of a process with potentially less effort than doing so by hand, but it may also help reveal the scientific principles governing how the process' mechanisms change over time.
Smart Sentiment Analyzer for Bengali-English based on Hybrid Model

This paper describes an intelligent sentiment analyzer incorporating linguistic knowledge. Our proposed method comprises three stages i) corpus construction, ii) classification using machine learning tools, and iii) linguistic knowledge integration in post processing stage if any misclassification occurs due to minor problem. Here, we applied nine supervised and ensemble learning approaches. From the experiments it is observed that the naïve Bayesian classifier with linguistic knowledge provides better accuracy (93%) on an average over the other classifiers using the 4-fold cross validation. Positive or negative or confused emotions with corresponding emoticons for both English and Bengali languages are determined and the results are demonstrated via an easy to use web based interface.
X-DOG: An Intelligent X-ray-based Dangerous Goods Detection and Automatic Alarm System

X-ray-based security inspection systems are widely used in public places, such as airports, train stations and other critical locations with a large-scale of crowds. However, security checks manually are not efficient enough. Current X-ray-based automatic detection methods, including deep learning and conventional ways, usually face low accuracy, poor universality, and other weakness. In this paper, we proposed an X-ray-based dangerous goods detection scheme named SSD-X, an improved SSD (Single Shot Multi-Box Detector) object detection algorithm, and built X-DOG: an intelligent X-ray-based dangerous goods detection and automatic alarm system with SSD-X. First, in consideration of the position uncertainty and the overlap of objects, multiple data enhancement is utilized to effectively improve the accuracy and ameliorate the overfitting phenomenon. To solve the problem of unbalanced positive and negative samples in detection algorithms, focal loss is adopted to the confidence loss function so as to accelerate the rate of convergence. What's more, soft-NMS is added to enhance performance of detection of dense objects. We built a portable detecting system X-DOG implemented with SSD-X, which can run on mobile platform. Real time detecting and automatic alarming functions are implemented in X-DOG. Compared with the baseline algorithm, SSD-X shows the better performance in our experiments.
Two Party Generation of SM9 Digital Signature

SM9 is an identity-based cryptography algorithm published by the State Cryptography Administration of China, which has great application value because of its high security and convenience. However when applied in an end device with no cryptographic hardware, the private key may suffer from potential leakage. When applied in a resource constrained device, the device is incapable of performing the complicated computation of the SM9 digital signature scheme. To address such problems, in this paper, three schemes of two party generation of SM9 digital signature are proposed. Comparing to traditional IBC schemes, the proposed schemes can ensure the security of user's private key by secret sharing between an end device and a dedicated cryptographic service system which runs in a computer or server, when signing a message with the private key is needed, the two devices will collaborate to generate the signature with their respective secret shares. The proposed schemes have promising prospect in various applications.
A new hybrid algorithm based on binary gray wolf optimization and firefly algorithm for features selection

In classifying diseases, many datasets have a number of redundant features that do not affect classification accuracy. There are several evolutionary algorithms that are used to determine the feature and reduce dimensional patterns such as the gray wolf optimization (GWO) and the firefly algorithm (FFA). In this paper, a hybrid optimization algorithm BGWO_FFA was proposed to find the optimal subset of datasets. BGWO_FFA benefits from the ability of both GWO and FFA to find the best subset of features in search space. The results show that BGWO_FFA significantly outperformed the results of the algorithm BGWO as it showed efficient and high accuracy through the mean squared error (MSE) and the number of features selection.
Towards Understanding Architectural Effects on Knowledge Distillation

Knowledge distillation is a promising model compression solution, which adopts an apprenticeship (teacher-student) learning approach. Even referring to the same teacher network, it has reported different distillation performance according to a used student network architecture in current works. To tackle this issue, we investigate how scaling depth and width over layers influences learning capability based on distillation. Our experiment results show that depth scaling is a more determinant factor than width for selecting a smaller network in knowledge distillation.
Deep Features Representation of Word Image for Keyword Spotting in Historical Mongolian Document Images

Due to degradation of historical Mongolian documents, a task for retrieving them is challenging. In the field of document image retrieval, keyword spotting technology is an alternative when optical character recognition is infeasible. Representation of word images plays a very important role in keyword spotting. In this paper, various of convolutional neural networks have been used for representing word images of historical Mongolian documents. To be specific, activations of the fully-connected layer in convolutional neural network are extracted and taken as representation vectors of word images. And then, similarity can be calculated between their representation vectors of word images. Several classic structures of convolutional neural networks have been compared with each other and the best one has been determined. Furthermore, convolutional neural network has been also compared with several baselines and the state-of-the-art method on a dataset of historical Mongolian documents. Experimental results indicates that the performance of convolutional neural network is superior to these baseline and state-of-the-art methods.
A Sentiment Classification Model Based on Bi-directional LSTM with Positional Attention for Fresh Food Consumer Reviews

Aiming at the problem that it is difficult to identify the consumer' attitude and satisfaction more accurately on food quality and cold chain logistics services from a large number of online reviews based on fresh food e-commerce, a neural network model, BLSTM-PA-CNN, which combines Bidirectional Long Short-Term Memory (BLSTM) and Positional Attention (PA) mechanism, was proposed and applied to sentiment classification of consumer reviews. The PA mechanism increases the weight of sentiment-related words so that sentiment-related words play a decisive role in the whole consumer reviews. Experimental results on the JD Fresh dataset show that the BLSTM-PA-CNN model has a higher accuracy rate than conventional Convolutional Neural Networks (CNN), conventional BLSTM, and two conventional models that separately integrate PA mechanism. Meanwhile, the model also provides an idea from the perspective of consumers for fresh food e-commerce to evaluate the service quality of their own logistlcs services and food quality.
Modeling Emotions for Training in Immersive Simulations (METIS): A Cross-Platform Virtual Classroom Study

Virtual training environments (VTEs) using immersive technology have been able to successfully provide training for technical skills. Combined with recent advances in virtual social agent technologies and in affective computing, VTEs can now also support the training of social skills. Research looking at the effects of different immersive technologies on users' experience (UX) can provide important insights about their impact on user's engagement with the technology, sense presence and co-presence. However, current studies do not address whether emotions displayed by virtual agents provide the same level of UX across different virtual reality (VR) platforms. In this study, we considered a virtual classroom simulator built for desktop computer, and adapted for an immersive VR platform (CAVE). Users interact with virtual animated disruptive students able to display facial expressions, to help them practice their classroom behavior management skills. We assessed effects of the VR platforms and of the display of facial expressions on presence, co-presence, engagement, and believability. Results indicate that users were engaged, found the virtual students believable and felt presence and co-presence for both VR platforms. We also observed an interaction effects of facial expressions and VR platforms for co-presence (p = .018 <; .05).
Does One Size Fit All? Exploring the Contribution of Text features, Text content, and Grade of Use on Comprehension

Readability systems have once more become prominent in policy and practice because of recommendations in the Common Core State Standards. This study revisited two features of current text analysis (readability) systems: their generalizability to all grade levels and to all content areas. A database that encompassed texts across the grade bands and content areas and included aggregate comprehension performance on the texts was used to: (a) describe how the text features (i.e., word frequency, word length, sentence length) varied at different grade levels and within different subject areas and (b) examine if the prediction of comprehension with the text features was moderated by the grade or content area of the text. Results indicated that texts did having differing levels of various word features along both grade and content lines especially in the area of sentence length. In addition, content and grade moderated the relationship between sentence length and comprehension.
Is it hard to learn a classifier on this dataset?

Identifying how hard it is to achieve a good classification performance on a given dataset can be useful in data analysis, model selection, and meta-learning. We hypothesize that the dataset clustering indices which capture the characteristics of a dataset are related to the respective classification complexity. In this work, we propose a method for determining the empirical classification complexity of a dataset based on its clustering indices. We model this mapping problem as a supervised classification task where the estimated clustering indices of a given dataset form the features and with an indicator variable representing its classification complexity as the label. For the experiments, we use a set of clustering and classification algorithms spanning different modeling assumptions. To test whether the given dataset is complex, we estimate its clustering indices and feed it to the trained complexity classifier to output the prediction. Our approach is simple, but very effective and robust across many datasets and classifiers. We evaluate our method using 60 publicly available datasets.
An Authorship Protection Technology for Electronic Documents Based on Image Watermarking

In the field of information technology, information security technologies hold a special place. They ensure the security of the use of information technology. One of the urgent tasks is the protection of electronic documents during their transfer in information systems, including smart systems. This paper proposes a technology for protecting electronic documents containing digital images. The main idea is that the electronic document authorship can be protected by embedding digital watermarks in the images that are contained in this document. The paper considers three cases of using the proposed technology: full copying of an electronic document, copying of images contained in the document, and copying of text. It is shown that in all three cases, the authorship confirmation can be successfully implemented. Some areas of the proposed technology application are described, including augmented reality applications. Computational experiments are conducted with robust watermarking algorithms that can be used within the technology. A scenario of technology implementation is proposed which provides for the joint use of different class algorithms.
Exploring user profiles based on their explainability requirements in interactive systems

Depending on the way they are designed, interactive systems may inadvertently influence the opinions, choices and actions of their users. To avoid this problem, there has been a growing demand for this type of system to be able to explain to users how their outputs are generated. This demand is called the "right to an explanation", or the explainability requirement. User perceptions are still little addressed in this context, for example: (1) do people value software explainability? (2) what are the typical user profiles in terms of their demand for explainability? This work seeks to elucidate users' perception and to identify profiles of users in terms of their perceptions of explainability requirement. The method consists of a questionnaire, data mining algorithms and statistical analysis to uncover and analyze the profiles of users. In a case study conducted with 61 people, we found 6 profiles, each one representing a different perception and type of requirement about software explainability. The implications of these profiles for the design of interactive systems are discussed.
Probabilistic Features on Simplex Manifold in Predictive Data Modelling

In this paper, we propose a probabilistic feature mapping technique for proportional data where the features are learned in an unsupervised manner. In computer vision, count data are very relevant and we can represent that as proportion which can be modeled with distributions for proportional data. We are interested in computing probabilistic features using scaled Dirichlet and shifted scaled Dirichlet distributions and map with discriminative features to improve classification accuracy. The parameters of these distributions is updated using maximum likelihood approach. Our experimental results show that the proposed feature mapping technique improves the accuracy of baseline kernels of support vector machines (SVM) for image classification tasks.
Identification of Tones with Noises by Artificial Intelligence

The paper presents the results of the application of artificial backpropagation neural networks in identification of signal frequency tones with different RMS noise level. The Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) training algorithms were applied in the processes of neural synthesis. Three-layer with 35 hidden neurons and four-layer architectures with 22 in the first and 11 neurons in the second hidden layer in hyperbolic tangent transfer functions with accuracies 96.00% and 98.00% in LM were selected. For SCG with softmax output activation function a neural network with the best accuracy 94.3% in 29 hidden neurons was synthesized.
A Comparative Study of Various Artificial Intelligence Based Agents for the Game of Angry Birds With and Without Splitting

Abstract In a game of angry birds, birds are fired from a slingshot and are targeted towards stationary pigs located at different fixed distances from the slingshot. The angry birds have to be fired in such a way that it lands as close as possible to the pigs’ location. The goal is to develop an artificial intelligence-based model that would play the angry birds game based on the past human experience. In this game, the user will give the initial velocity and the angle of projection. Based on these parameters, the shot will be played, and the outcome is stored as a tuple consisting of the initial velocity, the angle of projection, and the location of pigs that have not been destroyed in a database. The machine learning-based agent reads the data from the database, trains itself based on the outcome of previous shots stored in the database, and plays the best possible shot according to the data retrieved from the database. Two machine learning models have been proposed, which are the K Nearest Neighbours model and the Naive Bayes model. The third model is the stochastic gradient descent model, which plays a shot based on the minimization of the distance between the angry bird and the pig using an objective function in terms of the initial velocity and splitting angle. The performance of both these agents has been compared with the human agent’s performance in terms of the average number of wins per 100 games.