Reviewerly/exHarmony huggingface dataset:
Improving Neural Network Robustness Through Neighborhood Preserving Layers

One major source of vulnerability of neural nets in classification tasks is from overparameterized fully connected layers near the end of the network. In this paper, we propose a new neighborhood preserving layer which can replace these fully connected layers to improve the network robustness. Networks including these neighborhood preserving layers can be trained efficiently. We theoretically prove that our proposed layers are more robust against distortion because they effectively control the magnitude of gradients. Finally, we empirically show that networks with our proposed layers are more robust against state-of-the-art gradient descent based attacks, such as a PGD attack on the benchmark image classification datasets MNIST and CIFAR10.
Max-Variance Convolutional Neural Network Model Compression

In this paper, we present a method for convolutional neural network model compression which is based on the removal of filter banks that correspond to unimportant weights. To do this, we depart from the relationship between consecutive layers so as to obtain a factor that can be used to assess the degree upon which each pair of filters are coupled to each other. This allows us to use the unit-response of the coupling between two layers so as to remove pathways int he network that are negligible. Moreover, since the back-propagation gradients tend to diminish as the chain rule is applied from the output to the input layer, here we maximise the variance on the coupling factors while enforcing a monotonicity constraint that assures the most relevant pathways are preserved. We show results on widely used networks employing classification and facial expression recognition datasets. In our experiments, our approach delivers a very competitive trade-off between compression rates and performance as compared to both, the uncompressed models and alternatives elsewhere in the literature. pages = 271-279.
Certified Invoicing Software

The recent tax legislative changes created conditions for the dematerialization of documents, allowing waiving invoice printing, encouraging the adoption of an electronic invoicing and document archiving system. The consequent sustainability and cost reduction measures are allowing the optimization of invoicing and archive procedures, driving companies to invest in the development of technological instruments, incorporating a philosophy of innovation and sustainability. Entrepreneurial entities can take advantage of the increasing use of telework, due to the recent pandemic, by shifting to perform functions remotely. Thus, there is a need to optimize business processes and endeavour technological innovations, taking adequate, quick, and effective decisions. A model can be applied to systematize and simplify the decision-making process related to the Portuguese tax framework and requirements for certified invoicing software or for electronic document archiving. Understanding these requirements can speed up the development or acquisition of a proper invoicing or archiving software.
Cost-sensitive selection of variables by ensemble of model sequences

Many applications require the collection of data on different variables or measurements over many system performance metrics. We term those broadly as measures or variables. Often data collection along each measure incurs a cost, thus it is desirable to consider the cost of measures in modeling. This is a fairly new class of problems in the area of cost-sensitive learning. A few attempts have been made to incorporate costs in combining and selecting measures. However, existing studies either do not strictly enforce a budget constraint, or are not the ‘most’ cost effective. With a focus on classification problems, we propose a computationally efficient approach that could find a near optimal model under a given budget by exploring the most ‘promising’ part of the solution space. Instead of outputting a single model, we produce a model schedule—a list of models, sorted by model costs and expected predictive accuracy. This could be used to choose the model with the best predictive accuracy under a given budget, or to trade off between the budget and the predictive accuracy. Experiments on some benchmark datasets show that our approach compares favorably to competing methods.
A new type of spectral mapping theorem for quantum walks with a moving shift on graphs

The conventional spectral mapping theorem for quantum walks can only be applied for walks employing a shift operator whose square is the identity. This theorem gives most of the eigenvalues of the time evolution U by lifting the eigenvalues of an induced self-adjoint matrix T onto the unit circle on the complex plane. We acquire a new spectral mapping theorem for the Grover walk with a shift operator whose cube is the identity on finite graphs. Moreover, graphs we can consider for a quantum walk with such a shift operator is characterized by a triangulation. We call these graphs triangulable graphs in this paper. One of the differences between our spectral mapping theorem and the conventional one is that lifting the eigenvalues of $$T-1/2$$ onto the unit circle gives most of the eigenvalues of U.
Robust Uncalibrated Rectification With Low Geometric Distortion Under Unbalanced Field of View Circumstances

Rectification is a standard process in every system that requires multiviews. Existing algorithms largely work on similar field of view (FoV) cases where the two views are mostly identical. Dissimilarities between different FoVs can generate unexpected errors during the optimization process, resulting in a large amount of rectification errors and unwanted geometric distortion. In this study, we present a full pipeline to rectify uncalibrated images captured by cameras that have dissimilar FoVs under the constraints of geometric distortion. The proposed method contains two main parts: Field of View Neutralization and Rectification with Adaptive Geometric Constraints. The Field of View Neutralization module estimates the transformation matrix to compensate for the imbalance between different views. In addition, this module improves the overall quality of correspondences by removing misleading feature matching pairs. Finally, by applying an adaptive optimization process with the geometric constraints involved, our method addresses the overdistortion issue while maintaining small rectification errors. Extensive experiments are conducted to demonstrate the robust performance of the proposed method. Besides the existing datasets, we provide our dissimilar FoVs dataset with multiple baselines to examine the performance. Our method outperforms the existing algorithms in terms of both rectification errors and geometric distortion rates.
Improved Encryption Towards Data Security in Serverless Computing

Serverless computing is growing rapidly due to its rapid adoption by the cloud providers and tenants in terms of its scalability, elasticity, flexibility and ease of deployment. Such increase in deployment of serverless computing makes the research to rethink on its security aspects. Since, the serverless security computing may undergo problems due to malicious users or hackers. In this paper, a secure and an efficient access control system is designed for serverless security computing for both knowledge and resource sharing using attributed based encryption. Initially, the data is encrypted using user attributes; further the data is split into cipher text. It is finally decrypted using a decryption algorithm and then the shares of the cipher text are distributed in the network and the encapsulated texts are stored in the serverless system. The performance on security analysis shows that the proposed method achieves improved data security in serverless environment than the existing methods.
Analysis of Artificial Intelligence Applied in Video Games

Deep reinforcement learning is growing faster than ever before in the artificial intelligence world. As its applications and accomplishments proliferate, the challenges it faces will also increase in number. In this paper, the author focuses on deep reinforcement learning being applied to the field of games and video games. S some of the achievements in various types of games, from 2D perfect information environment (board games) to 3D imperfect information environment (first person perspective games) and the various challenges that DRL faces when being applied to the field will be analyzed. Challenges such as sample efficiency, exploration & exploitation trade-off, and delayed & sparse rewards will be discussed. In addition, some solutions are suggested. The solutions might also be applied to the various game examples in order to improve their performance.
Dialogue Act Recognition using Branch Architecture with Attention Mechanism for Imbalanced Data

Dialogue act recognition is a sequence labeling task that maps the dialogue act tag to each utterance in a conversation. Previous works on dialogue act recognition have investigated many methods, such as using Bi-LSTM-CRF model to improve accuracy. However, these methods ignore the problem caused by the imbalanced distribution of the data. In this paper, we target at dealing with the class imbalance problem on dialogue act recognition, and propose a branch architecture to predict different level data. The whole framework reflects a hierarchical pattern. The branches can induce global regularization, which is conducive to the utterance layer, help LSTM model to capture the features for minority classes. We also exploit self-attention mechanism after utterance layer to capture dependencies among words. Experimental results on a mandarin dialogue corpus, called CASIA-CASSIL corpus, show that our framework significantly outperforms other methods. And our experimental results also indicate the effectiveness of punctuation on the branch model and the interaction between two branches.
ExpressionHash: Securing Telecare Medical Information Systems Using BioHashing

The COVID-19 outbreak and its medical distancing phenomenon have effectively turned the global healthcare challenge into an opportunity for Telecare Medical Information Systems. Such systems employ the latest mobile and digital technologies and provide several advantages like minimal physical contact between patient and healthcare provider, easy mobility, easy access, consistent patient engagement, and cost-effectiveness. Any leakage or unauthorized access to users’ medical data can have serious consequences for any medical information system. The majority of such systems thus rely on biometrics for authenticated access but biometric systems are also prone to a variety of attacks like spoofing, replay, Masquerade, and stealing of stored templates. In this article, we propose a new cancelable biometric approach which has tentatively been named as “Expression Hash” for Telecare Medical Information Systems. The idea is to hash the expression templates with a set of pseudo-random keys which would provide a unique code (expression hash). This code can then be serving as a template for verification. Different expressions would result in different sets of expression hash codes, which could be used in different applications and for different roles of each individual. The templates are stored on the server-side and the processing is also performed on the server-side. The proposed technique is a multi-factor authentication system and provides advantages like enhanced privacy and security without the need for multiple biometric devices. In the case of compromise, the existing code can be revoked and can be directly replaced by a new set of expression hash code. The well-known JAFFE (The Japanese Female Facial Expression) dataset has been for empirical testing and the results advocate for the efficacy of the proposed approach.
Impact of Non-Functional Requirements on the Success of Ubiquitous Systems

With the recent advancements of technology, Ubiquitous Systems have rapidly become popular all over the world. It is a new paradigm that focuses on smooth integration of technology in human environments enabling users to access information and functionality anytime and anywhere. Software development companies nowadays increasingly invest in the ubiquitous system development projects in order to stay competitive and survive in the IT Industry. Success of ubiquitous system development projects heavily depends on Nonfunctional user requirements. Identification of the nonfunctional requirements is challenging since it represents the quality attributes of the system and are not directly measurable. This quantitative research aims to evaluate the different types of non-functional requirements that significantly contribute to the success of ubiquitous system development projects. This study was based on the data collected from the software industry in Sri Lanka. The results of this study indicate that both the product-related and organizational-related nonfunctional requirements strongly affect the ubiquitous systems success. The findings provide insights to the vendors of ubiquitous system development companies in the software industry.
Q Learning applied on the Board Game Dominion

Machine learning methods have been tested to many different board games in the past few years, with Monte Carlo Trees and artificial neural networks been the most popular approaches. In this paper we apply the Q Learning method on the card game dominion, in order to train an agent to develop a winning strategy. To avoid a large number of states, we try to define which variables describe the environment of the game to the agent, in a more substantive way. Also a new action selection method is tested, in an effort to accelerate the training time.
Design and Implementation of Automated Image Handwriting Sentences Recognition using Hybrid Techniques on FPGA

The validation of documents such as recognition of optical character, the sign which is written by hand are the main drawbacks involved in the identification of human and their addresses, codes of the post written on the envelops, manuscript evaluation, understanding the transactions of money and documents of the bank that are written in the English language. The conceptual model was written by hand for the real-time application that deals with the handwritten identification enables a comprehensive computerized system to identify the data written by hand which is more efficient and is free from noise. The proposed framework consists of filters based on Probabilistic Patch (PPB), identification, and analysis of the Canny edge. With the application of a Probabilistic Patch-based filter, the recursive speckle noise and additive Gaussian noise are processed. The words in the document are obtained by using the structure of Lifting transformation, the edges of the word are identified with help of Canny edge recognition. At last, the database validates the text as correct or incorrect. With the application of the Embedded Development Kit (EDK) and Software Development Kit (SDK), the entire framework is developed. The hardware used is in this work is Virtex-5 FPGA board which is the integration of SDK and EDK with XC5VLX50T as the part name.
Medicine Recommendation System For Diabetes Using Prior Medical Knowledge

Medicine recommendation system could assist doctors in making an accurate diagnosis and predict medicine. According to CDC reports, more than 250,000 people die by medication error; for that matter, many deep learning models were previously proposed to solve the high order correlations of diabetic medicine for diabetic patients. However, they are failed to address the benefit of prior medical knowledge from experienced doctors. This study proposes a recommendation method called (Diabetes Medicine Recommendation System) DIMERS model, which combines a prior medical knowledge of doctors with bidirectional Long Short-Term Memory (BiLSTM). In this study, DIMERS, first, preprocesses general tests of diabetic patient test results and medicines with a complex data achievement strategy. After that, we use a weighted block with prior medical knowledge to enhance the learning and explainable abilities of deep neural networks (DNN). Our study's overall performance was excellent. We train and validate using the real world Ruijin hospital and drug bank datasets that contain 25,280 patients within three months gaps and 63 types of diabetes medicines.
End-to-End Automated Exploit Generation for Processor Security Validation

Editor's notes: This article presents an end-to-end tool that, starting from a processor and a set of security-critical invariants, automatically generates exploits to help designers find security threats. -Rosario Cammarota, Intel Labs -Francesco Regazzoni, University of Amsterdam and Universitàdella Svizzera Italiana
Deep Factorization Machines network with Non-linear interaction for Recommender System

In recent years, leveraging the characteristics of users’ historical behavior to predict click-through rates (CTRs) has become a key point of interest in studies of recommender systems. Although theoretical and experimental investigations of CTR models have increased substantially, most models focus on linear feature interaction; however, crucial user characteristics in the real world are discovered implicitly by non-linear features. In this paper, we propose a novel model that integrates the advantages of linear and non-linear feature interaction. Our deep factorization machines network with non-linear interaction for recommend systems (DFNR) model identifies non-linear feature interactions by designing a new Non-linear interaction (NL-interaction) layer. We also incorporate a deeper multilayer perceptron (MLP) than other CTR models, which yields more accurate information about higher-order feature interactions. The MLP in the proposed model is unique because we use the residual structure to correct problems caused by a deeper network structure. Findings show that our DFNR model performs better on a CTR prediction task compared to other models. Results demonstrate the effective-ness of our model based on its non-linear interaction layer and deeper neural network architecture.
Semantic Image Inpainting via Maximum Likelihood

Current approaches involve deep learning in solving an image inpainting task. We propose a meta-algorithm in which we can set a probabilistic distribution on images. The set distribution can be a classic one, e.g. Normal distribution, or a modern one, e.g. PixelCNN++. Our first observation is relatively unexpected: if a learnt distribution generates reasonable images, then this does not make it a good candidate for inpainting via our proposed algorithm and vice-versa (if a learnt distribution gives reasonable results on image inpainting via our algorithm, then this does not make it is a good candidate for sampling a new image). Our second observation is that although the visual results of the state-of-the-art method are superior to ours, the training time for our method is lower. Hence, one can experiment faster with our method to see, for example, if the desired inpainting is learnable. Moreover, using a specific distribution, our algorithm can be trained also on high-quality RGB images, like 1024 times 1024 pixels. As for the experiments, we included visual results and some quantitative comparisons. The Google Colab notebook with the code and the demo is available at: https://github.com/aciobanusebi/mle-inpainting.
Research on the Improvement of College English Learning by Artificial Intelligence

English has become a popular language in the world, and English is becoming more and more routine in our lives, so the importance of college English teaching is becoming increasingly obvious. Artificial intelligence (AI) has been in the shape of breakthrough and development, especially in language translation. It has been able to comprehensively improve the efficiency of college English teaching and learning process, improve the learning and teaching experience and make personalized learning a reality in all aspects of learning tutoring, teaching evaluation and teaching scenes. On the basis of discussing the advantages of AI in English learning, such as accurate testing, pronunciation correction, intelligent evaluation, data tracking and efficiency improvement, this article discusses the core elements of language learning listening, speaking and reading in the aspect of writing, putting forward some strategies for the application of AI in English learning, such as improving the freshness of listening resources, creating a “mother tongue” communication environment, increasing reading entertainment and enhancing writing enthusiasm.
Towards Automatic Construction of Multi-Network Models for Heterogeneous Multi-Task Learning

Multi-task learning, as it is understood nowadays, consists of using one single model to carry out several similar tasks. From classifying hand-written characters of different alphabets to figuring out how to play several Atari games using reinforcement learning, multi-task models have been able to widen their performance range across different tasks, although these tasks are usually of a similar nature. In this work, we attempt to expand this range even further, by including heterogeneous tasks in a single learning procedure. To do so, we firstly formally define a multi-network model, identifying the necessary components and characteristics to allow different adaptations of said model depending on the tasks it is required to fulfill. Secondly, employing the formal definition as a starting point, we develop an illustrative model example consisting of three different tasks (classification, regression, and data sampling). The performance of this illustrative model is then analyzed, showing its capabilities. Motivated by the results of the analysis, we enumerate a set of open challenges and future research lines over which the full potential of the proposed model definition can be exploited.
Graphical Framework For Review Spammer Group Detection Using Metadata

Nowadays, many people depending on online reviews for the purchasing decision of a product/ service. One of the characteristics of an online review system is that anyone can post a review that allows spammers to compose fake reviews. Recently, these spammers work as groups to intensify their activities and for maximum profit gains. Few works are concentrated on the detection of group spammers compared to individual review/reviewer spamming. This work proposes a framework to detect spammer groups using graph- based algorithms with five group spamming features and also proposes a new group spamming feature, Group Rating Similarity (GRS) based on the review rating score. The results show that the proposed framework performs well with five features when comparing with the existing work having seven features. Also, the proposed feature (GRS) shows better performance in discriminating spam and non-spam when experimented on realworld review datasets from the Yelp website.
Comparative Study of Multi-label Classification, Ensemble Based Learning and Artificial Neural Network for Cervical Cancer Prediction

Cervical cancer is a major cause of death in women, affecting over five hundred thousand women worldwide each year. It is one of the leading causes of death in women. Technology plays an indispensable role in medicine and health to assess cancer research studies. In this study, we integrate diverse machine learning models in order to predict the presence of the cancer. The data set is characterized by 32 features and 4 labels—Biopsy, Hinselmann, Cytology and Schiller. Neural Network and Ensemble methods are applied to predict the four target variables independently. Naïve Bayes, K-Nearest Neighbour, Logistic Regression, Support Vector Machine and Decision Tree are used for Multi-label classification. Stacking classifier, which is an ensemble based machine learning technique, provided the best accuracy for classifying each target variable, with the best one being 98.02%. In Multi-label classification the best mean accuracy is 91.44%, given by decision tree, while the best accuracy given by neural network is 91.44% for Hinselmann.