Reviewerly/exHarmony huggingface dataset:
Scene Generation from Backgrounds to Objects and Anything in Between: A Deep Learning Robotics Survey

The recent rapid progress of deep learning algorithms in generating realistic images, especially in Generative Adversarial Networks (GAN) and Variational Auto-Encoders (VAE), has helped advance new applications. Examples of such applications range from generating and manipulating new synthetic data for self-driving cars, to building/urban architectures, to interior design, and gaming. Furthermore, several applications have benefited from deep learning generative advancement, such as robotics manipulations in structured and unstructured environments, virtual fashion clothes try-on, and item identification on the go. This survey paper provides a review of techniques for image generation from background outdoor scenes, to building facades and objects, and anything in between. In particular, we will cover scene generation such as outdoor landscapes, building facades and indoor scenes. For each category, we will compare the existing state of the art algorithms and techniques, and discuss their performance and gaps limitations on a wide variety of inputs. Additionally, we will discuss challenges and future trends to advance the state of the art in realistic image generation.
A Powerful Holonic and Multi-Agent-Based Front-End for Medical Diagnostics Systems

Despite the current hype about the use of decision support systems, especially in more demanding application areas, such systems are not yet popular in the field of medical diagnosis. The reason for this is the usual method of how a diagnosis is achieved. Basically, the process of medical diagnosis consists of two important steps. As a first step, a doctor always tries to gather as much relevant information and data as possible about the patient and their complaints by performing tests, measuring conditions, and using medical equipment. The result is a solid source of information that is used in the second step, the calculation of the medical diagnosis. The first step lays the foundation for a correct diagnosis. The better the data, the better the diagnosis. However, due to the abundance of diseases, there are many possible paths from the beginning of the first encounter with the patient to the end of the data collection. And often this data collection process is already guided by a doctor's suspicion of a particular disease (category). This implies that the two steps for a medical diagnosis are often not consecutive steps, but rather interlock. Very often, after the first step, the diagnosis is already pretty clear. Unfortunately, the state-of-the-art medical diagnostic systems can only be used after the first step has been completed. The very important first step is left to the experience, knowledge, and intuition of the medical staff to complete. That is what this chapter is about. It motivates and introduces an AI-based front-end for medical diagnostic systems that controls and guides the first step of a medical diagnosis, the so-called history and physical examination. Because of the requirements for such a front-end, we concluded that a system that combines the holonic paradigm, multi-agent technology and machine learning technology would best solve the problem at hand. As will be proved in the simulation part of the paper the proposed system is highly adaptive, scalable, flexible, reliable, and robust.
Exploration of Software Implementation on Cloud Accounting and Security System Towards Accounting Practices Case Study from A Private Company in Indonesia

Cloud accounting is an innovation in accounting development that acts as a form of ERP system combined with cloud computing in software as a service. This study aims to explore the benefits and risks due to the adoption of the system in corporate accounting practices. Especially in the security system and compliance with applicable accounting policies. The study was conducted with qualitative research methods with descriptive approach. Data collection techniques are done by interview, observation, and literature study. This research uses the TOE framework and the characteristics of Parkerian Hexad to explore the risks and benefits of implementing cloud accounting systems in companies both in terms of data confidentiality and economic security. Research shows that using cloud accounting simplifies the accounting process. and the output and application are in accordance with the applicable policies.
Adversarial Example Defense Based on the Supervision

In recent years, deep learning has developed rapidly and has shown great performance on many challenging machine learning tasks, such as image classification, natural language processing, and speech recognition. However, researchers have recently discovered that deep learning models have security risks and are easily affected by adversarial examples. The adversarial example is a sample formed by deliberately adding subtle perturbation that is invisible to the human in the dataset. It can make the classification classify incorrectly with a high degree of confidence, which poses more challenges for deep learning research. In this paper, we propose a defense model based on the supervision mechanism. The model adds supervision layers to the original convolutional neural network and improves the robustness and defense ability of the model by improving the loss function. The LeNet-5 and VGG networks are used as the original network models. The experimental results on MNIST and CIFAR-10 confirm that the method proposed in this paper will effectively increase the difficulty of the attackers.
Open-Ended Learning of Reactive Knowledge in Cognitive Robotics Based on Neuroevolution

Reactive knowledge corresponds to implicit knowledge in the human brain, that is, unconscious knowledge such as reflexes that are executed without "thinking". It is a key aspect in human development, and it is also a key aspect in cognitive architectures for robots, mainly because it avoids inefficient action selection procedures and allows addressing higher-level cognitive processes that make use of it. This paper deals with the acquisition of this type of knowledge in a cognitive architecture for open-ended learning. We propose a method for the learning of policies (reactive knowledge) trough evolution from deliberative models by means of neuroevolution. It is interesting to see in the results presented that this approach of learning reactive knowledge instead of exhaustively selecting the appropriate action every instant of time provides equivalent/better results and more efficient action sequences.
Image Classification Algorithm Based on Big Data and Multilabel Learning of Improved Convolutional Neural Network

More and more image materials are used in various industries these days. Therefore, how to collect useful images from a large set has become an urgent priority. Convolutional neural networks (CNN) have achieved good results in certain image classification tasks, but there are still problems such as poor classification ability, low accuracy, and slow convergence speed. This article mainly introduces the image classification algorithm (ICA) research based on the multilabel learning of the improved convolutional neural network and some improvement ideas for the research of the ICA based on the multilabel learning of the convolutional neural network. This paper proposes an ICA research method based on multilabel learning of improved convolutional neural networks, including the image classification process, convolutional network algorithm, and multilabel learning algorithm. The conclusions show that the average maximum classification accuracy of the improved CNN in this paper is 90.63%, and the performance is better, which is beneficial to improving the efficiency of image classification. The improved CNN network structure has reached the highest accuracy rate of 91.47% on the CIFAR-10 data set, which is much higher than the traditional CNN algorithm.
Template-Based Minor Embedding for Adiabatic Quantum Optimization

Quantum annealing (QA) can be used to quickly obtain near-optimal solutions for quadratic unconstrained binary optimization (QUBO) problems. In QA hardware, each decision variable of a QUBO should be mapped to one or more adjacent qubits in such a way that pairs of variables defining a quadratic term in the objective function are mapped to some pair of adjacent qubits. However, qubits have limited connectivity in existing QA hardware. This has spurred work on preprocessing algorithms for embedding the graph representing problem variables with quadratic terms into the hardware graph representing qubits adjacencies, such as the Chimera graph in hardware produced by D-Wave Systems. In this paper, we use integer linear programming to search for an embedding of the problem graph into certain classes of minors of the Chimera graph, which we call template embeddings. One of these classes corresponds to complete bipartite graphs, for which we show the limitation of the existing approach based on minimum odd cycle transversals (OCTs). One of the formulations presented is exact and thus can be used to certify the absence of a minor embedding using that template. On an extensive test set consisting of random graphs from five different classes of varying size and sparsity, we can embed more graphs than a state-of-the-art OCT-based approach, our approach scales better with the hardware size, and the runtime is generally orders of magnitude smaller. Summary of Contribution: Our work combines classical and quantum computing for operations research by showing that integer linear programming can be successfully used as a preprocessing step for adiabatic quantum optimization. We use it to determine how a quadratic unconstrained binary optimization problem can be solved by a quantum annealer in which the qubits are coupled as in a Chimera graph, such as in the quantum annealers currently produced by D-Wave Systems. The paper also provides a timely introduction to adiabatic quantum computing and related work on minor embeddings.
Balancing Data Within Incremental Semi-supervised Fuzzy Clustering for Credit Card Fraud Detection

As the number of online financial transactions increases, the problem of credit card fraud detection has become quite urgent.Machine learning methods, including supervised and unsupervised approaches, have been proven to be effective to detect fraudulent activities.In our previous work presented at EUSFLAT2019 we proposed the use of an incremental semi-supervised fuzzy clustering that processes both labeled and unlabeled data as a stream to create a classification model for credit card fraud detection.However, we observed that the results of the method were affected by data unbalancement.Indeed credit card fraud data are highly imbalanced since the number of fraudulent activities is far less than the genuine ones.In this work, to deal with the high data unbalance, different resampling methods are investigated and their empirical comparison is reported.
The Knowledge Base for Automating the Architecting of Software Systems

This article describes an approach to knowledge base (KB) formation. KB may be used for automating the software systems (SS) architecting based on the experience of previous projects.
Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future

Climate change poses a major challenge to humanity, especially in its impact on agriculture, a challenge that a responsible AI should meet. In this paper, we examine a CBR system (PBI-CBR) designed to aid sustainable dairy farming by supporting grassland management, through accurate crop growth prediction. As climate changes, PBI-CBR’s historical cases become less useful in predicting future grass growth. Hence, we extend PBI-CBR using data augmentation, to specifically handle disruptive climate events, using a counterfactual method (from XAI). Study 1 shows that historical, extreme climate-events (climate outlier cases) tend to be used by PBI-CBR to predict grass growth during climate disrupted periods. Study 2 shows that synthetic outliers, generated as counterfactuals on an outlier-boundary, improve the predictive accuracy of PBI-CBR, during the drought of 2018. This study also shows that an case-based counterfactual method does better than a benchmark, constraint-guided method.
Private Decision Tree Evaluation by a Single Untrusted Server for Machine Learnig as a Service

In this paper, we propose the first private decision tree evaluation (PDTE) schemes which are suitable for use in Machine Learning as a Service (MLaaS) scenarios. In our schemes, a user and a model owner send the ciphertexts of a sample and a decision tree model, respectively, and a single server classifies the sample without knowing the sample nor the decision tree. Although many PDTE schemes have been proposed so far, most of them require to reveal the decision tree to the server. This is undesirable because the classification model is the intellectual property of the model owner, and/or it may include sensitive information used to train the model, and therefore the model also should be hidden from the server. In other PDTE schemes, multiple servers jointly conduct the classification process and the decision tree is kept secret from the servers under the assumption they do not collude. Unfortunately, this assumption may not hold because MLaaS is usually provided by a single company. In contrast, our schemes do not have such problems. In principle, fully homomorphic encryption allows us to classify an encrypted sample based on an encrypted decision tree, and in fact, the existing non-interactive PDTE scheme can be modified so that the server classifies only handling ciphertexts. However, the resulting scheme is less efficient than ours. We also show the experimental results for our schemes.
Learning Spatial–Temporal Background-Aware Based Tracking

Discriminative correlation filter (DCF) based tracking algorithms have obtained prominent speed and accuracy strengths, which have attracted extensive attention and research. However, some unavoidable deficiencies still exist. For example, the circulant shifted sampling process is likely to cause repeated periodic assumptions and cause boundary effects, which degrades the tracker’s discriminative performance, and the target is not easy to locate in complex appearance changes. In this paper, a spatial–temporal regularization module based on BACF (background-aware correlation filter) framework is proposed, which is performed by introducing a temporal regularization to deal effectively with the boundary effects issue. At the same time, the accuracy of target recognition is improved. This model can be effectively optimized by employing the alternating direction multiplier (ADMM) method, and each sub-problem has a corresponding closed solution. In addition, in terms of feature representation, we combine traditional hand-crafted features with deep convolution features linearly enhance the discriminative performance of the filter. Considerable experiments on multiple well-known benchmarks show the proposed algorithm is performs favorably against many state-of-the-art trackers and achieves an AUC score of 64.4% on OTB-100.
Constraining Logits by Bounded Function for Adversarial Robustness

We propose a method for improving adversarial robustness by addition of a new bounded function just before softmax. Several studies hypothesize that small logits (inputs of softmax) by logit regularization contributes to adversarial robustness of deep learning. Following this hypothesis, we analyze norms of logit vectors at the optimal point under the assumption of universal approximation and explore new methods for constraining logits by addition of a bounded function before softmax. We theoretically and empirically reveal that small logits by addition of a common activation function, e.g., hyperbolic tangent, do not improve robustness since input vectors of the function (pre-logit vectors) can have large norms. From the theoretical findings, we develop the new bounded function. The addition of our function contributes to adversarial robustness because it makes logit and pre-logit vectors have small norms. Since our method only adds one activation function before softmax, it is easy to combine our method with adversarial training. Our experiments demonstrate that our method is comparable to logit regularization methods in terms of robustness against untargeted attacks without adversarial training. Furthermore, it is superior or comparable to logit regularization methods and a recent defense method (TRADES) when using adversarial training.
Parameter estimation for a discrete time model driven by fractional Poisson process

In this article, we study the parametric problem of estimating the coefficient for a discrete time model driven by a fractional Poisson noise, when high-frequency observations are given. We consider weighted least squares and maximum likelihood estimators. Thus, asymptotic behavior of the estimators is proved and a simulation study is shown to illustrate our results.
Experimental verification of the inertial theorem control protocols

An experiment based on a trapped ytterbium ion validates the inertial theorem for the SU(2) algebra. The qubit is encoded within the hyperfine states of the atom and controlled by RF fields. The inertial theorem generates analytical solutions for non-adiabatically driven systems that are 'accelerated' slowly, bridging the gap between the sudden and adiabatic limits. These solutions are shown to be stable to small deviations, both experimentally and theoretically. By encoding a two-level system into hyperphine structure of a trapped ytterbium, we explore the high control over the system dynamics in order to validate range of applicability of the inertial theorem in our system. For large deviations from the inertial condition, the experimental results show that the phase remains accurate while the amplitude diverges, so the inertial theorem has good robustness in the phase estimate. As a result, we experimentally showed that the inertial solutions pave the way to rapid quantum control of closed, as well as open quantum systems.
Simplify Your Neural Networks: An Empirical Study on Cross-Project Defect Prediction

Malhotra, Ruchika Khan, Abuzar Ahmed Khera, AmritEnsuring software quality, when every industry depends on software, is of utmost importance. Software bugs can have grave consequences and thus identifying and fixing them becomes imperative for developers. Software defect prediction (SDP) focuses on identifying defect-prone areas so that testing resources can be allocated judiciously. Sourcing historical data is not easy, especially in the case of new software, and this is where cross-project defect prediction (CPDP) comes in. SDP, and specifically CPDP, have both attracted the attention of the research community. Simultaneously, the versatility of neural networks (NN) has pushed researchers to study the applications of NNs to defect prediction. In most research, the architecture of a NN is arrived at through trial-and-error. This requires effort, which can be reduced if there is a general idea about what kind of architecture works well in a particular field. In this paper, we tackle this problem by testing six different NNs on a dataset of twenty software from the PROMISE repository in a strict CPDP setting. We then compare the best architecture to three proposed methods for CPDP, which cover a wide range of scenarios. During our research, we found that the simplest NN with dropout layers (NN-SD) performed the best and was also statistically significantly better than the CPDP methods it was compared with. We used the area under the curve for receiver operating characteristics (AUC-ROC) as the performance metric, and for testing statistical significance, we use the Friedman chi-squared test and the Wilcoxon signed-rank test.
Where is our Booth? Clan Organization at the Alþingi, Þingvellir, Iceland in Virtual Reality

Geography influences the long-term success or failure of a community. For example, your booth location at the local farmer’s market could result in greater sales of fresh produce if you are positio...
Nonlinear Lagrangean Neural Networks

Recently, Fagerholm, Friston, Moran and Leech advanced a class of linear neural network models complying with Lagrangean dynamics. In the present effort, we explore the possibility of extending the Lagrangean approach to nonlinear models. We present a Lagrangean formalism for a family of nonlinear neural network models, and investigate its main mathematical features.
AES-Based Android Video Encryption and Decryption App

Encryption is a major requirement in today’s world as it boosts the security of the data that everyone shares all over the Internet. Videos are the means of communication in several industries and can be private. Encrypting a video file to a text file using the AES encryption algorithm increases the security provided while sharing such content over the Internet. Currently, apps to encrypt text files and images exist for the Android OS, and this paper proposes the next step in that direction - an Android app for video encryption and decryption. The results obtained via the method proposed in this paper are nearly 10 times better than the existing results.
Transformation of the Project Space in the Digital Economy: Content and Development

The article examines the content and transformation of the project space in the digital economy. It presents a review of the state of the issue in academic sources, clarifies the content of the concept of project space in the digital economy, identifies the prerequisites for its study, and reveals its structure. The project space is considered as a specialized component of the economic space. The research results show that the formation and transformation of the project space in the digital economy require an appropriate institutional environment, system conditions, and infrastructure. The aspects considered in the paper make it possible to expand the scope of possibilities for solving an important problem for the modern economy: the formation of a theoretical and methodological basis for the study of the project space in the digital economy, increasing its role and significance in the processes of socio-economic development of regions and states.
VIDENS: Vision-based User Identification from Inertial Sensors

In this paper we propose the VIDENS (vision-based user identification from inertial sensors) approach, which transforms inertial sensors time-series data into images that represent in pixel form patterns found over time, allowing even a simple CNN to outperform complex ad-hoc deep learning models that combine RNNs and CNNs for user identification. Our evaluation shows promising results when comparing our approach to some relevant existing methods.
Explainable AI Using MAP-Independence

In decision support systems the motivation and justification of the system’s diagnosis or classification is crucial for the acceptance of the system by the human user. In Bayesian networks a diagnosis or classification is typically formalized as the computation of the most probable joint value assignment to the hypothesis variables, given the observed values of the evidence variables (generally known as the MAP problem). While solving the MAP problem gives the most probable explanation of the evidence, the computation is a black box as far as the human user is concerned and it does not give additional insights that allow the user to appreciate and accept the decision. For example, a user might want to know to what extent a variable was relevant for the explanation. In this paper we introduce a new concept, MAP-independence, which tries to formally capture this notion of relevance, and explore its role towards a justification of an inference to the best explanation.