Reviewerly/exHarmony huggingface dataset:
Scene Designer: a Unified Model for Scene Search and Synthesis from Sketch

Scene Designer is a novel method for searching and generating images using free-hand sketches of scene compositions; i.e. drawings that describe both the appearance and relative positions of objects. Our core contribution is a single unified model to learn both a cross-modal search embedding for matching sketched compositions to images, and an object embedding for layout synthesis. We show that a graph neural network (GNN) followed by Transformer under our novel contrastive learning setting is required to allow learning correlations between object type, appearance and arrangement, driving a mask generation module that synthesizes coherent scene layouts, whilst also delivering state of the art sketch based visual search of scenes.
An Approach for Synthesis of Magnitude Comparators

The paper presents an approach for the synthesis of a magnitude comparator comparing two five-bit binary numbers. The magnitude comparator is implemented in Logisim and tested for different input combinations. Then the circuit is built in the environment of ISE Project Navigator needful for programming the FPGA-based laboratory board, developed at the University of Ruse and used in the educational process. The work is related to the project in the course “Pulse and Digital Devices” for students-bachelors of the specialties “Electronics” and “Internet and Mobile Communications”.
Soil pH Prediction using Artificial Intelligence

The pH value of soil is a critical parameter which governs the fertility of soil. In Uttarakhand, due to very encouraging climatic conditions like temperature, moisture and humidity, the soil has an appropriate pH value, and hence it is very fertile, due to which, the agriculture has become a significant source of income for Uttarakhand people. This research article is based upon the secondary data pertaining to soil of Kumaun region of Uttarakhand. This available data has been used to develop and train RBF NN (radial basis function neural network) and FIS (subtractive clustering based fuzzy inference system), on MATLAB, for predicting the pH of soil out of the given minerals concentration e.g. P, K, Fe, Mn, Cu. Eventually, the prediction performance of both the designed models are compared in terms of mean square prediction error.
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning

Deep Reinforcement Learning (DRL) is a promising approach for teaching robots new behaviour. However, one of its main limitations is the need for carefully hand-coded reward signals by an expert. We argue that it is crucial to automate the reward learning process so that new skills can be taught to robots by their users. To address such automation, we consider task success classifiers using visual observations to estimate the rewards in terms of task success. In this work, we study the performance of multiple state-of-the-art deep reinforcement learning algorithms under different types of reward: Dense, Sparse, Visual Dense, and Visual Sparse rewards. Our experiments in various simulation tasks (Pendulum, Reacher, Pusher, and Fetch Reach) show that while DRL agents can learn successful behaviours using visual rewards when the goal targets are distinguishable, their performance may decrease if the task goal is not clearly visible. Our results also show that visual dense rewards are more successful than visual sparse rewards and that there is no single best algorithm for all tasks.
Tracking of Object with Occlusion based on Normalized Cross Correlation and Kalman Filter Estimation

This paper proposes an algorithm that uses Normalized Cross Correlation (NCC) and Kalman Filter (KF) for object tracking. Occlusion during visual tracking reduces the tracking performance and may even lead to track loss. Partial occlusion may cause target template get corrupted. During complete occlusion, false target tracking may take place as actual target is not present in the scene. This paper proposes a method combination of correlation tracking based on NCC and Kalman filter based Outlier Detection algorithm for robust tracking during occlusion. The outlier detection algorithm works based on adaptive bound for innovation in Kalman filter and detects presence of occlusion. Once the outlier is detected, the prediction of Kalman filter estimation is used to predict the target position. The results of track during occlusion in presence of false target show the robustness of this approach.
Detection of Leukemia Using Convolutional Neural Network

Leukemia which is commonly known as blood cancer is a fatal type of cancer that affects white blood cells. It usually originates from the bone marrow and causes the development of abnormal blood cells called blasts. The diagnosis is made by blood tests and bone marrow biopsy which involve manual work and are time consuming. There is a need for development of an automatic tool for the detection of white blood cell cancer. Therefore, in this work, a classification model using Convolutional Neural Network with Deep Learning techniques as a basis is proposed. This work was implemented using Keras library with TensorFlow as backend. This model was trained and evaluated on cancer cell dataset C_NMC_2019 which includes white blood cell regions segmented from the microscopic blood smear images. The model offers an accuracy of 91% for training and 87% for testing which is satisfactory.
Long-context Transformers: A survey

Current Transformer architecture falls behind in terms of the computational complexity of its attention mechanism, namely being O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). That problem leads to this model being unable to efficiently work with long sequences. There were a lot of attempts which tries to deal with this problem. In this paper, we considered the most up-to-date methods and how these methods compare to each other in different tasks. We address their limitations and how well they perform, and which ones are most versatile and could be used as a base for future works.
Image Denoising Using Nonlocal Regularized Deep Image Prior

Deep neural networks have shown great potential in various low-level vision tasks, leading to several state-of-the-art image denoising techniques. Training a deep neural network in a supervised fashion usually requires the collection of a great number of examples and the consumption of a significant amount of time. However, the collection of training samples is very difficult for some application scenarios, such as the full-sampled data of magnetic resonance imaging and the data of satellite remote sensing imaging. In this paper, we overcome the problem of a lack of training data by using an unsupervised deep-learning-based method. Specifically, we propose a deep-learning-based method based on the deep image prior (DIP) method, which only requires a noisy image as training data, without any clean data. It infers the natural images with random inputs and the corrupted observation with the help of performing correction via a convolutional network. We improve the original DIP method as follows: Firstly, the original optimization objective function is modified by adding nonlocal regularizers, consisting of a spatial filter and a frequency domain filter, to promote the gradient sparsity of the solution. Secondly, we solve the optimization problem with the alternating direction method of multipliers (ADMM) framework, resulting in two separate optimization problems, including a symmetric U-Net training step and a plug-and-play proximal denoising step. As such, the proposed method exploits the powerful denoising ability of both deep neural networks and nonlocal regularizations. Experiments validate the effectiveness of leveraging a combination of DIP and nonlocal regularizers, and demonstrate the superior performance of the proposed method both quantitatively and visually compared with the original DIP method.
An Energy-Aware Multi-Criteria Federated Learning Model for Edge Computing

The successful convergence of Internet of Things (IoT) technology and distributed machine learning have leveraged to realise the concept of Federated Learning (FL) with the collaborative efforts of a large number of low-powered and small-sized edge nodes. In Wireless Networks (WN), an energy-efficient transmission is a fundamental challenge since the energy resource of edge nodes is restricted. In this paper, we propose an Energy-aware Multi-Criteria Federated Learning (EaMC-FL) model for edge computing. The proposed model enables to collaboratively train a shared global model by aggregating locally trained models in selected representative edge nodes (workers). The involved workers are initially partitioned into a number of clusters with respect to the similarity of their local model parameters. At each training round a small set of representative workers is selected on the based of multi-criteria evaluation that scores each node representativeness (importance) by taking into account the trade-off among the node local model performance, consumed energy and battery lifetime. We have demonstrated through experimental results the proposed EaM C-Flmodel is capable of reducing the energy consumed by the edge nodes by lowering the transmitted data.
Target tracking algorithm based on image matching and improved kernel correlation filter

In the classical kernel correlated target tracking algorithms, most of them initialize the target detector by importing the manually marked targets. Moreover, the anti-occlusion ability of the correlated filtering algorithm is poor, which leads to the model drift in complex scenes. This work combined the image matching technology with the target tracking technology. In the case of a certain similarity between the input image and the target, the improved SIFT algorithm SIFT-BRISK is used to complete the initialization of the target detector, which can realize the target tracking in more common cases. And then APCE confidence level is introduced to judge whether the target tracking performance is well, adapt the update strategy of keeping track in the high confidence, dynamically adjusting the tracking model in the low confidence, and improve the details of the kernel correlated target tracking algorithm. Thus, the drift of the training model is suppressed to a certain extent. Finally, the algorithm is tested under the OTB-2015 dataset and the result proves that the improved algorithm has better accuracy and higher speed than traditional target tracking algorithms.
Eye Gaze Estimation and Its Applications

The human eye gaze is an important non-verbal cue that can unobtrusively provide information about the intention and attention of a user to enable intelligent interactive systems. Eye gaze can also be taken as input to systems as a replacement of the conventional mouse and keyboard, and can also be indicative of the cognitive state of the user. However, estimating and applying gaze in real-world applications poses significant challenges. In this chapter, we first review the development of gaze estimation methods in recent years. We especially focus on learning-based gaze estimation methods which benefit from large-scale data and deep learning methods that recently became available. Second, we discuss the challenges of using gaze estimation for real-world applications and our efforts toward making these methods easily usable for the Human-Computer Interaction community. At last, we provide two application examples, demonstrating the use of eye gaze to enable attentive and adaptive interfaces.
Single-Index Model Tree

In this paper, a novel single-index model tree (SIMTree) is proposed. It adopts the recursive partitioning strategy and each data segment is modeled by a single-index model (SIM), which is a flexible extension of linear regression with non-parametric link functions. The proposed SIMTree has 2 major advantages: a) with only a few leaf nodes, it can achieve competitive predictive performance compared to complicated black-box models; b) SIMs fitted on each local data segment are intrinsically interpretable. However, using conventional techniques to build such a SIMTree can be extremely time-consuming. SIM estimation typically involves iterative optimization via Newton-type algorithms; such a resource-intensive estimation procedure is repeatedly used for fitting leaf node SIMs and the search of optimal splits. To make the computation burden affordable, an effective training algorithm is proposed as enabled by the efficient utilization of Stein's lemma and several accelerating strategies in the tree construction algorithm. Moreover, a new Python package simtree is developed with elegant visualization modules that can further facilitate the model interpretation. Numerical results on extensive regression datasets show that SIMTree is an accurate and interpretable machine learning model.
Finite-Time Error Bounds of Biased Stochastic Approximation With Application to TD-Learning

Motivated by the recent success of reinforcement learning algorithms, this paper studies a class of biased stochastic approximation (SA) procedures under a mild &#x201C;ergodicity-like&#x201D; assumption on the random noise sequence. Building on a multistep Lyapunov function that looks ahead to several future updates to accommodate the stochastic perturbations (thus gaining control over the bias), we prove a general result on the convergence of the SA iterates, and use it to derive non-asymptotic bounds on the mean-square error in the case of constant stepsizes. This novel viewpoint renders finite-time analysis of biased SA algorithms under a family of stochastic perturbations possible. For direct comparison with prior work, we demonstrate these bounds by applying them to TD-learning with linear function approximation, under the Markov chain observation model. The resultant finite-time error bound for TD-learning is the first of its kind, in the sense that it holds i) for the unmodified versions (i.e., without any modification to the updates) using even nonlinear approximators; as well as for Markov chains ii) under sublinear mixing conditions and iii) starting from any initial distribution, at least one of which has to be violated for existing results to be applicable.
Automatic multi-organ segmentation from abdominal CT volumes with LLE-based graph partitioning and 3D Chan-Vese model

This paper presents a fully automatic method for multi-organ segmentation from 3D abdominal CT volumes. Firstly, spines and ribs are removed by exponential transformation and binarization to reduce the disturbance to subsequent segmentation. Then, a Local Linear Embedding (LLE)-based graph partitioning approach is employed to perform initial segmentation for liver, spleen, and bilateral kidneys simultaneously, and a novel segmentation refinement scheme is applied composed of hybrid intensity model, 3D Chan-Vese model, and histogram equalization-based organ separation algorithm. Finally, a pseudo-3D bottleneck detection algorithm is introduced for boundary correction. The proposed method does not require heavy training or registration process and is capable of dealing with shape and location variations as well as the weak boundaries of target organs. Experiments on XHCSU20 database show the proposed method is competitive with state-of-the-art methods with Dice similarity coefficients of 95.9%, 95.1%, 94.7%, and 94.5%, Jaccard indices of 92.2%, 90.8%, 90.0%, and 89.5%, and average symmetric surface distances of 1.1 mm, 1.0 mm, 0.9 mm and 1.1 mm, for liver, spleen, left and right kidneys, respectively, and the average running time is around 6 min for a CT volume. The accuracy, precision, recall, and specificity also maintain high values for each of the four organs. Moreover, experiments on SLIVER07 dataset prove its high efficiency and accuracy on liver-only segmentation.
Virtual Learning Environments based on Beacons Devices

Due to current developments it is important that education go hand in hand with technology. Currently, there are several technological devices that allow a more didactic management of learning and beacons are some interesting devices to apply them in education. This paper presents a new methodology for virtual learning environments based on Beacons devices. Specifically, we developed a prototype that allows information to be displayed on a smartphone application. For this, the mobile device communicates with the beacon, which indicates the information that must be displayed in the application. Although any content can be used in this proposal, we show as example the curricular content of an educational center. The proposed prototype takes advantage of the operating principle of Bluetooth Low Energy (BLE). The use of Beacons allows Smartphone-Beacon automated communication. More specifically, a mobile application has been developed in the Integrated Development Environment (IDE) of Android Studio, which is responsible for scanning the iBeacon protocol and the automatic generation of the notification that links the end user to a web repository. Prototype tests were carried out on students and encouraging results were obtained. These results demonstrate that the learning process can be improved using our proposal, that is, education-oriented Beacons is an interesting option for internet-of-things (IoT) based education. A systematic review of the literature has not found a solution similar to the one presented.
Multi-class Bagged Proximal Support Vector Machines for the ImageNet Challenging Problem

We propose the new multi-class of bagged proximal support vector machines (MC-Bag-PSVM) for handling the ImageNet challenging problem with very large number of images and a thousand classes. Our MC-Bag-PSVM trains in the parallel manner ensemble binary PSVM classifiers used for the One-Versus-All (OVA) multi-class strategy on multi-core computer with GPUs. The binary PSVM model is constructed by bagged binary PSVM models built in under-sampling training dataset. The numerical test results on ILSVRC 2010 dataset show that our MC-Bag-PSVM algorithm is faster and more accurate than the state-of-the-art linear SVM algorithm. An example of its effectiveness is given with an accuracy of 75.64% obtained in the classification of ImageNet-1000 dataset having 1,261,405 images in 2048 deep features into 1,000 classes in 29.5 min using a PC Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores and Gigabyte GeForce RTX 2080Ti 11 GB GDDR6, 4352 CUDA cores.
Using Artificial Intelligence for Anomaly Detection Using Security Cameras

Identifying suspicious activity in public areas is a common concern in machine learning studies. However, this task is not usually trivial. In this article, we propose a method for recognizing anomalies in the public transportation environment to guarantee the safety of its passengers. The method in question consists of a classifier based on Convolutional Neural Networks (CNN). As input to this model, we use images from Closed-Circuit Television (CCTV) cameras already present in vehicles for security reasons. The addressed problem became challenging for reasons such as the lack of standardization of equipment, the low quality of the images provided, and the poor positioning of the cameras. In addition, a dataset, which has a high imbalance between the classes, was built. We evaluated four CNN architectures on the dataset to validate the proposed method. Experiments on the created dataset showed that the proposal of this project achieved promising results.
Comparison between Code Clone Detection and Model Clone Detection

In software, clones are syntactically or semantically identical of two code fragments. Cloning by either deliberately or by coincidence. Cloning has pros and cons in software development. Bug propagation may be caused by cloning from original to copied segments. Cloning may lead to difficulties in software maintenance. Cloning is helpful in many functions like code reusability, inheritance, and libraries. Software developers before scripting the code design the model for it. Cloning can exist at any stage either at the design or coding phase, which influences on development, quality, and maintenance of the software. All software has constraints like budget and time are associated. In software development, life cycle budget and time constraints depend on irregularity and risk. These irregularities and risks can be minimized by identifying clones. There are different kinds of clones in Code and Model. In this paper, we present a concise summary of design and coding phase clone detection of these irregularities and risk as well as their for's and against of code clone detection and model clone detection.
Distributed Feature Selection Considering Data Pricing Based on Edge Computing in Electricity Spot Markets

With the rapid development of information technology, the multisource heterogeneous data containing meaningful information have been significantly generated by various edge devices in Internet of Energy, which is one of essential foundations of many knowledge discovery tasks based on edge computing. For some complicated tasks, essential features are owned by different data sellers offering data by blockchains. With limited budgets, buying features are crucial steps in knowledge discovery tasks in electricity spot markets, especially for learning-based algorithms. However, there are lack of proper data pricing mechanisms tailored to dynamic learning processes. Besides, existing methods cannot efficiently employ edge computing servers to obtain optimal policies for selecting features according to dynamic pricing with limited budgets. To overcome such drawbacks, a data pricing mechanism is proposed in this article, which consists of static and dynamic pricing parts. Based on this mechanism, given limited budgets, a feature selection (FS) algorithm considering multiple new factors is proposed, which offers near-optimal solutions for FS at different scenarios. Numeric results show the effectiveness of the proposed algorithms.
Obtaining secure business process models from an enterprise architecture considering security requirements

Purpose Security requirements play an important role in software development. These can be specified both in enterprise architecture models and in business processes. Enterprises increasingly carry out larger amounts of business processes where security plays a major role. Business processes including security can be automatically obtained from enterprise architecture models by applying a model-driven architecture approach, through a CIM to CIM transformation. The aim of this article is to present the specification of transformation rules for the correspondence between enterprise architecture and business process model elements focusing on security. Design/methodology/approach This work utilizes motivational aspects of the ArchiMate language to model security in the business layer of enterprise architectures. Next, a set of transformation rules defined with the Atlas Transformation Language are utilized to obtain the correspondence of the enterprise architecture elements in a business process, modelled with a security extension of BPMN. Findings A total of 19 transformation rules have been defined. These rules are more complex than element to element relations, as they take into consideration the context of the elements for establishing the correspondence. Additionally, the prototype of a tool that allows the automatic transformation between both models has been developed. Originality/value The results of this work demonstrate the possibility to tackle complex transformations between both models, as previous literature focuses on semantic correspondences. Moreover, the obtained models can be of use for software developers applying the model-driven approach.