Reviewerly/exHarmony huggingface dataset:
Solving the Shepherding Problem: Imitation Learning Can Acquire the Switching Algorithm

A single shepherd dog can herd a flock of sheep to a gate. Despite a heuristic algorithm of a dog based on adaptive switching between collecting the sheep when they are too dispersed and driving them once they are aggregated, it remains unknown how the dog learns the algorithm of switching. In fact, reinforcement learning models have not succeeded so far in reproducing the switching algorithm without explicitly making two strategies. Here, we show that an imitation learning model can reproduce the switching algorithm, that is, the dog learns the algorithm from demonstrations by an expert. We also confirmed that the dog does not simply copy the demonstrations but learns the required task by showing that it can herd more sheep than those in the given demonstrations.
Steering Angle Estimation for Self-driving Car Based on Enhanced Semantic Segmentation

Common approaches for semantic segmentation using Convolutional Neural Networks (CNN) based around conventional U-shapes architectures were widely used. However, failures to retrieve global context information and memory issues made such models unable to compete against modern architectures considering accuracy and real-time capability. In this paper, an efficient method maintaining equivalent accuracy of a previous segmentation network and skillfully making a model more light-weighted for real-time inference was proposed. More concretely, we managed to alleviate five out of 17 million trainable parameters, which effectively reduce the amount of computation of the original PSPNet by 30% using the backbone of CSPNet. Our proposed network implementation achieved 73 mIoU scores on our custom dataset and reached 15 fps regarding real-time inference. We deployed the trained model on the multifunctional hardware and then connected it to a golf car to jointly navigate the natural environment and traffic sign detection task. Accordingly, the STM32 board and servo motor were used for controlling the steering wheel through a track-and-wheel drive system. As for the traffic sign detection task, we employed a small-size Yolov5 trained on the TT100K dataset running around 60fps and attained real-time performance with sufficient accuracy.
Knowledge-based decision support for machine component design: A case study

This paper describes research conducted during a project with a multinational company that focuses on product design. The project tackles two different goals: providing sales staff with a tool that allows them to autonomously handle routine requests, and providing the company’s engineers with a decision support system to help them design products for more challenging application areas. For the first goal, we make use of a deterministic decision process, represented in the recent Decision Model and Notation (DMN) standard. For the second goal, we propose a constraint-based method. There, we use the IDP system to provide a number of interactive functionalities based on a logical representation of the relevant constraints. To ensure that the system is maintainable, we want the constraints to be updated by the engineers themselves. The IDP language is not ideal for this. Instead, we propose the cDMN notation, which extends the user-friendly DMN to constraints.
Three-Dimensional Photon Counting Optical Encryption With Enhanced Visual Quality and Security Level

In this paper, we propose three-dimensional (3D) photon counting double random phase encryption (DRPE) with enhanced visual quality and security level. Conventional 3D photon counting DRPE can encrypt the data quickly by using 4f optical system and random phase masks with enhanced the security because 3D photon counting technique is used. 3D photon counting DRPE extracts photons from the encrypted data by using statistical methods such as Poisson random process and the visual quality of the decrypted data can be enhanced through the 3D reconstruction process. However, it still has a problem to visualize the data when we extract extremely few photons. To improve the security and the visual quality of the decrypted data, we propose the random amplitude reconstruction process in the encryption stage. Our proposed method reconstructs the amplitude of the encrypted data at the random depth. Thus, the shifting pixel value and depth information can be another important key for decryption through the random process. Therefore, it can be effective in decrypting data securely and the visual quality of the decrypted data can be enhanced. Finally, Through the random reconstruction process in decryption process, our proposed method can enhance the security and visual quality simultaneously. To verify our proposed method, we carry out the simulation and optical experiment.
QSDB—a graphical Quorum Sensing Database

The human microbiome is largely shaped by the chemical interactions of its microbial members, which includes cross-talk via shared signals or quenching of the signalling of other species. Quorum sensing is a process that allows microbes to coordinate their behaviour in dependence of their population density and to adjust gene expression accordingly. We present the Quorum Sensing Database (QSDB), a comprehensive database of all published sensing and quenching relations between organisms and signalling molecules of the human microbiome, as well as an interactive web interface that allows browsing the database, provides graphical depictions of sensing mechanisms as Systems Biology Graphical Notation diagrams and links to other databases. Database URL: QSDB (Quorum Sensing DataBase) is freely available via an interactive web interface and as a downloadable csv file at http://qsdb.org.
Automatic Detection of Violence in Video Scenes

Violence detection from video stream using machine learning is a very rising field of inspection due to its great contribution to achieving peace and security and saving people's lives by automatically early-detecting violent acts and alarming those responsible to interfere. Knowing its significance, A Convolutional Neural Network (CNN) has been designed and implemented to tackle this problem, along with two classical classifiers, namely, support vector machine and random forest, in order to detect violence in video streams. The CNN is used both as a classifier and as a feature extractor whose outputs are fed into the other two classifiers. A data structure called ‘packets' was developed to refer to the input of our CNN network, packets are used to train the model on short clips where a packet consists of 15 sampled frames that make up one second of a video. So the input to the CNN is a subsampling of 3D volume of video frames. The problem is posed as a binary classification. Using four different datasets one of which is a combination of YouTube videos collected and annotated by the authors mixed with a dataset found on kaggle but also filtered by the authors. In addition, three other benchmark datasets is used. Our model was trained using supervised learning on a set of normal and violent videos, and tested using three separate classifiers whose results are compared together and then compared to other state-of-the-art approaches. Finally, transfer learning was implemented via cross validating models that where trained on a certain dataset and tested on another.
Energy-Aware Scheduling for Tasks with Target-Time in Blockchain based Data Centres

Cloud computing infrastructures have intended to provide computing services to end-users through the internet in a pay-per-use model.The extensive deployment of the Cloud and continuous increment in the capacity and utilization of data centers (DC) leads to massive power consumption.This intensifying scale of DCs has made energy consumption a critical concern.This paper emphasizes the task scheduling algorithm by formulating the system model to minimize the makespan and energy consumption incurred in a data center.Also, an energyaware task scheduling in the Blockchain-based data center was proposed to offer an optimal solution that minimizes makespan and energy consumption.The established model was analyzed with a target-time responsive precedence scheduling algorithm.The observations were analyzed and compared with the traditional scheduling algorithms.The outcomes exhibited that the developed solution incurs better performance with a response to resource utilization and decreasing energy consumption.The investigation revealed that the applied strategy considerably enhanced the effectiveness of the designed schedule.
Generation-based vs. Retrieval-based Conversational Recommendation: A User-Centric Comparison

In the past few years we observed a renewed interest in conversational recommender systems (CRS) that interact with users in natural language. Most recent research efforts use neural models trained on recorded recommendation dialogs between humans, supporting an end-to-end learning process. Given the user's utterances in a dialog, these systems aim to generate appropriate responses in natural language based on the learned models. An alternative to such language generation approaches is to retrieve and possibly adapt suitable sentences from the recorded dialogs. Approaches of this latter type are explored only to a lesser extent in the current literature.
Federated Learning Architecture for Bearing Fault Diagnosis

Federated learning (FL) is a distributed machine learning and it can obtain the participants, such as many companies or personal mobiles. The key point of federated learning is that it does not require the participants to send their data to the others, and FL only upload the gradients or weights of model trained by local data of each participant. Therefore, in this study, we combine the C fraction and gradient aggregation to implement the FL architecture for diagnosis of bearing fault. Finally, in our experiments, even if only a small number of clients participant in training, the testing accuracy can reach to 99 %. Furthermore, we use the number of turns to evaluate the impact of C fraction in testing.
Which UX Aspects Are Important for a Software Product?

Questionnaires are a popular method to measure User Experience (UX). These UX questionnaires cover different UX aspects with their scales. However, UX includes a huge number of semantically different aspects of a user's interaction with a product. It is therefore practically impossible to cover all these aspects in a single evaluation study. A researcher must select those UX aspects that are most important to the users of the product under investigation. Some papers examined which UX aspects are important for specific product categories. Participants in these studies rated the importance of UX aspects for different product categories. These categories were described by a category name and several examples for products in this category. In principle, the results of these studies can be used to indicate which UX aspects should be measured for a particular product in the corresponding product category. This is especially useful for modular frameworks, e.g., the UEQ+, that allow to create a questionnaire by selecting the relevant scales from a catalog of predefined scales. In this paper, it is investigated how accurate the UX aspect suggestions derived from category-level studies are for individual products. The results show that the predicted importance of a UX aspect from the category is fairly precise.
Concentrating Competency Profile Data into Cognitive Map of Knowledge Diagnosis

The paper describes the process of aggregating primary learning data in the form of learning digital footprints for managing educational process using cognitive visualization techniques. The over-arching process of transition from the learning data to competency diagrams and concentrate them into Cognitive Maps of Cnowledge Diagnosis that. The competency profile, represented as a radar-chart diagram, is compressed into a cognitive map, allowing interpreting this information during decision making by faculty and administrative staff. This allows generalizing results for groups of students. The results of using competency profiles and respective cognitive maps to analyze the results of summative assessments, final, and cross-curricular exams are provided as an illustration of the proposed approach.
Neural networks based on vectorized neurons

As the main research content of artificial intelligence, the artificial neural network has been widely concerned because of its excellent performance in the fields such as computer vision and natural language processing since it was proposed in the 1940s. The neuron model of the traditional neural network was proposed by McCulloch and Pitts in 1943 (MP neurons), But MP neurons is too simple to representing biological neurons. Based on this, this paper studies the attention mechanism and proposes vectorized neuron and its activation function. Firstly, we propose vectorized neurons, then use the attention mechanism to dynamically generate connection weights between vectorized neurons. Nextly, we construct a new type of neural network with vectorized neurons, which we called neural functional group (NFG). Finally, we tested the proposed neural functional group model on two tasks: image classifcation and few-shot learning. The vectorized neuron can be conditionally activated through its activation function. Besides, the vectorized neuron has the potential of representing complex biological neurons, which is difficult for MP neuron. The experimental results show that it can achieve higher accuracy with fewer parameters than convolutional neural networks (CNN) and capsule networks in image classication task; it also competitive to CNN based feature extractor in few-shot learning task.
Towards Scale and Position Invariant Task Classification Using Normalised Visual Scanpaths in Clinical Fetal Ultrasound

We present a method for classifying tasks in fetal ultrasound scans using the eye-tracking data of sonographers. The visual attention of a sonographer captured by eye-tracking data over time is defined by a scanpath. In routine fetal ultrasound, the captured standard imaging planes are visually inconsistent due to fetal position, movements, and sonographer scanning experience. To address this challenge, we propose a scale and position invariant task classification method using normalised visual scanpaths. We describe a normalisation method that uses bounding boxes to provide the gaze with a reference to the position and scale of the imaging plane and use the normalised scanpath sequences to train machine learning models for discriminating between ultrasound tasks. We compare the proposed method to existing work considering raw eyetracking data. The best performing model achieves the F1-score of 84% and outperforms existing models.
Blood Group Classification System Based on Image Processing Techniques

The present paper proposes a novel system that automatically classifies the eight different blood groups according to the ABO and Rh blood group systems. The proposed system is developed by applying MATLAB's image processing techniques on the blood sample images. These images are acquired from the laboratory using the slide test. It utilizes a mean filter for removing noise from blood sample images. In addition, the Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for enhancing the image characteristics analysis. The proposed system also utilizes the automated threshold strategy (Otsu's approach) for obtaining the blood samples binary images. Since, adding the three antigens (A, B, D) to the blood samples works on clumping the samples or not, and this, in turn, works on the variation number of objects and holes in the different blood groups' samples. Therefore, the "Bwboundaries" Matlab function is applied for the first time in the proposed system to count the number of objects and holes for blood group classification. The database used in the proposed system consists of 600 images collected from different laboratories. The suggested system has many advantages such as simplicity, speeding up processing time, reducing human interference and errors, decreasing the risk of transfusion reactions, and high accuracy, even in remote places. As well, it can be used by a lab technician or any user who doesn't have any previous knowledge of the blood group detection technique. In addition to using this system to assist in several applications such as premarital tests, blood transfusion as well as paternity tests.
Local Factor Models for Large-Scale Inductive Recommendation

In many domains, user preferences are similar locally within like-minded subgroups of users, but typically differ globally between those subgroups. Local recommendation models were shown to substantially improve top-K recommendation performance in such settings. However, existing local models do not scale to large-scale datasets with an increasing number of subgroups and do not support inductive recommendations for users not appearing in the training set. Key reasons for this are that subgroup detection and recommendation get implemented as separate steps in the model or that local models are explicitly instantiated for each subgroup. In this paper, we propose an End-to-end Local Factor Model (Elfm) which overcomes these limitations by combining both steps and incorporating local structures through an inductive bias. Our model can be optimized end-to-end and supports incremental inference, does not require a full separate model for each subgroup, and has overall small memory and computational costs for incorporating local structures. Empirical results show that our method substantially improves recommendation performance on large-scale datasets with millions of users and items with considerably smaller model size. Our user study also shows that our approach produces coherent item subgroups which could aid in the generation of explainable recommendations.
Length of Pseudorandom Binary Sequence Required to Train Artificial Neural Network Without Overfitting

The artificial neural network (ANN) has been applied to the various fields due to its capability to process complicated nonlinear functions involving a large amount of data.A pseudorandom binary sequence (PRBS) is commonly used to train the ANN since the PRBS is easily generated by using a linear feedback shift register and has a correlation function which is peaked at zero delay but is almost zero at other delays.However, when the PRBS length is not sufficiently long (compared to the input size of the ANN), the ANN trained by the PRBS could suffer from the overfitting where the ANN describes the behavior of the training sequence very well, but does poorly on new data inputs.In this paper, we provide a minimum length of the PRBS required by the ANN to avoid the overfitting for a given input size of the ANN.For this purpose, we analyze the minimum length of the input sequence required to estimate the PRBS pattern through theoretical study.These analyses are confirmed by numerical simulation.The findings of this paper would be used to select the PRBS length for training the ANN.
Blind Deblurring Based on a Single Luminance Channel and L1-Norm

To improve the image quality of the deblurring results restored by existing blind deblurring method, an effective image blind deblurring method based on a single channel and L1-norm is proposed for the blurry images.Firstly, the blur influence on different channels in common colorspaces are analyzed and concluded to verify that the transformed channel for deblurring is reasonable.Then a more suitable fidelity term is proposed for increase image details and restraining ringing effects.Finally, the deblurring model in a single luminace channel is generated and solved by semi-quadratic regularization method.The comprehensive experiments on several representative blind deblurring methods to evaluate the performances in terms of objective evaluation, subjective vision and running time.The results have demonstrated that the propopsed method can ensure the deblurring performance and meanwhile decrease the running time.The deblurring performance of many different images reveals that our method is practical in image deblurring.
Deepfake videos: synthesis and detection techniques – a survey

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.
CNN-Based Continuous Authentication on Smartphones with Auto Augmentation Search

In this paper, we present CAuSe, a CNN-based Continuous Authentication on smartphones using Auto Augmentation Search, where the CNN is specially designed for deep feature extraction and the auto augmentation search is exploited for CNN training data augmentation. Specifically, CAuSe consists of three stages of the offline stage, registration stage and authentication stage. In the offline stage, we utilize auto augmentation search on the collected data to find an optimal strategy for CNN training data augmentation. Then, we specially design a CNN to learn and extract deep features from the augmented data and train the LOF classifier after 95 features are selected by PCA in the registration stage. With the trained CNN and LOF classifier, CAuSe identifies the current user as a legitimate user or an impostor in the authentication stage. Based on our dataset, we evaluate the effectiveness of optimal strategy and the performance of CAuSe. The experimental results demonstrate that the strategy of Time-Warping(0.6)+Time-Warping(0.6) reaches the highest accuracy of 93.19% with data size 400 and CAuSe achieves the best authentication accuracy of 96.93%, respectively, comparing with other strategies and classifiers.
Design of a Weighted Average Chaotic System for Robust Chaotic Operation

In this paper, we propose a novel technique to attain a robust 1-D chaotic system. The robustness of the system is characterized by the presence of an uninterrupted chaotic region throughout the entire parameter space with excellent chaotic properties. This wide chaotic region is a crucial characteristic for practical applications where parameter disturbance may push the system out of chaotic operation. Our proposed system is comprised of the weighted average of multiple 1-D chaotic seed maps and is a generalized framework for developing robust chaos using any number of seed maps. The uninterrupted chaotic range and improved chaotic entropy is demonstrated using the bifurcation plot, Lyapunov exponent, Shannon entropy, correlation coefficient, and Kolmogorov entropy. We also demonstrate how cascading can be used to further improve the ergodicity of the proposed system. The proposed method can be useful in many applications such as chaos-based cryptography, secure communication, reconfigurable logic gate, random number generation, and so on.