Reviewerly/exHarmony huggingface dataset:
Optimal QoE-Fair Resource Allocation in Multipath Video Delivery Network

A steadily increasing number of users consume videos over the Internet. In current video platforms, players run a control algorithm that dynamically chooses the video bitrate to match the time-varying network bandwidth. Such an algorithm strives to improve the quality individually perceived by users. Consequently, this control architecture leads, in the optimal case, to maximize the average quality perceived collectively by all users rather than to a quality-fair distribution of resources, possibly leading to user abandonment for those users receiving a lower quality. Therefore, we argue that well-designed video delivery networks should gracefully degrade the perceived quality equally for all users when resources become scarce. In this paper, we propose the Multi-Commodity Flow Problem (MCFP) optimization framework to address the issue of designing a QoE-fair optimal allocation strategy. We show how to make the resulting problem tractable for video platforms serving massive audiences. The performance of the proposed optimal fair resource allocation strategy is tested through realistic simulations involving thousands of concurrent users on two real networks by varying both the total load on the network and the system parameters.
Definition modeling: literature review and dataset analysis

&lt;abstract&gt;&lt;p&gt;Definition modeling, the task of generating a definition for a given term, is a relatively new area of research applied in evaluating word embeddings. Automatic generation of dictionary quality definitions has many applications in natural language processing, such as sentiment analysis, machine translation, and word sense disambiguation. Additionally, definition modeling is also helpful for evaluating the quality of word embeddings. As more research is done in this field, the need for a summary of different applications, approaches, and obstacles grows apparent. This review provides an overview of the current research in definition modeling and a list of future directions and trends.&lt;/p&gt;&lt;/abstract&gt;
A density estimation approach for detecting and explaining exceptional values in categorical data

Abstract In this work we deal with the problem of detecting and explaining anomalous values in categorical datasets. We take the perspective of perceiving an attribute value as anomalous if its frequency is exceptional within the overall distribution of frequencies. As a first main contribution, we provide the notion of frequency occurrence . This measure can be thought of as a form of Kernel Density Estimation applied to the domain of frequency values. As a second contribution, we define an outlierness measure for categorical values that leverages the cumulated frequency distribution of the frequency occurrence distribution. This measure is able to identify two kinds of anomalies, called lower outliers and upper outliers , corresponding to exceptionally low or high frequent values. Moreover, we provide interpretable explanations for anomalous data values. We point out that providing interpretable explanations for the knowledge mined is a desirable feature of any knowledge discovery technique, though most of the traditional outlier detection methods do not provide explanations. Considering that when dealing with explanations the user could be overwhelmed by a huge amount of redundant information, as a third main contribution, we define a mechanism that allows us to single out outstanding explanations . The proposed technique is knowledge-centric , since we focus on explanation-property pairs and anomalous objects are a by-product of the mined knowledge. This clearly differentiates the proposed approach from traditional outlier detection approaches which instead are object-centric . The experiments highlight that the method is scalable and also able to identify anomalies of a different nature from those detected by traditional techniques.
Research in Traffic Accident Analysis Based on Multi-source Data Fusion

The rapid economic development and the increase of car ownership have led to the rise of traffic accidents, and the means of measuring by tape measure, photographing and videotaping used in traffic accident processing can no longer adapt to the needs of rapid accident processing and cause analysis. In this paper, the scene data obtained by UAV and 3D laser scanner are fused to realize the digitization of the accident scene. Secondly, the fused scene point cloud data is used as the basis for accident reconstruction in PC-Crash to obtain accident process data and generate a 3D model of the accident scene at the same time. Finally, the accident scene and the accident process are reproduced in 3D in PreScan to analyze the causes of the accident and formulate corresponding accident prevention measures. The accident investigation and analysis method of multi-source data fusion proposed in this paper can provide some reference for accident investigation, accident analysis and accident prevention.
Pathfinding algorithm based on rotated block AOR technique in structured environment

&lt;abstract&gt; &lt;p&gt;Harmonic potential fields are commonly used as guidance in a global approach for self-directed robot pathfinding. These harmonic potentials are generated using Laplace's equation solutions. The computation of these harmonic potentials often requires the use of immense amounts of computing resources. This study introduces a numerical technique called Rotated Block Accelerated Over-Relaxation (AOR), also known as Explicit Decoupled Group AOR (EDGAOR), to deal with pathfinding problem. Several robot navigation simulations were performed in a static, structured, known indoor environment to validate the efficiency of the suggested approach. The paths generated by the simulations are shown using several different starting and target positions. The performance of the proposed approach in computing harmonic potentials for solving pathfinding problems is also discussed.&lt;/p&gt; &lt;/abstract&gt;
Feature extraction from images using integrated photonic convolutional kernel

Optical neural networks are expected to solve the problems of computational efficiency and energy consumption in neural networks.Herein, we experimentally implemented a 2 × 2 photonic convolutional kernel (PCK) using four on-chip micro-ring resonators (MRRs) and demonstrated feature extraction for images with different convolutional kernels.We trained a simple convolutional neural network model to recognize the MNIST dataset and used our PCK devices for processing in the first convolutional layer, achieving a recognition rate of 91%, which further verified the feasibility of MRRs for convolution operations.In addition to the source, all silicon photonic devices used can be monolithically integrated and feature good scalability, which is important for realizing large-scale, low-cost optical neural networks.
Qrator: An Interest-Aware Approach to ABR Streaming Based on User Engagement

Conventional approaches to adaptive bitrate (ABR) streaming fail to further improve the quality of experience (QoE) as they are indifferent to video semantics and user behaviors by focusing solely on the network conditions. These ABR schemes cannot enhance the perceptual video quality as they neglect the users’ varying degree of interest (DoI) over video sections and may experience QoE degradation due to frequent rebuffers caused by user behaviors such as selectively watching the interesting sections. To this end, this article proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Qrator</i> , which is the very first attempt to utilize timestamps and likes as user engagement data to infer users’ genuine interests and further elevate the user-perceived QoE. Based on timestamps and likes in video comments, Qrator improves the overall QoE by considering DoI variations and user behaviors in performing interest-aware rate adaptation and prefetching. Qrator can be widely applied to conventional ABR approaches without significantly modifying their implementations. Evaluation results show that Qrator can heighten the bitrates of user interest sections without degrading the average bitrate. Furthermore, applying Qrator under user behavior patterns can reduce the rebuffering ratio and the number of rebuffers by 32% and 31%, respectively, while maintaining other QoE metrics to a similar extent.
An Energy-Efficient Reconfigurable AI-Based Object Detection and Tracking Processor Supporting Online Object Learning

This letter presents an energy-efficient reconfigurable AI-based object detection and tracking processor for smart drone/robot applications. Several techniques have been proposed to achieve high energy efficiency while supporting flexible object detection and tracking tasks with online object learning, including a reconfigurable object detection and tracking architecture with reconfigurable neural network (NN) engine, an online object learning architecture with shared NN inference and learning engine and automatic label generation engine, and a layer- and stride-aware NN computing technique. Compared with several state-of-the-art designs, the proposed design achieves better energy efficiency (2.13 mJ/frame), while supporting flexible object detection and tracking tasks with online object learning.
MHGSO: A Modified Hunger Game Search Optimizer Using Opposition-Based Learning for Feature Selection

Feature selection (FS) is one of the crucial pre-processing tasks in many data mining, machine learning, and pattern recognition applications. It facilitates limiting the feature count, dimensionality of datasets, and overfitting. Feature selection methods are developed to explore the benefits with good accuracy outcomes. This paper proposes a novel modification to the conventional Hunger Games Search optimization using the concept of opposition-based learning (OBL) to solve the FS problem. Here, the opposition-based learning enables the searching ability of HGSO to determine the optimal solution by looking in random directions and in the opposite directions as well, simultaneously. Moreover, three binarization approaches, namely transfer function (TF), great value priority (GVP), and angle modulation (AM), have been incorporated with modified HGSO and investigated to study the effective feature selection ability of the modified optimizer. The simulation results have been obtained using standard datasets for accuracy, fitness value, and a number of features, along with a convergence curve for each dataset. The obtained results demonstrate better performance compared to the Support Vector Machine (SVM) approach over most of the datasets for effective feature selection.
HOG-Based Object Detection Toward Soccer Playing Robots

Object detection is a computer vision technology that detects objects in an image or video primarily using feature extraction and classification. Object detection is the most essential aspect in robots that enables it to sense, think and act effectively. The purpose of this research is to develop an intelligent vision system on soccer playing Nao robot to detect objects on the soccer environment, which is essential for playing the game. In this paper we present an object detection for soccer playing Nao robots based on histogram of oriented gradient (HOG). HOG involves image conversion, gradient calculation, spatial orientation spinning and block normalization that extracts the primary features from the image. The support vector machine (SVM) classifier is used for classifying the two class of concern: Nao and ball. The results show that the proposed method of object identification performs satisfactorily well in detecting Nao and the ball with an accuracy of 0.875 which is better than available works in the literature.
Enhancing Comprehension of the Internal Structure of the Atom with a Virtual 3D Environment

An application in a 3D environment called AR Elements was used to improve the understanding of abstract concepts and maintain high school students' motivation in studying inorganic chemistry. Concepts such as the atom, valence electrons, and orbitals were evaluated. The application was developed with Unity and Autodesk Maya software for 3D modeling, then a didactic sequence was applied to a group of 87 high school students to evaluate academic performance. The results showed a 20% increase in the average evaluations of these topics compared to the previous two years. Eighty-nine per cent of students surveyed at the end of this research perceived that their learning improved using this tool.
Predicting Outcomes for Cancer Patients with Transformer-Based Multi-task Learning

Cancer patients often experience numerous hospital admissions as a result of their cancer and treatment, which can negatively impact treatment progress and quality of life. Accurately predicting outcomes for cancer patients is therefore crucial in providing personalised care and improving patient outcomes. Existing models leveraging deep learning with Electronic Health Record (EHR) data to predict outcomes for cancer patients are limited, despite the demonstrated success of these approaches with cancer imaging data and non-cancer EHR applications. Additionally, current methods focus on single-task predictions, and increasing evidence suggests jointly training a model on two related tasks can improve predictive performance. To address these limitations, we propose a Transformer-based Multi-Task (TransMT) model that captures relationships between diagnosis codes and sequential hospital visits to simultaneously predict related outcomes for hospitalised cancer patients. Experiments conducted on two public datasets show the proposed model outperforms both single-task and recurrent neural network approaches in predicting future diagnosis and hospital readmission, and demonstrates the benefits of using deep learning with EHR data for cancer-related research.
A Fast Method for Explanations of Failures in Optimization-Based Robot Motion Planning

The transparent interaction between an operator and a robot system is essential for successful task completion. This requires a mutual understanding of decisions and processes in order to provide accurate diagnoses and troubleshooting alternatives in the event of a failure. Due to inaccuracies in the environmental perception or planner configuration, errors can occur in robot motion planning that are hard to understand by the operator. In this work we present a method that is able to provide explanations for motion planning failures quickly. In the context of optimization-based planners, failures origin from planning constraints can be identified using an adaption of the FastDiag algorithm. It is able to provide one preferred minimal diagnosis in logarithmic time, also for large constraint sets. To evaluate the applicability of the proposed method, experiments are conducted that compare the computational performance to an existing method while considering different parameters such as number of constraints and requested diagnoses.
Publishing LOD Vocabularies in Any Schema with Semantics.gr

Semantics.gr is an innovative, cutting-edge platform developed by the National Documentation Centre (EKT) for the creation, curation, interlinking and publishing of vocabularies, thesauri, classifications and authority files (collectively referred to as Vocabularies in this paper) as Linked Open DataQuery. Besides SKOS, Semantics.gr can accommodate vocabularies of any data model that can be expressed as an OWL ontology. Through a user-friendly GUI, a user can represent a particular data model (i.e. SKOS or MADS/RDF) by configuring the relevant OWL classes and properties and setting appropriate constraints as a Vocabulary Schema. The Vocabulary Schema, in turn, provides the blueprint for the term forms through which a user adds or updates individual terms. To date, we have represented various established data models in Semantics.gr, such as SKOS, EDM's contextual classes, MADS/RDF (for person names and organizations) and Schema.org, creating, in parallel, several custom Vocabulary Schemas capturing specialized vocabulary needs. We have also developed several Vocabularies that we use, on one hand, as controlled vocabulary lists in our digital repositories as well as to enrich 3rd party data in the national cultural aggregator, SearchCulture.gr, and the scientific data aggregator, OpenArchives.gr. Vocabularies are published as RDF and also via a Web API under an open license and are, therefore, free for any entity to reuse for their purposes in their own systems. Semantics.gr is launching as a free service to organizations that wish to create and publish their vocabularies as LOD, with the intention to become a central public platform for publishing and re-using authoritative LOD Vocabularies and to develop an active community around the service.
Improved YOLOv5 in Remote Sensing Slender and Rotating Target Detection

Although the deep-learning based object detecting methods for remote sensing imagery have achieved very high accuracy on most of the targets, the slender and rotating targets in remote sensing images, such as bridges and harbors, are exceptional. The problem is that the predefined anchor boxes usually cannot well cover these targets. Thus, in this study an improved YOLOv5 model is proposed to better detect the slender rotating targets in remote sensing imagery. Firstly, the size of input images is doubled to maintain the integrity of the target. Secondly, the deformable convolution (DCN) is applied in the backbone net-work during feature extraction to increase the coverage of the feature and decrease the interruption by the background while extracting the slender rotating targets. Finally, besides length and width of a bounding box, the aspect ratio of the bounding box is also added to the loss function to emphasize the im-portance of the uniqueness of the slender rotating targets and to improve the prediction accuracy subse-quently. The three modifications have been applied to the YOLOv5 model respectively and together on DOTA dataset. The results show that the proposed method can better detect slender and rotating targets in remote sensing images and the accuracy for bridge and harbor has been improved to 52.1&#x0025; and 75.16&#x0025; respectively.
Using UTAUT for Blockchain Assessment

With over 1,000 public and private Blockchains (coinmarketcap.com, 2020), an acceptance model can facilitate an objective and comparative assessment of existing and future solutions. The novelty of this paper is a new methodology of assessing blockchains using the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) alongside the distinguishing characteristics of blockchain implementations: trust, utility, and support. The paper distinctly aggregates existing approaches to present a concise assessment model. The distinguishing characteristics of trust, utility, and support in the context of UTAUT are beneficial for comparing and evaluating the myriad of blockchain implementations. The research encourages the examination of existing and future solutions using the UTAUT-focused attributes. The study has broader implications for blockchain solutions by providing contextual constructs necessary for acceptance and use.
The use of Big Data and Artificial Intelligence: to prevent and detect fraud

The development and increased adoption of different IT trends is fostering the evolution and application of Big Data and Artificial Intelligence in many contexts of society and different business sectors. The financial services are the leading field in investment in advanced technology for continuous monitoring, transaction analysis, anomaly detection, data review, pattern recognition and other emerging tech- niques. In this article, we are going to analyse the role played by Big Data and artificial intelligence for fraud detection, analysing algorithms applied to prevent and detect payment frauds and the use of these techniques in the context of corporate fraud and investigations against financial statement frauds.
Face Mask Detection in Public Places Using Small CNN Models

The spread of coronavirus can be prevented among the people in a crowded place by making face mask mandatory so that the droplets from the mouth and nose would not reach the masses nearby. The negligence of some people, i.e., by not wearing the mask, causes the spread of this pandemic. Therefore, persons who do not wear masks should be tracked at the entrance to public venues such as malls, institutions, and banks. The mechanism proposed warns if the individual is wearing or not wearing the mask. The proposed system is built in a small CNN model to integrate any low-end devices with minimal cost. The small CNN model like ShuffleNet and Mobilenetv2 are evaluated in Transfer Learning and Deep Learning but the Deep Learning model has better performance than the Transfer Learning. Again, the Deep Learning approach, i.e., mobilenetv2 plus Support Vector Machine achieved 99.5% accuracy, 99.01% sensitivity, 100% precision, 100% FPR, 99.51% F1 score, 99.01% MCC, and 99.01% kappa coefficient.
Skin Cancer Diagnosis and Medical Service System Based on Deep Learning Models

Skin cancer is a kind of common disease that seriously does harm to peoples’ life quality, and its diagnosis is of great importance. This paper aims to apply a Convolutional Neural Network (CNN) model to diagnose skin cancer automatically to provide a reference for doctors and an application containing a website, a chatbot and a speech synthesis to provide patients with an electronic diagnosis service. We used image normalization to preprocess the data, as well as data augmentation which overcomes the overfitting problem to some extent. Our proposed model has a convolutional layer and a MaxPooling layer. Then a Dropout layer follows to reduce overfitting. After that, there are a Flatten layer and two Dense layers. Adam is used as the optimizer and the loss function is sparse categorical cross entropy. As a result, our model reached a training accuracy of 95.56% and a validation accuracy of 90.95% in diagnosing different kinds of skin diseases. The training loss is 0.1099 and the validation loss is 0.3356. For our website application, it realized functions of storage of users’ information, chatting service and voice announcement of diagnose results. In conclusion, it can diagnose skin diseases accurately and provide convenience for automatically modern medical services.
Real Time Hand Gesture Recognition in Industry

With the 4th industrial revolution and the increased use of cobots in the industries comes many opportunities for new generation control panels. In this article, we proposed to develop a deep learning model to recognize in real time 10 different gestures that can be used to interact with a cobot. We proposed a new dataset containing gestures that can be used in an industrial context. The videos were taken from a computer webcam and then processed to remove the noise created by the background by isolating the movement of the gray scale images. We proposed to extract the spatio-temporal features by the combination of 3D convolution and LSTM layers. We also proposed a real time method to recognize our gestures, the frames are captured continuously and fed to the model to get a prediction every 2.4 seconds. Our experimental results show for 8 out of 10 gestures, a recognition rate of more than 90%. Furthermore, an interface was created to test our method in real time and to add new classes of gestures to be recognized by our model.