Reviewerly/exHarmony huggingface dataset:
Dendrograms-based disclosure method for evaluating cluster analysis in the IoT domain

The Internet of Things (IoT) generates huge amount of data at an extremely fast pace. Thus, it is important to classify such data objects into different groups or clusters in order to gain some valuable insights from data. This paper aims to develop a dendrograms-based method for 3D visualization of hierarchical clustering for multidimensional data which can be collected from IoT devices and open databases. This method is built on hierarchical clustering algorithm which is simple and efficient. It presents areas of the selected clusters and their objects on a plane, according to the coordinates defined by the open dendrogram. It defines rules for visualization of the dendrogram and allows to find the nature of clusters. The paper also proposes quantitative indicators of localization of objects and evaluation of clusters being formed. The proposed method is evaluated using IoT-based dataset prepared in two different forms. The proposed method significantly improves the quality of visualization and evaluation of cluster analysis results. It is also efficient as the time complexity is significantly less for factorial analysis.
The impact of engineering students’ communication behavior on the teams’ performance (case study: Chemical process engineering classes)

Abstract The activity of modern engineer requires the ability to work in a multi-discipline team and the ability to hand over clearly the professional information to the other team members who are specialists from another field. Therefore, according to the researches graduates from engineering universities lack skills of business communication and team work interaction. That is why the organization of work of future engineers in small groups fulfilling different team roles and the use of moderation methods for the achievement of the effective team cooperation will enable the achievement of the goal of necessary cooperation skills development.
Color Texture Description Based on Holistic and Hierarchical Order-Encoding Patterns

Local binary pattern (LBP), as one of the most representative texture operators, has attracted much attention in computer vision and pattern recognition. Many LBP variants were developed in the literature. However, most of them were designed for gray images and their performance remains to be improved for color images. In this paper, we propose a novel color image descriptor named Holistic and Hierarchical Order-Encoding Patterns (H2OEP) for texture classification. In H2OEP, the holistic order-encoding pattern compactly encodes color order variation tendencies for each pixel in color space. The hierarchical order-encoding pattern leverages min ordering, median ordering and max ordering to encode local neighboring relationships across different color channels. Finally, the generated order-encoding patterns are aggregated via central pixel encoding to build 3D joint histograms for image representation. Experiments on four benchmark texture databases demonstrate the effectiveness of the proposed descriptor for color texture classification.
Feature Redundancy Mining: Deep Light-Weight Image Super-Resolution Model

Despite the great success achieved by deep convolutional neural network (CNN)-based models in the single image super-resolution (SISR) problem, the requirement of high computational complexity, accompanied with the deep CNN models, makes it less applicable in embedded devices, e.g., mobile phones. Recently, deep light-weight models for the SISR problem have been in demand for industrial applications, and have caught the attention of many researchers. The strategies of cascading several small networks and multi-path feature extraction have shown their effectiveness in most of the existing methods. In this paper, by considering the correlation and redundancy of feature maps, we propose a feature information mining network to efficiently investigate the features, for the SISR problem. Experiment results show that our proposed model achieves the best balance between the performance and the model size, compared with other competitive deep SR models.
BERT Multilingual and Capsule Network for Arabic Sentiment Analysis

Sentiment Analysis (SA) is one of the fast-growing research tasks in Natural Language Processing (NLP), which aims to identify the attitude of online users or communities regarding to a specific topic, service, or product. It helps to evaluate the overall tonality of a document to make a good decision in the right direction. Arabic SA still growing slowly compared to English and Chinese languages because of some challenges including lack of corpora, Arabic language complexity, and the spread of many local Arabic dialects. The current study presents a work-in-progress for Arabic SA based on capsule network and Google BERT multi-languages.BERT Google language model achieved state-of-the-art results for several NLP tasks, that aim to pre-train deep bidirectional (right and left) representations on the context in all layers. Recently, capsule networks become one of the most successful techniques in image processing and NLP, it is consists of a group of neurons that represents different features of the same entity. Each layer in a capsule network contains many capsules that encode spatial information and the likelihood of existing input data. Not like traditional neural techniques, capsule networks allow the model to capture the likeliness of each feature and its variants, which affects positively the quality of the final predicted features. The proposed model was trained based on a small dataset. Although, the results were encouraging and competitive compared to the state-of-the-art Arabic SA models.
Total Estimation from RGB Video: On-line Camera Self-Calibration, Non-Rigid Shape and Motion

In this paper we present a sequential approach to jointly retrieve camera auto-calibration, camera pose and the 3D reconstruction of a non-rigid object from an uncalibrated RGB image sequence, without assuming any prior information about the shape structure, nor the need for a calibration pattern, nor the use of training data at all. To this end, we propose a Bayesian filtering approach based on a sum-of-Gaussians filter composed of a bank of extended Kalman filters (EKF). For every EKF, we make use of dynamic models to estimate its state vector, which later will be Gaussianly combined to achieve a global solution. To deal with deformable objects, we incorporate a mechanical model solved by using the finite element method. Thanks to these ingredients, the resulting method is both efficient and robust to several artifacts such as missing and noisy observations as well as sudden camera motions, while being available for a wide variety of objects and materials, including isometric and elastic shape deformations. Experimental validation is proposed in real experiments, showing its strengths with respect to competing approaches.
Reflection on digital language teaching, learning, and assessment in times of crisis: a view from Italy

This chapter is a reflective account of the author’s experience as a teacher of English at the University of Pavia during the first wave of the SARS-CoV-2 pandemic. It considers the design and delivery of an English for architecture and construction engineering course as well as the assessment stage of a text analysis course. It proceeds by presenting and discussing the decisions implemented as a consequence of the crisis situation and reflects on principles of English language teaching, learning, and assessment in general and English for Specific Purposes (ESP) in particular. In doing so, it addresses the book project rationale as an opportunity to reflect on the adjustments made to various planning and design factors informing language education during the health crisis and thought to be generalisable to language teaching, learning, and assessment in the global digital world. It concludes with thoughts on what the future of digital language teaching, learning, and assessment could look like.
CNN-Based Spoken Term Detection and Localization without Dynamic Programming

In this paper, we propose a spoken term detection algorithm for simultaneous prediction and localization of in-vocabulary and out-of-vocabulary terms within an audio segment. The proposed algorithm infers whether a term was uttered within a given speech signal or not by predicting the word embeddings of various parts of the speech signal and comparing them to the word embedding of the desired term. The algorithm utilizes an existing embedding space for this task and does not need to train a task-specific embedding space. At inference the algorithm simultaneously predicts all possible locations of the target term and does not need dynamic programming for optimal search. We evaluate our system on several spoken term detection tasks on read speech corpora.
SEG-COVID: A Student Electronic Guide within Covid-19 Pandemic

Most of the applications (apps) used in different universities are limited in their features and not very popular among students. However, the importance of these apps has remarkably increased due to the covid-19 pandemic. In this paper, we present a Student Electronic Guide within Covid-19 pandemic (SEG-COVID) which is basically a personal electronic guide that is used by students to get instant information about various academic areas. The app has a number of useful features, and most importantly, a highly trained built in chatbot that responses to both Arabic and English written queries and covers a wide range of academic topics ranging from basic to complex. Moreover, SEG-COVID was practically tested and the feedbacks were very promising.
English Web-Based Teaching Supervision Based on Intelligent Face Image Perception and Processing for IoT

In this paper, the Internet of Things (IoT) with intelligent face perception and processing function is used to supervise online English teaching. In the intelligent learning environment, learners mainly learn by watching the information presentation screen of the learning content, i.e., the learning screen, which is the main environment for learners to learn and is the main channel for information interaction between learners and the learning content. The color matching, layout, graphic decoration, and background texture of the learning screen have a significant impact on learners’ emotions, interests, motivation, and effect in the learning process. On the contrary, the accurate identification of learners’ emotions is the basis for building a harmonious emotional interaction in the wisdom learning environment and is an important means to judge learners’ learning status, which is of great significance to promote learners’ wisdom learning. In addition to providing learners with personalized learning contents and learning paths, the learning images presented by the intelligent learning environment should also be compatible with learners’ emotional states and visual emotional preferences and can play a role in regulating and stimulating learners’ learning emotions. The system works well in the testing process, which verifies the feasibility, rationality, and effectiveness of our application of face perception to online teaching effectiveness monitoring, and can be combined with the old result-oriented effectiveness monitoring method for online teaching, with certain theoretical research significance and practical application value.
Object features and face detection performance: Analyses with 3D-rendered synthetic data

This paper is to provide an overview of how object features from images influence face detection performance, and how to select synthetic faces to address specific features. To this end, we investigate the effects of occlusion, scale, viewpoint, background, and noise by using a novel synthetic image generator based on 3DU Face Dataset. To examine the effects of different features, we selected three detectors (Faster RCNN, HR, SSH) as representative of various face detection methodologies. Comparing different configurations of synthetic data on face detection systems, it showed that our synthetic dataset could complement face detectors to become more robust against features in the real world. Our analysis also demonstrated that a variety of data augmentation is necessary to address nuanced differences in performance.
Research on Micro-blog Text Presentation Model Based on Word2vec and TF-IDF

It is of great practical significance to mine valuable information from micro-blog data, assist government departments to manage network environment and find potential business opportunities for enterprises. The clustering accuracy of topic detection depends on the quality of the text representation model. Micro-blog is short, scattered, data redundancy and so on. Therefore, traditional text representation technology methods will lead to high dimensional sparsity of semantic matrix. And the semantic information related to the topic is lost, resulting in insufficient clustering effect. In this paper, a text representation model based on the improved Word2vec and TF-IDF is constructed, and it is applied to topic detection. The comparative experiment shows that the Word2vec &TF-IDF model has a better effect of text expression and improves the accuracy of micro-blog text clustering.
Pretest or not to pretest? A preliminary version of a tool for the virtual character standardization

This work-in-progress paper deals with pretesting of the virtual characters in VR psychological experiments. To make pretesting easier, the tool based on the combination of WebGL technology, Unity engine and psychological questionnaire was created. To test its efficacy we asked 13 judges to rate 15 models differing in terms of realism. The results and future directions in the development of the tool are discussed in this paper.
Some considerations on the factorization of state probabilities in knowledge structures

In most of the work on probabilistic knowledge structures, no restrictions apply to the probability distribution on the collection of knowledge states. The number of parameters needed to describe this distribution can thus be very large. An exception is provided by the Simple Learning Model (SLM) in which, by means of assumptions on the learning process, the distribution is built from other parameters. However, the application of the SLM is limited to learning spaces. The present work generalizes the SLM by suggesting a method to build the state probabilities as products of the probabilities of single (or groups of) items. The construction follows from first factorizing the probability distribution into the product of marginal and conditional probabilities based on the blocks of an ordered exact cover of the item domain, and then by imposing suitable constraints on these probabilities. A specific ordered exact cover of the item domain, distinguishing minimal from non-minimal items, is shown to be of particular interest as it allows to recover the SLM and to generalize it to a wider class of regular knowledge structures.
Identifying First-Order Lowpass Graph Signals Using Perron Frobenius Theorem

This paper is concerned with the blind identification of graph filters from graph signals. Our aim is to determine if the graph filter generating the graph signals is first-order lowpass without knowing the graph topology. Notice that lowpass graph filter is a common prerequisite for applying graph signal processing tools for sampling, denoising, and graph learning. Our method is inspired by the Perron Frobenius theorem, which observes that for first-order lowpass graph filter, the top eigenvector of output covariance would be the only eigenvector with elements of the same sign. Utilizing this observation, we develop a simple detector that answers if a given data set is produced by a first-order lowpass graph filter. We analyze the effects of finite-sample, graph size, observation noise, strength of lowpass filter, on the detector’s performance. Numerical experiments on synthetic and real data support our findings.
Tracking Fast Moving Objects by Segmentation Network

Tracking Fast Moving Objects (FMO), which appear as blurred streaks in video sequences, is a difficult task for standard trackers, as the object position does not overlap in consecutive video frames and texture information of the objects is blurred. Up-to-date approaches tuned for this task are based on background subtraction with a static background and slow deblurring algorithms. In this article, we present a tracking-by-segmentation approach implemented using modern deep learning methods that perform near real-time tracking on real-world video sequences. We have developed a physically plausible FMO sequence generator to be a robust foundation for our training pipeline and demonstrate straightforward network adaptation for different FMO scenarios with varying foreground.
Improving Cross-Domain Slot Filling with Common Syntactic Structure

Cross-domain slot filling is a challenging task in spoken language understanding due to the differences in text genre across domains. In this paper, we attempt to solve this task by exploiting the syntactic structures of user utterances, because these syntactic structures are actually accessible and can be shared between utterances from different domains. To this end, we propose a novel Syntactic Structure Encoder (SSE) module and incorporate it into a detection-prediction framework. SSE introduces graph convolutional network (GCN) to learn the common structures from multiple source domains, which are helpful to better adaptation on the target domain. Experimental results conducted on SNIPS dataset show that our model significantly outperforms the state-of-the-art approach in cross-domain slot filling. Specifically, our model outperforms the best model by ∼4% and ∼5% F1-scores under the 20-example and 50-example settings, respectively.
Integrating Machine Learning Data with Symbolic Knowledge from Collaboration Practices of Curators to Improve Conversational Systems

This paper describes how machine learning training data and symbolic knowledge from curators of conversational systems can be used together to improve the accuracy of those systems and to enable better curatorial tools. This is done in the context of a real-world practice of curators of conversational systems who often embed taxonomically-structured meta-knowledge into their documentation. The paper provides evidence that the practice is quite common among curators, that is used as part of their collaborative practices, and that the embedded knowledge can be mined by algorithms. Further, this meta-knowledge can be integrated, using neuro-symbolic algorithms, to the machine learning-based conversational system, to improve its run-time accuracy and to enable tools to support curatorial tasks. Those results point towards new ways of designing development tools which explore an integrated use of code and documentation by machines.
A Short Description of an Ankle-Actuated Seated VR Locomotion Interface

We present an overview of the interface that allows users to perform foot-tapping movements in a seated posture to emulate walking sensation in virtual reality (VR). The interface comprises an impedance controlled robotic foot platform that moves while resisting the user's foot-tapping movements. The platform's angular movement is used to recognize the walking intention of the user and animate the VR avatar accordingly. The impedance characteristics of the platform are controlled to emulate different VR walking terrains.
Research on parallel principal component analysis based on ternary optical computer

Principal component analysis (PCA) is a widely used algorithm in the field of artificial intelligence and statistical analysis. When dealing with a large number of high-dimensional data, the traditional PCA algorithm has some shortcomings, such as slow processing speed, high power consumption, low computational efficiency. By taking advantage of several characteristics which include large number of data bits, easy-expansion of data bits, and parallel computing in ternary optical computer (TOC), the implementation scheme of parallel PCA algorithm is studied. Through the analysis of the traditional PCA algorithm, the parallel parts are found out and parallel design is carried out. Then, the elaborate implementation flow of the algorithm is given, and the clock cycle and hardware resources of the algorithm are also analyzed. Finally, the validity of the parallel scheme is proved by experiments. Compared with the traditional PCA algorithm, the PCA algorithm based on TOC has better time performance, operational efficiency, and gives full play to the advantage of ternary optical computer in processing complex computation.
Cost-Friendly Differential Privacy of Smart Meters Using Energy Storage and Harvesting Devices

Cost-friendly differential privacy (CDP) of smart meters can be preserved by an appropriate charging and discharging mechanism that uses rechargeable batteries (RBs) to generate Laplace distributed random noise. However, the existing CDP methods have several issues. First, the maximum discharge rate of an RB requires to vary with the maximal consumption of houses. Second, the probability of an RB to charge/discharge depends on the demand, regardless of the state-of-charge (SoC) of an RB. Third, in extreme SoC (near-empty or almost fully charged) of an RB, no noise added to the demand. To overcome these, we propose a mechanism in which a novel probability density function is designed to generate near Laplace distributed random noise. We also utilize a renewable energy source with small storage in cascade with an RB to enhance performance. Both theoretical analysis and simulations are performed to demonstrate the effectiveness of our proposed method.
Asymptotic Entanglement Sudden Death in Two Atoms with Dipole–Dipole and Ising Interactions Coupled to a Radiation Field at Non-Zero Detuning

We investigate the time evolution and asymptotic behavior of a system of two two-level atoms (qubits) interacting off-resonance with a single mode radiation field. The two atoms are coupled to each other through dipole–dipole as well as Ising interactions. An exact analytic solution for the system dynamics that spans the entire phase space is provided. We focus on initial states that cause the system to evolve to entanglement sudden death (ESD) between the two atoms. We find that combining the Ising and dipole–dipole interactions is very powerful in controlling the entanglement dynamics and ESD compared with either one of them separately. Their effects on eliminating ESD may add up constructively or destructively depending on the type of Ising interaction (Ferromagnetic or anti-Ferromagnetic), the detuning parameter value, and the initial state of the system. The asymptotic behavior of the ESD is found to depend substantially on the initial state of the system, where ESD can be entirely eliminated by tuning the system parameters except in the case of an initial correlated Bell state. Interestingly, the entanglement, atomic population and quantum correlation between the two atoms and the field synchronize and reach asymptotically quasi-steady dynamic states. Each one of them ends up as a continuous irregular oscillation, where the collapse periods vanish, with a limited amplitude and an approximately constant mean value that depend on the initial state and the system parameters choice. This indicates an asymptotic continuous exchange of energy (and strong quantum correlation) between the atoms and the field takes place, accompanied by diminished ESD for these chosen setups of the system. This system can be realized in spin states of quantum dots or Rydberg atoms in optical cavities, and superconducting or hybrid qubits in linear resonators.