Reviewerly/exHarmony huggingface dataset:
Crowd behavior anomaly detection using correlation of optical flow magnitude

Abstract Nowadays, crowd monitoring is a contentious issue. Because of the increasing population and diversity of human activities, crowd scenarios in the real world are becoming more common, demanding the need for an automotive anomaly detection system. Crowd behavior is influenced by the thoughts and attitudes of others around them. An unexpected event can turn a peaceful crowd into a riot. A mechanism based on optical flow must be implemented to compensate for all of these factors. The amount of motion present in two successive frames is estimated using optical flow. It includes information on velocity in the x &amp; y plane, along with magnitude and line of action. By means of “anomalous event” in this paper is quick and sudden dispersal of the crowd. For detecting an event the magnitude of two successive frames should be taken into account followed by estimating a correlation. We expect a high correlation, slight motion, and low rate of change in velocities at non-anomalous events, but as soon as an anomalous event occurs, the correlation begins to decrease with a significant change in velocity and large motion vectors. The methodology was tested on a dataset from the University of Minnesota that included 11 movies from three different circumstances. Almost all anomalous occurrences in videos were successfully detected using this method.
Binomial adjective doublets in Japanese: A Relational Morphology account

Abstract Adjectives whose stems consist of two elements (or binomial adjectives) are becoming increasingly productive in colloquial Japanese. Unlike the stems of conventional adjectives, the stems of numerous, but not all, innovative binomial adjectives can have two distinct inflectional behaviors (e.g., daru-omo-{i / na} ‘be languid and heavy’, aza-kawai-i / aza-kawa-na ‘be shrewd and cute’) and have different syntactic properties depending on the suffix. Within the framework of Relational Morphology, the current study proposes that two independently established word-formation rules, represented as “sister schemas,” account for the apparent derivational gaps in these adjectives and their difference in productivity. I -forms are crucially based on particular subschemas, such as X- kawai-i ‘be X and cute’ and kuso -Y- i ‘be very Y’, and are not fully productive, whereas na -forms have detailed morphophonological constraints and are notably productive. The two suffixes are available only if an adjective stem meets the structural and semantic requirements of both schemas.
Method for Calculating Thermodynamic Properties of Poorly Studied Liquids

The article discusses the use of a virtual model of isochoric heating of various liquids in educational laboratories and in practical classes. The article proposes a method for obtaining information about the thermodynamic properties of poorly studied liquids at pressures significantly higher than atmospheric pressure, when only the density, isobaric heat capacity and the speed of sound at atmospheric pressure are known. The basis of the method is the assumption of a linear dependence of fluid pressure on temperature in an isochoric process. Approbation of the method on the example of toluene up to pressures of 50 MPa gave satisfactory results.
Research on Semisupervised Affective Interaction Technology in Immersive Virtual Teaching Environment

The separation of time and space in immersive virtual teaching makes students unable to realize emotional communication, which may affect students’ mental health. In recent years, the use of affective computing technology to solve the problem of affective loss in distance education has become a key research topic. In order to realize the problem of emotion interaction in immersive virtual teaching, a semisupervised support vector machine- (SVM-) based affective interaction model was proposed. First, the natural language sequences of students in the virtual teaching environment are preprocessed using a statistical-based framing method, and mutual information and expected cross-entropy are used as feature selection methods. Then, a vector space model based on TF/IDF feature term weights is proposed to implement the feature vector representation of natural language sequences. Finally, after the constructed sentiment space, a semisupervised SVM is employed as the classifier to complete the affective interaction computation. The experimental results of emotion classification show that the proposed model is able to determine and understand the emotional state more accurately than other traditional models and significantly improves the training speed. In addition, the proposed model can provide emotional encouragement or emotional compensation according to the specific emotional state of the learner.
Increasing image segmentation accuracy on small datasets by merging multiple inferences on augmented images

Supervised machine learning depends on training a model to mimic previous labeled results. The problem with a small dataset is that data augmentation is necessary to increase the generalization of the model to future images, but we have observed that future images won’t necessarily be in the same domain as the augmented images. To alleviate this problem we applied image segmentation multiple times on the same image by using the same data augmentation techniques on the image in question, and then we merged the results using a priority based on the class weights used when training the model. Merging the segmentation results from the augmented images increased the mean-intersection-over-union over the inference results that used a single image.
A Binocular Vision-Guided Puncture Needle Automatic Positioning Method

In the traditional Magnetic Resonance Imaging(MRI)-guided breast puncture surgery, doctors perform puncture surgery manually based on the MRI images and experience. The puncture needle may not be able to reach the location of the lesion accurately due to the inexperience or fatigue of the doctor. In this paper, a binocular vision-guided automatic positioning method for breast puncture needles is proposed. First, the combined image processing algorithm and the three-frame difference method are used to detect the needle tip; then, the puncture needle is guided to the target position by position-based visual servoing. The experimental results show that the average error of feature point detection is within 3 pixels, and the final positioning error is within 2.5mm. Compared with the traditional manual positioning method, the positioning accuracy of this method is higher, and the puncture process is also more stable, which improves the efficiency of surgery.
Using of Polynomial and THD Methods to the Depth Map of Shurau Formation that Obtained from 2D Seismic Interpretation to Structural Map Construction in Ismail Region, Northern Iraq

The 2D seismic data were analyzed and interpreted in Isamil region in northwestern Arbil Governorate, northern Iraq. The structural and tectonic situation of the region was constructed through the study of Shurau Formation (Oligocene). The polynomial analytical method applied to the depth map of Shurau Formation produced two maps these are regional trend map of the formation in addition to the residual structural map. The Total Horizontal Derivative method application supports the identification of the lineaments of possible faults and tectonic boundaries in the study area. The final structural map indicates that the regional trend of the study area is toward the ENE direction. The local structural features (Anticlines, Synclines, and Faults) found mostly trending NW-SE coincide with the main trend in the folded zone. This study shows the importance of using the polynomial and the Total Horizontal Derivative methods in the interpretation of the depth map of formation obtained from seismic data.
OpenTFV: An Open Domain Table-Based Fact Verification System

The prevalence of misinformation, both online and offline, has prompted a great demand of fact verification. Table-based fact verification aims to check whether a textual claim is supported or refuted based on relational tables. However, most of the existing approaches are in a closed-domain setting, which may not be realistic in practice. To address this problem, in this paper, we introduce OpenTFV, a user-friendly system that supports open domain table-based fact verification. Given a claim input by an end-user, OpenTFV retrieves the relevant tables, and provides a verification result for each table with an intuitive interpretation in natural language. We have implemented OpenTFV and demonstrated OpenTFV in two representative scenarios, COVID-19 claims fact verification based on academic tables and general fact verification on Wiki-tables.
Blockchain based Transcript Processing

Blockchain has emerged as a revolutionary technology which provides secure and distributed mechanisms, from wider applications of cryptocurrencies to application areas such as property registration, insurance etc. It has features like distributed ledger that eliminate the need for a trusted third party to facilitate transactions. The emergence of smart contracts – blockchain-based protocols that can automatically execute a contract – has accelerated the spread of the technology to new application areas. Higher education is one field where suitable use-cases are prompting innovators to start using blockchain. With the world moving towards digitisation, there is a need to deliver data and important documents to students in higher education institutions via digital platforms while maintaining the integrity and confidentiality of the data so that it can be trusted with the guarantee that no unauthorised modification has taken place. To solve this problem we propose the utilization of blockchain in the field of education via a blockchain-based university transcript verification system and a blockchain based system for storing and processing examination answers.
Web Resource for Teaching Programming in the Form of Tournaments

The article presents a web resource intended for use in the practical lessons on programming in the process of IT students training. This resource supports the process of implementing tournaments between game computer programs developed by students, which allows us to add competitive and game elements to the learning process. The architecture of the developed resource and some aspects of its implementation are explained. The example of the practical application of designed web resource is shown.
An Object-Oriented Approach to Ontology Modelling in Specialists Education of Methods and Technologies of Artificial Intelligence

Training highly qualified specialists in modern digital and information technologies, including artificial intelligence (AI) methods and technologies tasks Higher educational institutions. Since the 2021/2022 academic year, a new educational program, "Artificial Intelligence", has been introduced at the Department of Applied Mathematics and Artificial Intelligence of the NRU "MPEI" when studying for a master's degree in Applied Mathematics and Computer Science. In new academic disciplines, "Applied Intelligent Systems" (AIS) and "Intelligent Decision Support Systems", students learn and acquire practical skills and competencies in AI models and methods and implement prototypes of modern AI systems for various purposes.
Distributed Constraint Programming based on Fuzzy Probability Applied to Aphasia Diagnosis

Fuzzy constraint programming is implemented to optimize a cost function facing vague constraints. In this paper, for the first time, a novel distributed constraint programming is developed by taking into account fuzzy and probability concepts. In the proposed approach, we use the human way of thinking by expressing constraints in the form of rules. The proposed approach can deal with different kinds of uncertainty. We model the content of constraints using not only fuzzy inequality constraints but also probability inequality. Then, a proposed distributed protocol is applied based on the cooperative constraints programming regulation framework An adaptive certainty is adjusted for the set of constraints depending on the relevant satisfaction degree. Then, the problem of constraint programming is converted to the optimization problem. This approach can be considered a step forward for modeling complex systems. The proposed approach has been explored by the application of aphasia diagnosis facing inconsistency in the clarification of aphasic syndromes. Even by requiring more executed time, the improvement of results’ accuracy is observed especially when compared to the results of alternative approaches applying the same database.
Deep learning-based medical diagnostic services: A secure, lightweight, and accurate realization1

In this paper, we propose CryptMed, a system framework that enables medical service providers to offer secure, lightweight, and accurate medical diagnostic service to their customers via an execution of neural network inference in the ciphertext domain. CryptMed ensures the privacy of both parties with cryptographic guarantees. Our technical contributions include: 1) presenting a secret sharing based inference protocol that can well cope with the commonly-used linear and non-linear NN layers; 2) devising optimized secure comparison function that can efficiently support comparison-based activation functions in NN architectures; 3) constructing a suite of secure smooth functions built on precise approximation approaches for accurate medical diagnoses. We evaluate CryptMed on 6 neural network architectures across a wide range of non-linear activation functions over two benchmark and four real-world medical datasets. We comprehensively compare our system with prior art in terms of end-to-end service workload and prediction accuracy. Our empirical results demonstrate that CryptMed achieves up to respectively 413 ×, 19 ×, and 43 × bandwidth savings for MNIST, CIFAR-10, and medical applications compared with prior art. For the smooth activation based inference, the best choice of our proposed approximations preserve the precision of original functions, with less than 1.2% accuracy loss and could enhance the precision due to the newly introduced activation function family.
Analysis and Handling Methods of Line Protection Action Events Caused by the Lightning Strike

This paper describes four events of protection action caused by the lightning strike in JIBEI from 2013 to 2018. In those events, there is no grounding or phase to phase fault while the line differential relay act. In addition, the inconsistency of protection action would be occurred between two sets of protection devices. In this paper, an overall analysis is made on the protection action process of four events and improving measures are put forward.
Breast Tumors Delineation for Mammogram Investigations Exploiting k-Means Algorithm and Associated Image Processing

Breast cancer early detection is critical for life-saving and fast healing. Radiologists can misdiagnose many cases due to the modality’s low accuracy, or lack of experience. We aim to customized a computer-assist detection (CAD) system to assist the radiologists in the early discovery of the malignants in the mammograms. This study involved 150 mammograms for patients with various breast masses thru two mammographies: (GE Sys., USA) (N = 80) and MAMMOMAT Revelation (Siemens, Germany) (N = 70)). Normal cases (N = 30) were used in the system as a control group to evaluate the specificity of false positive (FP) and true negative (TN) results. Mammogram images were processed by a custom algorithm (Normalization, moving average filtering, and K-means clustering) to automatically differentiate between normal and malignant regions. Although, experimental results were validated by the "Lunit inc. online software" to compare the tumor detection accuracy. However, all cases were re-checked and assessed by two qualified radiologist readers. The system presents reasonable outcomes concerning CAD for breast tumors regarding the qualified radiologist, where out of the 150 patients, twelve were missed by the custom CAD algorithm and five were missed by at least one of the radiologists, although the CAD sensitivity and specificity were 92.5% and 90%, respectively. The presented framework improves breast cancer detection from the mammogram investigation regarding two qualified radiologist readers with an overall accuracy of 92%. The presented CAD system could assist the radiologist to avoid missing the recognition of breast tumor cases, in addition to offering the possibility to improve careful tumor resection during surgery.
Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in India

Extreme weather conditions, which affect photovoltaic output power, can have a major impact on electricity generated by PV systems. In India, an annual PV power density of 2000kWh/m-2 may be used. Renewable energy (RE) is expected to play a rising part in the nations in coming years. The sun’s radiation is the primary source of renewable energy (RE). With the objective of predicting PV output power with the least amount of error in mind, it is vital to analyse the impact of major environmental parameters on it. The researchers looked at a variety of environmental factors in this study, including irradiance, humidity levels, meteorological conditions, wind velocity, PV global temperature and dust deposition. Countries such as India would gain immensely from this since it will increase the quantity of PV power generated in their national networks. ANN-based prediction models and multiple regression models were used to predict PV system hourly power output. There were three ANN models that predicted PV output power with RMSEs of 2.1436, 6.1555, and 5.3551, respectively, utilising all features using the correlation feature selection (CFS) or relief feature selection (ReliefF) approaches. It is possible to reduce bias to enhance accuracy by employing two distinct bias calculation methodologies, which were applied in this study. For example, the ANN model outperforms linear regression, M5P decision trees and GAUSSIAN process regression (GPR) models in terms of performance.
Are machines radically contextualist?

In this article, I describe a novel position on the semantics of artificial intelligence. I present a problem for the current artificial neural networks used in machine learning, specifically with relation to natural language tasks. I then propose that from a metasemantic level, meaning in machines can best be interpreted as radically contextualist. Finally, I consider what this might mean for human‐level semantic competence from a comparative perspective.
Attractive and repulsive training to address inter-task forgetting issues in continual learning

In continual learning over deep neural networks (DNNs), the rehearsal strategy, in which the previous exemplars are jointly trained with new samples, is commonly employed for the purpose of addressing catastrophic forgetting. Unfortunately, due to the memory limit, rehearsal-based techniques inevitably cause the class imbalance issue leading to a DNN biased toward new tasks having more samples. Existing works mostly focus on correcting such a bias in the fully connected layer or classifier. In this paper, we newly discover that class imbalance tends to make old classes even more highly correlated with their similar new classes in the feature space, which turns out to be the major reason behind catastrophic forgetting, called inter-task forgetting. To alleviate inter-task forgetting, we propose a novel class incremental learning method, called attractive & repulsive training (ART), which effectively captures the previous feature space into a set of class-wise flags, and thereby makes old and new similar classes less correlated in the new feature space. In our empirical study, our ART method is observed to be quite effective to improve the performance of the state-of-the-art methods by substantially mitigating inter-task forgetting. Our implementation is available at: https://github.com/bigdata-inha/ART/.
Tree-line Contradictory Risk Early Warning Technology of Rural Distribution Network Considering Severe Convective Weather

Tree-line contradiction refers to the conflict between planting trees in the power line erection space and ensuring the safe and stable operation of power lines. In severe convective weather, the tree-line contradiction is further intensified. Once the tree-barrier grounding fault is triggered, the power supply reliability of the rural distribution network will be seriously affected. Aiming at this, a zonal early warning mechanism for tree-line contradiction of rural distribution networks considering severe convective weather is proposed in this paper. Firstly, due to the imbalance of tree-barrier grounding fault records, the Synthetic Minority Oversampling Technique (SMOTE) algorithm is used to replace part of the majority class samples with minority class samples, and the data preprocessing is realized based on keeping the scale of the data set unchanged. Secondly, combined with six meteorological monitoring indexes of severe convective weather, a mapping model of severe convective weather and tree barrier grounding risk is established based on Extreme Learning Machine (ELM). Finally, the data of a rural distribution network in China is used for example analysis. The results show that the proposed model can effectively reflect the mapping relationship between severe convective weather and tree-barrier grounding faults, and accurately realize the zonal warning of tree-barrier grounding risk, which has good robustness and scalability.
Intelligent Assistance: Enhanced Real-time Patient-Specific Model for Fall Detection and Evaluation

Automated caretaking systems provide constant monitoring of people without human involvement. Intelligent frameworks automatically recognize and evaluate numerous sorts of data by making use of modern technologies. Human activities are complex practices that include continual together with interleaved activities. Complex activities can be quickly processed using fully connected quantum devices that work like the human brain. Human postures have the capability to reveal various movements of the body in diverse circumstances. An optimized approach is needed to integrate different environments and to recognize and explore various aspects of activities that improve acceptance rates. The proposed system can handle a wide range of body movements, various viewpoints, environmental changes, and obscuresituations. This research aims to design a real-time activityrecognitionsystem which tracks the movements of elderly people suffering from Parkinson's Disease(PD).
Relationship classification based on dependency parsing and the pretraining model

As an important part of information extraction, relationship extraction aims to extract the relationships between given entities from natural language text. Based on the pretraining model R-BERT, this paper proposes an entity relationship extraction method that integrates an entity dependency path and pretraining model, which generates a dependency parse tree by dependency parsing, obtains the dependency path of an entity pair via a given entity, and uses an entity dependency path to exclude information such as modifier chunks and useless entities in sentences. This model has achieved good F1 value performance on the SemEval2010 Task 8 dataset. Experiments on datasets show that dependency parsing can provide context information for models and improve performance.