Reviewerly/exHarmony huggingface dataset:
Design and Application of Intelligent Patrol System in Substation

Abstract With the continuous development and improvement of power grid technology, intelligence has become the development goal of power grid technology. By designing an intelligent substation inspection system, simplifies the operational tasks of inspectors, and makes inspection of power equipment more accurate, efficient, and traceable. In the implementation of the system, the mobile terminal is responsible for the collection, input, and upload of inspection data, and the server completes the data reception and statistics and displays it visually. And the application of the case proves that the intelligent patrol system is convenient and quick to operate, the social and economic benefits produced are significant, and it has strong practicability.
Development of Electromyography Signal Contraction Detection Using the Double Threshold Method for Controlling Wheelchair

The consequence of spinal cord injury (SCI) is the malfunction of some parts of the body. This observation especially applies to trauma at the cervical level, because both patients' hands and legs are no longer functioning. The purpose of this study was to determine the mechanism of the comparator treshold system in electric wheelchairs for patients with paralysis. The research contribution is to get a treshold mechanism that can make a wheelchair run smoothly without being affected by the strength of the user's unstable muscle contractions. To set the muscle reference value, connect the transmitter to Android using USB Cable. On Android there are 2 settings modes, namely manual and auto settings. The method used in auto mode is the transmitter will take 100 contraction and 100 relaxation data and multiplied by certain constants to get the upper and lower threshold values. In setting the threshold manually, the value is set based on the strength of the patient's contractions that are displayed on Android manually. Where in the threshold setting manually obtained response 93.3% of forward movement, 100% of turn left movement, 100% of turn right movement and 93.3% of backward movement. Meanwhile, the threshold setting automatically gets 80% response for forward movements, 93.3% for turn left movements, 86.6% for turn right movements and 93.3% for backward movements. The results of the tapping into 4 parts of the muscle can be implemented to detect muscle contractions so that the wheelchair can move according to orders. The problem obtained in this study is that there are many tapping points on the face so that it interferes with the comfort and time of installing electrodes on the face, also the patient's muscle reference setting system on Android still needs the help of others. In the future, it is expected to reduce tapping points and improve the efficiency of muscle threshold settings.
SMOTE and Nearmiss Methods for Disease Classification with Unbalanced Data

Unbalanced data are often encountered in practice. They complicate the search for a model suitable for classification. This is because the number of individuals who have a history of a disease is less than the number of individuals who do not. We analyse the IFLS 5 data on medical history of a set of patients. We split the dataset in the proportion 80:20 to training and test subsets. Of course, both datasets are unbalanced, with only a small minority of patients who had a stroke. We apply the SMOTE and Nearmiss methods and evaluate the rate of correct classification. After being treated using the two methods, the training data was transformed into balanced data. The classification process is carried out to test the comparison of the effectiveness of the two methods in solving the problem of unbalanced data. Based on the results obtained, it can be concluded that the Nearmiss method is better than SMOTE in balancing the data. It was obtained by comparing several measures such as accuracy, F-score, Kappa, sensitivity, and specificity on the SMOTE and Nearmiss methods.
CDC: Color-Based Diffusion Model with Caption Embedding in VBS 2022

With the rapid development of the internet and technology, the amount of information that people need to store is exploding. This leads to a burden on search engines which are required to qualify the need of seeking items within seconds or even less. Therefore, information retrieval tasks are getting more and more attention in the research community. Video Browser Showdown (VBS) is one of the annual competitions where researchers can evaluate and compare their works with others on the provided benchmarks. Given a query, which can be in form of a text or a short video, the system is supposed to return the video that is closely relevant to the information in the query. In this work, we introduce CDC: a video browser system using our proposed Color-based Diffusion model and the Caption embedding method inherited from the current state-of-the-art visual-language model (Oscar). To the best of our knowledge, Oscar is currently holding the best performance in cross-modal vision-language modeling, while the color-based feature with diffusion helps enhance the searching process.
Cranial meninges reconstruction based on convolutional networks and deformable models: applications to longitudinal study of normal aging

The cranial meninges are membranes enveloping the brain. The space between these membranes contains mainly cerebrospinal fluid. It is of interest to study how the volumes of this space change with respect to normal aging. In this work, we propose to combine convolutional neural networks (CNNs) with nested topology-preserving geometric deformable models (NTGDMs) to reconstruct meningeal surfaces from magnetic resonance (MR) images. We first use CNNs to predict implicit representations of these surfaces then refine them with NTGDMs to achieve sub-voxel accuracy while maintaining spherical topology and the correct anatomical ordering. MR contrast harmonization is used to match the contrasts between training and testing images. We applied our algorithm to a subset of healthy subjects from the Baltimore Longitudinal Study of Aging for demonstration purposes and conducted longitudinal statistical analysis of the intracranial volume (ICV) and subarachnoid space (SAS) volume. We found a statistically significant decrease in the ICV and an increase in the SAS volume with respect to normal aging.
Alignment Conservativity Under the Ontology Change

Recently, many methods have appeared to solve the problem of the evolution of alignment under the change of ontologies. The main challenge for them is to maintain consistency of alignment after applying the change. An alignment is consistent if and only if the ontologies remain consistent even when used in conjunction with the alignment. The objective of this work is to take a step forward by considering the alignment evolution according to the conservativity principle under the change of ontologies. In this context, an alignment is conservative if the ontological change should not introduce new semantic relationships between concepts from one of the input ontologies. The authors give methods for the conservativity violation detection and repair under the change of ontologies and they carry out an experiment on a dataset adapted from the Ontology Alignment Evaluation Initiative. The experiment demonstrates both the practical applicability of the proposed approach and shows the limits of the alignment evolution methods compared to the alignment conservativity under the change of ontologies.
IFSE - Personalized Quiz Generator and Intelligent Knowledge Recommendation

This paper has presented an AI-based quiz sub-system that customizes personalized exercises for individual learners together with accurate instant feedback and knowledge recommendation after taking quizzes, eventually aiming to assist learners with self-regulated learning. A knowledge-based quiz generation algorithm and a set of intelligent feedback recom-mendation algorithms are proposed, which are designed for a large number of exercise materials from various courses to numerous students' self-learning and, generically, for all kinds of knowledge domains. Intelligent feedback for student exercise (IFSE) application has been developed and integrated into the Moodle learning management system in order to conduct field experiments and to be evaluated by university students.
Combining pattern-based CRFs and weighted context-free grammars

We consider two models for the sequence labeling (tagging) problem. The first one is a Pattern-Based Conditional Random Field (PB), in which the energy of a string (chain labeling) x=x1⁢…⁢xn∈Dn is a sum of terms over intervals [i,j] where each term is non-zero only if the substring xi⁢…⁢xj equals a prespecified word w∈Λ. The second model is a Weighted Context-Free Grammar (WCFG) frequently used for natural language processing. PB and WCFG encode local and non-local interactions respectively, and thus can be viewed as complementary. We propose a Grammatical Pattern-Based CRF model (GPB) that combines the two in a natural way. We argue that it has certain advantages over existing approaches such as the Hybrid model of Benedí and Sanchez that combines N-grams and WCFGs. The focus of this paper is to analyze the complexity of inference tasks in a GPB such as computing MAP. We present a polynomial-time algorithm for general GPBs and a faster version for a special case that we call Interaction Grammars.
Satellite imagery band ratio for mapping the open pit mines: A preliminary study&amp;#160;

&amp;lt;p&amp;gt;Indices are designed to differentiate land use and land cover classes to avoid misinterpretation of landscape features. The resemblances of spectral reflectance of mines with urban built-up and barren land cause difficulties in identification of objects. Open pit mines of Rampura-Agucha for Zn and Pb were selected for this study. The freely available data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was selected from the year of 2001 and 2003. It is observed that b1-b5/b1+b5 equation of ASTER imagery significantly differentiate Zn-Pb mine from urban settlement and other features. The reflected range (&amp;amp;#181;m) for b1 and b5 is 0.52-0.60 (Visible and Near-Infrared) and 2.145-2.185 (Shortwave Infrared) respectively. The pixel values indicate higher reflectance of open pit suggesting feasibility of equation for differentiating it from barren and built-up area. The mine is rich in sphalerite followed by galena, pyrite and pyrrhotite in different proportions of abundance. Spectral reflectance depends on type of minerals hence need further studies to develop the index according to specific minerals and mines. In the mining regions, the role of temperature, moisture content, vegetation covers and high concentration of pollutants in variation of spectral reflectance are highly important. The developed index would be beneficial for tracing the extent of overburden dumps, tailings and mines at faster rate.&amp;lt;/p&amp;gt;
Site classification of seismic recording stations of Garhwal region of earthquake early warning system for Uttarakhand, India

Site classification is vital to carry out seismic hazard analysis of a region and get the damage patterns caused by earthquakes. In the present study, the ground motion of earthquakes recorded from 2015 to 2019 at 84 sites of the seismic network array of Earthquake Early Warning System for Uttarakhand are analyzed for site classification purposes. The predominant period from the mean horizontal to vertical spectral ratio curves was estimated. The classification schemes devised by Japan Road Association and National Earthquake Hazards Reduction Program are applied to classify the sites. Along with this, two other site classification indexes schemes are also applied for classification purposes. Data winnowing techniques are used over the ground motion records to pick out desirable quality records. Conclusively, the site class with the highest recurrence rate amongst the used methods is selected as the final class for that particular site. The effect of magnitude, distance and depth on horizontal to vertical spectral ratio are described and concluded that these factors do not significantly affect the ratio curves. The average horizontal to vertical spectral ratio curves obtained for all the sites matches well with the existing literature. The classification of a few sites are verified from the classification done by other methods in recent studies. It is evident from the results that the classification done in this study matches well with them.
Mariangela Sedda e l’Argentina: lo spagnolo imparato e l’italiano insegnato

Since the 1980s women’s writing on the subject of migration has developed both in Argentina and in Italy. This article analyses the novels Oltremare (2004) and Vincendo l’ombra (2009) by Mariangela Sedda, which talk about two sisters’ exchange of letters between Italy and Argentina from 1913 to 1943. Through the characters this study considers both the learning of Spanish and the teaching of Italian in relation to the background of Italian immigration. The enduring bond between Italy and Argentina is demonstrated by the portentous migratory phenomenon, and the evolution of Italian from a language of immigrants to a language of culture.
Keyword Search Based on Unsupervised Pre-Trained Acoustic Models

Speech keyword search (KWS) is the task of automatically detecting the required keywords in continuous speech. Single-keyword detection can be regarded as the task of speech keyword wake-up. For many practical applications of these small vocabulary speech recognition tasks, it is costly and unnecessary to build a full large vocabulary speech recognition system. For tasks related to speech keyword search, insufficiency in data resources remains the main challenge so far. Speech pre-training has become an effective technique, showing its superiority in a variety of tasks. The key idea is to learn effective representations in settings where a large amount of unlabeled data is available to improve the performance while labeled data of downstream tasks are limited. This research focuses on the combination of unsupervised pre-training and keyword search based on the Keyword-Filler model and introduces unsupervised pre-training into speech keyword search. The research selects pre-trained model architecture Wav2vec2.0 including XLSR. The research results show that training with feature extracted by pre-trained model performs better than the baseline. In the case of low-resource condition, the baseline performance drops significantly, while the performance of the pre-trained tuned model does not decrease but even increases slightly in some intervals. It can be seen that the pre-trained model can be tuned to achieve better performance on very little data. This shows the advantage and application value of keyword search based on unsupervised pre-training.
Detecting Deepfakes with Deep Learning and Gabor Filters

The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue color information. The purpose of this paper is to give the reader a deeper view of (1) enhancing the efficiency of distinguishing fake facial images from real facial images by developing a novel model based on deep learning and Gabor filters and (2) how deep learning (CNN) if combined with forensic tools (Gabor filters) contributed to the detection of deepfakes. Our experiment shows that the training accuracy reaches about 98.06% and 97.50% validation. Likened to the state-of-the-art methods, the proposed model has higher efficiency.
Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring

Abstract Education technologies (EdTech) are becoming pervasive due to their cost-effectiveness, accessibility, and scalability. They also experienced accelerated market growth during the recent pandemic. EdTech collects massive amounts of students’ behavioral and (sensitive) demographic data, often justified by the potential to help students by personalizing education. Researchers voiced concerns regarding privacy and data abuses (e.g., targeted advertising) in the absence of clearly defined data collection and sharing policies. However, technical contributions to alleviating students’ privacy risks have been scarce. In this paper, we argue against collecting demographic data by showing that gender—a widely used demographic feature—does not causally affect students’ course performance: arguably the most popular target of predictive models. Then, we show that gender can be inferred from behavioral data; thus, simply leaving them out does not protect students’ privacy. Combining a feature selection mechanism with an adversarial censoring technique, we propose a novel approach to create a ‘private’ version of a dataset comprising of fewer features that predict the target without revealing the gender, and are interpretive. We conduct comprehensive experiments on a public dataset to demonstrate the robustness and generalizability of our mechanism.
Vietnamese Speech-based Question Answering over Car Manuals

This paper presents a novel Vietnamese speech-based question answering system QA-CarManual that enables users to ask car-manual-related questions (e.g. how to properly operate devices and/or utilities within a car). Given a car manual written in Vietnamese as the main knowledge base, we develop QA-CarManual as a lightweight, real-time and interactive system that integrates state-of-the-art technologies in language and speech processing to (i) understand and interact with users via speech commands and (ii) automatically query the knowledge base and return answers in both forms of text and speech as well as visualization. To our best knowledge, QA-CarManual is the first Vietnamese question answering system that interacts with users via speech inputs and outputs. We perform a human evaluation to assess the quality of our QA-CarManual system and obtain promising results.
Modern Web Frameworks: A Comparison of Rendering Performance

Recent years have seen the rise of a new generation of UI frameworks for web application development. These frameworks differ from previous generations of JavaScript frameworks in that they define a declarative application development model, where transitions in the state of the UI are managed by the framework. This potentially greatly simplifies application development, but requires the framework to implement a rendering strategy which translates changes in application state into changes in the state of the UI. The performance characteristics of these rendering strategies have thus far been poorly studied. In this article, we describe the rendering strategies used in the frameworks Angular, React, Vue, Svelte and Blazor, which represent some of the most influential and widely used modern web frameworks. We find significant differences in the scaling of costs in their rendering strategies with potentially equally significant practical performance implications. To verify these differences, we implement a number of benchmarks that measure the scaling of rendering costs as an application grows in complexity. The results of our benchmarks confirm that under certain circumstances, performance differences between frameworks can range up to several orders of magnitude when performing the same tasks. Furthermore, we find that the relative performance of a rendering strategy can be effectively estimated based on factors affecting the input sizes of render loops. The best performing rendering strategies are found to be ones which minimize input sizes using techniques such as compile-time optimization and reactive programming models.
In the Land of MMUs: Multiarchitecture OS-Agnostic Virtual Memory Forensics

The first step required to perform any analysis of a physical memory image is the reconstruction of the virtual address spaces, which allows translating virtual addresses to their corresponding physical offsets. However, this phase is often overlooked, and the challenges related to it are rarely discussed in the literature. Practical tools solve the problem by using a set of custom heuristics tailored on a very small number of well-known operating systems (OSs) running on few architectures. In this article, we look for the first time at all the different ways the virtual to physical translation can be operated in 10 different CPU architectures. In each case, we study the inviolable constraints imposed by the memory management unit that can be used to build signatures to recover the required data structures from memory without any knowledge about the running OS. We build a proof-of-concept tool to experiment with the extraction of virtual address spaces showing the challenges of performing an OS-agnostic virtual to physical address translation in real-world scenarios. We conduct experiments on a large set of 26 different OSs and a use case on a real hardware device. Finally, we show a possible usage of our technique to retrieve information about user space processes running on an unknown OS without any knowledge of its internals.
Web Service Recommendation based on Graph Attention Network (GAT-WSR)

In recent years, Web APIs have been greatly developed, which makes it convenient to develop Mashups from abundant sources of Web APIs. Nonetheless, it synchronously brings difficulties to choose suitable APIs for a Mashup. The existing web service recommender systems based on collaborative filtering only mine the historical behavior data of users, resulting in one-sided recommendation results. However, service recommendations should not only meet the functional needs of users, but also consider non-functional features to enhance the diversity of recommendations. In this work, we propose a method that can comprehensively use the information of functional semantics, non-functional features, and service invocation behavior. At the same time, we found that the relationship between Mashups and APIs is essentially a kind of graph data. By building a graph data network and directly inputting information into the GAT, nodes can extract their neighbors&#x0027; information, so as to predict whether there are edges between nodes and complete the recommendation of APIs for Mashups. In view of this, this paper proposes a Mashup service recommendation model based on a GAT, which generalizes Mashups and APIs into network nodes, combines the text feature vector representation of nodes with other non-functional features. We use the GAT to capture the different weight contributions of different neighbor nodes, mix node information to output new node features. The extracted features are used for edge prediction tasks, so as to recommend appropriate APIs for Mashup applications. The results show that our model outperforms some state-of-art models in web service recommendations.