Reviewerly/exHarmony huggingface dataset:
Vibro-Acoustics of a Mandar

Mandar is a percussion musical instrument used in the eastern part of India. It is popular among the tribe people and is similar to Mridangam and Dhol. This is widely used in Jharkhand, West Bengal and near boarder region. There is no analytical numerical and experimental analyses documentation available on it. The primary objective of this study is to understand the behavior of vibration of the mandar membrane and to justify whether it is following harmonics? This is justified by experimental and numerical analyses. The numerical analysis is performed in the ‘FEA SOFTWARE (Simulia Abaqus 6.14),’ and the experimental analysis is performed by using Chladni’s experimental setup which helps us to extract the mode shapes of the vibrating mandar membrane. On the basis of both experimental and numerical analyses, we found the mode shape of mandar membrane does not follow harmonics.
Privacy Preserving Survival Prediction

Predictive modeling has the potential to improve risk stratification of cancer patients and thereby contribute to optimized treatment strategies and better outcomes for patients in clinical practice. To develop robust predictive models for decision-making in healthcare, sensitive patient-level data is often required when developing the training models. Consequently, data privacy is an important aspect to consider when building these predictive models and in subsequent communication of the results. In this study we have used Graph Neural Networks for survival prediction, and compared the accuracy to state-of-the-art prediction models after applying Differential Privacy and k-Anonymity, i.e. two privacy-preservation solutions. By using two different data sources we demonstrated that Graph Neural Networks and Survival Forests are the two most well-performing survival prediction methods when used in combination with privacy preservation solutions. Furthermore, when the predictive model was built using clinical expertise in the specific area of interest, the prediction accuracy of the proposed knowledge based graph model drops by at most 10% when used with privacy preservation solutions. Our proposed knowledge based graph is therefore more suitable to be used in combination with privacy preservation solutions as compared to other graph models.
Implementation of speech processing algorithms based on Singular Value Decomposition and Hidden Markov Model

This article discusses a new approach to processing speech commands based on Singular Value Decomposition and Hidden Markov model. Using this approach in real conditions has reduced the computational load on the processor and the recognition accuracy of speech command increased.
Multi-Source Domain Adaptation with Sinkhorn Barycenter

We describe a multi-source and unsupervised domain adaptation method using Sinkhorn barycenters, which, given the labeled data in multi-source domains and unlabeled data in a target domain, uses the optimal transport Sinkhorn distance to measure gaps between data distributions in the source and target domains. For end-to-end classification learning, the feature extractor and classifier are simultaneously estimated on the basis of two criteria: the minimization of the Sinkhorn distance for the source and target domains and the minimization of the classification loss for the source domains. The first criterion is based on the assumptions that domain-invariant features would be captured in a latent feature space obtained by minimizing the Sinkhorn distance among all domains and that the space would be close to the Sinkhorn barycenter. Experiments on image classification using the Digit-Five dataset, which is comprised of digit datasets from five different domains, demonstrated that our method outperforms other state-of-the-art methods.
Roberta-based Encoder-decoder Model for Question Answering System

Question answering is an essential component of the chatbot where it can facilitate users retrieve tremendous information from online websites. Traditional question answering system are built upon the conventional deep learning sequence models, where they are failed to capture sufficient semantic features due to the complexity of the question answering task. To tackle the above challenges, we propose a novel approach, equipped with a Roberta-based encoder-encoder framework, to extract the underlying semantic features more effectively. The experimental results on a well-studied dataset, i.e., SQUAD, show that our proposed method outperforms the baseline models in term of both EM and F1 evaluation metrics, which proves our model effectiveness.
Comparing Methods to Visualize Orientation of Photographs: A User Study

We present methods to visualize characteristics in collections of historical photographs, especially focusing on the presentation of spatial position and orientation of photographs in relation to the buildings they depict. The developed methods were evaluated and compared in a user study focusing on their appropriateness to gain insight into specific research questions of art historians: 1) which buildings have been depicted most often in a collection of images, 2) which positions have been preferred by photographers to take pictures of a given building, 3) what is the main perspective of photographers regarding a specific building. To analyze spatial datasets of photographs, we have adapted related methods used in the visualization of fluid dynamics. As these existing visualization methods are not suitable in all photographic situations—especially when a multitude of photographs are pointing into diverging directions—we have developed additional cluster-based approaches that aim to overcome these issues. Our user study shows that the introduced cluster-based visualizations can elicit a better understanding of large photographic datasets concerning real-world research questions in certain situations, while performing comparably well in situations where existing methods are already adequate.
Navigation by Revealing Trade-offs for Content-Based Recommendations

Abstract Conversational recommender systems have been introduced to provide users the opportunity to give feedback on items in a turn-based dialog until a final recommendation is accepted. Tourism is a complex domain for recommender systems because of high cost of recommending a wrong item and often relatively few ratings to learn user preferences. In a scenario such as recommending a city to visit, conversational content-based recommendation may be advantageous, since users often struggle to specify their preferences without concrete examples. However, critiquing item features comes with challenges. Users might request item characteristics during recommendation that do not exist in reality, for example demanding very high item quality for a very low price. To tackle this problem, we present a novel conversational user interface which focuses on revealing the trade-offs of choosing one item over another. The recommendations are driven by a utility function that assesses the user’s preference toward item features while learning the importance of the features to the user. This enables the system to guide the recommendation through the search space faster and accurately over prolonged interaction. We evaluated the system in an online study with 600 participants and find that our proposed paradigm leads to improved perceived accuracy and fewer conversational cycles compared to unit critiquing.
Efficient Fully Anonymous Public-Key Trace and Revoke with Adaptive IND-CCA Security

We aim to efficiently design a unified, cost-effective primitive exhibiting two mutually orthogonal functionalities, namely subscribed users anonymity and public-key traitor traceability in the context of Broadcast Encryption (BE), and propose an explicit construction of identity-based Fully Anonymous Public-Key Trace and Revoke (FAnoPKTR) scheme that is obtained by coupling the Identity-Based Encryption (IBE) framework with the collusion-secure optimal probabilistic fingerprinting codes. In addition to being adaptively secure, our design is proven to be INDistinguishable Chosen-Ciphertext Attack (IND-CCA) secure under asymmetric Decisional Bilinear Diffie-Hellman Type-3 (DBDH-3) assumption in standard security model without random oracles. Our asymmetric Type-3 bilinear pairing-based scheme has communication bandwidth that grows with the size of a subscriber set for any encryption, and the user secret-key size is constant. Moreover, our decryption algorithm is faster, which requires only three asymmetric pairings to recover the encrypted broadcast message.
Recognition Of Facial Expression By Smart Music Player Via Human Face Mood Recommendation

Identification of a person's intention, feeling and cognitive state requires the use of facial expressions. By building a suggestion system, it may be possible to aid a user in deciding which music to listen to, lowering the user's stress levels. To recognize facial emotion expression, researchers used YOLOv4, a type of one-shot, end-to-end deep neural network. YOLO v4 obtains progressive leads to time period, using 43.5 percent AP operating at 65 independent authorities in a Tesla V100. No one has used YOLOv4 to our knowledge.
Cultivation of white oyster mushroom (Pleuorotus ostreatus) on some ligno-celluloses materials

Abstract White oyster mushroom ( Pleurotus ostreatus ) is a mushroom that is often consumed by the public and cultivated because it has a high nutritional content and a wide variety of essential amino acids, proteins, fats, minerals, and vitamins. This study aims to analyze the response of several ligno-celluloses materials to the growth of white oyster mushrooms, to compare the treatment of several ligno-celluloses materials to the growth of white oyster mushrooms, and to determine the appropriate concentration on the growth of white oyster mushrooms. The experimental design used in this study was a Factorial Completely Randomized Design (CRD). As the result of the interaction test, the P-value obtained is 0.999&gt;0,05, thus h0 is not rejected. The test of seeds obtained p-value 0.005&lt;0,05 h0 is rejected, based on the test of the influence of planting media obtained p-value 0,000&lt;0,05 h0 is rejected. From the graph of the average comparison, it is known that the type of F3 seed on the M4 planting medium caused the highest duration of mycelium growth compared to the influence of the type of seed and other growing media.
Llrsb: Privacy-Preserving Epidemic Infection Control Scheme Through Lattice-Based Linkable Ring Signature in Blockchain

Epidemics, such as COVID-19, have serious consequences globally, of which the most effective way to control the infection is contact tracing. Nowadays, research releated to privacy-preserving epidemic infection control for has been conducted, nevertheless, current researches do not regard the authenticity of records and infection facts as well as poor traceability. Moreover, with the emergence of quantum computing, there is a bottleneck in upholding privacy, security and efficiency. Our paper proposes a privacy-preserving epidemic infection control scheme through lattice-based linkable ring signature in blockchain, called LLRSB. Firstly, our scheme adopts a blockchain with three ledgers to store information in a distributed manner, which offers transparency and immunity from the Single Point of Failure(SPoF) and Denial of Service (DoS) attacks. Moreover, we design a lattice-based linkable ring signature scheme to secure privacy-preserving of epidemic infection control. Significantly, we are the first to propose a privacy-preserving epidemic infection control scheme that can resist quantum computation attacks and achieve traceability. Security analysis indicates that our privacy-protected infection control scheme ensures unconditional user anonymity, record unforgeability, signature linkability, link nonslanderability and contact traceability. Finally, the comprehensive performance evaluation demonstrates that our scheme has an efficient time-consuming, storage consumption and system communication overhead and is practical for epidemic and future pandemic privacy-preserving.
Real-time privacy risk quantification in online social networks

Matching the anonymous profile of an individual in an online social network (OSN) to their real identity raises serious privacy concerns as one can obtain sensitive information about that individual. Previous work has formulated the profile matching risk in several different ways and has shown that there exists a non-negligible risk of matching user profiles across OSNs. However, they are not practical to convey the risk to OSN users in real-time. In this work, using the output of such formulation, we model the profile characteristics of users that are vulnerable to profile matching via machine learning and make probabilistic inferences about how the vulnerabilities of users change as they share new content in OSNs (or as their graph connectivity changes). We evaluate the generated models in real data. Our results show that the generated models determine with high accuracy whether a user profile is vulnerable to profile matching risk by only analyzing their publicly available information in the anonymous OSN. In addition, we develop optimization-based countermeasures to preserve the user's privacy as they share their OSN profile with third parties. We believe that this work will be crucial for OSN users to understand their privacy risks due to their public sharings and be more conscious about their online privacy.
Masked Face Recognition based on Attention Mechanism and FaceX-Zoo

Recently, due to the outbreak of the COVID-19 epidemic in the world, wearing face masks has become a trend, which brings difficulties to the traditional face recognition technologies that do not actively focus on the upper part of the face. This paper proposes a novel method for masked face recognition based on attention mechanism and FaceX-Zoo (an open-source method of JD.COM). In order to make the module focus on the regions around the eyes, we integrated the CBAM (Convolutional Block Attention Module) attention mechanism into ResNet50 and MobileFaceNet network. Furthermore, the FaceX-Zoo method was used to generate masked face images to improve the module performance. Experiment results show that the proposed approach can improve the performance of masked face recognition compared with competitive approaches.
On the issue of legal regulation of the qualifications of a teacher of legal disciplines at a university

In modern Russian universities, there are frequent complaints from law students that a teacher who reads a particular discipline is not a specialist in the field of this discipline. The article analyzes the norms of modern legislation on higher education, as well as federal state educational standards of higher education in the direction of training “Jurisprudence” for clear requirements for narrow scientific and (or) practical specialization of teachers of legal disciplines.
Topic-to-Text Generation with PMI-IR Additional Semantic Information

The purpose of this paper is to study the use of a deep learning method to make a machine automatically generate coherent text related to a topic based on user-specified multiple topic words. The existing research on topic-to-essay generation is mainly based on the topic words inputted by users, but the result of model generation is not satisfactory due to the limited semantic information of the subject words that can be obtained from the input. To solve this problem, this paper proposes a topic-to-essay generation model (PMI-IR similarity model) based on the corpus of keywords with additional semantic information. According to the similarity relationship between the input subject words and corpus words, this paper designed and constructed a corpus-based PMI-IR similarity computing similarity network. In the process of generation, the words with high similarity to the input topic words are selected as additional semantic information to be introduced into the model. This model is superior to the existing models in both manual evaluation and BLEU automatic evaluation. The text paragraphs generated by the model are closer to the topic, and the generated text paragraphs are more fluent and coherent.
A Comparative Analysis of Dimensionality Reduction Methods for Genetic Programming to Solve High-Dimensional Symbolic Regression Problems

Genetic Programming (GP) is a powerful evolutionary algorithm that has a wide range of real-world applications. High-dimensional symbolic regression (HDSR) is an important yet challenging application of GP. In this paper, a comparative study is conducted to investigate and to discuss the effectiveness of dimensionality reduction (DR) techniques in assisting GP for HDSR problems. Three popular DR techniques, which are the Pearson Correlation Coefficient (PCC), the Principal Component Analysis (PCA), and the Maximal Information Coefficient (MIC), are selected for comparison and discussion. The experimental results showed that considering only correlation during DR is not effective enough to provide a suitable reduced set of problem dimensions, and that GP with DR may perform worse than its counterpart without DR. Meanwhile, we propose a novel two-phase DR method, considering both correlation and redundancy. The proposed method can give a more reasonable set of reduced dimensions, which can effectively improve the performance of GP on HDSR problems.
Deep Neural Models for Key-Phrase Indexing

The association of key-phrases allows a more efficient search for an article of interest, since key-phrases indicate an article's main idea and allow to align the researcher's interest in that article easily. The authors themselves often report these key-phrases. And most of the documents available on the Web do not yet have key-phrases assigned to them. The manual association of keyphrases is not feasible, considering a large amount of data currently available. To ensure intellectualization of key-phrase extraction, it is proposed to use deep learning models in this work, particularly a concatenated model approach. This work is devoted to studying and developing methods for key-phrase extraction based on word embedding (Glove) and Recurrent Neural Network (RNN). Three different RNN based models: Simple Bi-LSTM model—I, concatenated Bi-LSTM model—II, and concatenated Bi-LSTM model—III are proposed. From various aspects, the proposed models show consistent performance over existing approaches on three publically available & widely acceptable datasets viz Inspec, SemEval2010 and DUC.
Robustness of two-qubit and three-qubit states in correlated quantum channels

We investigate how the correlated actions of quantum channels affect the robustness of entangled states. We consider the Bell-like state and random two-qubit pure states in the correlated depolarizing, bit flip, bit-phase flip, and phase flip channels. It is found that the robustness of two-qubit pure states can be noticeably enhanced due to the correlations between consecutive actions of these noisy channels, and the Bell-like state is always the most robust one. We also consider the robustness of three-qubit pure states in correlated noisy channels. For the correlated bit flip and phase flip channels, the result shows that although the most robust and most fragile states are locally unitary equivalent, they exhibit different robustness in different correlated channels, and the effect of channel correlations on them is also significantly different. However, for the correlated depolarizing and bit-phase flip channels, the robustness of two special three-qubit pure states is exactly the same. Moreover, compared with the random three-qubit pure states, they are neither the most robust states nor the most fragile states.
The Threat of Social Engineering and The Safety of Companies

Social engineering is a method of attack aimed at the state, organization, or individual. It focuses on the weakest point in the use of information and communication technologies, specifically the human factor. The article deals with the issue of social engineering with a focus on the attack and the possibilities of defense against it in a commercial and manufacturing company. Several questionnaire studies were conducted, which found that only a small number of staff had been trained in the past against similar attacks. Companies still underestimate their protection, investing in new technologies, but already investing less in training employees who use them. At the same time, social engineering largely focuses on them. The article, therefore, identifies social engineering, its ways of carrying out the attack. Furthermore, the article briefly summarizes its threats as well as its history, along with an overview of the first attacks. For greater orientation in practice, the individual types of attackers, the progress of their attacks, and the relevant technical aspects are described here. An analysis of the current state of safety in the existing manufacturing and trading company was performed, namely, the specific directives that are currently in force. These were compared with current needs and appropriate measures were proposed. Based on this analysis, recommendations for improving the state of security against social engineering attacks in all companies, in general, are described at the end of the article. The most frequently used methods in practice are listed according to the survey, followed by the establishment of safety recommendations. Individual technologies are constantly evolving, newer applications are being launched on the market, and at the same time, more advanced methods for data protection need to be developed.
Design and Implementation of a New Intelligent Vehicle Safety Monitoring System based on Multi-source Sensors

At present, China's road traffic safety situation is grim, so how to effectively curb traffic accidents is an urgent problem to be solved. Vehicle safety is the main body of road safety and monitoring and early warning the whole process of vehicle driving is an effective way to improve driving safety and reduce accident rate. In this paper, the comprehensive application of multi-source sensors is taken as the means, and the processing and analysis of associated big data is taken as the basic method. Firstly, the key factors affecting road traffic safety are analyzed, and based on this, the sensor selection is realized. Secondly, the system architecture is studied and the software and hardware design of the system are completed. Finally, the field vehicle experiment is carried out, the typical experimental data are analyzed, and the prototype system verification is completed. The system has been linked with the big data system of the traffic police in Hunan Province currently, and conducted a joint test for the intercity bus and vehicle carrying dangerous goods, laying a foundation for effectively improving the safety of vehicles and roads.
Comparison of Emotional Speech Detection Algorithm for Glottal Closure Instants

Epochs or glottal closure instants are periods of high vocal tract stimulation during speech production. Among other applications, GCI extraction is utilized in prosody modification, speech synthesis, and voice source analysis as a pre-processing step in speech technology. . Many of the GCI detection techniques developed throughout time are compatible with speech signals produced by modal phonation, thus these algorithms work well with them.” For emotive speech, however, a comparison of several cutting- edge GCI detection methods is required. Emotional speech and well-known evaluation criteria are used in this research to evaluate GCI detection techniques. This collection contains recordings of seven distinct emotions, as well as factual information like electro glotto graphy(EGG). All six GCI identification algorithms perform well in neutral emotion speech processing, but they struggle in high arousal speech processing (anger and joy). GCI detection in emotional speech may be aided by local average pitch period estimates, according to the findings of the study.