Reviewerly/exHarmony huggingface dataset:
Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors

Breast cancer is a global epidemic, responsible for one of the highest mortality rates among women. Ultrasound imaging is becoming a popular tool for breast cancer screening, and quantitative ultrasound (QUS) techniques are being increasingly applied by researchers in an attempt to characterize breast tissue. Several different quantitative descriptors for breast cancer have been explored by researchers. This study proposes a breast tumor classification system using the three major types of intratumoral QUS descriptors which can be extracted from ultrasound radiofrequency (RF) data: spectral features, envelope statistics features, and texture features. A total of 16 features were extracted from ultrasound RF data across two different datasets, of which one is balanced and the other is severely imbalanced. The balanced dataset contains RF data of 100 patients with breast tumors, of which 48 are benign and 52 are malignant. The imbalanced dataset contains RF data of 130 patients with breast tumors, of which 104 are benign and 26 are malignant. Holdout validation was used to split the balanced dataset into 60% training and 40% testing sets. Feature selection was applied on the training set to identify the most relevant subset for the classification of benign and malignant breast tumors, and the performance of the features was evaluated on the test set. A maximum classification accuracy of 95% and an area under the receiver operating characteristic curve (AUC) of 0.968 was obtained on the test set. The performance of the identified relevant features was further validated on the imbalanced dataset, where a hybrid resampling strategy was firstly utilized to create an optimal balance between benign and malignant samples. A maximum classification accuracy of 93.01%, sensitivity of 94.62%, specificity of 91.4%, and AUC of 0.966 were obtained. The results indicate that the identified features are able to distinguish between benign and malignant breast lesions very effectively, and the combination of the features identified in this research has the potential to be a significant tool in the noninvasive rapid and accurate diagnosis of breast cancer.
Attention-Based Temporal-Frequency Aggregation for Speaker Verification

Convolutional neural networks (CNNs) have significantly promoted the development of speaker verification (SV) systems because of their powerful deep feature learning capability. In CNN-based SV systems, utterance-level aggregation is an important component, and it compresses the frame-level features generated by the CNN frontend into an utterance-level representation. However, most of the existing aggregation methods aggregate the extracted features across time and cannot capture the speaker-dependent information contained in the frequency domain. To handle this problem, this paper proposes a novel attention-based frequency aggregation method, which focuses on the key frequency bands that provide more information for utterance-level representation. Meanwhile, two more effective temporal-frequency aggregation methods are proposed in combination with the existing temporal aggregation methods. The two proposed methods can capture the speaker-dependent information contained in both the time domain and frequency domain of frame-level features, thus improving the discriminability of speaker embedding. Besides, a powerful CNN-based SV system is developed and evaluated on the TIMIT and Voxceleb datasets. The experimental results indicate that the CNN-based SV system using the temporal-frequency aggregation method achieves a superior equal error rate of 5.96% on Voxceleb compared with the state-of-the-art baseline models.
Encrypted Speech Retrieval Scheme Based on Multiuser Searchable Encryption in Cloud Storage

In order to solve the problem of multiuser security sharing and privacy protection of the speech data in cloud storage and realize efficient encrypted speech retrieval, an encrypted speech retrieval scheme based on multiuser searchable encryption was proposed. Firstly, the ciphertext-policy attribute-based encryption (CP-ABE) and searchable encryption (SE) are combined to support a multiuser searchable speech encryption scheme, which achieves the encryption and fine-grained access control of the speech data. Secondly, the Mel frequency cepstral coefficient (MFCC) feature of the original speech that is used as the input of the long- and short-term memory network (LSTM) is extracted to perform the deep semantic feature extraction as the speech keywords. Finally, the speech keywords are encrypted to generate a secure index, bound together with the encrypted speech, and then stored in the cloud. During the user retrieving, the keywords of query speech are extracted by utilizing the trained LSTM to generate the search trapdoor of the user and then uploaded to the cloud server, and the Euclidean distance is used for matching the security index with the search trapdoor. In addition, a proxy server is introduced to execute the partial ciphertext decryption operations to reduce computation overhead and storage space. The theoretical analysis and experimental results show that the proposed scheme has higher security and retrieval accuracy and can realize the secure storage of the massive speech data and multiuser data sharing.
Simulation of a Low Concentrator Photovoltaic System Using COMSOL

The use of photovoltaic (PV) systems presents a great solution to high energy demand. Many factors limit the output of PV systems. One method of increasing the output of PV systems is to employ concentrators. The function of these concentrators is to increase the amount of solar radiation falling on a PV panel using optical devices. In this work, a simulation of a low concentrated photovoltaic system (LCPV) (V-trough model) will be conducted using COMSOL Multiphysics software package. The ray-tracing technique, based on the finite-element method, was used to study the performance of a V-trough without the incorporation of a tracking system. By investigating the effect of the mirrors’ inclination angles on the performance of the system, the optimum inclination angles were determined. The simulation was done for a non-tilted concentrator photovoltaic (CPV) system if placed in different geographical locations in Saudi Arabia with the inclination of the mirrors being changed every hour of the daylight. It was found that the concentration ratio of the suggested model increased for the city of Jeddah, for example, by 171% and 131% for double and partial coverage cases, respectively. In order to reduce the operation cost, the simulation was repeated with the restriction of the mirrors’ inclination to only three positions during the day. The concentration ratio decreased in this case by not more than 14%. When mirrors were fixed throughout the day, the concentration ratio dropped to about 50%. Such simulations will assist in investigating different designs of PV systems prior to their manufacturing. In addition, it could assist in determining the best geographic location for such CPV systems.
A Joint Multi-task Architecture for Document-level Aspect-based Sentiment Analysis in Vietnamese

The increasing demands of e-commerce websites have caused a vast amount of opinions about Internet users' products and services.Therefore, the aspect-based sentiment analysis (ABSA) has attracted much attention from academia and industry due to its application in many real-life problems in recent years.This problem is a complex task that aims to extract both the aspects and sentiments from the text input.Aspect Category Detection and Sentiment Polarity Classification are two of the challenging subtasks of ABSA, which detect a set of pre-defined categories and corresponding sentiment polarity for a given review.This paper presents an effective joint multi-task architecture based on neural network models to solve two tasks in the document-level ABSA datasets.Our model is designed to predict the whole mentioned aspect categories and corresponding sentiment polarities on the document-level datasets.We trained our model jointly on two tasks simultaneously and utilized the additional information of aspect category detection task for predicting the aspect categories and its sentiments for the specific domain.Our architecture can explore the hidden correlated information between categories and polarities in the review.Experiments on two Vietnamese language datasets in the restaurant domain and hotel domain demonstrated that our model outperforms the previous state of the art methods on two benchmark document-level dataset.
Revisiting the Feasibility of Public Key Cryptography in Light of IIoT Communications

Digital certificates are regarded as the most secure and scalable way of implementing authentication services in the Internet today. They are used by most popular security protocols, including Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS). The lifecycle management of digital certificates relies on centralized Certification Authority (CA)-based Public Key Infrastructures (PKIs). However, the implementation of PKIs and certificate lifecycle management procedures in Industrial Internet of Things (IIoT) environments presents some challenges, mainly due to the high resource consumption that they imply and the lack of trust in the centralized CAs. This paper identifies and describes the main challenges to implement certificate-based public key cryptography in IIoT environments and it surveys the alternative approaches proposed so far in the literature to address these challenges. Most proposals rely on the introduction of a Trusted Third Party to aid the IIoT devices in tasks that exceed their capacity. The proposed alternatives are complementary and their application depends on the specific challenge to solve, the application scenario, and the capacities of the involved IIoT devices. This paper revisits all these alternatives in light of industrial communication models, identifying their strengths and weaknesses, and providing an in-depth comparative analysis.
Hierarchical Collaborated Fireworks Algorithm

The fireworks algorithm (FWA) has achieved significant global optimization ability by organizing multiple simultaneous local searches. By dynamically decomposing the target problem and handling each one with a sub-population, it has presented distinct property and applicability compared with traditional evolutionary algorithms. In this paper, we extend the theoretical model of fireworks algorithm based on search space partition to obtain a hierarchical collaboration model. It maintains both multiple local fireworks for local exploitation and one global firework for overall population distribution control. The implemented hierarchical collaborated fireworks algorithm is able to combine the advantages of both classic evolutionary algorithms and fireworks algorithms. Several experiments are provided for in-depth analysis and discussion on the proposed algorithm. The effectiveness of proposed strategy is demonstrated on the benchmark test suite from CEC 2020. Experimental results validate that the hierarchical collaborated fireworks algorithm outperforms former fireworks algorithms significantly and achieves similar results compared with state-of-the-art evolutionary algorithms.
Lecturers’ information literacy experience in remote teaching during the COVID-19 pandemic

The advent of coronavirus disease 2019, or COVID-19, continues to trigger several important disruptions/innovations in practically every sector around the world. Additionally, the impacts are predominant in certain educational systems and in creating opportunities. Previous studies had addressed possible effective methods in handling distant learning and student interactions. This qualitative study explored lecturers' information literacy experience during online classes as a result of the pandemic. Semi-structured interview techniques were applied among participants, made up of 15 lecturers in the Humanities Faculty, Diponegoro University, Indonesia, and thematic analysis was used to analyze the data obtained. The results showed the focus of lecturers' information literacy experience was primarily on student interactions and knowledge of various online learning platforms. However, information repackaging was a significant initial consideration during virtual classes, after identifying salient student characteristics. In summary, the present study have contributed to the theoretical understanding of information literacy and may be of benefit to the teaching faculties for enhancing teaching and learning activities, as well as providing student support.
Online Activity Recognition Combining Dynamic Segmentation and Emergent Modeling

Activity recognition is fundamental to many applications envisaged in pervasive computing, especially in smart environments where the resident's data collected from sensors will be mapped to human activities. Previous research usually focuses on scripted or pre-segmented sequences related to activities, whereas many real-world deployments require information about the ongoing activities in real time. In this paper, we propose an online activity recognition model on streaming sensor data that incorporates the spatio-temporal correlation-based dynamic segmentation method and the stigmergy-based emergent modeling method to recognize activities when new sensor events are recorded. The dynamic segmentation approach integrating sensor correlation and time correlation judges whether two consecutive sensor events belong to the same window or not, avoiding events from very different functional areas or with a long time interval in the same window, thus obtaining the segmented window for every single event. Then, the emergent paradigm with marker-based stigmergy is adopted to build activity features that are explicitly represented as a directed weighted network to define the context for the last sensor event in this window, which does not need sophisticated domain knowledge. We validate the proposed method utilizing the real-world dataset Aruba from the CASAS project and the results show the effectiveness.
Bitcoin as a Safe Haven during COVID-19 Disease

In this paper, we investigate the role of Bitcoin as a safe haven against the stock market losses during the spread of COVID-19. The performed analysis was based on a regression model with dummy variables defined around some crucial dates of the pandemic and on the dynamic conditional correlations. To try to model the real dynamics of the markets, we studied the safe-haven properties of Bitcoin against thirteen of the major stock market indexes losses using daily data spanning from 1 July 2019 until 20 February 2021. A similar analysis was also performed for Ether. Results show that this pandemic impacts on the Bitcoin status as safe haven, but we are still far from being able to define Bitcoin as a safe haven.
Study on Enterprise Financial Risk Prevention and Early Warning System Based on Blockchain Technology

To address the challenges of financing SMEs, breaking the limitations of traditional financing models, a blockchain-based financial risk prevention and early warning system for businesses has been proposed and an in-depth study of the system has been undertaken. As a scientific and technological means to strengthen the disclosure and sharing of information such as credit records and risk early warning, The key to getting financial institutions to lend to SMEs is to avoid financial credit risk in the value chain. Considering SMEs located in supply chains as an object of study, using the logical relationships between block chains and financial credit risk management chains, three principles of supply chain management are proposed by analyzing the logical relationships between supply chains and financial credit control chains. Chain using blockchain technology, namely: the principle of recognizing the trust of major enterprises, the principle of sharing information about the financing process and the principle of general cooperation for successful financing. Based on this principle, an evolutionary game model is used to analyze the evolutionary path of endorsement fiduciary relationships between major enterprises and SMEs, the influencers of evolutionary equilibrium, the evolutionary path of cooperation between commercial banks and SMEs in financing, the evolutionary path under the control of third-party institutions, and influencers. The research shows that the financing activities of commercial banks and small and medium-sized enterprises are based on the trust endorsement of core enterprises in the supply chain, and the intervention of third-party regulators will help to promote the consistent cooperation and information sharing of all participants in the supply chain, so as to promote the smooth completion of supply chain financing.
Predictive Analysis and Simulation of College Sports Performance Fused with Adaptive Federated Deep Learning Algorithm

With the widespread use of intelligent teaching, data containing student performance information continues to emerge, and artificial intelligence technology based on big data has made a qualitative leap. At present, the prediction of college students’ sports performance is only based on the past performance, and it does not reflect the student’s training effect very well. In order to solve these problems, this paper puts forward the analysis and simulation of college sports performance fusion with adaptive federated deep learning algorithm, aiming to study the influencing factors of student sports performance and suggestions for improvement. This paper uses an adaptive federated learning method and a personalized federated learning algorithm based on deep learning and then proposes a student performance prediction method. These methods integrate the quantitative methods of motor skill assessment and establish standards for college students, which are good standards for evaluating college students’ sports skills. This paper adopts the performance prediction framework and then establishes the sports performance prediction model. Through the analysis of sports performance analysis examples, it is concluded that the model proposed in this paper can accurately predict the student’s sports performance, and the average accuracy rate of each sports item has reached 91.7%.
Imagining a Neuroqueer Technoscience

The rise of mobile communication applications and technologies presents promising therapeutic and accessibility-related interventions for neurodivergent users. However, top-down approaches in human-computer interaction (HCI) research often prioritize the needs and goals of allistic and neurotypical researchers and secondary stakeholders in media creation. Furthermore, media technologies are created with a one-size-fits-all approach, with the intent of rehabilitating or curing neurodivergent ways of being. This article imagines neuroqueer technoscience as an extension of crip technoscience that amplifies new styles of relationality, self-expression, and communication practices within the development of information and communication technologies (ICTs). Using an interdisciplinary framework informed by crip technoscience and human-computer interaction research, the author presents three tenets for mediating neuroqueer subjectivities.
Distinctions Organize Information in Mind and Nature: Empirical Findings of Identity–Other Distinctions (D) in Cognitive and Material Complexity

The transdisciplinary importance of distinctions is well-established as foundational to such diverse phenomena as recognition, identification, individual and social identity, marginalization, externalities, boundaries, concept formation, etc., and synonymous general ideas, such as thingness, concepts, nodes, objects, etc. Cabrera provides a formal description of and predictions for identity–other distinctions (D) or “D-rule” as one of four universals for the organization of information that is foundational to systems and systems thinking, as well as the consilience of knowledge. This paper presents seven empirical studies in which (unless otherwise noted) software was used to create an experiment for subjects to complete a task and/or answer a question. The samples varied for each study (ranging from N = 407 to N = 34,398) and were generalizable to a normal distribution of the US population. These studies support—with high statistical significance—the predictions made by DSRP theory regarding identity–other distinctions including its: universality as an observable phenomenon in both mind (cognitive complexity) and nature (ontological complexity) (i.e., parallelism); internal structures and dynamics; mutual dependencies on other universals (i.e., relationships, systems, and perspectives); role in structural predictions; and efficacy as a metacognitive skill. In conclusion, these data suggest the observable and empirical existence, universality, efficacy, and parallelism (between cognitive and ontological complexity) of identity–other distinctions (D).
Employing Mobile Technologies to Investigate the Association Between Abstraction Skills and Performance in Environmental Studies in Early Primary School

In the modern digital era, the impressive development of mobile technologies has entailed the reformation of orientations, dispositions and stances in education, changing the dominant approaches to learning. In this article, we introduce the novel idea of the synergistic development of computational thinking skills and environmental awareness in the early primary school years, by employing mobile technologies.
Quantum Circuits for the Preparation of Spin Eigenfunctions on Quantum Computers

The application of quantum algorithms to the study of many-particle quantum systems requires the ability to prepare wavefunctions that are relevant in the behavior of the system under study. Hamiltonian symmetries are an important instrument, to classify relevant many-particle wavefunctions, and to improve the efficiency of numerical simulations. In this work, quantum circuits for the exact and approximate preparation of total spin eigenfunctions on quantum computers are presented. Two different strategies are discussed and compared: exact recursive construction of total spin eigenfunctions based on the addition theorem of angular momentum, and heuristic approximation of total spin eigenfunctions based on the variational optimization of a suitable cost function. The construction of these quantum circuits is illustrated in detail, and the preparation of total spin eigenfunctions is demonstrated on IBM quantum devices, focusing on 3- and 5-spin systems on graphs with triangle connectivity.