Reviewerly/exHarmony huggingface dataset:
NAAQA: A Neural Architecture for Acoustic Question Answering

The goal of the Acoustic Question Answering (AQA) task is to answer a free-form text question about the content of an acoustic scene. It was inspired by the Visual Question Answering (VQA) task. In this paper, based on the previously introduced CLEAR dataset, we propose a new benchmark for AQA, namely CLEAR2, that emphasizes the specific challenges of acoustic inputs. These include handling of variable duration scenes, and scenes built with elementary sounds that differ between training and test set. We also introduce NAAQA, a neural architecture that leverages specific properties of acoustic inputs. The use of 1D convolutions in time and frequency to process 2D spectro-temporal representations of acoustic content shows promising results and enables reductions in model complexity. We show that time coordinate maps augment temporal localization capabilities which enhance performance of the network by ∼ 17 percentage points. On the other hand, frequency coordinate maps have little influence on this task. NAAQA achieves 79.5% of accuracy on the AQA task with ∼ four times fewer parameters than the previously explored VQA model. We evaluate the performance of NAAQA on an independent data set reconstructed from DAQA. We also test the addition of a MALiMo module in our model on both CLEAR2 and DAQA. We provide a detailed analysis of the results for the different question types. We release the code to produce CLEAR2 as well as NAAQA to foster research in this newly emerging machine learning task.
Research on the Application of Artificial Intelligence Video Feedback System in College Basketball Shooting Teaching

Objective: To test the effect of the artificial intelligence video feedback system in the basketball shooting teaching in Colleges and universities. Methods: 24 male basketball players from XX University were randomly divided into experimental group (n=12) and control group (n=12). The experimental group and the control group were given shooting learning for 8 weeks. During each teaching period, the experimental group was intervened with 10-minute video feedback system, while the control group was not intervened. Results: After the experiment, the shooting percentage and the performance of action skill evaluation of the experimental group were significantly higher than those before the experiment (p=0. 032<0.05). After the experiment, the control group in the fixed-point shot and jump shot shooting percentage and action skill evaluation results were higher than before the experiment (p=0.158>0.05). After the experiment, the experimental group in the fixed-point shot and jump shot shooting percentage and action skill evaluation results were significantly higher than the control group (p=0. 026<0.05). Conclusion: The application of artificial intelligence video feedback system in college basketball shooting teaching is helpful to: Students can get the action points of shooting technique more richly and intuitively; Help students quickly establish the technical action model and spatial thinking concept of shooting; The correction and consolidation of shooting techniques are more personalized and timely.
Entanglement distribution in multi-platform buffered-router-assisted frequency-multiplexed automated repeater chains

Abstract Realization of a globe-spanning quantum network is a current worldwide goal, where near and long term implementations will benefit from connectivity between platforms optimized for specific tasks. Towards this goal, a quantum network architecture is herewith proposed whereby quantum processing devices based on NV − colour centers act as quantum routers (QR) and, between which, long-distance entanglement distribution is enabled by spectrally-multiplexed quantum repeaters based on absorptive quantum memories in rare-earth ion-doped crystals and imperfect entangled photon-pair sources. The inclusion of a quantum buffer structure between repeaters and routers is shown to, albeit the increased complexity, improve the achievable entanglement distribution rates in the network. Although the expected rate and fidelity results are presented for a simple linear network (point-to-point), complex topologies are compatible with the proposed architecture through the inclusion of an extra layer of temporal multiplexing in the QR’s operation. Figures of merit are extracted based on parameters found in the literature for near-term scenarios and attest the availability of the proposed buffered-router-assisted frequency-multiplexed automated repeater chain network.
Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models

Abstract In this paper, an ordinal multilevel latent Markov model based on separate random effects is proposed. In detail, two distinct second-level discrete effects are considered in the model, one affecting the initial probability vector and the other affecting the transition probability matrix of the first-level ordinal latent Markov process. To model these separate effects, we consider a bi-dimensional mixture specification that allows to avoid unverifiable assumptions on the random effect distribution and to derive a two-way clustering of second-level units. Starting from a general model where the two random effects are dependent, we also obtain the independence model as a special case. The proposal is applied to data on the physical health status of a sample of elderly residents grouped into nursing homes. A simulation study assessing the performance of the proposal is also included.
Online navigation to journal articles: How are journal articles retrieved by researchers and students at an academic institution; a quantitative examination of HTTP referer [sic] data

This quantitative case study employs weblog data supplied by six major academic journal publishers, known as HTTP referer [sic] data from Washington State University IP address ranges to reveal the sites that researchers and students use to retrieve academic journal literature. HTTP referrers to academic journal articles were categorized as follows; Search Engines (37%), Library Discovery Services (10.04%), the individual publisher itself (15.85%), free A & I services (7.90%) and absent data (termed no referrers) (25.77%). Within the category of search engines, Google and Google Scholar combined to account for almost all of the referrers. There was no evidence from the referrer data that Social Networking Sites or Courseware sites were substantial referrers. There were large variations in results between the individual publishers.
Research on the Practice and Application of Information Technology in Physical Education

Abstract With the popularity of the Internet, Information Technology (hereinafter referred to as IT) has been applied to many fields of education, especially Physical Education (hereinafter referred to as PE). Through IT, colleges can complete the information exchange of PE through the media. Through IT, we can use modern teaching technology to complete diversified teaching, which will lead to a variety of teaching process reform. With the development of multimedia technology, the application of IT in PE can stimulate students’ interest in learning. Through IT, students can break through the key and difficult points in teaching, which can make students better establish the correct action representation. By strengthening students’ physique, we can develop students’ physique, which will enhance students’ ability. First of all, this paper analyzes the importance of IT to PE. Then, this paper puts forward some problems. Finally, this paper puts forward some suggestions, which can be better applied to PE.
An Energy-Efficient Deep Neural Network Accelerator Design

This paper introduces an energy-efficient design method for Deep Neural Network (DNN) accelerator. Although GPU is widely used for DNN acceleration, its huge power consumption limits practical usage on mobile devices. Recent DNN accelerators are dedicated to high energy-efficiency to realize real-time DNN acceleration with low power consumption. But a hardware-oriented algorithm is essential for realistic implementation. Therefore, various techniques of network compression are applied with the DNN accelerators that utilize several schemes to reduce computational complexity in trade of accuracy loss. This work studies the optimization schemes and presents a DNN accelerator architecture by hardware-software co-optimization.
Developing Digital Competence of Teachers of Humanitarian Disciplines in the Conditions of Distance Education

The relevance of the research is explained by the necessity of developing digital competence of teachers of Humanitarian disciplines at the higher education institutions in the conditions of the quarantine measures to prevent the spread of COVID-19. It is particularly challenging for teachers of Humanitarian disciplines, who are not specialists in the digital sphere as their profession is focused on humans. The purpose of the paper is to define the influence of the quarantine on the work of teachers at the higher education institutions, revealing the level of the development, challenges and peculiarities of enhancing the digital competence of teachers of Humanitarian disciplines, theirs needs in education and giving recommendations how to arrange education and make administrative decisions. The methods of the research are survey, theoretical analysis, others. The results are to define the peculiarities of the influence of the quarantine on the arrangement of distance learning, the peculiarities of developing digital competence of teachers during the quarantine, their needs in education, the challenges in conducting mass distance learning, the determined complex of scientific approaches to developing digital competence. The practical significance of the research refers to developing electronic educational resource as a set of methodological tools that provides all stages of the educational process, based on the results of diagnosing teachers’ digital competence.
Parkinson’s disease Diagnosis using a Combined Deep Learning Approach

Parkinson's disease, which often affects the elderly, is one of the most dangerous neurodegenerative disorders. The prime characteristics observed in people with this disorder are: bradykinesia i.e. slowness of movement, rigidity, tremor and instable posture. One of the most commonly observed changes is in the handwriting of a person with Parkinson's disease. Due to the tremor effects on the hands, drawing a spiral shape in particular becomes difficult for those with Parkinson's. In this work, we have considered this as the primary criteria for our diagnosis system. Deep learning is an enhancement of Machine learning and has presently demonstrated efficiency to analyze and diagnose unstructured datasets and, hence to provide the best care for patients. In this paper, an early diagnosis technique combining LeNet and Long Short Term Memory (LSTM) has been proposed for Parkinson's disease.
Research on Airport Target Recognition under Low-Visibility Condition Based on Transfer Learning

Operational safety in the airport is the focus of the aviation industry. Target recognition under low visibility plays an essential role in arranging the circulation of objects in the airport field, identifying unpredictable obstacles in time, and monitoring aviation operation and ensuring its safety and efficiency. From the perspective of transfer learning, this paper will explore the identification of all targets (mainly including aircraft, humans, ground vehicles, hangars, and birds) in the airport field under low-visibility conditions (caused by bad weather such as fog, rain, and snow). First, a variety of deep transfer learning networks are used to identify well-visible airport targets. The experimental results show that GoogLeNet is more effective, with a recognition rate of more than 90.84%. However, the recognition rates of this method are greatly reduced under the condition of low visibility; some are even less than 10%. Therefore, the low-visibility image is processed with 11 different fog removals and vision enhancement algorithms, and then, the GoogLeNet deep neural network algorithm is used to identify the image. Finally, the target recognition rate can be significantly improved to more than 60%. According to the results, the dark channel algorithm has the best image defogging enhancement effect, and the GoogLeNet deep neural network has the highest target recognition rate.
A Machine Learning-Based Approach to Password Authentication Using Keystroke Biometrics

Keystroke authentication systems, though they are getting more popular, are a rather common method of data/network access. The typing dynamics apply to the automatic process of recognizing or verifying an individual’s identity depending on the form and style of tapping on a keyboard. It allows to authenticate individuals through their way of typing their password or a free text on a keyboard. In this paper, we perform analysis of machine learning algorithms on a keystroke dynamics-based data set with features like Hold time, Keyup-Keydown time and Keydown-Keydown time. This research is based on our methodology of using support vector machines (polynomial & radial basis kernels), random forest algorithm and artificial neural networks to recognize users based on their keystroke patterns. The review analysis shows a great result in user identification based on keystroke patterns with artificial neural networks showing better results among the three algorithms implemented at 91.8% accuracy.
A robust hybrid active contour model based on pre-fitting bias field correction for fast image segmentation

An array of existing active contour models is prone to suffering from the deficiencies of poor anti-noise ability, initialization sensitivity, and slow convergence. In order to handle these problems, a robust hybrid active contour method based on bias correction is proposed in this research paper The energy functional is formulated through incorporating the adaptive edge indicator function and level set formulation driven by bias field correction. The adaptive edge indicator function, which is formulated based on image gradient information, is utilized to detect object boundaries and accelerate the segmentation in the homogeneous region. The level set formulation is constructed based on an improved criterion function, in which bias field information is considered. Specifically, the bias field distribution is approximated through the local mean gray value algorithm as a prior. Moreover, a new regularized function is proposed so as to maintain the stability of curve evolution. The segmentation process is implemented by the optimized energy function and the novel regularized term. Compared to previous active contour models, the modified active contour method can yield more precise, stable, and efficient segmentation results on some challenging images.
Data Privacy Preserving Recommendations for Social Media

To deliver personalized services to users, recommendations systems are highly important. Despite the numerous advantages, implementing personalized recommendation systems usually necessitates the collection of users' personal data, especially online usage, which unnecessarily exposes users to significant privacy concerns. As a result, developing a functional privacy-preserving framework to prevent sensitive information from being inferred against inference attacks is critical. The main idea is to mask the users' information such that the original data is transformed with data anonymization to comply with data security and privacy regulation. As a result, using different entropy-based metrics, our system will quantify privacy leakage, which provides effective privacy protection for individuals.
Research on college English teaching model based on computer network technology

With the rapid development of social and economic globalization, China starts to pay more and more attention to college students’ English language education. At present, the rapid development of network technology in China has an obvious influence on People’s daily life, work and study, especially on China’s education cause. As a result of the influence, English language education of our country has been constantly reforming its teaching model, so as to improve the comprehensive English ability of college students and enable them to contribute to the development of China’s economic globalization. In this paper the research mainly focuses on the teaching model of college students’ English language education at current stage, and the proposal of education platform based on network technology, so as to promote college students’ comprehensive English ability and meet the requirements of current social development for college English teaching.
IAS: Intelligent Attendance System Based on Hybrid Criteria Matching

The traditional fingerprint-based presence system and the geographic location on the mobile phone are not always efficient. First of all, it may leak personal information and privacy. Besides, it is easy to cause a fraud when users alter the GPS information of their mobile phones. What’s more, the mode of the traditional attendance system is not flexible enough. In our demonstration, we construct an intelligent attendance system (abbreviated as IAS), which is based on hybrid criteria matching. The front-end of IAS is WeChat applet. In IAS, we take hybrid criteria matching model as the core. The model organically combines multiple matching criteria in order to ensure the strength of attendance management. The innovation of the system is hybrid criteria matching and multiple operation modes. The hybrid criteria matching model associates a variety of matching criteria such as image matching, Wi-Fi matching and geographic location matching, which can increase the supervision intensity of attendance on the basis of ensuring the information security of users. Meanwhile, this system also provides managers with multiple operation modes, such as periodic attendance mode and personnel batch management mode, to meet the needs of multiple attendance activities, and evidently reduces the burden on managers, which lead to higher efficiency.
Different Diagnostic Aids and the Improved Scope of Establishing Early Breast Cancer Diagnosis

A good prognosis of cancer therapies mainly depends on how early the condition is being diagnosed and hence the initiation of appropriate treatment measures. In view of availability and affordability of a wide array of diagnostic approaches and methods, an attempt is made to provide a brief insight into each technique that leads to a noninvasive mode of determining the cancerous condition at an early stage. Many of these modern methods utilize radioisotopes to determine the extent of the condition in three-dimensional anatomical manners unlike the invasive histological examination of biopsies. A sound knowledge of these modern methods and understanding the importance of early diagnosis and treatment response are much needed for the better prognosis and well-being of the condition. This paper presents the detailed review of types of cancer, its diagnosis, treatments and also the recent methods of diagnosis using deep learning techniques.
Secure Deduplication Method Based on Autoencoder

Message lock encryption is the basis of current secure deduplication research. However, since the theoretical basis of randomized Message-locked encryption (R-MLE) is bilinear mapping, the computational overhead is large. Although this method has semantic security and supports data deduplication, it is not practical. This paper solves the core problem that the deduplication method based on R-MLE is not efficient. The method of this paper does not take the improvement of encryption algorithm efficiency as a breakthrough, but introduces the autoencoder commonly used in image processing, which greatly improves the deduplication efficiency by greatly reducing the number of tag comparisons. Theoretical analysis and experiments show that the autoencoder has similarity and can be used to quickly filter tags that do not need to be compared. Compared with the local sensitive hash algorithm, autoencoder has randomness and higher security. The method of this paper can not only substantially improve the efficiency of R-MLE-based secure deduplication method, but also introduce artificial intelligence into secure deduplication research, which is expected to become a new research hotspot.
Prototype of College Student Attendance Using Radio Frequency Identification (RFID) at Musamus University

Abstract Student attendance in the classroom is one indicator for lecturers in learning activities. At Musamus University, student attendance has been managed digitally. The system is known as SIMAKAD. The weakness of SIMAKAD in doing attendance is in the process of entering data. Student attendance data can be inputted by the lecturer after completing the manual signature process by students. This can cause errors in entering data into the system. Whereas in the industrial revolution of the 4.0 era, the transformation of digital data in education was needed. For example, with the presence of Radio Frequency Identification (RFID) technology. The student attendance prototype using RFID was designed as a solution to solve problems in entering student attendance data at SIMAKAD. RFID technology can identify each student while attending college through reading the student TAG ID. By adding Internet of Things (IoT) capabilities, the results of reading student IDs will be sent directly through the internet and stored in a database server. The database can be directly used by lecturers as monitoring attendance of lectures. So no need to use manual absence.
The Effectiveness of Phrase Skip-Gram in Primary Care NLP for the Prediction of Lung Cancer

Neural models that use context-dependency in the learned text are computationally expensive. We compare the effectiveness (predictive performance) and efficiency (computational effort) of a context-independent Phrase Skip-Gram (PSG) model and a contextualized Hierarchical Attention Network (HAN) model for early prediction of lung cancer using free-text patient files from Dutch primary care physicians. The performance of PSG (AUROC 0.74 (0.69–0.79)) was comparable to HAN (AUROC 0.73 (0.68–0.78)); it achieved better calibration; had much less parameters (301 versus > 300k) and much faster (36 versus 460 s). This demonstrates an important case in which the complex contextualized neural models were not required.
Method for Assessing the Impact of Electric Energy Quality Indicators on the Technical Condition of Asynchronous Electric Motors

The paper proposes a new method for supporting the diagnostic decision-making in assessing the impact of electrical energy quality indicators (EQI) on the technical condition of asynchronous electric motors (TC of AM). This method is based on combining a heterogeneous cognitive model (HCM) with different types of vertices, fuzzy cause-and-effect relationships to obtain additional data on TC of AM; an artificial feedforward neural network (ANN) and the methodology of fuzzy cognitive modeling under conditions of measuring and expert data. The paper offers theoretical grounds for development of methods and decision support models in the diagnosis of AM (based on system-wide principles) and a generalized scheme of the proposed method. The authors considered a detailed numerical example of the developed method application for estimating the EQI on TC of AM in the oil industry. The method proposed provides obtaining the more complete and accurate data on TC of AM, as well as making scientifically based diagnostic decisions on AM serviceability.