Reviewerly/exHarmony huggingface dataset:
Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution

Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.
Taylor saves for later: Disentanglement for video prediction using Taylor representation

Video prediction is a challenging task with wide application prospects in meteorology and robot systems. Existing works fail to trade off short-term and long-term prediction performances and extract robust latent dynamics laws in video frames. We propose a two-branch seq-to-seq deep model to disentangle the Taylor feature and the residual feature in video frames by a novel recurrent prediction module (TaylorCell) and residual module, based on a novel principle for feature separation. TaylorCell can expand the video frames’ high-dimensional features into the finite Taylor series to describe the latent laws. In TaylorCell, we propose the Taylor prediction unit (TPU) and the memory correction unit (MCU). TPU employs the first input frame’s derivative information to predict the future frames, avoiding error accumulation. MCU distills all past frames’ information to correct the predicted Taylor feature from TPU. Correspondingly, the residual module extracts the residual feature complementary to the Taylor feature. Due to the characteristic of the Taylor series, our model works better on datasets with short-range spatial dependencies and stable dynamics. On three generalist datasets (Moving MNIST, TaxiBJ, Human 3.6), our model reaches and outperforms the state-of-the-art model in the short-term and long-term forecast, respectively. Ablation experiments demonstrate the contributions of each module in our model.
Speaker anonymization by modifying fundamental frequency and x-vector singular value

Speaker anonymization is a method of protecting voice privacy by concealing individual speaker characteristics while preserving linguistic information. The VoicePrivacy Challenge 2020 was initiated to generalize the task of speaker anonymization. In the challenge, two frameworks for speaker anonymization were introduced; in this study, we propose a method of improving the primary framework by modifying the state-of-the-art speaker individuality feature (namely, x-vector) in a neural waveform speech synthesis model. Our proposed method is constructed based on x-vector singular value modification with a clustering model. We also propose a technique of modifying the fundamental frequency and speech duration to enhance the anonymization performance. To evaluate our method, we carried out objective and subjective tests. The overall objective test results show that our proposed method improves the anonymization performance in terms of the speaker verifiability, whereas the subjective evaluation results show improvement in terms of the speaker dissimilarity. The intelligibility and naturalness of the anonymized speech with speech prosody modification were slightly reduced (less than 5% of word error rate) compared to the results obtained by the baseline system.
Machine Learning and Small Data

Abstract Commonly used machine learning applications seem to relate to big data. This article provides a gentle review of machine learning and shows why machine learning can be applied to small data too. An example of applying machine learning to screen irregularity reports is presented. In the example, the support vector machine and multinomial naïve Bayes methods were used and compared. The performance of machine learning was compared to human experts in terms of flagging records to be excluded from equating. The application of machine learning seemed to be successful, although the data only consisted of a couple of thousand records. Recommendations in using machine learning are provided.
Hourly Photovoltaic Power Forecasting Using CNN-LSTM Hybrid Model

In addition to being environmentally friendly, Photovoltaic (PV) power generation has gained popularity globally because it is also abundant in nature. However, the stochastic nature of its radiative energy has made its use undesirable to some consumers at various levels. In this manuscript, a state-of-the-art hybrid deep learning model is introduced to forecast PV power on an hourly basis. The CNN-LSTM model is a hybrid of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models. This hybrid model was trained using historical data of Johannesburg city with two different data compositions. The first experiment involved training the model with 80% of data collected over one year from Johannesburg city. The second experiment involved training the model with 80% of ten-year historical data from the same city. Using Root Mean Square Error (RMSE) as the evaluation metric, the results obtained were compared with results from CNN and LSTM time series models. It was discovered that the CNN-LSTM model outperformed all the other models used in the same experiment. For instance, at 1,000 epochs and with ten years of data composition, the CNN-LSTM hybrid model gave an RMSE value of 7.21 Watts/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> while the LSTM and CNN time series models recorded 7.30 Watts/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 12.67 Watts/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> respectively. This outcome indicates that a hybrid of CNN and LSTM for short-term PV power forecasting performs better than when the models are used individually. It is suggested that deploying the CNN-LSTM model for short-term PV power forecasting in Johannesburg’s solar farms could be beneficial in stabilizing the PV power connection to the smart grid.
Utilizing Bidirectional Encoder Representations from Transformers for Answer Selection

Pre-training a transformer-based model for the language modeling task in a large dataset and then fine-tuning it for downstream tasks has been found very useful in recent years. One major advantage of such pre-trained language models is that they can effectively absorb the context of each word in a sentence. However, for tasks such as the answer selection task, the pre-trained language models have not been extensively used yet. To investigate their effectiveness in such tasks, in this paper, we adopt the pre-trained Bidirectional Encoder Representations from Transformer (BERT) language model and fine-tune it on two Question Answering (QA) datasets and three Community Question Answering (CQA) datasets for the answer selection task. We find that fine-tuning the BERT model for the answer selection task is very effective and observe a maximum improvement of 13.1% in the QA datasets and 18.7% in the CQA datasets compared to the previous state-of-the-art.
InVesa 1.0: The Conceptual Framework of Interactive Virtual Academic Advisor System based on Psychological Profiles

Interactive Virtual Academic System Prototype (InVesa) is a conceptual automated system dedicated to students where a theory of personality test, Holland Code (RIASEC) is integrated to aid students in selecting the ideal elective subjects for their Cognitive Science course. With InVesa, rather than giving alerts to academic advisors, students are assigned personalized advice by the system on the recommended elective subjects based on their RIASEC result. A student may choose to accept a list of subjects' recommendations from the system or to retake the RIASEC test until they are satisfied with the suggested subjects provided by the system. In addition, students with less time meeting their academic advisors to consult on their subject enrolment may avoid general issues that are common in the standard appointment registration system, such as multiple registrations and long queues when scheduling an appointment. It can be concluded that the proposed application meets the requirements. The proposed application provides a convenient and efficient solution to gain the required information and will benefit the targeted users, which achieves and fulfills the forth of Sustainable Development Goals (SDG) of Quality Education for better education quality for all in terms of gender, age and status.
Phone-Level Prosody Modelling With GMM-Based MDN for Diverse and Controllable Speech Synthesis

Generating natural speech with a diverse and smooth prosody pattern is a challenging task. Although random sampling with phone-level prosody distribution has been investigated to generate different prosody patterns, the diversity of the generated speech is still very limited and far from what can be achieved by humans. This is largely due to the use of uni-modal distribution, such as single Gaussian, in the prior works of phone-level prosody modelling. In this work, we propose a novel approach that models phone-level prosodies with a GMM-based mixture density network(MDN) and then extend it for multi-speaker TTS using speaker adaptation transforms of Gaussian means and variances. Furthermore, we show that we can clone the prosodies from a reference speech by sampling prosodies from the Gaussian components that produce the reference prosodies. Our experiments on LJSpeech and LibriTTS dataset show that the proposed method with GMM-based MDN not only achieves significantly better diversity than using a single Gaussian in both single-speaker and multi-speaker TTS, but also provides better naturalness. The prosody cloning experiments demonstrate that the prosody similarity of the proposed method with GMM-based MDN is comparable to recent proposed fine-grained VAE while the target speaker similarity is better.
Neural Emotion Director: Speech-preserving semantic control of facial expressions in “in-the-wild” videos

In this paper, we introduce a novel deep learning method for photo-realistic manipulation of the emotional state of actors in “in-the-wild” videos. The proposed method is based on a parametric 3D face representation of the actor in the input scene that offers a reliable disentanglement of the facial identity from the head pose and facial expressions. It then uses a novel deep domain translation framework that alters the facial expressions in a consistent and plausible manner, taking into account their dynamics. Finally, the altered facial expressions are used to photo-realistically manipulate the facial region in the input scene based on an especially-designed neural face renderer. To the best of our knowledge, our method is the first to be capable of controlling the actor's facial expressions by even using as a sole input the semantic labels of the manipulated emotions, while at the same time preserving the speech-related lip movements. We conduct extensive qualitative and quantitative evaluations and comparisons, which demonstrate the effectiveness of our approach and the especially promising results that we obtain. Our method opens a plethora of new possibilities for useful applications of neural rendering technologies, ranging from movie post-production and video games to photo-realistic affective avatars.
Exploring data structure alternatives in the RDB to NoSQL document store conversion process

NoSQL databases have emerged as an alternative to relational databases, which do not meet all the currently imposed scenarios. Large applications that handle a variety of data formats often use several types of databases and the need to migrate data between them is common. There are several approaches that perform this type of conversion. However, the process of choosing the ideal data structuring for the application requirements is not a trivial task. In this paper, we present a RDB to NoSQL conversion approach composed by steps for defining, evaluating and comparing candidate NoSQL schemas (data structuring) in relation to the applications access pattern, before migrating RDB data to NoSQL document store. We present a set of query-based metrics and scores to assist the user in the schema selection process and a framework to migrate RDB data to NoSQL format. Finally, we present experiments to evaluate the benefits of our approach and the correlation between metrics results and query implementation effort after migrating RDB to NoSQL.
Quantum estimation of a time-dependent perturbation

We analyze the estimation of a time-dependent perturbation acting on a continuously monitored quantum system. We describe the temporal fluctuations of the perturbation by a hidden Markov model, and we combine quantum measurement theory and classical filter theory into a time-evolving hybrid quantum and classical trajectory. The forward-backward analysis that permits smoothed estimates of classical hidden Markov models has a counterpart in the theory of retrodiction and past quantum states. As a specific example, we apply our hybrid trajectory and past-quantum-state theory to the sensing of a fluctuating magnetic field by microwave interrogation of a single quantum spin.
User Perception of Data Breaches

<roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Background:</b></roman> Data breaches happen when an unauthorized party gains access to personally identifiable information. They are becoming more common and impactful, raising serious concerns for individuals as well as companies. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Literature review:</b></roman> Although there is considerable literature on users’ mental models in security and privacy, there has been limited study of mental models related to data breaches. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Research questions:</b></roman> 1. How do users understand data breaches? 2. What are their perceptions of the causes, responsibilities, and consequences, as well as possible prevention and appropriate follow up? <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Methodology:</b></roman> We explored end-user understanding of internet data breaches by conducting a study with 35 participants. They were asked to draw their understanding of data breaches and answer some open-ended and closed-ended questions afterwards. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Results/discussion:</b></roman> Although their drawings varied in detail and complexity, we identified four patterns in the participants’ drawings: they illustrated abstractions of attacks to gain administrator access, end-user access, backdoor access, or access using database server vulnerabilities. We found that participants had a basic model of how an internet data breach happens, but with significant uncertainties regarding system vulnerabilities, causes, consequences, prevention methods, and follow-up steps after a breach. <roman xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Conclusions:</b></roman> In all, end-user mental models of internet data breaches are basic and show gaps that emphasize the need for improved communication to increase users’ awareness and help them hold companies accountable.
Sentimental Knowledge Graph Analysis of the COVID-19 Pandemic Based on the Official Account of Chinese Universities

With the advent of the new media mobile Internet era, the network public opinion in colleges and universities, as an extension of social network public opinion, is also facing a crisis in the prevention, control, and governance system. In this paper, the Fiddler was used to collect the comments and other relevant data of the COVID-19 topic articles on the WeChat Official Accounts of China’s top ten universities in 2020. The BILSTM_LSTM sentiment analysis model was used to analyze the sentiment tendency of the comments, and the LDA topic model was used to mine the topics of the comments with different emotional attributes at different stages of COVID-19. Based on sentiment analysis and text mining, entities and relationships in the theme graph of public opinion events in colleges and universities were identified, and the Neo4j graph database was established to construct the sentimental knowledge graph of the pandemic theme of university public accounts. People’s attitudes in university public opinion are easily influenced by a variety of factors, and the degree of emotional disposition changes over time, with the stage the pandemic is in, and with different commentators; official account opinion topics change with the development of the time stage of the pandemic, and students’ positive and negative comment topics show a diverse trend. By incorporating topic mining into the sentimental knowledge graph, the graph can realize functions such as the emotion retrieval of comments on university public numbers, a source search of security threats in university social networks, and monitoring of comments on public opinion under the theme of the pandemic, which provides new ideas for further exploring the research and governance system of university network public opinion and is conducive to preventing and resolving campus public opinion crises.
A Video-Quality Driven Strategy in Short Video Streaming

In recent years, fueled by the rapid advances in high-speed mobile networks, streaming short-form videos over mobile devices (e.g., TikTok) is ubiquitous among mobile users. Despite its widespread application, our investigation revealed that the video quality experienced by the viewers exhibits substantial fluctuations over different short video sessions, so the Quality-of-Experience (QoE) is in fact far from optimal. To tackle this problem, this work develops a novel strategy called Quality Aware Short Video Streaming (QASVS), which employs deep reinforcement learning to automatically generate a quality-driven bitrate decision model to dynamically determine the bitrate for each session. Extensive evaluations show that QASVS can maintain much more consistent video quality across the video sessions. Moreover, compared to the state-of-the-art algorithms, it can improve the video quality by 11.1%~27.9% and significantly reduce the rebuffering events. Therefore, QASVS offers an effective solution for high-performance short video services.
Save the Earth: Teaching Environment Studies using Augmented Reality

Today, augmented reality (AR) technology is regarded as a significant technology in education. AR can enhance educational content and learning experience. The present paper analyzes the use of AR in teaching environmental studies to young students. In this work, multiple AR markers interact with one another to create an environmental atmosphere for students. Students could conceptualize the complex issue of global warming through visualization via AR technology. The proposed system is easy to operate for the novice as well as old teachers. Responses of teachers regarding the effectiveness of the system were analyzed on the SUS scale. Results showed that the teachers favored the system while teaching EVS in their classrooms.
A Systematic Evaluation of EM and Power Side-Channel Analysis Attacks on AES Implementations

The effectiveness of coarse- and fine-grained electromagnetic (EM) side-channel analysis (SCA) attacks, as well as power SCA attacks, are empirically evaluated on implementations of the Advanced Encryption Standard (AES) algorithm. Coarse-grained EM and power SCA attacks use a single sensor configuration to measure the aggregated EM emanation or power consumption for a large set of encryptions, and then analyze this set of signals to recover all encryption key bytes. In contrast, fine-grained EM SCA attacks first perform high-resolution scans with relatively small probes in multiple orientations to localize on-chip information leakage, and then use a specific probe configuration for each key byte to collect and analyze signals. The fine-grained EM SCA attacks are found to be up to >70× more effective than coarse-grained EM and power SCA attacks when extracting the key from 3 implementations of 128-bit AES. They are constrained, however, by the potentially prohibitive cost of the initial search to identify effective probe configurations. Search protocols, categorized according to the threat model, to reduce this one-time acquisition cost are presented and are found to require ~8–15× fewer measurements compared to an exhaustive search.
FORECASTING BITCOIN PRICES WITH THE ANFIS MODEL

Recently, Bitcoin has gained great importance in the cryptocurrency market with the highest market capitalization. Investors and researchers have attempted to find out the drivers of Bitcoin prices and if they are predictable. However, there is only limited research in the literature that identifies the most effective economic and technical variables for predicting Bitcoin prices using machine learning models. Thus, in this study, the future Bitcoin prices utilizing several economic and technical factors using the ANFIS model are aimed to forecasted between 01.05.2013 - 26.02.2021 periods. The findings show that the ANFIS model produced accurate and consistent predicting results that are in line with the real data. As a result, investors who wish to make a profit by predicting future Bitcoin values might consider using the ANFIS approach as a forecasting tool.
Development and Implementation of a Digital Manufacturing Demonstrator for Engineering Education

The fourth industrial revolution (Industry 4.0) offers enhanced processing efficiencies and reduced downtime, however there is a lack of manufacturing training equipment for those seeking practical training in the key underpinning digital technologies. This paper reports on the development of a digital manufacturing training demonstrator, called the PERFORM turbine demonstrator, which incorporates four aspects of Industry 4.0, that of the Internet of Things (IoT), Augmented Reality (AR), the digital twin and additive manufacturing (3D printing). The program involves the printing of a polymer turbine and its testing using a turbine demonstrator system. This incorporates the use of an Arduino microcontroller, and sensors to monitor the systems variable pump speed, turbine speed, temperature, and humidity. A cloud-based platform allows for data logging and passing to the AR application, visualization, and system control. The design and development of the system is presented, alongside the results of pilot training programme, which involved undergraduate engineering students and industry trainees, on the practical application of Industry 4.0. This paper demonstrated the effectiveness of the PERFORM demonstrator in successfully integrating different digital technologies in the same system.