Reviewerly/exHarmony huggingface dataset:
Towards retina-quality VR video streaming

Virtual reality systems today cannot yet stream immersive, retina-quality virtual reality video over a network. One of the greatest challenges to this goal is the sheer data rates required to transmit retina-quality video frames at high resolutions and frame rates. Recent work has leveraged the decay of visual acuity in human perception in novel gaze-contingent video compression techniques. In this paper, we show that reducing the motion-to-photon latency of a system itself is a key method for improving the compression ratio of gaze-contingent compression. Our key finding is that a client and streaming server system with sub-15ms latency can achieve 5x better compression than traditional techniques while also using simpler software algorithms than previous work.
Changing expectations related to digitalisation and socialisation in higher education. Horizon scanning of pre- and post-COVID-19 discourses

Based on related academic and semi-academic discourse, this paper aims to investigate the impacts of the COVID-19 pandemic on important actors and their expectations in the higher education (HE) sector. As open organisations, higher education institutions (HEIs) are influenced and shaped by different stakeholders’ numerous and often controversial demands. While HEIs strive to meet key actors’ needs, these expectations have a determinative role in the future of HEIs. Therefore, the future-oriented horizon scanning method was used for mapping the explicit demands of actors and for analysing alterations in expectations due to the pandemic. The horizon scanning showed that one of the most pressing expectations of HEIs in Central-Eastern Europe (CEE) was digitalisation even before the pandemic. Due to the pandemic, the awaited digitalisation in HE was realised within a few weeks, and it affected all actors. The tangible daily experience of the digital mode of education changed the priorities and expectations of the actors. In addition, this unexpected situation brought to the surface HEIs’ hidden potentials, resources and responsibilities. Although the role of digitalisation in the future of HE is clearly manifested, the impacts of social restrictions as well as the effects of the digitalisation of learning and life in general were perceived primarily in the field of socialisation. As a result, the need for socialisation has increased. The article highlights the dynamic interconnection between digitalisation and socialisation, and the changing expectations and voices of stakeholders, which should be considered when HEIs choose their future paths in the post-COVID-19 era.
FATNN: Fast and Accurate Ternary Neural Networks

Ternary Neural Networks (TNNs) have received much attention due to being potentially orders of magnitude faster in inference, as well as more power efficient, than full-precision counterparts. However, 2 bits are required to encode the ternary representation with only 3 quantization levels leveraged. As a result, conventional TNNs have similar memory consumption and speed compared with the standard 2-bit models, but have worse representational capability. Moreover, there is still a significant gap in accuracy between TNNs and full-precision networks, hampering their deployment to real applications. To tackle these two challenges, in this work, we first show that, under some mild constraints, computational complexity of the ternary inner product can be reduced by 2×. Second, to mitigate the performance gap, we elaborately design an implementation-dependent ternary quantization algorithm. The proposed framework is termed Fast and Accurate Ternary Neural Networks (FATNN). Experiments on image classification demonstrate that our FATNN surpasses the state-of-the-arts by a significant margin in accuracy. More importantly, speedup evaluation compared with various precision is analyzed on several platforms, which serves as a strong benchmark for further research. Source code and models are available at: https://github.com/MonashAI/QTool
Solving the Max-3-Cut Problem with Coherent Networks

Many computational problems are intractable through classical computing and, as Moore's law is drawing to a halt, demand for finding alternative methods in tackling these problems is growing. Here, we realize a liquid light machine for the NP-hard max-3-cut problem based on a network of synchronized exciton-polariton condensates. We overcome the binary limitation of the decision variables in Ising machines using the continuous-phase degrees of freedom of a coherent network of polariton condensates. The condensate network dynamical transients provide optically-fast annealing of the XY Hamiltonian. We apply the Goemans and Williamson random hyperplane technique, discretizing the XY ground state spin configuration to serve as ternary decision variables for an approximate optimal solution to the max-3-cut problem. Applications of the presented coherent network are investigated in image-segmentation tasks and in circuit design.
Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification

As a research hotspot in the field of natural language processing (NLP), sentiment analysis can be roughly divided into explicit sentiment analysis and implicit sentiment analysis. However, due to the lack of obvious emotion words in the implicit sentiment analysis task and because the sentiment polarity contained in implicit sentiment words is not easily accurately identified by existing text-processing methods, the implicit sentiment analysis task is one of the most difficult tasks in sentiment analysis. This paper proposes a new preprocessing method for implicit sentiment text classification; this method is named Text To Picture (TTP) in this paper. TTP highlights the sentiment differences between different sentiment polarities in Chinese implicit sentiment text with the help of deep learning by converting original text data into word frequency maps. The differences between sentiment polarities are used as sentiment clues to improve the performance of the Chinese implicit sentiment text classification task. It does this by transforming the original text data into a word frequency map in order to highlight the differences between the sentiment polarities expressed in the implicit sentiment text. We conducted experimental tests on two common datasets (SMP2019, EWECT), and the results show that the accuracy of our method is significantly improved compared with that of the competitor's. On the SMP2019 dataset, the accuracy-improvement range was 4.55-7.06%. On the EWECT dataset, the accuracy was improved by 1.81-3.95%. In conclusion, the new preprocessing method for implicit sentiment text classification proposed in this paper can achieve better classification results.
Analysis of a Huge Amount of Network Traffic Based on Quantum Machine Learning

A method for analyzing network traffic based on quantum machine learning is presented. A method for network traffic encoding into quantum computer terms is developed. The experimental results show the superiority of the proposed approach over traditional machine learning methods for detecting network attacks.
Evaluation of Computer-Aided Detection (CAD) in Screening Automated Breast Ultrasound Based on Characteristics of CAD Marks and False-Positive Marks

The present study evaluated the effectiveness of computer-aided detection (CAD) system in screening automated breast ultrasound (ABUS) and analyzed the characteristics of CAD marks and the causes of false-positive marks. A total of 846 women who underwent ABUS for screening from January 2017 to December 2017 were included. Commercial CAD was used in all ABUS examinations, and its diagnostic performance and efficacy in shortening the reading time (RT) were evaluated. In addition, we analyzed the characteristics of CAD marks and the causes of false-positive marks. A total of 1032 CAD marks were displayed based on the patient and 534 CAD marks on the lesion. Five cases of breast cancer were diagnosed. The sensitivity, specificity, PPV, and NPV of CAD were 60.0%, 59.0%, 0.9%, and 99.6% for 846 patients. In the case of a negative study, it was less time-consuming and easier to make a decision. Among 530 false-positive marks, 459 were identified clearly for pseudo-lesions; the most common cause was marginal shadowing, followed by Cooper's ligament shadowing, peri-areolar shadowing, rib, and skin lesions. Even though CAD does not improve the performance of ABUS and a large number of false-positive marks were detected, the addition of CAD reduces RT, especially in the case of negative screening ultrasound.
Image Steganography Using Blowfish Algorithm and Transmission via Apache Kafka

Steganography refers to a message or record that is hidden within a virtual photograph, audio/video document or any physical object. There are extraordinary carrier document formats that can be used which includes textual content steganography, image steganography and audio/video steganography. Picture steganography is famous because of their frequency on the net. It is a method used to hide the data inside the cover photo. The Least Significant Bit (LSB) embedding is one such method in which the least bit of the picture's pixel is replaced with a statistics bit. In this proposed system blowfish encryption will be used. The primary attention is concerned with enforcing steganography for photographs, with an advanced safety and picture resolution. The main goal here is to hide the sensitive information and secure up the embedded data by encrypting it.
Volcanic Ash Region Path Planning Based on Improved A-Star Algorithm

In civil aviation flight path planning, in order to effectively reduce the safety threat caused by the volcanic ash area to the civil aviation flight, factors such as the speed and acceleration of the aircraft in the volcanic ash area must be considered. In this paper, we propose an improved A-star algorithm by adopting the concept of potential collision set and using the velocity vector idea of optimal reciprocal collision avoidance (ORCA). The improved A-star algorithm selects the optimal speed range of the aircraft from the limited elements, obtains the speed and acceleration set of the aircraft in the volcanic ash area, calculates the flight path through the displacement increment, continuously refreshes the global starting point to the target point, and finally obtains the final path planning results by calculating the total cost value. The improved A-star algorithm is used to plan two flight paths from Madrid to Cairo and Algiers to Rome in volcanic ash areas. The verification results show that the improved A-star algorithm optimizes the flight path planning in the volcanic ash area and has the advantages of less search nodes, a small search range, and short computing time.
The hard-working virtual agent in the service encounter boosts customer satisfaction

Virtual agents (VAs) are used increasingly as representatives of the firm in retail and service settings – particularly in online environments. Existing studies indicate that the customer's experience is enhanced if VAs resemble humans, which seems to imply that what has been learned over the years in research about the influence of the human employee's behavior on customer satisfaction may be applicable also to VA behavior. This study explores one factor, effort, which has a positive impact on customer satisfaction when it characterizes the human employee in service encounters. Although a VA (i.e., a computer program) cannot experience effort, it was assumed that human sensitivity to other humans' effort, and a tendency to anthropomorphize non-human agents, would make human customers susceptible to effort-expending signals when they interact with a VA. To examine this assumption, data were collected from customers who had been interacting with existing VAs. The results indicate that three specific behaviors (engaging in personal conversation, listening, and display of warmth) boost the customer's perceptions of VA effort, and that perceived VA effort has a positive impact on customer satisfaction.
Evaluation Method of Wushu Teaching Quality Based on Fuzzy Clustering

The existing teaching quality evaluation methods cannot calculate the distance between the data points of teaching dataset and the center points of the large density grid, which leads to the poor classification of teaching data and the low accuracy of teaching quality evaluation. Therefore, a method of teaching quality evaluation of Wushu based on fuzzy clustering is proposed. In order to improve the comprehensiveness of teaching quality evaluation, the lost data of teaching resources were recovered. Based on this, the grid index of martial arts teaching data is established, and the relationship model between teaching quality and martial arts achievement is constructed. Based on the characteristics of Wushu teaching resources, the fuzzy clustering method is introduced to calculate the distance between each data point and the grid center with high data density. The experimental results show that the evaluation accuracy of the teaching quality is high, and the lost data can be accurately recovered. The evaluation efficiency, reliability, and stability of Wushu teaching quality are ideal.
Foreign direct investment and economic growth in Sub-Saharan Africa: the complementary role of economic freedom and financial market fragility

In line with the neoclassical growth theory, this study examines the complementary role of economic freedom and financial fragility in the foreign direct investment (FDI) – growth nexus for 36 Sub-Saharan (SSA) Africa countries spanning the period 1998 to 2016. The study hypothesises that for FDI to translate into growth, financial market development that account for fragility and economic freedom should be present in the host country. The uniqueness of this study lies in the use of financial market data that accounts for financial market fragility and aggregate score of economic freedom. Using fully modified ordinary least squares technique, the study reveals that economic freedom and a well-developed financial market complement FDI in promoting economic growth in SSA. This study highlights the need to strengthen the financial system and promote economic freedom to enhance FDI’s effect on economic growth.
Hybrid Blockchain-Enabled Security in Cloud Storage Infrastructure Using ECC and AES Algorithms

Due to the high growth of Internet of Things and cloud computing services it has brought great changes within the human lifestyle in various fields such as (medical, agricultural, educational, military, environmental, etc.). So, it was necessary to understand the building blocks' basic of the Internet of Things and identify weaknesses and data security of user. Blockchain plays an important role in the implementation of security aspects in cloud computing. In this work, we have analyzed the security aspects of data stored in the cloud through hybrid security system enabled with blockchain technology using cryptographic algorithms ECC and AES. Our framework is having higher security with more efficiency than others.
Extracting New Temporal Features to Improve the Interpretability of Undiagnosed Type 2 Diabetes Mellitus Prediction Models

Type 2 diabetes mellitus (T2DM) often results in high morbidity and mortality. In addition, T2DM presents a substantial financial burden for individuals and their families, health systems, and societies. According to studies and reports, globally, the incidence and prevalence of T2DM are increasing rapidly. Several models have been built to predict T2DM onset in the future or detect undiagnosed T2DM in patients. Additional to the performance of such models, their interpretability is crucial for health experts, especially in personalized clinical prediction models. Data collected over 42 months from health check-up examinations and prescribed drugs data repositories of four primary healthcare providers were used in this study. We propose a framework consisting of LogicRegression based feature extraction and Least Absolute Shrinkage and Selection operator based prediction modeling for undiagnosed T2DM prediction. Performance of the models was measured using Area under the ROC curve (AUC) with corresponding confidence intervals. Results show that using LogicRegression based feature extraction resulted in simpler models, which are easier for healthcare experts to interpret, especially in cases with many binary features. Models developed using the proposed framework resulted in an AUC of 0.818 (95% Confidence Interval (CI): 0.812-0.823) that was comparable to more complex models (i.e., models with a larger number of features), where all features were included in prediction model development with the AUC of 0.816 (95% CI: 0.810-0.822). However, the difference in the number of used features was significant. This study proposes a framework for building interpretable models in healthcare that can contribute to higher trust in prediction models from healthcare experts.
Efficient phase-shift error compensation algorithm based on probability density function

In non-digital projection systems, the inaccuracy of phase shifts is one of the most important nonlinear errors. In this paper, an efficient and universal compensation algorithm is proposed, which does not need to find the exact phase shift amounts, but to compensate the periodical errors by a universal two-coefficient error model. With this model, only two error coefficients should to be searched for any multi-step phase shift algorithm. To find the error coefficients, a correlation process and a tool known as the probability density function (PDF) are used. First, a set of pre-simulated PDF curves are generated with the different error coefficients. Second, a practical PDF curve from captured fringes is calculated. Finally, the correlation process is carried out to locate the most similar simulated PDF curve, whose error coefficients are the practical ones. In this paper, we demonstrate the compensation for three, four and five-step phase-shifting algorithms separately. Experimental results show that this method has high universality, and can meet the requirements of rapid and high accuracy measurement.
Egocentric Pose Estimation from Human Vision Span

Estimating camera wearer's body pose from an egocentric view (egopose) is a vital task in augmented and virtual reality. Existing approaches either use a narrow field of view front facing camera that barely captures the wearer, or an extended head-mounted top-down camera for maximal wearer visibility. In this paper, we tackle the egopose estimation from a more natural human vision span, where camera wearer can be seen in the peripheral view and depending on the head pose the wearer may become invisible or has a limited partial view. This is a realistic visual field for user-centric wearable devices like glasses which have front facing wide angle cameras. Existing solutions are not appropriate for this setting, and so, we propose a novel deep learning system taking advantage of both the dynamic features from camera SLAM and the body shape imagery. We compute 3D head pose, 3D body pose, the figure/ground separation, all at the same time while explicitly enforcing a certain geometric consistency across pose attributes. We further show that this system can be trained robustly with lots of existing mocap data so we do not have to collect and annotate large new datasets. Lastly, our system estimates egopose in real time and on the fly while maintaining high accuracy.
A Study on Reversible Data Hiding Technique Based on Three-Dimensional Prediction-Error Histogram Modification and a Multilayer Perceptron

In the past few years, with the development of information technology and the focus on information security, many studies have gradually been aimed at data hiding technology. The embedding and extraction algorithms are mainly used by the technology to hide the data that requires secret transmission into a multimedia carrier so that the data transmission cannot be realized to achieve secure communication. Among them, reversible data hiding (RDH) is a technology for the applications that demand the secret data extraction as well as the original carrier recovery without distortion, such as remote medical diagnosis or military secret transmission. In this work, we hypothesize that the RDH performance can be enhanced by a more accurate pixel value predictor. We propose a new RDH scheme of prediction-error expansion (PEE) based on a multilayer perceptron, which is an extensively used artificial neural network in plenty of applications. The scheme utilizes the correlation between image pixel values and their adjacent pixels to obtain a well-trained multilayer perceptron so that we are capable of achieving more accurate pixel prediction results. Our data mapping method based on the three-dimensional prediction-error histogram modification uses all eight octants in the three-dimensional space for secret data embedding. The experimental results of our RDH scheme show that the embedding capacity greatly increases and the image quality is still well maintained.
Dynamic-Aware Federated Learning for Face Forgery Video Detection

The spread of face forgery videos is a serious threat to information credibility, calling for effective detection algorithms to identify them. Most existing methods have assumed a shared or centralized training set. However, in practice, data may be distributed on devices of different enterprises that cannot be centralized to share due to security and privacy restrictions. In this article, we propose a Federated Learning face forgery detection framework to train a global model collaboratively while keeping data on local devices. In order to make the detection model more robust, we propose a novel Inconsistency-Capture module (ICM) to capture the dynamic inconsistencies between adjacent frames of face forgery videos. The ICM contains two parallel branches. The first branch takes the whole face of adjacent frames as input to calculate a global inconsistency representation. The second branch focuses only on the inter-frame variation of critical regions to capture the local inconsistency. To the best of our knowledge, this is the first work to apply federated learning to face forgery video detection, which is trained with decentralized data. Extensive experiments show that the proposed framework achieves competitive performance compared with existing methods that are trained with centralized data, with higher-level security and privacy guarantee.
TweezBot: An AI-Driven Online Media Bot Identification Algorithm for Twitter Social Networks

In the ultra-connected age of information, online social media platforms have become an indispensable part of our daily routines. Recently, this online public space is getting largely occupied by suspicious and manipulative social media bots. Such automated deceptive bots often attempt to distort ground realities and manipulate global trends, thus creating astroturfing attacks on the social media online portals. Moreover, these bots often tend to participate in duplicitous activities, including promotion of hidden agendas and indulgence in biased propagation meant for personal gain or scams. Thus, online bots have eventually become one of the biggest menaces for social media platforms. Therefore, we have proposed an AI-driven social media bot identification framework, namely TweezBot, which can identify fraudulent Twitter bots. The proposed bot detection method analyzes Twitter-specific user profiles having essential profile-centric features and several activity-centric characteristics. We have constructed a set of filtering criteria and devised an exhaustive bag of words for performing language-based processing. In order to substantiate our research, we have performed a comparative study of our model with the existing benchmark classifiers, such as Support Vector Machine, Categorical Naïve Bayes, Bernoulli Naïve Bayes, Multilayer Perceptron, Decision Trees, Random Forest and other automation identifiers.