Reviewerly/exHarmony huggingface dataset:
AUIF: An Adaptive User Interface Framework for Multiple Devices

Nowadays, it is becoming increasingly common for users to use multiple devices in their daily lives. Smartphones, tablets, laptops and other devices constitute a multi-device environment for users. Making full use of the multi-device environments so that users can work conveniently and efficiently has been a challenging problem in the field of user interface (UI) research. Here, we present AUIF, a web-based application framework for dynamically creating distributed user interfaces (DUIs) across multiple devices. We propose multi-device collaboration patterns and UI components allocation algorithm and combine them in our implementation. A prototype system based on AUIF is designed as an illustration of concept. Finally, user experiments based on the prototype system are carried out, and the result verifies the effectiveness of AUIF in actual use.
Research on Plug-and-Play Twin-Field Quantum Key Distribution

In this paper, we have proposed a plug-and-play twin-field quantum key distribution scheme that has passive mode-matching characteristics of quantum signals and can be operated stably. Also, we have experimentally demonstrated the implementation feasibility of our proposed scheme.
Real-Time Forensic Marking Method based on Tiles Parallel Processing

This paper proposes a real-time forensic mark technology based on the HEVC/MPEG-H Part.2(H.265) standards. This technology ,on a GPU multi-core basis, utilizes the Entropy Coding technology only to approach the QTC (Quantified Transform Coordinators) area and inserts the mark by applying the XOR operation to the n-bits of the forensic mark bitstream and the key based pseudo random n-bits. Compared to the previous researches, the PSNR was 10dB higher, and the insertion speed increased by 70 times at 8K and 13 times at 4K. And robustness shows an average of 84.37% in 8K video.
Generative Adversarial Neural Networks for Text-to-Image Synthesis of Dog Images

Computer vision has been making advancements in the recent years with generative algorithms. A set of artificial intelligence algorithms creates their own data based on the training set they are trained on. Out of all generative algorithms that exist, generative adversarial neural networks are making rapid strides due to their ability to mimic the data distribution of the training data that they are trained upon which can be constrained using a text caption for the images. Using this concept of conditioning, the generative adversarial neural network, a text-to-image synthesis model can be created that gives out realistic images. This paper will explore the concept of creating a text-to-image synthesis for dog images that are conditioned based on certain caption words.
Distributed representation of tags for Active Zero Shot learning

Extreme multi-labeled classification (XMLC) refers to the problem of tagging items to its most relevant subset of class labels from an extremely large set of labels. Since it is practically difficult to obtain training instances for each of such a large set of labels, the problem is better tackled by a zero-shot approach, where training instances of only a small subset of labels are used. In this paper, we focus on active zero-shot classification, where one can select which subset of labels to obtain training data for. The challenge is to intelligently select a small subset of labels (seen classes) for which to obtain training data, such that the trained model can accurately predict a large number of unseen labels. We present a model named Distributed Representation of Tags for Active Zero-Shot Learning (DiRTAZL), which learns label similarities from an external knowledge source, to intelligently select the seen classes. We experimented on three data sets to demonstrate that our proposed model is superior to prior models for active zero-shot learning paradigm.
Agricultural Products Risk Assessment Model Based on Enhanced RNN

In recent years, with the improvement of the people’s living conditions and the arousal of consumers’ consciousness, the problem of Agricultural Product Quality Safety becomes a hot topic that more and more people pay attention to. This paper presents a safety risk assessment model for agricultural product quality and safety. The effectiveness and feasibility of applying neural network models to Risk Assessment are analyzed. Aiming at the problems of the disappearance of gradients in traditional recurrent neural networks when stretching the length of the network, an improved recurrent neural network algorithm is proposed. It can predict the risks better, and finally, achieve the purpose of providing a solid foundation for improving the agricultural product quality and risk early warning system in the future, thereby effectively reducing food safety risks.
Unsupervised Topology Assessment in Smart Homes

Nowadays, a wide range of IOT devices are deployed in a variety of environments and settings to enhance the quality of human life. With a huge amount of data being generated from them, privacy is becoming a very big concern. To determine the level of privacy breach that can be achieved, we introduce in this paper, an unsupervised approach to visualize the sensor network, which in turn divulges the indoor topology of a smart home. The results are obtained from a smart environment by conducting a series of deductions and analysis on sensor datasets generated by a smart home. The experimental results demonstrate that our approach is able to deduce room-level sensor topology for a smart home even without the knowledge of any activity label or any prior information about the environment.
A Critical Evaluation of Validation Practices in the Forensic Acquisition of Digital Evidence in South Africa

Accepted digital forensics practice requires the tools used in the forensic acquisition of digital evidence to be validated, meaning that the tools perform as intended. In terms of Sect. 15 of the Electronic Communications and Transactions Act 25 of 2002 in South Africa, validation would contribute to the reliability of the digital evidence. A sample of digital forensic practitioners from South Africa was studied to determine to what extent they make use of validated forensic tools during the acquisition process, and how these tools are proven to be validated. The research identified significant concerns, with no validation done, or no proof of validation done, bringing into question the reliability of the digital evidence in court. It is concerning that the justice system itself is not picking this up, meaning that potentially unreliable digital evidence is used in court.
Development of Risk Controlling Mechanism and Tools for Agile Projects in Telecommunications

We developed the mechanism and tools of risk controlling in agile projects for companies in information and communication (ICT) industry. The relevance of this topic is underpinned by (1) the growing number of failures of agile projects due to realization of unique set of risks; and (2) the fact that standard agile practices neglect important risk management steps. The paper fills the research gaps in this area and addresses inefficiencies in explicit agile risk management frameworks. The results include the development of the mechanism's concept and the block diagram for scrum framework. They also include risk controlling tools: risk-based release planning, system of key risk indicators and agile risk controlling effectiveness evaluation tree. Unlike peers mentioned in existing studies the mechanism provides holistic approach to the agile risk management while keeps the spirit of agile. It addresses risk governance issues, ensures integration and coordination of risk management activities both inside the team and across the company and provides for lightweight but efficient tools. The mechanism can be adapted for other agile frameworks and can be applied by scrum masters and project managers in ICT industry as well as by the researches who are interested in further enhancement in agile risk management practices.
Keyphrase generation for Vietnamese administrative documents: a collaborative approach

Keyphrases of a given document can be considered as its condensed summary. Unsupervised models focus on extracting keyphrases based only on the information contained in that document without interacting with other documents. While a good performance supervised learning model for keyphrase generation requires a massive effort to build training data, which can not generalize to new domains. Moreover, according to human perception, a user would comprehend the topic expressed in a document better if that user has already read other documents that express the same topic. Based on the above idea, we proposed a collaborative keyphrase generation system (CollabKG): a novel semi-supervised method by leveraging limited labeled data. The amount of labeled data will be enriched over time by the user. In our work, we conduct research on a large scale dataset consisting of 500,000 Vietnamese administrative documents. In CollabKG, each document is represented as a feature vector, and a cluster pruning algorithm is employed to accelerate finding the most similar documents. The generated keyphrases were manually evaluated for relevance and accuracy. In the final, the result we achieved shows high ratification. Therefore, we can conclude that CollabKG has good performance and fits a real-time system.
Location tracking technique for Regional ENF Classification Using ARIMA

Recently, a digital forensics technology has emerged that uses electrical network frequency (ENF) signals depending on the geographical environment for power supply. Through ENF data training, the technology finds the area where the signal occurred when any data was given. At this time, forecasts are usually made through interpolation based on trained data. In this paper, we proposed a location tracking method that does not require separate interpolation in the case of when trying to find the grid where the signal appears. The network frequency signal is collected from the streaming videos of the online multimedia services and the ENF signal is extracted using the secondary interpolation FFT (QIFFT) in the collected file. Subsequently, the extracted ENF signals are grouped into a constant size and trained through the Automatic Integrated Moving Average (ARIMA). Then, analyze the coefficients of regional ENF data to find meaningful sorting values in each region. This suggested how to track where online files are played and verified the accuracy of the predicted locations.
Distributed Semi-Private Image Classification Based on Information-Bottleneck Principle

In this paper, we propose a framework for semi-privacy-preserving image classification. It allows each user to train a model on her/his own particular data class, after which the output features are shared centrally. The model parameters are never shared. Individual users each use an auto-encoder to empirically ascertain their private data distribution. The resulting features are sufficiently discriminative between the private datasets. A central server aggregates all labeled output features together with a subset of the private data into a final classifier over all classes from all users. The latter forms a trade-off between privacy and classification performance. We demonstrate the viability of this scheme empirically and showcase the privacy performance compromise.
Self-supervised Light Field View Synthesis Using Cycle Consistency

High angular resolution is advantageous for practical applications of light fields. In order to enhance the angular resolution of light fields, view synthesis methods can be utilized to generate dense intermediate views from sparse light field input. Most successful view synthesis methods are learning-based approaches which require a large amount of training data paired with ground truth. However, collecting such large datasets for light fields is challenging compared to natural images or videos. To tackle this problem, we propose a self-supervised light field view synthesis framework with cycle consistency. The proposed method aims to transfer prior knowledge learned from high quality natural video datasets to the light field view synthesis task, which reduces the need for labeled light field data. A cycle consistency constraint is used to build bidirectional mapping enforcing the generated views to be consistent with the input views. Derived from this key concept, two loss functions, cycle loss and reconstruction loss, are used to fine-tune the pre-trained model of a state-of-the-art video interpolation method. The proposed method is evaluated on various datasets to validate its robustness, and results show it not only achieves competitive performance compared to supervised fine-tuning, but also outperforms state-of-the-art light field view synthesis methods, especially when generating multiple intermediate views. Besides, our generic light field view synthesis framework can be adopted to any pre-trained model for advanced video interpolation.
SIDEKICKS AND SOCIETY

Using an etic lens, this study deals with identifying and deliberating upon culture in the United Kingdom as represented in the literary works of Arthur Conan Doyle, Roald Dahl and JK Rowling. We posit that literature is an art form that invariably represents the zeitgeist, the beliefs and the biases of the society the author inhabited (either directly through social commentary or indirectly through allegory and subtext). This cultural paradigm changes over time as societies evolve. Building upon our prior work, this study looks at evaluating the culture of the UK, using Hofstede's Cultural Dimensions of Power Distance and Masculinity, and looking specifically at the characters in the works of these authors. Arguably, minor characters, that are generally used for world-building, are the ones that better represent the society contemporary to the author, bespoke for the audience. We conducted four online workshops with Indian participants well-versed with the chosen texts. Through these workshops we analyzed minor characters for the Power Distance and Masculinity they reflect. Furthermore, even more meta discussions about societies and characters unfolded, giving us a rich tapestry of the cultural evolution of the United Kingdom's society. We found that the sets of characters in the fictional universes of the stories had grown to become more diverse and the authors had grown more bold with their criticisms of society over time. This paper also lays the groundwork for future culture studies that look into literature of a society and builds a platform for a deeper and more insightful understanding of persona building and design fiction.
Net2Net Extension for the AlphaGo Zero Algorithm

The number of residual network blocks in a computer Go program following the AlphaGo Zero algorithm is one of the key factors to the program’s playing strength. In this paper, we propose a method to deepen the residual network without reducing performance. Next, as self-play tends to be the most time-consuming part of AlphaGo Zero training, we demonstrate how it is possible to continue training on this deepened residual network using the self-play records generated by the original network (for time saving). The deepening process is performed by inserting new layers into the original network. We present in this paper three insertion schemes based on the concept behind Net2Net. Lastly, of the many different ways to sample the previously generated self-play records, we propose two methods so that the deepened network can continue the training process. In our experiment on the extension from 20 residual blocks to 40 residual blocks for $$9 \times 9$$ Go, the results show that the best performing extension scheme is able to obtain 61.69% win rate against the unextended player (20 blocks) while greatly saving the time for self-play.
Date Augmentation for Constellation and Eye Diagrams Using Conditional Generative Adversarial Nets

For the constellation diagrams and eye diagrams in the field of optical research, a data augmentation method using the CGAN algorithm is proposed, to solve the problem of insufficient training data.
What-if QoS Prediction of Cloud-hosted Web Services via Domain Adaptation in Evolutionary Scenarios

In order to ensure that the web application can continue to provide high-quality services after implementing the bandwidth management schemes, the administrators usually need to predict the Quality of Service(QoS) in advance according to the hypothetical changes of the web system. In the existing research, few pay attention to the prediction of QoS in bandwidth-driven evolutionary scenarios. In this paper, we propose a solution comprised of automated data mining skills and transfer learning techniques to predict the response time of web services in bandwidth-driven evolutionary scenarios. We choose a suitable approach of domain adaptation according to the characteristics of the QoS prediction problems in this paper. There are mainly three contributions in this paper: 1) we adopt an automated data mining approach to extract features for prediction. 2) To our knowledge, it is the first time to apply domain adaptation to the QoS prediction of web services in evolutionary scenarios. 3) We perform some experiments to evaluate the effectiveness and stability of the proposed solution, including two types of evolutionary scenarios under a realworld web application.
Determination of the Helpfulness of Physics Exam Study Methods

Studying for physics exams can be difficult and stressful, especially during a student's introductory year in physics. For students who do not plan to major in physics, the desire to do well is based less on understanding concepts and more on achieving a better grade. For this reason, students want to study as efficiently as possible by using the most optimal study methods. We have taken surveys over the past three years to determine how students study for exams and compared that to their exam grades. We found that students who studied using methods that they rated as more helpful did better on the exams. By utilizing the study results, we are able to present our current and future students with study methods that have been rated as being more helpful, and give them advice on ways to optimize their study time for exams.
A Framework of Knowledge Management in Classroom Action Research on Cloud Computing for Pre-Service Teachers

Thepurpose of this article was to design a framework of knowledge management in classroom action research on cloud computing for pre-service teachers. The research comprised two steps: 1) analyzing and synthesizing the relevant document and research study related to knowledge management in classroom action research on cloud computing for pre-service teachers and 2) designing a framework of knowledge management in classroom action research on cloud computing for pre-service teachers. Data were analyzed using content analysis. The results of this article were as follows: the framework consists of three components, namely 1) KM (SECI Model), 2) CAR Process and 3) Cloud-Based Tools.
An Integrated Method for Realtime 2D Hand Pose Detection

We present an integrated, real-time approach for 2D hand pose detection from a monocular RGB image, with a common backbone shared between the bounding box detector and the keypoint detector subnets. This is in contrast to traditional methods which use two separate models for hand localization and keypoint detection with no sharing of features. We build on the popular RetinaNet architecture for object detection and introduce an integrated model which performs both hand localization and keypoint detection in real-time. We evaluate our approach on two different datasets and show evidence that our model obtains accurate results.
On Sky Imaging Analysis and Deep Learning for Photovoltaic Output Nowcasting

In nowcasting of photovoltaic (PV) output, all-sky images can be utilized as an exogenous data input to improve the accuracy of the prediction. In this paper, we first investigate the correlations between sky images and power outputs of three different types of PV panel installations: fixed array, single-axis tracking array, and dual-axis tracking array. This has not been sufficiently addressed in the literature. Based on the correlation analysis, we conduct an image processing-based PV output nowcasting. Instead of directly using the original sky images as input of convolutional neural network (CNN) to learn features, we propose a pre-processing step to extract the statistical features embedded in the sky images. Then PV output prediction is implemented by a recurrent neural network (RNN)-based model. The experiments results show that the proposed light-weighted deep learning model can attain a promising forecast accuracy.
Specific development of additional professional professional programs taking into account the target audience

The article deals with the design and development of additional professional programs. The article focuses on the problems of document preparation and the content of training. The author considers one of the conditions for attracting students to additional professional education in the framework of the development of a higher educational organization’s economic independence. The author determines the possibilities of coordinating an additional professional program for students of different specialties with professional standards.