Reviewerly/exHarmony huggingface dataset:
Research on Representation Learning Method by Incorporating External Information

The Knowledge Graph (KG) is composed of structured information in the form of entities and relationships in a specific domain. The application of knowledge graph is affected by high computational and storage costs. Therefore, methods that can map high-dimensional knowledge graphs to low-dimensional spaces are needed. In addition to structured information, there is also many unstructured information related to entities in various modalities. This paper studies the knowledge graph embedding model, which not only considers the structured information contained in the knowledge graph in the form of entities and relationships, but also considers the unstructured information expressed in the form of text and numerical literals. We analyze and compare other advanced and classic methods, and evaluate the performance of different methods under the same setting for the general task of link prediction.
Speaking to write: examining language learners’ acceptance of automatic speech recognition as a writing tool

This mixed-methods one-shot study examines L2 writers’ perceptions of using Automatic Speech Recognition (ASR) to write using the Technology Acceptance Model (TAM), based on three criteria: usefulness, ease of use, and intention to use. After receiving training on Google voice typing in Google Docs, 17 English as a Second Language (ESL) students carried out two ASR-based writing tasks over a two-hour period. After the treatment, participants filled in a TAM-informed survey and participated in semi-structured interviews to measure their perceptions based on the target criteria. Findings indicate positive perceptions of ASR as a writing tool in terms of usefulness (language learning potential) and ease of use (e.g. user-friendly voice commands). We believe that these positive perceptions might lead to an intention to continue to use ASR, suggesting that the technology has L2 pedagogical potential.
Impact of IoT and Big Data Application to Business Performance

Many companies have promoted their business performances by applying emerging technologies. However, knowledge of their application impact on business performanceis still lack. Few factors are identified such as: 1) Business Process Improvement, 2) Marketing Strategies, 3) Business Management Innovation, 4) Business Models and Organizational Culture, and 5) Privacy and Ethics. This paperaims to develop model of applying IoT and Big Dataon business performance. Responses are solicited from 112 managers. Observation by Smart PLS3.0 is conducted on their responses with Goodness of Fit (GoF) 0.69 compared to required 0.38. Thus, the model is robust and accurate. Findings show that: 1) Business Model Innovation correlates with Business Performance ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{r}=0.368$</tex> ), 2) Business Model and Organization Culture does not correlate with Business Model Innovation, 3) Business Process Improvement correlates with Business Model Innovation ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{r}=0.674$</tex> ), 4) Business Process Improvement correlates with Marketing Strategies ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{r}=0.503$</tex> ), 5) Marketing Strategies correlates with Business Performance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{r}=0.424),\ 6)$</tex> Privacy and Ethics correlates with Marketing Strategies ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{r}=4.33$</tex> ).
Development of CCTV Stream Annotation Tool for Preparing Office Occupancy Learning Dataset

We were planning to develop a machine learning system to determine the number of occupants through stream data using existing CCTVs in the office. There, we developed an annotation tool and its system that can easily label the actual access occupant data from CCTV stream data to obtain the data needed for learning.
Design of A Backbone without Pretraining

The excellent performance of deep learning depends on the strong representation ability of its backbone. As a conventional means of most backbones, pretraining can make the model obtain high accuracy, but it also brings some disadvantages that can not be ignored: first, the structures of the backbones need pretraining are fixed, they are difficult to modify and migrate across tasks; second, the pretraining process needs to consume huge computing power. To solve this problem, we propose a backbone named RVNet (Residual VGGNet), which can make the model converge quickly without pretraining. The design of RVNet is divided into the following two steps: firstly, the residual convolutional layer (RCL) is designed by referring to the residual skill and BN layer, which can prevent the gradient from disappearing and restrain the data distribution. At the same time, The introduced 1* 1 convolution layer can improve the nonlinearity of the model while controlling the number of feature maps’ channels; then, based on VGGNet-19, the designed RCLs replace the original 3* 3 convolution layer to improve the representation ability of the backbone. We take the person re-identification (Re-ID) task as the research object, and prove the effectiveness and superiority of RVNet through a series of ablation experiments.
Image quality assessment in patient-assisted versus standard compression: A population-based randomized trial in a Breast Cancer Screening Program in Spain

Thank you very much for joining EUSOBI 2021 ONLINE and celebrating the Breast Cancer Awareness Month with us! This year we had a terrific programme and we are indebted to Dr. Ritse Mann and his Programme Committee for all their hard work in putting this together. We had a wonderful selection of international speakers who gave the latest updates in all your favourite topics and ensured that you are kept up to date with the latest developments in breast imaging. A special focus was given to the interactivity of this year’s programme, by adding expert discussions and concluding with the first virtual edition of our famous Breast Interpretation Quiz. Thanks to all of you who submitted abstracts and therefore having been an important part of this scientific event. We are also very grateful for the support of our industry partners who are listed here: https://www.eusobi.org/congress-sponsors-2/ Most of them have contributed to the extension of our scientific programme by offering interesting sessions and workshops. Have a look by scrolling through all sessions. All sessions have been recorded and are available on-demand.
Philosophical reception in modern art history

This article explores the applicability of philosophical reception in terms of analysis of the phenomenon and the essence of art and artworks. The goal of this research lies in substantiation of the need for proliferation of the philosophical reception in art history for the holistic study of art in the aspect of its &amp;ldquo;ontologization&amp;rdquo;. Emphasis is placed on the fact that modern art history sets the tone in examination of the formal and substantive sides of art, its types and genres. Methodological and theoretical framework for art history is the philosophy of art, which recently however does not hold the leading position in the analysis of art, and even yields to aesthetics. The problem of comprehending the essence of art and the value role of artwork in life of a person and society remains open. It is rather not the subject of close attention of the art historians, but may become such. Thus, the art history analysis should be complemented with the philosophical reception. Namely this aspect is given special attention in this research. Philosophical reception is a peculiar algorithm for understanding the meanings and ideas of the artwork, as well as realization of the essence of this phenomenon. Art is often viewed as one of the forms of collective consciousness (along with science, mythology, etc.); however, the philosophical reception helps to establish the value-semantic nature of art that prevails over the social and aesthetic. Comprehension of any genre or type of art through the prism of art history is essentially refracted in the philosophical reception. In this case, the researcher is able not only assess the style and genre nature of the artwork, but also to trace its position in the turn of eras, personality concepts, ideologies, and worldviews. It is undoubtedly gives an apparent advantage to any modern researcher who aims to perceive the essence of art.
Rediscovering Neighborhood History Through Augmented Reality

Augmented Reality (AR) goes beyond conventional methods of engagement with the public. It offers an interactive method to expand visualization techniques in civic engagement necessary for museum exhibitions that are focused on cultural and social issues. This paper discusses four AR projects conducted by a partnership between a history museum and a university in the City of Charlotte, USA. These projects, which utilize a variety of AR technologies, support a series of community events that are aimed at expanding overall public participation with a goal of increasing awareness of neighborhood history through data visualization and storytelling.
Big Data Analysis for Multispectral Images Recognition Based on Deep Learning

The scientific article considers multiclass classification, which is used to solve problems of recognising multispectral images. In particular, we are solving this problem using a neural network model, which is created using the Python programming language with the TensorFlow library. The article also describes the stages of establishing and optimal parameters of the model. The topical problem of multispectral image recognition is investigated. Neural networks are used for the solution. The neural network model, its number of layers, activator functions, optimiser and number of neurons in each layer were successfully selected. An accuracy of 97&#x0025; is achieved for this application Using these optimal parameters.
Deep Q-Network based Tracking and Slope Climbing Controller Design of Mobile Robot and Flatbed Vehicle

Thanks to the technological change, the industry 4.0 has arrived. Artificial intelligence let software and machines to sense and learn on their own activities. In these research field, formation control of mobile robots is a popular topic. From rescue task to environment exploration, many of the applications require the robots to perform group patterns. To achieve these works, the control process could be simplified into multiple leader-follower relationships which control the followers to achieve the specific relative position with respect to the leaders. However, in particular situations, the tracking process should further consider the relative pose when both robots reach the desired relative position such as dynamic combination of robots. This paper proposes a Deep Q-Network based leader-follower tracking method with a climbing-up combination process. The control algorithm depends on the robot vision of the follower without any global measurements, and the whole process focuses on the relative pose between the mobile robot (follower) and the flatbed vehicle (leader).
MMT

This paper presents the Metrics Monitoring Tool (MMT) that was developed in university graduate and undergraduate courses on software project work in 2014-2016. The tool aims to support project members, project managers and upper management in reporting and monitoring software and project metrics for their easier and more effective utilization. The paper covers the development process of the tool, evaluation assessment, its current composition and features. The paradigm applied in this study is Design Science Research and the methods for evaluation include prototype, expert evaluation, case study and technical experiment. Data was collected from the tool users by two questionnaires. As a result, MMT was evaluated to ease the metrics handling, while several aspects related to the richness of functionalities and usability still require further development.
Describing Environmental Information in Videos Using Machine Learning

The previous researches of video captioning task have focused on human actions or objects in videos, however, environmental information such as place, time, weather among others is also important information to understand videos. Therefore, in this paper, we create a new dataset which adds environmental information labels to MSVD dataset and train the machine learning model to analyze environmental information from videos. We apply R(2+1)D which is a 3D CNN model to extract video features and S2VT which is a RNN model to encode the video features and to decode the environmental information. The reason why we define the problem as a sequence to sequence problem, not multilabel classification, is that the input is a video, which is a sequence of frames, and the output is also related with each other. For example, if the place label is outside, then next label would be weather. We analyze the experimental results based on BLEU, METEOR, ROUGE-L, and CIDEr and it shows the competitive results compared to the state-of-the-art video captioning model.
An Image Encryption Method based on Pixel-level Diffusion and Bit Flip

With the rapid development of multimedia technology, digital images are increasingly used. These applications bring great convenience to users but increase the risk of information leakage. According to the granularity of operation, the existing image encryption methods can be divided into pixel-level and bit-level. The pixel-level method has high encryption efficiency, but does not break the linear relationship totally; the bit-level method has high security, but it needs to convert pixel value to 8 bits, which causes the low encryption efficiency. The current research on the hybrid encryption method performs pixel-level and bit-level operations in series. Although the methods can improve security to some extent, they still unsolve the inefficiencies problem. In this regard, we proposed a pixel-level bit-level hybrid diffusion image encryption method based on the traditional permutation-diffusion structure. The randomness is significantly improved by calculating the diffusion order through a double chaotic matrix. Meanwhile, we embedded bit flip into the diffusion process to further enhance the security of encryption. The experimental results show that our method achieves better results in five security metrics (pixel histogram analysis, key sensitivity, plaintext sensitivity, pixel correlation, and information entropy) compared with existing image encryption methods. Also, the time required for encryption is reduced by 6% compared to mainstream efficient encryption algorithms.
Low-Resolution IR Sensor-based Occupancy Detection Scheme for Smart Buildings

Accurate occupancy ditection plays a vital role in the control system of smart buildings, such as energy distribution, comfort adjustment, etc. Traditional indoor occupancy detection schemes are mainly based on RGB camera, PIR sensor, environmental sensors, etc. There are some weaknesses with these methods, such as privacy intrusion, difficulty in deployment, poor portability. According to investigations, low-resolution infrared radiation (IR) array sensors can solve the deficiencies of the above solutions. However, the IR array sensor-based method still has a few limitations, such as, the noise inherent in detected data, difficulty in multi-person detection, the lack of software and hardware interaction system. Based on the above defects, we design a new occupancy detection scheme based on low-resolution IR sensors. This scheme uses detection equipment placed in the doorway to determine the in and out status of people to achieve the purpose of occupancy detection. Among them, the combination of three frame difference and background subtraction deal with the difficulty of background segmentation. Then, a two-pass connected area division method is used as a classifier to detect multiple people in the target area. Finally, in order to facilitate real-time user perception in the construction-related information, we design a We Chat applet with strong interactive capabilities. A series of simulation experiments and actual tests verify the effectiveness of the proposed scheme.
Optimized Coloring Algorithm Based on Non-local Neighborhood Search

Image coloring is the process of adding color to grayscale images with the assistance of a computer. This paper proposes an optimal coloring algorithm based on non-local neighborhood search to better deal with boundary color penetration when colorizing grayscale images. First, the K non-local texture most similar neighborhoods are searched based on the image texture features. Then, based on the neighborhood pixels with similar intensity having similar color, the group of similar neighborhood pixels is further searched. The search results take into account not only the pixel texture features but also the light intensity distribution. Finally, the neighborhood pixels are introduced into the optimized coloring method, and the image is colored. The experimental results show that the way can effectively reduce boundary color penetration by non-local propagation of color information. In the case of only a tiny amount of color smearing, the method in this paper can provide better visual effects.
Pyramid Dilated Attention Network for Action Segmentation

Action segmentation has been widely studied with the development of temporal convolution networks. However, the correlations of frames with different time intervals are still not well explored. Especially, in untrimmed videos, frames always play different roles. Consecutive frames can provide local spatiotemporal information, and distant frames provide global information. Therefore, applying attention in the same dimension cannot exploit such differences. In addition, untrimmed videos generally contain thousands of frames, and directly applying attention to the whole video would be computationally heavy and inefficient. In this paper, we propose a dilated attention module (DAM), which builds attention maps in a dilated manner, rather than on the whole sequence. To explore correlations between frames with different intervals, we propose a pyramid dilated attention network (PDAN), which uses higher dimension features to exploit the relationships with a short interval to get the local information and uses lower dimension features to study the correlations with a long interval to explore the global information. When MS-TCN is equipped with the PDAN, the state-of-the-art performance is achieved on three challenging datasets.
Parallel Branch-and-Bound – Comparing Serial and OpenMP Implementations in a Color Sorting Game

Branch-and-Bound algorithm is very diverse in its applications, one of which is the gaming industry, especially for complicated games that need optimization in their search for a solution from a large search space. For a thorough study, the Branch-and-Bound algorithm will be implemented in a serial and parallel (using Open Multi-Processing) approach to compare time required to solve the game. Results showed that for easy levels, the serial program performed better, while for difficult levels (with more flasks and colors) the parallel program outperformed the serial program with a speed up of more than 1,000x. Future work can replicate the algorithm on different types of games and explore different approaches such as Backtracking in a parallel approach.
4-Character Captcha Recognition Based on the CNN

Nowadays, in the case that many automatic robots and web crawlers retrieve web resources maliciously or illegally, more and more websites add the captcha technology to identify whether website visitors are human beings. To test whether a captcha generation system is easy to recognise and help to improve such a system, the author has constructed a 4-character captcha recognition model based on the convolutional neural network via a Python programming experiment. In this experiment, the captcha library in Python helped to generate all captcha images and datasets. Firstly, the author generated 500,000 original single-character captcha images from the corresponding python library. Secondly, images were preprocessed to create the dataset of single-character images. Thirdly, the author built the CNN model and trained it with the dataset generated in step 2. Then, a new input captcha could be segmented into four characters and recognised in sequence by the well-trained model. Eventually, the evaluation result shows that single-character recognition accuracy is 97.3% and 4-character captcha recognition accuracy is 96.2%. The most common mistakes the model made are recognizing “0” as “D”, “5” as “S”, “Y” as “V”.
Large Scale String Analytics in Arkouda

Large scale data sets from the web, social networks, and bioinformatics are widely available and can often be rep-resented by strings and suffix arrays are highly efficient data structures enabling string analysis. But, our personal devices and corresponding exploratory data analysis (EDA) tools cannot handle big data sets beyond the local memory. Arkouda is a framework under early development that brings together the productivity of Python at the user side with the high-performance of Chapel at the server-side. In this paper, an efficient suffix array data structure design and integration method are given first. A suffix array algorithm library integration method instead of one single suffix algorithm is presented to enable runtime performance optimization in Arkouda since different suffix array algorithms may have very different practical performances for strings in various applications. A parallel suffix array construction algorithm framework is given to further exploit hierarchical parallelism on multiple locales in Chapel. A corresponding benchmark is developed to evaluate the feasibility of the provided suffix array integration method and measure the end-to-end performance. Experimental results show that the proposed solution can provide data scientists an easy and efficient method to build suffix arrays with high performance in Python. All our codes are open source and available from GitHub (https://github.com/Bader-Research/arkouda/tree/string-suffix-array-functionality).
AMG: An Auto-Tuning Method for Spark GraphX Running on GPU Cluster

With the explosive growth of social networking services and traffic information, graph structured data has promoted the development of analytical tools. GraphX is an API of Apache Spark for graph analytics. NVIDIA has created a library named RAPIDS to enable the execution of Spark 3.0 including GraphX on GPUs. It has a lot of configurable options related to GPU which can significantly affect GraphX programs' performance. However, the default options of Spark GraphX may not lead to the optimal performance for a given GraphX program and its input data and thus they need to be carefully tuned. To address these issues, we propose an automatic tuning approach named AMG to efficiently identify the optimal option for a given GraphX program-input pair running on a GPU cluster. AMG innovates two techniques: 1) it leverages Random Forest to construct performance models; 2) it employs Bayesian Optimization to seek the optimal option for a given GraphX program-input pair. We use three typical GraphX programs to evaluate AMG. The experimental results show that compared with the default options, AMG dramatically improves the performance by a factor of 3.99× on average and up to 4351×.