Reviewerly/exHarmony huggingface dataset:
Event causality extraction based on Transformer sequence annotation model

Text data such as research reports and announcements in the financial field contain a large amount of event causality that can be extracted and thus applied to downstream tasks such as prediction and Q&A. Traditional event causality extraction methods extract through sentence templates, which cannot cope with multiple pairs of causality in a sentence. This paper considers the event causality extraction task as a sequential annotation task. The event causality labels are divided into ”core noun in the cause”, ”predicate or state in the cause”, ”central word”, ”core noun in result”, and ”predicate or state in result”. We proposed using the Transformer sequence annotation model based on lexicon matching to identify and extract event causality. The F1 value of the Transformer model reaches 58.70 %, and the F1 of BERT+Transformer comes the highest, 69.49 %.
Tuning Automatic Summarization for Incident Report Visualization

We present a machine learning approach to improve the accuracy of summarized incident report visualizations for cyber security. We extend a recent incident report summarization method by training a Bayesian hierarchical model to optimize the summarization algorithm's weights. We also train a flat model and a neural network as alternative models to compare against our hierarchical model. Summaries generated by our hierarchical model achieve higher accuracy than the other methods, with an AUC 0.2 higher than the unweighted method while achieving comparable summarization size. We further demonstrate that visualizations of the hierarchical model's summaries are at least as useful the unweighted method's summaries, and possibly more useful.
Sports Image Decomposition Strategy Based on Moreau Envelope and Depth Recovery

Sports image decomposition technology is utilized broadly in different fields, and the technologies of sports image action identification situated on sports picture transformation technologies may be suitable. This paper utilizes the Moreau envelope and depth recovery for sports image decomposition strategy, an order of work such as a collection of sports image characteristic distillation, and action identification must be achieved in beginning and started with texture functions as well as various relevant functions. And both algorithms have to be utilized to finish the sports picture-oriented sports action identification technology at the lowest time expense. For the improvement of the latest sports image industry form, which is also strongly developed, the people’s affection for sports image is gaining as powerful and creates an improvement of sports image industry still achieving a lot of advantages. The parameters recognition rate and accuracy are compared with various techniques. The proposed methods are effective to achieve the perfect sports image decomposition.
Controllable Person Image Synthesis GAN and Its Reconfigurable Energy-efficient Hardware Implementation

At this stage, how to controllably generate higher quality person image is still the challenge of person image synthesis. At the same time, the update of image synthesis network is far ahead of its hardware implementation. Therefore, this paper proposes a GAN network for person image synthesis that can generate high quality person image with controllable pose and attributes. The newly designed network is more convenient for hardware implementation while ensuring that the generated image is controllable. This paper also designs a synthesizable library for GAN to pursue faster hardware reconfiguration. We completed the new model proposed in this paper based on this library. Finally, the proposed network achieves better results both quantitatively and qualitatively compared with previous work. Compared with GPU and CPU, the hardware implementation based on FPGA can achieve the highest energy efficient of 73.67 GOPS / W.
A-DROP Scoring System in Predicting Mortality within 30 Days of Hospitalization in Community-acquired Pneumonia Patients at H. Adam Malik General Hospital Medan

BACKGROUND: Community-acquired pneumonia (CAP) is the leading cause of death among infectious diseases, contributing significantly to patient morbidity and mortality. Therefore, an accurate initial assessment of CAP severity should be performed immediately to determine the prognosis before starting the management. A-DROP scoring system is one of the initial assessments. AIM: This study aimed to determine the accuracy of the A-DROP scoring system in predicting mortality within 30 days of hospitalization at H. Adam Malik General Hospital Medan. METHODS: This is an observational study with a retrospective and cohort study. Data were obtained from the medical records of 76 CAP patients hospitalized from January 2018 to December 2018. Each patient was assessed with an A-DROP scoring system and the presence or absence of mortality within 30 days of hospitalization. The data were processed using statistical analysis to calculate the area under curve (AUC) on the receiver operating characteristic (ROC) curve. The optimal cutoff point is also analyzed using the Youden index. RESULT: The ROC curve analysis showed that the AUC was 0.772 (95% CI: 0.666–0.978, p &lt; 0.001). The accuracy of A-DROP scoring system is good (AUC:0.7−0.8). The optimal of the Youden index is 0.428 at the cutoff point A-DROP score &gt;1.5, so that the optimal cutoff point is A-DROP score ≥2. CONCLUSION: The A-DROP scoring system has good accuracy in predicting mortality within 30 days of hospitalized CAP patients. The A-DROP scoring system has an accuracy similar to the PSI (Pneumonia Severity Index) and the CURB-65 scoring system.
Blended Experiential Learning with e-Portfolios Learning to Enhance Creative Imagination

Our research developed out of concerns about whether design students currently have enough creative imagination because of COVID-19, changing design studies to online learning. This paper describes an empirical study testing the effect of Blended Experiential Learning with e-Portfolios Learning on the Creative Imagination of design students to compare the differences in the effectiveness of design students' Creative Imagination scores. It was hypothesized that first-year design students learning through blended experiential learning with e-Portfolios would have higher Creative Imagination scores when compared to those in the control group learning by not following the format. The multistage sampling method was used to select the study population, and the sample consisted of 111 participants. The test was run in two steps, including 1) the try-out, conducted with the sample group of 45 participants using the Dependent T-Test to compare the pre-test and post-test Creative Imagination scores, and 2) the trial run, conducted with the sample group of 33 participants (learning by following the format), and the control group of 33 participants (learning by not following the format) to compare the Creative Imagination scores. MANOVA was then used to test whether at least one component of the creative imagination test scores of the experimental group was higher than the control group. The Test of Creative Imagery Abilities (TCIA) assessment was used to collect the data, which was then analysed using the T-test. Doi: 10.28991/ESJ-2022-SIED-03 Full Text: PDF
Border Management Using Internet of Things

Single shot multi-box detector is the quickest approach that employs a single layer of neural network to recognize items from an image and video. The system employs numerous photos to recognize items and label them with the appropriate class label. The suggested approach is used in conjunction with a multilayer convolutional neural network to improve computational performance by using a greater number of default boxes. This results in more accurate identification. Raspberry Pi, Pi cam and Raspbian operating system have been used in this work. In this paper, an ultrasonic sensor has been used to measure the distance of the object. The camera starts livestream when the object is closer than a threshold distance. A standard dataset named coco.names is used which can detect 80 different types of objects. The livestream video output is downscaled to an extent where the single shot multi-box detector algorithm cannot detect and recognize the objects. Next, a pre-trained faster super resolution convolutional neural network is used for super resolution to upscale the livestream video. As a result, single shot multi-box detector algorithm can detect and recognize the objects present in the video. In this work, the single shot multi-box detector algorithm has also been tested for detection of an object after enhancing the quality of a low-resolution livestream video using super resolution pretrained model.
Transferring Learnt Features from Deep Neural Networks trained on Structured Data

Structured data is a widely used type of data with numerous applications in training machine learning models. However, training deep learning models require a lot of data, which may not be present for all use-cases. In addition to this, training these models could get very expensive as the data increases. Transfer learning can be a solution to these problems. It involves reusing features from trained models on the same or similar tasks, however, it has not been explored much for structured data yet. In this paper, an approach is proposed to transfer learnt features from the embedding layers present commonly in deep neural networks for structured data along with a format for effective portability of these trained embeddings. Experimentally, it is observed that the proposed method resulted in faster training and the model parameters start at a better point compared to parameters of a randomly initialized model, resulting in lesser training costs as well.
Automatic Generation of Test Documents Based on Knowledge Extraction

In view of the existing shortcomings in the automatic generation of test documents in the past, a method of generating test reports based on knowledge extraction was proposed based on the theory of data-to-text generation. The correlation between the header and the cell was identified by analyzing the logical structure of the spreadsheet that records the original test data, and test knowledge was represented according to the normalized test ontology. Then, the entire test report was gradually formed by filling it into a partial document template. In this way, the problem of iterative software development caused by flexible test recording was solved, which facilitated the effective utilization of test results. Moreover, the bottom-up document structure supports content presentation on-demand, thus improving the readability of test reports.
Stationary Behavior of Constant Stepsize SGD Type Algorithms

Stochastic approximation (SA) and stochastic gradient descent (SGD) algorithms are work-horses for modern machine learning algorithms. Their constant stepsize variants are preferred in practice due to fast convergence behavior. However, constant stepsize SA algorithms do not converge to the optimal solution, but instead have a stationary distribution, which in general cannot be analytically characterized. In this work, we study the asymptotic behavior of the appropriately scaled stationary distribution, in the limit when the constant stepsize goes to zero. Specifically, we consider the following three settings: (1) SGD algorithm with a smooth and strongly convex objective, (2) linear SA algorithm involving a Hurwitz matrix, and (3) nonlinear SA algorithm involving a contractive operator. When the iterate is scaled by 1/√α, where α is the constant stepsize, we show that the limiting scaled stationary distribution is a solution of an implicit equation. Under a uniqueness assumption (which can be removed in certain settings) on this equation, we further characterize the limiting distribution as a Gaussian distribution whose covariance matrix is the unique solution of an appropriate Lyapunov equation. For SA algorithms beyond these cases, our numerical experiments suggest that unlike central limit theorem type results: (1) the scaling factor need not be 1/√α, and (2) the limiting distribution need not be Gaussian. Based on the numerical study, we come up with a heuristic formula to determine the right scaling factor, and make a connection to the Euler-Maruyama discretization scheme for approximating stochastic differential equations.
Ride With Me: Exploring Group Road Cycling Through Contextual Design

Group sports such as cycling promote a healthy lifestyle and contribute to an active social life among individuals with a common interest. HCI research has mainly focused on supporting individual riders like commuters, young children learning to ride safely, and tourists using bikes. In this work, we present the results of a study exploring the work done by road cyclists while training together, following a Contextual Design methodology involving participants from three different cycling groups across different geographic regions. We share their strategies and challenges with group coordination and communication, and uncover a lack of support for sharing individual metrics such as effort in common tools.
Study of Video Suggestions based on Calendar Events

Present times work life is composed of many digital instruments. Every instrument is becoming smarter but making the calendar into smarter one has attracted little attention. The framework for this study is based on recommending videos based on the preset calendar events. The data that we will save will be analyzed 1) qualitatively, and 2) quantitatively. The qualitative analysis shows that choosing quality data as millions of videos are being uploaded on various platforms, every single second and we need a mechanism that can filter out videos and then can provide the relevant data to the users based on digital calendar. A Calendar is an instrument that is used to store events and by storing the events in the calendar and then using that stored words different recommendations can be made to the user on a timely basis. It can be beneficial in the field of healthcare, education, banks, and in every sector of society. The ability to search and book meetings in the calendars of others can be made advanced and individual can be made strongly connected to the digital tool. This advancement will help everyone in day-to-day life. Here two works must be done first is the event and the other is to filter the content based on the saved events. Various work has been done in the field of video suggestion, but very little work has been done in the field of the calendar. This approach will bring innovation in the field of a calendar and will prove to be a helping hand in suggestions too. For an instance let us assume that a pregnant lady saves in a calendar event, and we can provide recommendation to her to make her more well informed and suggest to her what to do and what not to do, let us take another example as small kids do not remember their exams so we can set an event and can provide him best help in the exams time. This advancement will help everyone in day-to-day life.
Digital image watermarking based on visual secret sharing scheme

Orthodox, VSS, visual secret sharing scheme to conceal hidden images of the spots, or printed out on trans-parencies or are encoded and stored in a digital format, so the audio/video, the lyrics. Stains and spots, what a noise-like pixels or as meaningful images may look like it, but this will cause the time and increase the risk of disruptions in the maintenance and transmission of the process. Therefore, in the traditional system of the visual secret sharing, this will create a problem for the possibility of transmitting the secret to himself and to the members of the group, which is involved in the VSS system. In order to solve this problem, we proposed new techniques in the field of the digital watermark on the images, with the help of a texture image, as well as a system for the visual and the replacement of the natural image, the secrets of the VSS of the hidden, the images with the help of different vehicles in order to protect the confidentiality of communications, and the participants during the transmission phase. We combine a process for the synthesis of a digital image as the textures in order to hide the secret message. Instead of using the existing cover image to hide secret messages, our algorithm conceals the original texture of the image and the codes of the secret message by means of a process of watermark or digital images. Natural resources can be pictures, it can be in any form, in both digital and printed media. We also offer the opportunity to hide in the secret, in order to reduce the risks of transmission of notices of default is AES-advanced encryption algorithm. The results show that the proposed method is an obvious way to solve this problem at the risk of the transmission SCD systems, as well as the concern for the reliability of the system. An alphabetical index of terms, information security, information Security, visual secret in exchange programs, as well as a watermark for digital images.
Spectral-Transfer-Tensor Method for Characterizing Non-Markovian Noise

With continuing improvements on the quality of fabricated quantum devices, it becomes increasingly crucial to analyze noisy quantum process in greater details such as characterizing the non-Markovianity in a quantitative manner. In this work, we propose an experimental protocol, termed spectral transfer tensor maps (STTMs), to accurately predict the Rivas-Huelga-Plenio non-Markovian measure of any Pauli channels without state preparation and measurement errors. In fact, for Pauli channels, STTM even allows the reconstruction of a highly precise noise power spectrum for qubits. At last, we also discuss how STTM can be useful to approximately characterize non-Markovianity of non-Pauli channels via Pauli twirling in an optimal basis.
Localizing Semantic Patches for Accelerating Image Classification

Existing works often focus on reducing the architecture redundancy for accelerating image classification but ignore the spatial redundancy of the input image. This paper proposes an efficient image classification pipeline to solve this problem. We first pinpoint task-aware regions over the input image by a lightweight patch proposal network called AnchorNet. We then feed these localized semantic patches with much smaller spatial redundancy into a general classification network. Unlike the popular design of deep CNN, we aim to carefully design the Receptive Field of AnchorNet without intermediate convolutional paddings. This ensures the exact mapping from a high-level spatial location to the specific input image patch. The contribution of each patch is interpretable. Moreover, AnchorNet is compatible with any downstream architecture. Experimental results on ImageNet show that our method outperforms SOTA dynamic inference methods with fewer inference costs. Our code is available at https://github.com/winycg/AnchorNet.
Knowledge and Educational Capital As Factors in the Formation of Intellectual Potential in the Context of the Modern Information Society

The article presents the issues related to the knowledge and educational capital of young people who, despite quality education and skills, often face difficulties in entering the labor market. The article reveals the essence and types of knowledge, distinguishes between information, knowledge and wisdom. A scheme of the educational development trajectory is proposed, taking into account the dual model of learning. The results of an empirical study conducted among students are presented, in the focus of which the process of creating educational capital is positioned. The authors have developed a methodological approach to the program for increasing the intellectual potential of students, built as an integral system of conceptual-target, content and analytical-effective components, aimed at personal transformation and new educational results, the content of which is determined by the supply of goals, meanings, values, priorities, way of thinking of the modern student.
Applications of Logistic Regression and Naive Bayes in Commodity Sentiment Analysis

Sentiment analysis is popular research which helps people perceive the trend of public opinion commented in separate social networking platforms. The aim of the paper is to investigate the real evaluation of goods when marketing tweets are eliminated and estimate the performance of two classifiers in sentiment analysis. The initial process is data collection used crawler with user comments on the series of ‘Xiaomi 11’ in Weibo. By collected data, pre-processing and features extracting can be instituted sequentially. We use Term Frequency-inverse Document Frequency to evaluate the significant of words then filter out common words, and Bag of Word is used to convert the texts into numerical features. Finally, we use Naive Bayes and Logistic Regression for the classification of twitters reviews and get the results that Logistic Regression Classifier preforms better than Naive Bayes Classifier. The analysis with former gives 3.572% more accuracy, 6.632% more precision and 2.81% more recall.
Toward Investigating the Violations Roles of Pattern Grime Occurrence in Software Design Patterns

Design patterns (DPs) are recurring solutions for software design problems. They are recommended and employed for their benefits and impact on software quality. However, the improper extension and implementation of design patterns raise the emergence of accumulating bad smells in DPs structure and behavior. Pattern grime occurrence is one of the bad smells in the design pattern. Grime highly reduces design pattern usability, testability, and adaptability, and even prevents their proper implementation. Despite that, pattern grime occurrence may have many severe violations and impacts on design patterns. For instance, grime occurrence contributes to build-up code smells, violates object-oriented principles, and degenerates pattern quality and code structure. This research focuses on investigating the violations roles of grime occurrence in design patterns and aims to propose a taxonomy for pattern grime violation types, to take the first step to develop a prediction model to detect pattern grime occurrence in the software design pattern. Thus, this research outlines a research project targeting the main research question: “how pattern grime is correlated, and how this might lead to violate design pattern principles, structure, and quality? We plan to answer this question through various stages. First, we investigate the commonalities and variability of pattern grime occurrence on design patterns. Secondly, we identify the types of grime roles violation in DPs to propose a taxonomy of pattern grime violation types. The results of these stages will help to identify a prediction model to predict pattern grime in the software design patterns.