Reviewerly/exHarmony huggingface dataset:
Using Histogram Skewness and Kurtosis Features for Detection of White Matter Hyperintensities in MRI Images

White matter hyperintensities are regions of hyperintensive signal in white brain matter that appear in T2 or FLAIR MRI imaging. They show demyelination, cerebral oedema, tumors, or angiogenesis. Their presence is associated with a number of neurological diseases such as dementia, cognitive impairment, depression, schizophrenia, or even cerebral small vessel disease and multiple sclerosis. WMH areas are thought to appear on MRI images even a few years before the clinical manifestation of certain neurological diseases. Automatic segmentation algorithms can help better understand white matter lesions as part of large-scale studies. It is believed that changes in lesions’ appearance can be monitored over time, thus their correlation with certain diseases can be better understood. This work describes the use of histogram features for the detection of white matter hyperintensities, with a focus on skewness and kurtosis. The first aim of this study is to find features that can be used to identify white matter hyperintensities; then, the authors propose a fully automatic detection algorithm based on a sliding window method to obtain local histograms, and a Support Vector Machine algorithm to perform binary classification. The authors explored the effects of different combinations of characteristics and the relationship between feature values and hyperintensity percentage in a disc-shaped window. The described study is preliminary and contains an initial examination of the effectiveness of using histogram features for the detection of white matter hyperintensities in MRI images.
Increasing Student’s Ability to Write Descriptive Text Using the Brainstorming Method

The focus of this research was on the problems and difficulties faced by students when they were writing descriptive text. The aim of this study was to improve student’s ability in writing descriptive text using brainstorming method. The subjects of this study were20 students of VII B of Jati Agung Junior High School participated as the subject of the study. The method used was classroom action research contained four components: planning, acting, watching, and reflecting. The instrument used for data collection were test and observation field notes. The findings of this study were two cycles. In the cycle 1, the students pre-test scores resulted in only 10% or two students passed standard criteria minimum score, and the post-test resulted 40% or four students passed standard minimum score criteria. While in the cycle 2 students gained 70% or 14 students passed standard minimum score (M&gt;75). In conclusion it be able that the brainstorming method can be used to improve the student’s ability in writing descriptive text.
Evaluating the Utility of Notional Machine Representations to Help Novices Learn to Code Trace

Code tracing involves simulating at a high level the actions the computer takes when executing a program. Given that students experience difficulties learning this fundamental skill, research is needed on how to effectively teach it. We report on two studies that investigate the pedagogical utility of various notional machine representations used to explain the mechanics of program execution. In study 1 (N = 44), we compared instruction using a concrete computer representation to an abstract table representation. In study 2 (N = 50), we tested if fading between representations improved learning over only providing one representation. The instruction in both studies was embedded in basic tutoring systems we implemented that served as testbeds for the present research. On average students did learn in each study, as evidenced by pretest to posttest gains, but the type of representation did not significantly affect learning; Bayesian statistics provided substantial evidence for this null result. We discuss potential explanations for our findings and suggest future research directions.
Designing Ethically-Integrated Assignments: It’s Harder Than it Looks

While the CS education community has successfully incorporated tech-ethics assignments and modules into computing courses, we lack a defined process for instructional design to create these materials from scratch across the curriculum. To enable the development of such a process, we explore two research questions: (1) What specific instructional design challenges emerge when creating ethically-integrated assignments for CS courses? And (2) what strategies might overcome them? We address these questions using Research through Design, a method for critically examining design processes. Applying this method to our own process of creating ethics-integrated CS assignments yielded four key challenges: identifying an ethical context, maintaining a technical focus, eliciting both ethical and technical thinking from students, and making the assignment practical for the classroom. Further, the Research through Design approach revealed process-level insights for addressing these challenges, which can apply across the computing curriculum. This paper also serves as a case study of Research through Design for CS education, highlighting the importance of the instructional design process and the behind-the-scenes challenges and design decisions that go into tech-ethics materials.
Analyzing the Performance of Real-Coded Genetic Algorithm with Control Locations for Multi-Robot Path Planning

The problem of navigating multi-robot systems through environments in a collision-free manner is becoming ever more important in the healthcare industry. An increasing interesting can be observed in optimization-based methods for path planning in multi robot systems. This study analyzes the performance of a Real-Coded Genetic Algorithm when planning paths for multi-robot systems with up to five robots. Our approach uses the control location method extended to multiple robot systems. The entire path-planning task was contained within the objective function, which meant that no custom modifications to the optimization algorithm were required, furthermore no constraints were placed on the robot displacements—the collision space was continuous. In total 625 simulations were performed over five custom collision maps and including five reruns of the procedure to minimize the effects of random number generator. Our findings indicated that the methods was viable in cases up to five robots, where the average success rate for path planning was more than 37% with a low number of generations and population size. Depending on the map the success rate could be as high as 80% for systems with 4 robots. Interestingly, avoiding robot-to-environment collisions was more difficult than robot-to-robot collisions, while the number of control locations proved to be more costly for the algorithm than the number of the robots. This might suggest that the algorithm is more suited to simpler collision spaces with higher number of robots.
The Effect of The Creative Drama Method on Pre-Service Physical Education Teachers' Classroom Management Self-Efficacy Beliefs and Communication Skills

This study aimed to reveal the effect of the creative drama method on the communication skills and classroom management self-efficacy beliefs of pre-service physical education teachers. The research group consisted of 47 pre-service physical education teachers (23 in the experimental group, 24 in the control group). The study followed an experimental design with pre-test and post-test control groups. While the experimental group conducted the classroom management lesson with the creative drama method for 10 weeks, the control group conducted it using the traditional method. Classroom management self-efficacy beliefs scale and communication skills scale were applied to the students in the first week before the classes began and in the last week after the classes finished. The study used a one-way analysis of covariance (ANCOVA) to compare the effectiveness of two different methods while keeping the common effect of students’ pre-test scores under check. The study results showed that using creative drama as a method in the classroom management lesson positively affects the classroom management self-efficacy beliefs and communication skills of pre-service physical education teachers.
Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning

Every year in many countries, there are fatal unintentional drownings in different water reservoirs like swimming pools, lakes, seas, or oceans. The existing threats of this type require creating a method that could automatically supervise such places to increase the safety of bathers. This work aimed to create methods and prototype solutions for detecting people bathing in water reservoirs using a multimodal imaging system and machine learning. Two types of cameras, RGB and thermal, were integrated and calibrated to form a multimodal imaging system. The system was designed and implemented to acquire real-world data for bathing people in swimming pools. The EfficientDet models were adapted and trained on collected data reaching at least 94% detection accuracy, with the highest result equal to 97.47%. The best accuracy obtained for the thermal data was lower and equal to 94.85%. However, thermal imaging allows observing scenes in low-light conditions or darkness. This could potentially highly improve the effectiveness of rescue missions, decreasing the death rates or improving the health of early rescued people. Thermal imaging could also be more acceptable regarding privacy, as high-frequency biometric features are not as easy to extract from thermal images as from high-resolution RGB images.
Tissue Pattern Classification with CNN in Histological Images

Tissue pattern is an important factor in morphological evaluation of tissue samples. It can be decisive in disease discrimination or for establishing disease subtypes. Tissue architecture can be generally described by pathologist as classical, hypocellular or hypercellular. This article presents a study used to establish classification convolutional neural network model for tissue compactness assessment. The VGG16 network was trained to classify image patches to create reliable heatmaps. The application of image augmentation, class-specific sampling, and hyperparameter tuning was used to prevent overfitting and increase the accuracy of the model. Based on the current results it can be concluded that the differentiation between hypo- and hypercellular tissue (compactness) is possible with application of deep learning classification model VGG16. We hope to find correlation of features related to tissue compactness with subtypes of the analysed disease that would become diagnostic markers or prognostic factors.
Airavata Metascheduler: A Reliable, Fault Tolerant, and Resource-Aware Job Scheduling Service

Software-as-a-service science gateways provide user interfaces and middleware for accessing scientific software deployed on remote high-performance computing resources and clusters. Selecting the resource to use for a particular job submission may be left to the user, who may need more information to make good choices when selecting from multiple options. To address this problem, we have designed and developed an extensible, scalable metascheduling system that can provide automated scheduling capabilities based on resource availability and other characteristics. We develop a system model based on queuing theory to guide our implementation and provide a basis for analysis. In particular, we derive an efficiency metric from these considerations. We implement the metascheduling system within the open-source Apache Airavata framework for science gateways as a supplemental service for guiding the job submission capabilities. We measure efficiency in representative scenarios, observing efficiencies of greater than 70% even in scenarios with high input rates and low job acceptance rates.
An Analysis on the Factors of English Learning Difficulties Faced by the Ninth Grade Student at SMP Swasta Banuhampu

This reasearch based on in the process of learning English. The problem was namely: “What are the factors of english learning difficulties for students in learning english? These problems were explained through observation and interviews with class IX students at SMP Swasta Banuhampu. In this study, researchers used qualitative research as a research methodology with a descriptive analysis research design. The research subjects were 21 students of the ninth grade and english teacher at SMP Swasta Banuhampu. Data obtained from observation and interviews. The results of this study inform that factors of students difficulties in learning english based on internal and eskternal factors. The researchers classify the problems they face from several factors of existing learning difficulties. The researcher group the problems from physiological factors into 2 general categories, namely: (1) pysical health (2) body defect. On psychological factors, the researcher group problems into 3 general categories, namely: (1)intelligence (2) interested (3) motivation. On the school factor, the researcher group problems into 3 general categories, namely: (1) parental support (2) noisy home atmosphere (3) economic problems. On school factors, the researcher group problems into 3 general categories, namely: (1) teaching method is not varied (2) english learning facilities are inadequate. On social factors, the researcher group problems into 2 general categories, namely (1) the living environment does not motivate to learn english, an (2) technological sophistication makes negligent in learning english.
TicketHub: Enabling Actionable Analysis of Support Requests With NLP

This paper presents TicketHub, an application that uses modern Natural Language Processing (NLP) techniques to provide a dynamic, temporal, and holistic view of High-Performance Computing (HPC) support requests (tickets). While the project was originally developed to derive insights into tickets submitted to Purdue's Rosen Center for Advanced Computing (RCAC), the architecture was designed to easily integrate with additional ticket sources and platforms. This project extends, and automates, previous ticket analysis efforts by utilizing modern semantic and unsupervised NLP techniques to better understand the context of messages, while remaining robust with respect to shifting ticket topics over time.
Students’ Perceptions of Using Digital Media in English Language Learning

This study aims to determine the perceptions of students learning English using various digital media at the Islamic university level. This research uses a qualitative descriptive method, using observation and interviews. The research involved 10 respondents representing the Institute of Islamic Religion (IAI) Tulang Bawang, Lampung. Of the 10 respondents who answered the questionnaire, 87 percent stated why they prefer digital media over non-digital media. The reasons for these respondents are that digital media is more attractive, modern, and practical, in favor of students, and does not require a lot of paper. In addition, digital media can help students become autonomous and independent learners. This can also be used as a reference for developing independent curriculum implementation at KKNI level 6 in higher education.
Alignment in Physics, Biology and Psychology

In a variety of phenomena of our experiences we find that these come into existence (only) thanks to the special way that two disparate items (in the broadest term) are coupled together, which coupling leads to the exclusion of the many more possibilities absent the coupling. Instances of this, here termed ”Alignment”, are discussed within a quantal formalism, for the collapse of wave packets in measurements, for the elongation of protein chains in a cell and for the emergence of awareness resulting from and linked to neural action. For the last, a dual Hilbert-space formulation of mental activities leads to the semi-quantitative explanation of R.N. Shepard’s seminal findings for reaction times in recalls.
Robust and Accessible True Random Number Generator Exploiting the Structural Frustration of Nematic Liquid Crystals

Abstract The study proposes a highly credible true random number generator based on the structural frustration of nematic liquid crystals, which refers to the appearance of two energetically equivalent structures in a nematic system. Frustration is induced in a simple liquid crystal cell by applying an electric field and observing optical textures under crossed polarizers. A clockwise or counterclockwise spiral image appears on 630 separate circular electrodes under the electric field, and random binary sequences are obtained by assigning a digit of 0 or 1 to each image. These sequences pass the strict statistical randomness tests of the National Institute of Standards and Technology with a pass rate of 0.992 or higher for the ten applicable tests and exhibit excellent uniformity. Furthermore, a simple method is introduced to convert the binary results to random decimal numbers with only a minimal amount of data processing.
Drones, Phones, and Stones: Visualization for Collaborative Digitization of Historic Cemeteries

Historic cemeteries face ravages of time, weather, and vandalism. We first captured hundreds of images for each of several unindexed historic cemeteries. We then used a high performance computing system to orthorectify these images into ultraresolution (billions of pixels), georeferenced, tiled images. Here, we describe the interactive visualization we developed using these tiled images to enable volunteers to collaboratively map graves and identify burials. Our ongoing work tests the capacity of this visualization to enable coordination between volunteers with distinct and often asynchronous assignments, including photography, mapping, research, and restoration.
The Mathematics of Scratch Seven

The well-received 2020 science fiction novel To Sleep in a Sea of Stars by Christopher Paolini introduced a novel card game, Scratch Seven, said to be played by Spacers in the 23rd century. The game appears to be based on a mathematical object called a Fano plane, which in turn seems to underlie important structural elements of the novel. This paper will only touch on literary criticism, the main foci are theoretic optimal play in Scratch Seven and useful playing advice in practice.
MSNet: A Seismic Phase Picking Network Applicable to Microseismic Monitoring

In the process of microseismic monitoring, the accuracy of seismic phase picking has a great influence on the precision of seismic localization. Recently, neural network models have achieved satisfying results in seismic phase picking. But these models are mainly applicable to natural earthquakes. In this letter, we present a fully convolutional network for microseismic phase picking called MSNet. The performance of the model is improved from three aspects: model structure, training data, and training strategy. MSNet has two decoders to allow for auxiliary learning, and its structure is improved with reference to state-of-the-art fully convolutional network. Our training data derives from the Stanford Earthquake Dataset (STEAD). It is selected and processed to meet our requirements. In the training process, a special early stopping strategy is used to obtain the model with best generalization performance. The model is tested on fracture-induced microseismic monitoring data from a coal mine in Shaanxi Province, China. Methods for comparison include short- and long-time average ratio, the original PhaseNet and a retrained PhaseNet. The number of correct P picks of MSNet is 18.7% more than PhaseNet, and MSNet achieves a 16.2% higher precision than PhaseNet on S phase. The event locating results verified the accuracy of MSNet. Our method provides an idea to build a microseismic phase picking model with better generalization performance and lower labor cost.
Frequency Disentangled Features in Neural Image Compression

The design of a neural image compression network is governed by how well the entropy model matches the true distribution of the latent code. Apart from the model capacity, this ability is indirectly under the effect of how close the relaxed quantization is to the actual hard quantization. Optimizing the parameters of a rate-distortion variational autoencoder (R-D VAE) is ruled by this approximated quantization scheme. In this paper, we propose a feature-level frequency disentanglement to help the relaxed scalar quantization achieve lower bit rates by guiding the high entropy latent features to include most of the low-frequency texture of the image. In addition, to strengthen the de-correlating power of the transformer-based analysis/synthesis transform, an augmented self-attention score calculation based on the Hadamard product is utilized during both encoding and decoding. Channel-wise autoregressive entropy modeling takes advantage of the proposed frequency separation as it inherently directs high-informational low-frequency channels to the first chunks and conditions the future chunks on it. The proposed network not only outperforms hand-engineered codecs, but also neural network-based codecs built on computation-heavy spatially autoregressive entropy models.
USGG: Union Message Based Scene Graph Generation

Scene graph generation (SGG) is designed to represent images by objects and their relationships. Existing works mainly attempt to strengthen object pair representations for SGG. However, most methods ignore the significant semantic information implied in union regions, which refers to the surrounding area of object pairs. In this paper, we propose a new union message based architecture, named as USGG, to profoundly exploit the relational semantics of unions to facilitate SGG. Concretely, we employ sufficient feature extraction to enhance the features of objects and unions. Next, we devise the Union Embedding Network to model the relational representations through two symmetric encoder-decoder branches. Moreover, the Union Fusion Network is designed to integrate the refined semantics by two-stage feature fusion. Extensive experiments are conducted on Visual Genome dataset, which demonstrates that the proposed approach achieves competitive performance against state-of-the-art methods on Recall, mean Recall and Zero Shot Recall metrics.
Reduced Complexity Multiscale CNN for in-Loop Video Restoration

Convolutional neural networks (CNNs) have shown promising improvements in video coding efficiency when included in traditional block-based codecs as a loop filter. Unfortunately, these coding gains are often accompanied by significant increases in complexity, measured by the number of multiply-accumulate (MAC) operations, that make them intractable in practice. As a result, there is considerable interest in reducing complexity for these CNN-based approaches. In previous work, we have shown that multiscale CNNs provide a path to reduce the associated MAC count. In this paper, we extend our work to consider channel grouping, spatial support limitations and shallower network depths to further reduce the MAC count of these multi-scale architectures. We demonstrate that the method can achieve an average VMAF bitrate reduction of 6.1% and 2.6% for all intra and random-access coding respectively, when compared to the evolving AV2 standard. Complexity is reduced to 1.85k MACs per pixel, which is a 390× reduction over previously published results.