Reviewerly/exHarmony huggingface dataset:
Anomaly Traffic Detection Based on Communication-Efficient Federated Learning in Space-Air-Ground Integration Network

In this paper, we study the architectures of space-air-ground integration network (SAGIN) proposed by domestic scientific research institutes, and put forward an collaborative federal learning architecture suitable for SAGIN to solve the problems of insecurity and low timeliness caused by traffic backhaul. An anomaly traffic detection method is proposed based on the requirements and characteristics of SAGIN. The problem that it is difficult to manually label and extract features in the traffic of SAGIN is solved through the improvement of deep learning algorithm. The challenge of lack of professionals labeling training set is solved by studying the method of semi supervision. The problem of artificial feature engineering is solved by studying the end-to-end anomaly traffic detection algorithm. Finally, we design a simulation environment for the anomaly traffic detection in SAGIN, and verify the feasibility and advanced nature of the proposed methods.
Evaluating large language models on a highly-specialized topic, radiation oncology physics

We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions. Because popular exams like AP Physics, LSAT, and GRE have large test-taker populations and ample test preparation resources in circulation, they may not allow for accurately assessing the true potential of LLMs. This paper proposes evaluating LLMs on a highly-specialized topic, radiation oncology physics, which may be more pertinent to scientific and medical communities in addition to being a valuable benchmark of LLMs.We developed an exam consisting of 100 radiation oncology physics questions based on our expertise. Four LLMs, ChatGPT (GPT-3.5), ChatGPT (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated against medical physicists and non-experts. The performance of ChatGPT (GPT-4) was further explored by being asked to explain first, then answer. The deductive reasoning capability of ChatGPT (GPT-4) was evaluated using a novel approach (substituting the correct answer with "None of the above choices is the correct answer."). A majority vote analysis was used to approximate how well each group could score when working together.ChatGPT GPT-4 outperformed all other LLMs and medical physicists, on average, with improved accuracy when prompted to explain before answering. ChatGPT (GPT-3.5 and GPT-4) showed a high level of consistency in its answer choices across a number of trials, whether correct or incorrect, a characteristic that was not observed in the human test groups or Bard (LaMDA). In evaluating deductive reasoning ability, ChatGPT (GPT-4) demonstrated surprising accuracy, suggesting the potential presence of an emergent ability. Finally, although ChatGPT (GPT-4) performed well overall, its intrinsic properties did not allow for further improvement when scoring based on a majority vote across trials. In contrast, a team of medical physicists were able to greatly outperform ChatGPT (GPT-4) using a majority vote.This study suggests a great potential for LLMs to work alongside radiation oncology experts as highly knowledgeable assistants.
Pair Programming In Introductory Programming Labs

This project looks at the practice of pair programming as a vehicle for improving the learning environment in introductory computer science labs, a nearly universal course for all engineering students.Pair programming is a practice in which two programmers work collaboratively at one computer, on the same design, algorithm, or code.Prior research indicates that pair programmers produce higher quality code in essentially half the time taken by solo programmers.A multiyear project is currently underway at North Carolina State University, looking at the efficacy of pair programming in an introductory Computer Science (CS1) course.Results indicate that student pair programmers are more self-sufficient in lab and are more likely to complete the class with a grade of C or better.The effect of pair programming on specific graded exams and projects are less clear.Demographic data, prior achievement scores, and current course scores were brought together with interview data, attitude surveys, and in-lab observations to guide the evaluation.As part of the in-lab data collection, novel techniques are being developed to better understand pair dynamics and student/instructor interactions.Collectively, these evaluative methods have guided the iterative implementation of paired programming instructional methods.Current challenges being addressed include lab instructor training, student/instructor concerns over equity in effort on assignments, pair dynamics in lab, and collaborative logistics of pair programming outside of lab.
Portfolio Online Evolution in StarCraft

Portfolio Online Evolution is a novel method for playing real-time strategy games through evolutionary search in the space of assignments of scripts to individual game units. This method builds on and recombines two recently devised methods for playing multi-action games: (1) Portfolio Greedy Search, which searches in the space of heuristics assigned to units rather than in the space of actions, and (2) Online Evolution, which uses evolution rather than tree search to effectively play games where multiple actions per turn lead to enormous branching factors. The combination of both ideas lead to the use of evolution to search the space of which script/heuristic is assigned to which unit. In this paper, we introduce the ideas of Portfolio Online Evolution and apply it to StarCraft micro, or individual battles. It is shown to outperform all other tested methods in battles of moderate to large size.
SpecINT: A framework for data integration over cheminformatics and bioinformatics RDF repositories

Many research centers and medical institutions have been accumulating a vast amount of various biological and chemical data over the past decade and this trend continues. Based on Linked Data vision, many semantic applications for distributed access to these heterogeneous RDF (Resource Description Framework) data sources have been developed. Their improvements have brought about a decrease of intermediate results and optimizing query execution plans. But still many requests are unsuccessful and they time out without producing any answer. Also, the applications which operate over repositories taking into consideration their specificities and inter-connections are not available. In this paper, the SpecINT is proposed as a comprehensive hybrid framework for data integration and federation in semantic data query processing over repositories. The SpecINT framework represents a trade-off solution between automatic and user-guided approaches, since it can create queries which return relevant results, while not being dependent on human work. The innovativeness of the approach lays in the fact that the coordinates of graph eigenvectors are used for the automatic sub-queries joining over the most relevant data sources within repositories. In this way searching can be effected without a common ontology between resources. In experiments, we demonstrate the potential of our framework on a set of heterogeneous and distributed cheminformatics and bioinformatics data sources.
Active Learning for New Domains in Natural Language Understanding

Stanislav Peshterliev, John Kearney, Abhyuday Jagannatha, Imre Kiss, Spyros Matsoukas. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
Financial structure and income inequality

We empirically investigate whether financial structures affect income inequality. Using panel data for 97 economies covering above two decades preceding the FinTech surge we uncover a non-monotonic relationship. More finance reduces income inequality up to a certain point, beyond which inequality rises if finance expands via market-based financing, while it does not when finance grows via bank lending. The finance-inequality relationship is not structurally affected by the occurrence of crises, while it is reinforced in countries featuring a higher degree of financial openness. Financial deepening always reduces inequality when it takes the form of improved financial access.
Weighted Direct Nonlinear Regression for Effective Image Interpolation

This paper proposes a learning-based image interpolation method based on weighted direct nonlinear regression.It attempts to learn the nonlinear relationship between the low-resolution patches and their corresponding high-resolution patches by using an external database of natural images.In the training phase, without being initialized to the same size as the high-resolution patches using bicubic interpolation, low-resolution patches are directly used for training, which will reduce the number of parameters.Dictionary learning combined with nearest neighbor searching based on the normalized correlation coefficient in the entire training set is proposed as a soft classification method.Afterward, a single hidden-layer feed-forward network with random input weights is adopted to learn the direct nonlinear mapping for the regression in each class.In the interpolation phase, the strategy of weighting multiple estimations is applied to enhance the interpolation performance.Furthermore, a refinement process is proposed to improve the interpolation performance with sharper edges and richer details.Extensive experimental results demonstrate that our proposed method is not only effective and efficient but also outperforms many state-of-the-art methods.
PROJECT-BASED LEARNING TO DEVELOP STUDENTS' ABILITY AND CREATIVITY IN WRITING NARRATIVE STORY

In English language teaching, project-based learning is not only able to optimize language skills but also other skills such as critical and creative thinking. This study tries to investigate the use of project-based learning to develop students� abilities and creativity in writing narrative text and the students� perception towards the implementation of project-based learning in writing course. This study employed a qualitative approach by involving second semester students who take writing course. The results of this study indicated that project-based learning can help the students to develop their ability and creativity in writing narrative text in several point including their understanding of the topic, their knowledge about narrative genre namely social function, structure of text, and language feature. In addition, the students also showed their creativity through creating their story line and designing the illustration of the story. Furthermore, the students gave positive views toward the implementation of project-based learning. They mentioned that they learned networking, collaborative learning, and team work through PBL. Thus, project-based learning can be one of alternative ways to teach writing in higher education.Keywords: narrative text; project based learning; student creativity; teaching writing; writing skills.
Undressing the Words: Prevalence of Profanity, Misogyny, Violence, and Gender Role References in Popular Music from 2006-2016

Are degrading sexual references more prevalent than non-degrading references in music popular with adolescents? The purpose of this study was to perform a content analysis of contemporary popular music with particular attention paid to the prevalence of violent, misogynistic, profane, and/or demeaning colloquial references. Billboard magazine was used to identify the top popular songs appearing on top music charts from 2006 through 2016. Data show songs with justified violence themes were most commonly found in hip-hop music, whereas songs with unjustified message themes were most often found in Pop music. The present study suggests that pop and hip-hop/rap music, genres popular with most adolescents today, use frequent references involving profanity, violence, and misogynistic themes.
Modifying Maiorana--McFarland Type Bent Functions for Good Cryptographic Properties and Efficient Implementation

Very recently, a class of cryptographically significant Boolean functions were constructed by Tang and Maitra [IEEE Trans. Inform. Theory, 64 (2018), pp. 393--402] by modifying the $\mathcal{PS}_{ap}$ class of bent functions. The basic ideas used in Tang--Maitra construction were derived from a modification of a subclass of bent functions which is defined over the finite field, and a concern was raised in the same paper whether the implementation of such functions will be as efficient as that of Maiorana--McFarland type bent functions. In this paper, we look at the concrete realization of such functions over a vector space and answer the question positively. The first part of this paper investigates how the finite field implementation of the functions can be viewed as simple truth tables. Next, we present a completely new construction that itself starts from Maiorana--McFarland bent functions which are straightforward concatenations of linear functions.
Use of VR Headsets in Education: A Systematic Review Study

In recent years, it can be said that the use of new virtual reality related technologies increased swiftly. Especially virtual reality headsets have become an important device in virtual reality and augmented reality applications. As a consequence of these developments, researchers started to investigate the use of virtual reality headsets in many fields. In this respect, the purpose of this study is to perform a systematic review with content analysis on the articles about the use of VR headsets in education in the Scopus database between 2014 and 2019. A total of 49 related article were found and examined to determine the major topics, research methods/models, most cited papers, data collection tools, participants and variables in these articles. Because no systematic review study has been conducted on this topic in literature, the study is considered to be important. As a result, it was found that the number of studies about the use of virtual reality headsets in education tends to increase by year. In addition, it was seen that quantitative methods as research methods, undergraduate students as participants, and pretest-posttests as data collection tools were the most favored ones. Besides, it was found that usability and experience were the most preferred dependent variables.
A novel technique for analysing histogram equalized medical images using superpixels

We present a novel technique to distinguish between an original image and its histogram equalized version. Histogram equalization and superpixel segmentation such as SLIC (simple linear iterative clustering) are very popular image processing tools. Based on these two concepts, we introduce a method for finding whether an image (grayscale) is histogram equalized or not. Because sometimes we see images that look visually similar but they are actually processed or changed by some image enhancement process such as histogram equalization. We can merely infer whether the image is dark, bright or has a small dynamic range. Moreover, we also compare the result of SLIC superpixels with three other superpixel segmentation algorithms namely, quick shift, watersheds, and Felzenszwalb’s segmentation algorithm
Evaluating the utility of elemental measurements obtained from factory-calibrated field-portable X-Ray fluorescence units for aquatic sediments

We assessed factory-calibrated field-portable X-ray fluorescence (pXRF) data quality for use with minimally-prepared aquatic sediments, including the precision of replicate pXRF measurements, accuracy of factory-calibrated pXRF values as compared to total digestion/ICP-OES concentrations, and comparability of calibrated pXRF values to extractable concentrations. Data quality levels for precision, accuracy, and comparability were not equivalent for element/analyzer combinations. All analyses of elements that were assessed for precision and accuracy on a single analyzer were both precise (<10% relative standard deviation) and accurate (r2 > 0.85) for K, Ca, Ti, Mn, Fe, and Zn. Calibrated pXRF values for Al, K, Ca, Ti, Mn, Fe, Cu, Zn, and Pb were within ∼10% relative difference of total digestion/ICP-OES concentrations. Calibrated pXRF values for Fe, Cu, Zn, As, and Pb were within ∼20% relative difference of extractable concentrations. Some elements had a higher level of data quality using specific analyzers, but in general, no pXRF analyzer had the highest level of data quality in all categories. Collectively, our data indicate that a wide range of factory-calibrated pXRF units are capable of providing high-quality total concentrations for the analysis of aquatic sediments.
Watersheds for Semi-Supervised Classification

Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered - How do the boundaries of the watershed operator behave? Which loss function does the watershed operator optimize? How does watershed operator relate with existing ideas from machine learning. In this letter, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, morphMedian, resulting in the maximum margin principle. It is then shown that watersheds form a particular class of morphMedian classifiers. Using the ensemble technique, watersheds are also extended to ensemble watersheds. These techniques are compared with relevant methods from the literature and it is shown that watersheds perform better than support vector machines on some datasets, and ensemble watersheds usually outperform random forest classifiers.
Full-Field Strain Shape Estimation from 3D SLDV

The ability to measure full-field strains is desirable for analytical model validation or characterization of test articles for which there is no model. Of further interest is the ability to determine if a given environmental test’s boundary conditions are suitable to replicate the strain fields the test article undergoes in service. In this work, full-field strain shapes are estimated using a 3D scanning laser Doppler vibrometer and several post-processing methods. The processing methods are categorized in two groups: direct or transformation. Direct methods compute strain fields with only spatial filtering applied to the measurements. Transformation methods utilize SEREP shape expansion/smoothing of the measurements in conjunction with a finite element model. Both methods are used with mode shapes as well as operational deflection shapes. A comparison of each method is presented. It was found that performing a SEREP expansion of the mode shapes and post-processing to estimate strain fields was very effective, while directly measuring strains from ODS or modes was highly subject to noise and filtering effects.
Does online anonymity undermine the sense of personal responsibility?

This article begins by exploring the media visibility of anonymity online, in particular in relation to trolls and online bullying. It then explores anonymity in the case of Chelsea Manning who leaked US military secrets but only lost anonymity sometime after her leak was made public. This discussion explores issues of responsibility and the reasons for being anonymous. The article then briefly discusses anonymity based on the discussion of the Manning case in relation to the Snowden case, where Snowden refused anonymity, and the Pentagon Papers. The article concludes by pointing out that issues of responsibility in relation to anonymity remain similar whether digital or not, but that anonymity now exists in a context in which there is greater ability to share information between many more people, faster and with radically lowered costs of publishing than before.
Augmented Reality for Quick and Intuitive Robotic Packing Re-Programming

Current manufacturing applications are subject to constant changes in production orders for their robotic systems to adapt to the dynamic nature of the market. Hence, reprogramming robots needs to be a fast, easy and effective process. In this demonstration, we present an augmented reality (AR) interface using HoloLens. Our interface provides an intuitive platform to re-program a robotic packing application through simple hand gestures and the information gathered by the HoloLens' spatial mapping functionality.
Cimind: A phonetic-based tool for multilingual named entity recognition in biomedical texts

Extracting concepts from biomedical texts is a key to support many advanced applications such as biomedical information retrieval. However, in clinical notes Named Entity Recognition (NER) has to deal with various types of errors such as spelling errors, grammatical errors, truncated sentences, and non-standard abbreviations. Moreover, in numerous countries, NER is challenged by the availability of many resources originally developed and only suitable for English texts. This paper presents the Cimind system, a multilingual system dedicated to named entity recognition in medical texts based on a phonetic similarity measure. Cimind performs entity recognition by combining phonetic recognition using the DM phonetic algorithm to deal with spelling errors and string similarity measures. Three main steps are processed to identify terms in a controlled vocabulary: normalization, candidate selection by phonetic similarity and candidate ranking. Cimind was evaluated in the 2016 and 2017 editions of the CLEF eHealth challenge in the CépiDC/CDC tasks. In 2017, it obtained on each corpus the following results: English dataset: 83.9% P, 78.3% R, 81.0% F1; French raw dataset: 85.7% P, 68.9% R, 76.4% F1; French aligned dataset: 83.5% P, 77.5% R, 80.4% F1. It ranked first in French and fourth in English in officials runs.
Mutual Information, the Linear Prediction Model, and CELP Voice Codecs

We write the mutual information between an input speech utterance and its reconstruction by a code-excited linear prediction (CELP) codec in terms of the mutual information between the input speech and the contributions due to the short-term predictor, the adaptive codebook, and the fixed codebook. We then show that a recently introduced quantity, the log ratio of entropy powers, can be used to estimate these mutual informations in terms of bits/sample. A key result is that for many common distributions and for Gaussian autoregressive processes, the entropy powers in the ratio can be replaced by the corresponding minimum mean squared errors. We provide examples of estimating CELP codec performance using the new results and compare these to the performance of the adaptive multirate (AMR) codec and other CELP codecs. Similar to rate distortion theory, this method only needs the input source model and the appropriate distortion measure.
Animasi Interaktif Pembelajaran Huruf dan Angka Menggunakan Model ADDIE

The use of technology in learning media is very necessary for all educational institutions. Creating a pleasant learning atmosphere through multimedia means in the form of interactive animations. It was felt effective to increase children's learning interest in recognizing letters and numbers at TK RA Nurul Iman Jakarta. Use the type of research and development with the ADDIE model. Calculation of the app's feasibility to produce interactive animations that fit your needs. The purpose of making this learning media helps students to be able to more easily understand and understand and not feel bored in learning to start from letters and numbers and how to pronounce them. After the learning process, students will work on the quiz and there will also be games to make the child's thinking speed better. In addition, this application can help teachers in the teaching and learning process that is considered very boring, with an average value of 82.6% showing this interactive animation application is very fun for the media to learn the introduction of numbers and letters.