Reviewerly/exHarmony huggingface dataset:
The complex conjugate invariants of Clifford groups

Nebe, Rains and Sloane studied the polynomial invariants for real and complex Clifford groups and they give a new conceptual proof that these invariants are spanned by the set of complete weight enumerators of certain self-dual codes, which was first proved by Runge using modular forms. The purpose of this paper is to show that very similar results can be obtained for the invariants of the complex Clifford group $$\mathcal {X}_m$$ acting on the space of conjugate polynomials in $$2^m$$ variables of degree $$N_1$$ in $$x_f$$ and of degree $$N_2$$ in their complex conjugates $$\overline{x_f}$$ . In particular, we show that the dimension of this space is 2, for $$(N_1,N_2)=(5,5)$$ . This solves affirmatively Conjecture 2 given by Zhu, Kueng, Grassl and Gross. In other words if an orbit of the complex Clifford group is a projective 4-design, then it is automatically a projective 5-design.
Language effects on acoustic voice variation within and between talkers

Acoustic voice spaces for English speakers are characterized mainly by variability in F0, the balance between higher harmonic amplitudes and inharmonic energy, and higher formant frequencies [JASA 146(4), 3011 (2019)]. We extended this investigation to another language to test the hypothesis that a few biologically relevant measures will emerge commonly across languages, while remaining variance will depend on the structure of the language. This hypothesis was tested against sentence productions from 5 female and 5 male speakers of Seoul Korean. Like English, Korean does not have tone or phonation contrasts, but Seoul Korean exhibits specific phrase intonation patterns. PCAs were performed on scaled values of F0, formant frequencies, spectral noise, source spectral shape, and their variability, measured from vowels and approximants. Results revealed striking similarities between the acoustic voice spaces derived from Korean speakers and those for English speakers. For Korean voices, F0 and variability in lower formant frequencies (i.e., vowel quality) accounted for the most acoustic variance within and across talkers, presumably due to Seoul speakers' systematic use of these measures for phrasal/accentual information. These measures were insignificant for English voices. Our findings suggest that acoustic voice spaces are shaped by both biologically and phonologically relevant factors. [Work supported by NIH/NSF.]
CounQER: A System for Discovering and Linking Count Information in Knowledge Bases

Predicate constraints of general-purpose knowledge bases (KBs) like Wikidata, DBpedia and Freebase are often limited to subproperty, domain and range constraints. In this demo we showcase CounQER, a system that illustrates the alignment of counting predicates, like staffSize, and enumerating predicates, like workInstitution $$^{-1}$$ . In the demonstration session, attendees can inspect these alignments, and will learn about the importance of these alignments for KB question answering and curation. CounQER is available at https://counqer.mpi-inf.mpg.de/spo .
A System for Explainable Answer Set Programming.

We present xclingo, a tool for generating explanations from ASP programs annotated with text and labels. These annotations allow tracing the application of rules or the atoms derived by them. The input of xclingo is a markup language written as ASP comment lines, so the programs annotated in this way can still be accepted by a standard ASP solver. xclingo translates the annotations into additional predicates and rules and uses the ASP solver clingo to obtain the extension of those auxiliary predicates. This information is used afterwards to construct derivation trees containing textual explanations. The language allows selecting which atoms to explain and, in its turn, which atoms or rules to include in those explanations. We illustrate the basic features through a diagnosis problem from the literature.
Drift error compensation for vision-based bridge deflection monitoring

Recently, an advanced video deflectometer based on the principle of off-axis digital image correlation was presented and advocated for remote and real-time deflection monitoring of large engineering structures. In engineering practice, measurement accuracy is one of the most important technical indicators of the video deflectometer. However, it has been observed in many outdoor experiments that data drift often presents in the measured deflection-time curves, which is caused by the instability of imaging system and the unavoidable influences of ambient interferences (e.g., ambient light changes, ambient temperature variations as well as ambient vibrations) in non-laboratory conditions. The non-ideal unstable imaging conditions seriously deteriorate the measurement accuracy of the video deflectometer. In this work, to perform high-accuracy deflection monitoring, potential sources for the drift error are analyzed, and a drift error model is established by considering these error sources. Based on this model, a simple, easy-to-implement yet effective reference point compensation method is proposed for real-time removal of the drift error in measured deflections. The practicality and effectiveness of the proposed method are demonstrated by in-situ deflection monitoring of railway and highway bridges.
Using Topic Models to Label Documents for Classification

Document classifiers are supervised learning models in which documents are assigned categories based on models that are trained on annotated datasets. In this paper, we use topic models to automatically assign categories to documents, which later are fed to document classification models. We perform experiments on several datasets in Vietnamese, collected from free online resources. Our method is promising and applicable to many datasets that have not been labeled.
CHATBOT PEMBELAJARAN BAHASA INGGRIS BERBASIS MEDIA SOSIAL

ABSTRACTSocial media has changed people's way of life but, unfortunately, most of them only use it for information exchange. This research tried to develop an application using a social media as a medium for students to learn English more efficiently. This application was in the form of a chatbot (answering robot) and designed to be capable of replacing the teacher's role in answering students' questions while they learn English. Their familiarity with the interface and navigation of social media made them easy to operate the integrated chatbot. This application was tried out on D3 students of English Study Program, State Polytechnic of Malang. The test results showed 98% of the respondents stated that the chatbot helped them learn English, 90% said that the material presented was quite interesting, and 72% stated that the chatbot application did not decrease the performance of their mobile phones. This showed that the collection of information and the help model through the Organized Exercise Items in the application helped students learn English independently.Keyword: chatbot, english learning, organized exercise items, social media,
Automatic Extraction of Locations from News Articles Using Domain Knowledge

With the increasing amount of digital data, it is becoming increasingly hard to extract useful information from text data, especially for resource-constrained languages. In this work, we report the task of language-independent automatic extraction of locations from news articles using domain knowledge. The work is tested on four languages namely, English and three resource-constrained languages: Assamese, Manipuri and Mizo, the lingua francas of three neighboring North-Eastern states of India namely Assam, Manipur, and Mizoram respectively. Our architecture is based on semantic similarity between similar words based on the popular word embedding, word2vec model coupled with the domain knowledge of the aforementioned regions. The model is able to detect the best possible detailed locations.
A Study on the Improvement of Segmentation Performance using Modified WASPP

본 논문에서는 DeepLab V3 알고리즘의 핵심인 ASPP(Atrous Spatial Pyramid Pooling)를 대신하기 위해 선행 연구한 WASPP(Wavelet Atrous Spatial Pyramid Pooling)의 mIOU 결과는 epoch=100에서 90.2%로 기존 ASPP 대비 유사하거나 약간 낮아서 성능 향상에는 미흡한 결과가 나왔다. 이를 개선하기 위해 웨이블릿 특성을 활용하면서 기존 ASPP보다 Semantic segmentation 객체 검출 성능 개선에 대해 검토하였다. 그 결과 기존 ASPP 대비 Loss 및 mIOU 부분에서 1%이상의 성능 향상이 이루어졌으며, image_6 (Monitor prediction) 영상에서는 DeepLab v3는 epoch=200 기준으로 여전히 인식 오류가 존재하나 변형 WASPP에서는 epoch=50에서 완벽히 개선되었다. 이것은 특정 영상에서 기존보다 200%이상 성능이 개선된 결과이다.
Undergraduate Research at a Liberal Arts College during a Time of Disruption

Due to the COVID-19 pandemic, the authors, who have expertise in acoustics, optics, and astrophysics, decided to pivot from the experimental components of their research and focus instead on computational studies. Many of their usual research practices were adapted, creating new techniques to optimize the remote research experience for their undergraduate students.
ImCLR: Implicit Contrastive Learning for Image Classification

Contrastive learning is an effective method for learning visual representations. In most cases, this involves adding an explicit loss function to encourage similar images to have similar representations, and different images to have different representations. In this paper, we introduce a clever construction for Implicit Contrastive Learning (ImCLR), primarily in the supervised setting: there, the network can implicitly learn to differentiate between similar and dissimilar images. Furthermore, this requires almost no change to existing pipelines, which allows for easy integration and for fair demonstration of effectiveness on a wide range of well-accepted benchmarks. Namely, there is no change to loss, no change to hyperparameters, and no change to general network architecture. We show that ImCLR improves the test error in the supervised setting across a variety of settings, including 3.24% on Tiny ImageNet, 1.30% on CIFAR-100, 0.14% on CIFAR-10, and 2.28% on STL-10. We show that this holds across different number of labeled samples, maintaining approximately a 2% gap in test accuracy down to using only 5\% of the whole dataset. We further show that gains hold for robustness to common input corruptions and perturbations at varying severities with a 0.72% improvement on CIFAR-100-C, and in the semi-supervised setting with a 2.16% improvement with the standard benchmark Π-model. We demonstrate that ImCLR is complementary to existing data augmentation techniques, achieving over 1% improvement on CIFAR-100 by combining ImCLR with CutMix over either baseline, and 2\% by combining ImCLR with AutoAugment over either baseline. Finally, we perform an ablation study.
Synthesis and characterization of iron Oxide (hematite) nanoparticles by sol-gel method

&#x0D; &#x0D; &#x0D; Iron oxide nanoparticles have excellent biomedical applications because of its large surface area. It can be used in drug delivery, cell separation, tissue repair and MRI. A Fe2O3 nanoparticle is synthesized chemically by sol-gel method. This method uses Ammonium hydroxide and Ethanol as a precursor for forming Fe2O3 nanoparticles. Thus Fe2O3 nanoparticles are characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), Energy dispersive X-ray spectroscopy (EDAX) and UV Spectroscopy. All the structural parameters such as lattice constants, unit cell volume, density, crystalline size, micro strain are calculated from the XRD results using Debye Scherrer’s formula. When annealing temperature increased from 400oC to 1000oC the average crystalline size of the Fe2O3 nanoparticles are increased from 18nm to 22nm. FTIR technique also confirms functional groups of the synthesized Fe2O3 nanoparticles. The SEM image indicates that Fe2O3 nanoparticles are approximately spherical in shape. Band gap of the Fe2O3 nanoparticles is founded using UV Spectroscopy and it is reported in paper.&#x0D; &#x0D; &#x0D;
REBOOTING OF THE SIMULATION TRAINING DURING A PANDEMIC

In a pandemic, all educational institutions were forced to switch to distance learning. The quick reaction of staff, students and teachers to the new conditions created allowed mobilizing internal intellectual resources for distance learning in the simulation center. Analysis of the effectiveness of the available electronic resources made it possible to flexibly and quickly restructure the educational process in the simulation center. In general, both students and teachers have gained invaluable experience in teaching and learning during total self-isolation and distance learning.
Teaching Essay Writing to the ELT Students

Essay writing ELT students’ is considered as one of the most important academic courses in asupposed program help develop students’ skill. The research subjects were 11 students of class2019 A STKIP PGRI Sidoarjo English Education study program. The instrument used was aquestionnaire and scoring rubric. The technique for data analysis is qualitative. In analyzingessay writing data with a questionnaire and scoring rubric are as follows. Taking the dataquestionnaire was conducted using 15 questionnaires in a student's response to learning essaywriting with Google Form with answers to yes and no. respondents who answered yes as much84,23% and than respondents who answered no as much 21,22%. Then got data based on scoringrubric was carried out by assessing and correcting the essay writing results of 11 students of class2019 A. The result were assessed using a scoring rubric consisted of 5 aspects, namely format,mechanics, content, organization, grammar and sentence structure. From the description above,the average value was 87.3. This study presents a research report on teaching essay writing to theELT students. The aim is to find out students ability in essay writing and students responses inessay writing classroom.
Comparing DASH Adaptation Algorithms in a Real Network Environment

Over the past few years, the Dynamic Adaptive Streaming over HTTP (DASH) standard has been widely adopted by video streaming services; this has led to a considerable amount of research on efficient adaptation algorithms to maximize users' Quality of Experience (QoE), but the comparison between algorithms is often flawed and unrealistic. Trace-based simulations or experiments in wired testbeds often fail to capture the full complexity and variability of a live network environment, and the comparison between them is often biased by the simplistic assumptions made about the simulation/testing setup. In this work, we implement four of the most representative adaptation algorithms and compare their performance in a real campus network. This allows us to have a fair comparison of the various algorithms' strengths and weaknesses in a representative scenario, improving the understanding of the dynamics of video streaming adaptation and highlighting the open problems in the field. The test results make a clear trade-off emerge, as algorithms designed for high fairness in a static scenario cannot deal with a dynamic channel and cross-traffic effectively and result in very low QoE levels, while more aggressive algorithms face rebuffering events in fast-varying scenarios.
Short-text intention recognition based on multi-dimensional dynamic word vectors

Abstract For short-texts with few words and sparse features, it becomes very important to use text context semantic information to enrich its text representation. At present, the text representation method of static word vectors is the same for the same word in different sentences. From a certain perspective, contextual semantic information is not fully utilized. Therefore, this paper proposes to use the combination of BERT and ELMo to mine more comprehensive contextual semantic information from different dimensions to enrich the text representation. The BERT model fully describes the characteristics of word level, sentence level and relationship between sentences. The ELMo model can solve the problem of polysemy. LSTM and Attention Mechanism are used to extract high-dimensional text features, and CRF is used to recognize intentions. Experiments show that the multi-dimensional dynamic word vector intention recognition model (MD-Intention) proposed in this paper has achieved good performance on both the ATIS-2 dataset and the SNIPS dataset.
Parameter accuracy analysis of weak-value amplification process in the presence of noise*

We theoretically introduce the statistical uncertainty of photon number and phase error to discuss the precision of parameters to be measured based on weak measurements. When the photon counting scheme is used, we discuss the relative accuracy of the system in the presence of phase error by using the orthogonal and nonorthogonal pre- and post-selected states, respectively. When using the measurement scheme of pointer shift, we discuss the measurement accuracy in the presence of phase error, pointer resolution, and statistical uncertainty. These results give a guide way to get the smallest relative precision and deepen our understanding about weak measurement.
VIRTUAL REALITY: DISTANT LEARNING IN INFORMATION SOCIETY

Due to current socio-cultural transformations, the system of national higher education has significantly reduced its effectiveness, which is indirectly prooved by numerous international ratings, where Russian universities keep far leading positions. Many attempts of the authorities to strengthen the financing of concrete Russian universities in the beginning of the XXI century were not successful and merely pushed aside the moment when it became obvious that the approaches used were not efficient to ensure the competitiveness of national education in the context of increasing globalization. In higher education system, the gap between the high-speed flow of information in online-education sector (distance education) and the slow passage of time in the traditional educational process is rapidly increasing. Currently, one of the possible alternatives for the development of education is lifelong learning, which is based on the development of distance (virtual) technologies. The paper discusses the ontological features of distance education, highlighted its positive and negative characteristics. The author highlights that modern education is a process and the result of mastering the knowledge and skills acquired in the context of the achievements of modern science and practical activity of people. It is revealed that in modern conditions, as a result of the lack of synchrony and balance between the virtual and real sectors of higher education, there can be a de-synchronization of the ontology of education. The paper explains that a complete transfer of education to the virtual area will lead not only to a serious deterioration in quality, but also to destructive changes in education as an institution aimed at the reproduction of society. The author makes conclusion that, in accordance with the trends of the world economy, national higher education will experience a shift in favor of a product with «growing surplus value», which will lead to the inevitability of high-tech, high-value, competitive higher education and low-cost depreciate «higher education», aimed at supporting the production of cheap uncompetitive labor, which is unclaimed outside the domestic market.
The Challenges of Digital Technologies for Nigeria

The world is saturated with information and communication technologies that have changed the way people work and connect to one another. These technologies play a great role in people’s interactions, their pursuit of education, and certain careers in life. For some scholars in the developed world, the era of the digital revolution is over; it is now the post-digital era. However, fora developing country like Nigeria, though digitalization is seen as a necessity, there are still many challenges hindering its progress. Digital technologies are perceived as a double-edged sword with positive and negative impacts. Apart from the digital divide, some of these technologies have been abused or misused. Hence, part of the humanization process for Nigeria with its complex historical-cultural context would entail addressing the challenges surrounding these technologies, aspiring to reduce their capacity to dehumanize. More people need to have access to and be educated on how to better understand and wisely use such technologies. The process of dealing with these challenges must involve the partnership and collaboration of government agencies, academia, and civil societies.
The Machine-Learning Approach of Reinforcement Learning

This chapter assesses alternative approaches of reinforcement learning that are developed by machine learning. The initial goal of this branch of artificial intelligence, which appeared in the middle of the twentieth century, was to develop and implement algorithms that allow a machine to learn. Originally, they were computers or more or less autonomous robotic automata. As artificial intelligence has developed and cross-fertilized with neuroscience, it has begun to be used to model the learning and decision-making processes for biological agents, broadening the meaning of the word ‘machine’. Theoreticians of this discipline define several categories of learning, but this chapter only deals with those which are related to reinforcement learning. To understand how these algorithms work, it is necessary first of all to explain the Markov chain and the Markov decision-making process. The chapter then goes on to examine model-free reinforcement learning algorithms, the actor-critic model, and finally model-based reinforcement learning algorithms.
Mailing It In? Results from a Field Experiment Encouraging Voting by Mail in a 2020 Primary

The ability to cast a mail ballot can be an important safeguard to the franchise. But because there are often additional procedural protections to ensure that a ballot cast in person is counted, voting by mail can also jeopardize people's ability to cast a ballot that is counted. An experiment carried out during the COVID-19 pandemic illustrates both of these forces. Philadelphia city officials randomly sent approximately 47,000 Philadelphia voters postcards encouraging them to apply to vote by mail in the lead-up to the June 2, 2020 primary election. While the intervention increased the share of Philadelphia voters who cast a mail ballot by 0.4 percentage points (p=0.017)--or 3%--many of these additional mail ballots counted only because a last-minute policy intervention allowing mail ballots postmarked by Election Day to count.
A Circular Adaptive Median Filter for Salt and Pepper Noise Suppression from MRI Images

An adaptive median filter with a circular kernel, named as circular adaptive median filter (CAMF) is proposed in this article, for denoising of magnetic resonance imaging (MRI) images corrupted by salt and pepper noise of varying noise densities. An adaptive operation is incorporated in the proposed filter by varying the size of the circular kernel according to the requirement. The effectiveness of the CAMF is compared with six other competitive networks, i.e., conic adaptive median filter (CoAMF), decision based filter (DBF), modified switching median filter (MSWM), recursive adaptive modified filter (RAMF), plus adaptive median filter (PAMF), and cross adaptive median filter (CrAMF). The performance of all the models is analyzed using peak signal to noise ratio (PSNR) and computational time. Moreover, a non-parametric statistical test is conducted to illustrate the pair wise comparison of other filter with the proposed one. It is observed that the proposed approach has demonstrated superior performance with respect to the two performance measures.