Reviewerly/exHarmony huggingface dataset:
Consortia and academic networks

Most academic institutions will be members of some form of consortia. In Consortia and academic networks: the reality of unfulfilled potential , Lamie and Hill ask whether these consortia are living up to their full potential. In exploring the motivations behind consortia, they present a typology that identifies and categorises the different forms of engagement and activity. What becomes evident is that the very nature and design of consortia are often at odds with their actual impact. Does that really matter? Even if the collaboration may be more in name than in practice, is the demonstration of willingness to work together at least one benefit, maybe even that first important step towards true partnership and impact.
Research on the Deep Integration of Curriculum Politics and Electromagnetic Field Teaching

Based on the characteristics of electromagnetic field course, this paper discusses the deep integration paths of curriculum politics and electromagnetic field teaching.First, it shows how to give full play to the key role of teachers in teaching and educating.Then, a number of measures such as designing teaching content, improving teaching strategies and assessment methods, have been taken to integrate the elements of curriculum politics into the teaching of electromagnetic field.The results shows that the organic combination of curriculum politics and professional education can lay the foundation for all-round and whole-process education.
THE EFFECT OF STUDENT TEAM ACHIEVEMENT DIVISION (STAD) ON STUDENTS’ GRAMMAR ACHIEVEMENT

This research had the purpose to figure out about the effect of Student Team Achievement Division (STAD) on Students’ Grammar Achievement especially with the usage of simple past tense at the eleventh grade of SMA Imelda Medan. The kind of this research was experimental study within pre-test treatment and post-test treatment either as the research’s instrument. That research's population was in SMA Imelda Medan of eleventh grade. There were consisting of grade XI MIA 1 as the control class and XI MIA 2 as the class of experimental. XI MIA 1 has 24 students while XI MIA 2 had 24 students. The researcher used SPSS 18 for calculation the data. The tcount ≥ ttable (2,829 ≥ 2,013) and the Sig. (2-tailed) was 0,007 lower than (alpha): 0,05. According to the observation of quantitative data, the Alternative Hypotheses (H a ) was accumulated and of the contrary, Null of Hypotheses (H o ) was rejected in the result of this research. It was meant that there was the significant effect amongst the students who are taught by applying Student Team Achievement Division (STAD) and all of those Students who are taught by using conventional method of teaching.
Exploring Efl Graduate Students’ Attitudes Toward, and Use of, Mobile Phones in Language Learning

Mobile phones offer unique opportunities for learners of English as a foreign language (EFL) to boost their language skills. Many studies have investigated the use of mobile applications in language learning, but little research has dealt with EFL graduate students. This study is an attempt to explore graduate EFL students’ attitudes toward the use of mobile phones in language learning and to shed light on the problems students face when using their mobile phones for language learning. For data collection, a 25-item questionnaire was developed and circulated online to 70 graduate EFL students at King Khalid University, Saudi Arabia. The results showed that students’ attitudes were positive, and there was a significant difference in attitudes in favour of female students. Also, the results revealed that most of the informants frequently use mobile phones, and also, there was a significant difference in favour of females. Additionally, the results showed that some students use smartphones effectively without facing any problems, whereas the majority of students face various challenges such as technical difficulties, the small size of the screen, distraction, becoming less patient, and getting tired. The findings indicated that further research is needed to scrutinize all these problems, with particular emphasis on health problems and distraction.
Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence

We investigate the reasons for the performance degradation incurred with batch-independent normalization. We find that the prototypical techniques of layer normalization and instance normalization both induce the appearance of failure modes in the neural network's pre-activations: (i) layer normalization induces a collapse towards channel-wise constant functions; (ii) instance normalization induces a lack of variability in instance statistics, symptomatic of an alteration of the expressivity. To alleviate failure mode (i) without aggravating failure mode (ii), we introduce the technique Proxy Normalization that normalizes post-activations using a proxy distribution. When combined with layer normalization or group normalization, this batch-independent normalization emulates batch normalization's behavior and consistently matches or exceeds its performance.
PSD Representations for Effective Probability Models

Finding a good way to model probability densities is key to probabilistic inference. An ideal model should be able to concisely approximate any probability, while being also compatible with two main operations: multiplications of two models (product rule) and marginalization with respect to a subset of the random variables (sum rule). In this work, we show that a recently proposed class of positive semi-definite (PSD) models for non-negative functions is particularly suited to this end. In particular, we characterize both approximation and generalization capabilities of PSD models, showing that they enjoy strong theoretical guarantees. Moreover, we show that we can perform efficiently both sum and product rule in closed form via matrix operations, enjoying the same versatility of mixture models. Our results open the way to applications of PSD models to density estimation, decision theory and inference. Preliminary empirical evaluation supports our findings.
PowerLinear Activation Functions with application to the first layer of CNNs

Convolutional neural networks (CNNs) have become the state-of-the-art tool for dealing with unsolved problems in computer vision and image processing. Since the convolution operator is a linear operator, several generalizations have been proposed to improve the performance of CNNs. One way to increase the capability of the convolution operator is by applying activation functions on the inner product operator. In this paper, we will introduce PowerLinear activation functions, which are based on the polynomial kernel generalization of the convolution operator. EvenPowLin functions are the main branch of the PowerLinear activation functions. This class of activation functions is saturated neither in the positive input region nor in the negative one. Also, the negative inputs are activated with the same magnitude as the positive inputs. These features made the EvenPowLin activation functions able to be utilized in the first layer of CNN architectures and learn complex features of input images. Additionally, EvenPowLin activation functions are used in CNN models to classify the inversion of grayscale images as accurately as the original grayscale images, which is significantly better than commonly used activation functions.
PENERAPAN MODEL PEMBELAJARAN VALUE CLARIFICATION TECHNIQUE MENGGUNAKAN METODE CERITA UNTUK MENINGKATKAN HASIL BELAJAR AGAMA KRISTEN SISWA KELAS III SD NEGERI 043935 SIMPANG KATEPUL KABUPATEN KARO

This study aims to determine the increase in student learning outcomes and activities in the subject matter of loving others after the implementation of the value clarification technique learning model using the story method in class III 043935 Public Elementary School Katepul Intersection in Karo Regency 2017/2018 academic year. This research is a classroom action research in two cycles. The research subjects were third grade students of SD Negeri 043935, Katepul Intersection, Karo Regency, 2017/2018 learning year, totaling 16 students. Student learning outcomes data obtained through tests while student learning activity data obtained through observation sheets. Data is processed by being described as a percentage using the minimum completeness criteria (KKM) reference. The results of the study show; 1) completeness of student learning outcomes increases by applying the value clarification technique learning model using the formative I story method shows an average of 73 with 69% classical completeness and formative II shows an average of 77 with 88% classical completeness or reaches classical completeness and occurs 19% increase; 2) student learning activities increase after the implementation of the value clarification learning model using the story method with the activities of students on average according to observers in Cycle I, among others, reading and understanding 57.12%, doing assignments 52.88%, group discussions 53.18 %, asking teachers 63.94%, and presentations 54.09%. While the activities of students on average according to observations in Cycle II include reading and understanding 86.21%, doing assignments 86.82%, discussing groups of 88.48%, asking teachers 88.94%, and presentations 89.09%.
Combinatorial Spectrum E-Auction for 5G Heterogeneous Networks: A Zether-Based Approach

5G heterogeneous network (HetNet) is a novel network topology that integrates various kinds of wireless access technologies such as 4G Long-Term Evolution (LTE), Wi-Fi, and so on. Despite greatly improving spectrum efficiency, it poses enormous challenges to spectrum e-auction. Firstly, due to high mobility, bidders may be interested in different spectrums in terms of time or geolocation. Secondly, one’s bidding value should be protected against rival bidders or adversaries to avoid vicious competition as well as privacy leakage. Thirdly, the ubiquitous HetNet requires a trustworthy distributed auction framework rather than a centralized auctioneer-based pattern. Aiming at overcoming these obstacles above, we proposed a blockchain-based combinatorial spectrum e-auction framework. Different from other blockchain-based solutions of using SGX to realize trust processing in the auction phase, we adopt Zether, a privacy-preserving smart contract, as the main building block. Besides, the bidding value is preserved from the beginning to the end, even though the time-consuming Paillier homomorphic encryption and garbled circuits are absent. We provide the auction security by leveraging <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi mathvariant="normal">Σ</mi> </math> -Bullets, a zero-knowledge proof mechanism. Theoretical analysis and extensive evaluation also indicate that our approach is better than the state-of-the-art works in terms of efficiency and effectiveness.
‘Tecnologica cosa’: Modeling Storyteller Personalities in Boccaccio’s ‘Decameron’

We explore Boccaccio’s Decameron to see how digital humanities tools can be used for tasks that have limited data in a language no longer in contemporary use: medieval Italian. We focus our analysis on the question: Do the different storytellers in the text exhibit distinct personalities? To answer this question, we curate and release a dataset based on the authoritative edition of the text. We use supervised classification methods to predict storytellers based on the stories they tell, confirming the difficulty of the task, and demonstrate that topic modeling can extract thematic storyteller “profiles.”
Elicitation of Basic PUT&amp;TAKE Verbs – An Experimental Approach

While previous research has mainly concentrated on the analysis of “specifying” PUT and TAKE verbs (like e.g. English SET, STICK, PULL or German STELLEN ‘cause to stand’, STECKEN ‘stick’/‘plug’, ZIEHEN ‘pull’), analyses on “basic verbs” (like e.g. English PUT and TAKE or French METTRE ‘put’ and PRENDRE ‘take’) have so far remained a desideratum. In this contribution, we will pursue an experimental approach to basic PUT and TAKE verbs in German. While German and English share the existence of a basic verb for source-oriented object movements, i.e. TAKE in English and NEHMEN in German, a basic verb for goal-oriented positional placement - i.e. for putting events - is missing in German, at least in the German standard varieties. Nevertheless, our experiment will show that nonstandard varieties of German provide potential candidates for basic PUT verbs among which especially the verb GEBEN ‘(lit.) give’ is about to achieve the status of a standard language variant.
Dynamic shape measurement for objects with patterns by Fourier fringe projection profilometry based on variational decomposition and multi-scale Retinex.

In practical measurement, we often need to measure the shape of objects with patterns or letters. As far as we know, no paper has ever reported the shape measurement for objects with patterns or letters by Fourier fringe projection profilometry (FPP). In this paper, we propose a method based on the variational decomposition TV−Hilbert−L2 model and multi-scale Retinex (MSR) to measure the shape of objects with patterns and letters by Fourier FPP. In this method, we first use the TV−Hilbert−L2 model to obtain the fringe part, then perform MSR enhancement on the fringe part, and finally decompose the enhanced fringe part with TV−Hilbert−L2 again. We evaluate the performance of this method via application to one computer-simulated noisy fringe projection pattern and two experimental fringe projection patterns with different types of patterns or letters, and comparison with the Fourier transform method, the variational image decomposition TV−Hilbert−L2 model. Furthermore, we apply the proposed method to the dynamic three-dimensional shape measurement of hand posture with pattern. The experimental results show that our method can effectively measure the dynamic shape of objects with patterns or letters from a single-frame fringe projection pattern.
Improving Deep Learning Interpretability by Saliency Guided Training

Saliency methods have been widely used to highlight important input features in model predictions. Most existing methods use backpropagation on a modified gradient function to generate saliency maps. Thus, noisy gradients can result in unfaithful feature attributions. In this paper, we tackle this issue and introduce a {\it saliency guided training}procedure for neural networks to reduce noisy gradients used in predictions while retaining the predictive performance of the model. Our saliency guided training procedure iteratively masks features with small and potentially noisy gradients while maximizing the similarity of model outputs for both masked and unmasked inputs. We apply the saliency guided training procedure to various synthetic and real data sets from computer vision, natural language processing, and time series across diverse neural architectures, including Recurrent Neural Networks, Convolutional Networks, and Transformers. Through qualitative and quantitative evaluations, we show that saliency guided training procedure significantly improves model interpretability across various domains while preserving its predictive performance.
Linguistic Dependencies and Statistical Dependence

Are pairs of words that tend to occur together also likely to stand in a linguistic dependency? This empirical question is motivated by a long history of literature in cognitive science, psycholinguistics, and NLP. In this work we contribute an extensive analysis of the relationship between linguistic dependencies and statistical dependence between words. Improving on previous work, we introduce the use of large pretrained language models to compute contextualized estimates of the pointwise mutual information between words (CPMI). For multiple models and languages, we extract dependency trees which maximize CPMI, and compare to gold standard linguistic dependencies. Overall, we find that CPMI dependencies achieve an unlabelled undirected attachment score of at most ≈ 0.5. While far above chance, and consistently above a non-contextualized PMI baseline, this score is generally comparable to a simple baseline formed by connecting adjacent words. We analyze which kinds of linguistic dependencies are best captured in CPMI dependencies, and also find marked differences between the estimates of the large pretrained language models, illustrating how their different training schemes affect the type of dependencies they capture.
Causal Abstractions of Neural Networks

Structural analysis methods (e.g., probing and feature attribution) are increasingly important tools for neural network analysis. We propose a new structural analysis method grounded in a formal theory of \textit{causal abstraction} that provides rich characterizations of model-internal representations and their roles in input/output behavior. In this method, neural representations are aligned with variables in interpretable causal models, and then \textit{interchange interventions} are used to experimentally verify that the neural representations have the causal properties of their aligned variables. We apply this method in a case study to analyze neural models trained on Multiply Quantified Natural Language Inference (MQNLI) corpus, a highly complex NLI dataset that was constructed with a tree-structured natural logic causal model. We discover that a BERT-based model with state-of-the-art performance successfully realizes the approximate causal structure of the natural logic causal model, whereas a simpler baseline model fails to show any such structure, demonstrating that neural representations encode the compositional structure of MQNLI examples.
Improving Vocabulary Using CTL at the Fourth Grade of SDN 072 Sukasari Bandung

Abstrak Penelitian ini dilakukan untuk mengetahui pencapaian kosakata siswa melalui CTL. Subjek penelitian ini adalah siswa kelas IV SDN 072 Sukasari. Pengumpulan data dilakukan dengan Penelitian Tindakan Kelas yang terdiri dari dua siklus. Setelah pelaksanaan kegiatan CTL pada siklus I dan siklus II nilai kosakata siswa semakin baik. Hal ini terlihat dari hasil nilai rata-rata postes siswa pada siklus 1 adalah 67,50 dan siklus 2 adalah 85,2. Hasil penelitian menunjukkan bahwa permainan huruf awal dapat meningkatkan kosakata siswa dan dapat memotivasi siswa untuk belajar bahasa Inggris. Kata kunci: CTL, Vocabulary, Young Learner Abstract This research conducted to find out students' vocabulary achievement through CTL. The subjects of this research were class IV of SDN 072 Sukasari. The data collected using Classroom Action Research including two cycles. After the implementation of CTL activities in first cycle and second cycle, the students’ vocabulary score is getting better. It can be seen from the result of the students’ post-test average score in cycle 1 was 67,50 and cycle 2 was 85,2. The result showed that Initial letter game can improved the students' vocabulary and can motivated students to learn English. Keyword: CTL, Vocabulary, Young Learner
The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation

Temporary syntactic ambiguities arise when the beginning of a sentence is compatible with multiple syntactic analyses. We inspect to which extent neural language models (LMs) exhibit uncertainty over such analyses when processing temporarily ambiguous inputs, and how that uncertainty is modulated by disambiguating cues. We probe the LM’s expectations by generating from it: we use stochastic decoding to derive a set of sentence completions, and estimate the probability that the LM assigns to each interpretation based on the distribution of parses across completions. Unlike scoring-based methods for targeted syntactic evaluation, this technique makes it possible to explore completions that are not hypothesized in advance by the researcher. We apply this method to study the behavior of two LMs (GPT2 and an LSTM) on three types of temporary ambiguity, using materials from human sentence processing experiments. We find that LMs can track multiple analyses simultaneously; the degree of uncertainty varies across constructions and contexts. As a response to disambiguating cues, the LMs often select the correct interpretation, but occasional errors point to potential areas of improvement
Natural Language to SPARQL Query Builder for Semantic Web Applications

SPARQL is a powerful query language for an evergrowing number of Semantic Web applications.Using it, however, requires familiarity with the language which is not to be expected from the general web user.This drawback has led to the development of Question-Answering (QA) systems that enable users to express their information needs in natural language.This paper presents a novel dependency-based framework for translating natural language queries into SPARQL queries, which is built on the idea of syntactic parsing.The translation process involves the following steps: extraction of the entities, extraction of the predicate, categorization of the query's type, resolution of lexical and semantic gaps between user query and domain ontology vocabulary, and finally construction of the SPARQL query.The proposed framework was tested on our closed-domain student advisory application intended to provide students with advice and recommendations about curriculum and scheduling matters.The advantage of our approach is that it requires neither any laborious feature engineering, nor complex model mapping of a query expressed in natural language to a SPARQL query template, and thus it can be easily adapted to a variety of applications.
SISTEM INFORMASI REKAPITULASI DATA REALISASI INVESTASI DI DINAS PENANAMAN MODAL, ESDM DAN TRANSMIGRASI PROVINSI GORONTALO

Abstract The recruitment system is essential to improve service to a job or activity. The activity of collecting data on the realization of investment at the Gorontalo Province Investment, ESDM, and Transmigration Office uses Microsoft Excel. This recapitulation activity has several obstacles that are the background of this research. The constraints experienced are the slow process of recapping, piling up of files so that there is a risk of damage or loss of data, and unable to display investment charts every quarter. The method used in this research is the prototype method. The final results obtained in this study are in the form of an information system for recapitulation of investment realization data to make it easier for the government to manage recapitulation activities, monitor the progress of investment every quarter, and be able to determine the budget plan to be set for the following year and data storage to be more effective and safer. Keywords : Recapitulation, Recapitulation Information System, Realization of Investment, Investment, Prototype.