Reviewerly/exHarmony huggingface dataset:
Question Answering Over Temporal Knowledge Graphs

Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area. Lack of broad coverage datasets has been another factor limiting progress in this area. We address this challenge by presenting CRONQUESTIONS, the largest known Temporal KGQA dataset, clearly stratified into buckets of structural complexity. CRONQUESTIONS expands the only known previous dataset by a factor of 340x. We find that various state-of-the-art KGQA methods fall far short of the desired performance on this new dataset. In response, we also propose CRONKGQA, a transformer-based solution that exploits recent advances in Temporal KG embeddings, and achieves performance superior to all baselines, with an increase of 120% in accuracy over the next best performing method. Through extensive experiments, we give detailed insights into the workings of CRONKGQA, as well as situations where significant further improvements appear possible. In addition to the dataset, we have released our code as well.
Evaluating Word Embeddings with Categorical Modularity

We introduce categorical modularity, a novel low-resource intrinsic metric to evaluate word embedding quality. Categorical modularity is a graph modularity metric based on the $k$-nearest neighbor graph constructed with embedding vectors of words from a fixed set of semantic categories, in which the goal is to measure the proportion of words that have nearest neighbors within the same categories. We use a core set of 500 words belonging to 59 neurobiologically motivated semantic categories in 29 languages and analyze three word embedding models per language (FastText, MUSE, and subs2vec). We find moderate to strong positive correlations between categorical modularity and performance on the monolingual tasks of sentiment analysis and word similarity calculation and on the cross-lingual task of bilingual lexicon induction both to and from English. Overall, we suggest that categorical modularity provides non-trivial predictive information about downstream task performance, with breakdowns of correlations by model suggesting some meta-predictive properties about semantic information loss as well.
Implicit Representations of Meaning in Neural Language Models

Does the effectiveness of neural language models derive entirely from accurate modeling of surface word co-occurrence statistics, or do these models represent and reason about the world they describe? In BART and T5 transformer language models, we identify contextual word representations that function as models of entities and situations as they evolve throughout a discourse. These neural representations have functional similarities to linguistic models of dynamic semantics: they support a linear readout of each entity's current properties and relations, and can be manipulated with predictable effects on language generation. Our results indicate that prediction in pretrained neural language models is supported, at least in part, by dynamic representations of meaning and implicit simulation of entity state, and that this behavior can be learned with only text as training data. Code and data are available at https://github.com/belindal/state-probes .
Gaussian Processes with Differential Privacy

Gaussian processes (GPs) are non-parametric Bayesian models that are widely used for diverse prediction tasks. Previous work in adding strong privacy protection to GPs via differential privacy (DP) has been limited to protecting only the privacy of the prediction targets (model outputs) but not inputs. We break this limitation by introducing GPs with DP protection for both model inputs and outputs. We achieve this by using sparse GP methodology and publishing a private variational approximation on known inducing points. The approximation covariance is adjusted to approximately account for the added uncertainty from DP noise. The approximation can be used to compute arbitrary predictions using standard sparse GP techniques. We propose a method for hyperparameter learning using a private selection protocol applied to validation set log-likelihood. Our experiments demonstrate that given sufficient amount of data, the method can produce accurate models under strong privacy protection.
Fair-Net: A Network Architecture For Reducing Performance Disparity Between Identifiable Sub-Populations

In real world datasets, particular groups are under-represented, much rarer than others, and machine learning classifiers will often preform worse on under-represented populations. This problem is aggravated across many domains where datasets are class imbalanced, with a minority class far rarer than the majority class. Naive approaches to handle under-representation and class imbalance include training sub-population specific classifiers that handle class imbalance or training a global classifier that overlooks sub-population disparities and aims to achieve high overall accuracy by handling class imbalance. In this study, we find that these approaches are vulnerable in class imbalanced datasets with minority sub-populations. We introduced Fair-Net, a branched multitask neural network architecture that improves both classification accuracy and probability calibration across identifiable sub-populations in class imbalanced datasets. Fair-Nets is a straightforward extension to the output layer and error function of a network, so can be incorporated in far more complex architectures. Empirical studies with three real world benchmark datasets demonstrate that Fair-Net improves classification and calibration performance, substantially reducing performance disparity between gender and racial sub-populations.
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining

While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles. This research gap is due, in part, to the lack of standardized datasets for summarizing online discussions. To address this gap, we design annotation protocols motivated by an issues--viewpoints--assertions framework to crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data. To create a comprehensive benchmark, we also evaluate these models on widely-used conversation summarization datasets to establish strong baselines in this domain. Furthermore, we incorporate argument mining through graph construction to directly model the issues, viewpoints, and assertions present in a conversation and filter noisy input, showing comparable or improved results according to automatic and human evaluations.
Tight Accounting in the Shuffle Model of Differential Privacy

Shuffle model of differential privacy is a novel distributed privacy model based on a combination of local privacy mechanisms and a secure shuffler. It has been shown that the additional randomisation provided by the shuffler improves privacy bounds compared to the purely local mechanisms. Accounting tight bounds, however, is complicated by the complexity brought by the shuffler. The recently proposed numerical techniques for evaluating $(\varepsilon,\delta)$-differential privacy guarantees have been shown to give tighter bounds than commonly used methods for compositions of various complex mechanisms. In this paper, we show how to obtain accurate bounds for adaptive compositions of general $\varepsilon$-LDP shufflers using the analysis by Feldman et al. (2021) and tight bounds for adaptive compositions of shufflers of $k$-randomised response mechanisms, using the analysis by Balle et al. (2019). We show how to speed up the evaluation of the resulting privacy loss distribution from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$, where $n$ is the number of users, without noticeable change in the resulting $\delta(\varepsilon)$-upper bounds. We also demonstrate looseness of the existing bounds and methods found in the literature, improving previous composition results significantly.
Outlier-resilient iterative-extended Kalman filter based on maximum correntropy criterion

The extended Kalman filter (EKF) is the most popular tool for state estimation of nonlinear stochastic systems. The conventional EKF can be refined via iterative re-linearization, leading to the so-called iterative EKF (IEKF). The performance of either EKF or IEKF will deteriorate in the presence of outliers. In this article, an outlier-resilient IEKF method is proposed using a more robust cost function, the correntropy, to replace the traditional mean squared error criterion. An iterative procedure is derived to maximize the correntropy criterion in a similar way to the Gauss–Newton optimization. Simulation results demonstrate the superiority of the proposed method as compared to the existing methods.
The Role of The Production Team for Live Streaming of BRI League 1 Sports Broadcast on Vidio.com

New Media is growing in today's digital era. The existence of technology has shifted the journalism system, starting with news appearing on online platforms that have an impact on news media organizations, which affect traffic, audience income, and media practices that are very epistemic as they adapt to produce content for digital platforms such as Facebook and Twitter. The new media platform that is currently quite popular is streaming. Streaming is content in the form of videos that are broadcast live via the internet without any editing or post-production processes. Through streaming, viewers not only watch but can provide comments and reactions while the streaming is taking place.&#x0D; Vidio.com, Genflix, UseeTV, and FirstMedia X are older than their competitors because they have been operating since 2014. They are still operating because they have an ecosystem that is closely related to enriching content. Vidio.com is under the auspices of the Emtek Group, which has a wide network of TV shows, making it easier to incorporate into digital platforms.&#x0D; Vidio.com broadcasts one of the broadcasts of Indonesian football sports events, namely BRI League 1. Apart from BRI League 1, on the Vidio platform there are also Indonesian football events league 2 and league 3. League 1 is the highest order in the professional football league in Indonesia. The champions in this 1st league will represent the country in the play-offs of the Asian Champions League. While the 2nd and 3rd place will represent Indonesia in the AFC Cup. Exclusive BRI Liga 1 broadcast on video. The League 1 competition was first formed by PSSI in 2008 under the name Indonesia Super League (ISL).
Undergraduate Student-Translators’ Difficulties in Translating English Word + Preposition Collocations to Arabic

English and Arabic have different types of collocations, i.e., groups of words that go together. This study aims to explore the difficulties that Saudi undergraduate student-translators have in translating English word + preposition collocations such as verb + preposition, noun + preposition, and adjective + preposition collocations to Arabic. A corpus of faulty word+ preposition collocations was collected from students-translators’ graduation projects to identify the types of translation errors, translation strategies, sources of translation errors and the contexts in which the translation errors occurred. A comparison of English and Arabic word + preposition collocations showed the following categories: (i) cases were the Arabic word + preposition collocations match those of their English equivalents in form and meaning (depend on يعتمد على, apologize for/to يعتذر لـ/عن , interested inمهتم بـ ) ; (ii) cases where a preposition is used in the English collocation but no preposition is used in the Arabic equivalent (wait for ينتظر ); (iii) cases where an Arabic preposition is used after a word but no such preposition is used in their English equivalent ( gave him tea قدم له الشاي, offered him a proposal عرض عليه اقتراح, stopped participating توقف عن المشاركة, lack somethingيفتقر إلى ). Results showed that the students mistranslated certain prepositions in word + preposition collocations. In 84% of the errors, the students substituted a preposition in the translation by a faulty one, in 13%, they added a preposition after an Arabic word that does not require a preposition, and in 3% they deleted a preposition from a translation that requires use of a preposition. In addition, 19% of the errors were interlingual (transfer errors from English) and 81% were intralingual due to inadequate competence in L1 (Arabic). 44% were extraneous errors, 21% were due to ignorance of Arabic language rules of preposition use and 18% were due to faulty common use of the preposition in the students’ local dialect. 86% were syntactic; 11% were semantic and 3% were stylistic errors. Results are reported in detail and implications for translation pedagogy are given.
On the work of the laboratory of computer-assisted instruction in the Russian language created by N. N. Algazina at Yelets State University named after I. A. Bunin

The paper is prepared in line with the retrospective analysis of the activity of the computer-aided instruction in the Russian language at Yelets State University named after I. A. Bunin. The scientific and practical conferences, scholarly disputes, workshops, and methodological seminars with methodologists and subject teachers conducted by the laboratory staff promoted N. N. Algazina’s ideas on computerisation of education and made a significant contribution to improving the theory and practice of using information and communication technologies in the process of teaching Russian. The paper concludes with the relevance of N. N. Algazina’s scientific and methodological legacy for research scientists and methodologists-practitioners who have yet to improve the process of teaching Russian in the context of digitization.
Review on Automobile Crime Prediction Model

Misconduct and crime of all sorts are the popular problems that bother humanity in general.This research work seeks to review and suggest the best solution to this problem.From this study, it was noticed that most research carried out has been focused on monitoring, controlling, and tracking automobile crimes, a process that has become difficult as in producing good results to adequately combat these crimes.Monitoring, tracking, and/or controlling automobile crimes are not most reliable due to certain conditions such as atmospheric or weather conditions, lack of knowledge, etc.So, with this challenge, this study reviews and suggest a better solution to this problem.If implemented, the suggested solution will help reduce the rate of automobile crimes by providing relevant authorities with good predicted results as information for battling automobile crime effectively.The suggested solution involves implementing a model using a good and best dataset alongside the best deep-learning algorithm.Finding shows that deep-learning algorithm provides successful, general, accurate, and fast result analysis.The obtained dataset should be filtered for noise and other unwanted values to ensure accuracy, reduce computation time, and provide an adequate understanding of the solution.
Research on the Construction of “Course Certificate Integration” Curriculum System for Cloud Computing Technology Application Major in Higher Vocational Colleges

Based on cloud computing technology in a cloud computing platform, cloud services, and cloud terminal professional talent demand has carried on the systematic analysis, from the professional requirements, work flow and the perspective of the actual work process, found that higher vocational cloud computing technology application roughly the training needs and direction of the professional talents, give full consideration to factors such as creative thinking, cultural quality, professional ability, In addition, practical employment and technological innovation are applied to construct a new curriculum system with deep integration of professional technical skills and vocational skills certificates, providing reference for higher vocational colleges to set up cloud computing technology and application major.
Research on the Construction Strategy of the Teaching Evaluation System of Vocational Undergraduate Education in China

Along with the process of the clustered industrial structure reforming, the concepts of vocational undergraduate education remain no longer traditionally market-oriented. Requirements for the workforce training are highly imposed to be well balanced with vocational education and roundabout personality development as a complete human being. Thus, in this article, a study of the evolution of political and academic situation of the existing vocational undergraduate universities in China is carried out. The distinctive characteristics of vocational undergraduate universities are presented and compared in the span of the domestic and overseas realms.
Discussion on the Cultivation Mode of Innovative and Entrepreneurial Talents in Higher Vocational Colleges under the Promotion of “Double-high Plan”

Under the “twin plan” in higher vocational colleges should change the talent training scheme, pay attention to promote the all-round development of students, inspire students’ innovative thinking and entrepreneurial motivation, creative talents should be brought into the education development, into students’ classroom teaching, the deepening innovation of entrepreneurship education reform, improve the quality of talents training in higher vocational colleges. This paper analyzes the background of innovation and entrepreneurship talent training in higher vocational colleges, finds out prominent problems, puts forward basic principles and implementation strategies, and hopes to provide suggestions for the reform of higher vocational colleges.
Analysis on the Path of Integrating Interactive Media Network Platform into Higher Education and Teaching Based on Mobile Terminal

This paper studies the development and construction of the “media integration” network platform based on mobile terminals in universities from the aspects of the concept change and team building of higher education, in order to provide modern ideas for the construction of higher education in the unused era.
Application of EDA Technology in Electronic Engineering Design

The rapid development of EDA technology makes it more and more widely used in modern electronic system design, &#x0D; including IC design, circuit board wiring, function verification and simulation, and testing.Electronic engineering design is the core &#x0D; course of cultivating electrical information majors, which helps cultivate students’ engineering ability, practical ability, innovative &#x0D; consciousness, systematic thinking, etc. Through the application of EDA Technology in all links of course implementation, &#x0D; including design scheme selection, unit circuit design and system debugging, the correctness and reliability of circuit design are &#x0D; greatly improved, and the application ability of students’ electronic design software is greatly exercised.
How to Enhance the Practical Teaching of Student Finance and Accounting Under the New Curriculum Reform

At the current stage, the new curriculum reform concept is deeply rooted in the hearts of the people. Under the influence of this, finance and accounting teachers begin to pay more and more attention to the subject status of students in teaching, &#x0D; pay more and more attention to meet the financial learning needs of students in teaching, pay attention to the training of students’ &#x0D; financial application ability in teaching. This paper discusses how to further optimize the teaching of finance and accounting from &#x0D; the perspective of the importance and diversity of practical teaching.
Language Awareness with a Focus on Lexical Variation

With the continuous development of English language teaching (ELT), language awareness has been studied as a teaching &#x0D; approach. Vocabulary knowledge is indispensable in all language skills, such as reading and speaking, listening, and writing. It &#x0D; is one of the constituent elements of language. This article is based on literature review and provides a lexical variation view on &#x0D; language awareness, and attempts to discuss the variability and flexibility with a focus on lexis, which reflects lexical awareness &#x0D; on language teaching from the perspective of English teachers. The study shows that Data-driven learning (DDL) approach has &#x0D; helped EFL learners to build lexical awareness to some degree. Furthermore, three reflections in teachers’ teaching are as follow: &#x0D; setting themes for peculiar words in each lesson will help students to easily understand the words; adjust teachers’ own language &#x0D; expression according to the actual response of students constantly; teach the language through the culture.
Preliminary Design of E-learning Learning Resource Interaction Based on Web Data Mining

According to the problems of low resource utilization efficiency, single learning content and lack of personalization in &#x0D; e-learning system, a personalized e-learning system based on Web data mining is designed by applying web mining and ontology &#x0D; technology. The system can provide more satisfying teaching methods and learning resources according to the characteristic information of learners’ knowledge structure and learning preference, and create a relatively personalized e-learning environment. &#x0D; Experiments show that ontology technology can fully improve the mining effect, improve the management efficiency of learning &#x0D; resource database, effectively promote students’ network learning, meet students’ personalized learning needs, and provide intelligent auxiliary means for system decision analysis.
Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models

Despite our best efforts, deep learning models remain highly vulnerable to even tiny adversarial perturbations applied to the inputs. The ability to extract information from solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as autonomous cars or machine learning models exposed as a service (MLaaS). Of particular interest are sparse attacks. The realization of sparse attacks in black-box models demonstrates that machine learning models are more vulnerable than we believe. Because these attacks aim to minimize the number of perturbed pixels measured by l_0 norm-required to mislead a model by solely observing the decision (the predicted label) returned to a model query; the so-called decision-based attack setting. But, such an attack leads to an NP-hard optimization problem. We develop an evolution-based algorithm-SparseEvo-for the problem and evaluate against both convolutional deep neural networks and vision transformers. Notably, vision transformers are yet to be investigated under a decision-based attack setting. SparseEvo requires significantly fewer model queries than the state-of-the-art sparse attack Pointwise for both untargeted and targeted attacks. The attack algorithm, although conceptually simple, is also competitive with only a limited query budget against the state-of-the-art gradient-based whitebox attacks in standard computer vision tasks such as ImageNet. Importantly, the query efficient SparseEvo, along with decision-based attacks, in general, raise new questions regarding the safety of deployed systems and poses new directions to study and understand the robustness of machine learning models.