Reviewerly/exHarmony huggingface dataset:
The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity

Humans are increasingly interacting with machines through language, sometimes in contexts where the user may not know they are talking to a machine (like over the phone or a text chatbot).We aim to understand how system designers and researchers might allow their systems to confirm its non-human identity.We collect over 2,500 phrasings related to the intent of "Are you a robot?".This is paired with over 2,500 adversarially selected utterances where only confirming the system is non-human would be insufficient or disfluent.We compare classifiers to recognize the intent and discuss the precision/recall and model complexity tradeoffs.Such classifiers could be integrated into dialog systems to avoid undesired deception.We then explore how both a generative research model (Blender) as well as two deployed systems (Amazon Alexa, Google Assistant) handle this intent, finding that systems often fail to confirm their nonhuman identity.Finally, we try to understand what a good response to the intent would be, and conduct a user study to compare the important aspects when responding to this intent.
A Query-Driven Topic Model

Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus.It has been increasingly widely adopted as a tool in the social sciences, including political science, digital humanities and sociological research in general.One desirable property of topic models is to allow users to find topics describing a specific aspect of the corpus.A possible solution is to incorporate domain-specific knowledge into topic modeling, but this requires a specification from domain experts.We propose a novel query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics, thus avoiding tedious work from domain experts.Our proposed approach is particularly attractive when the user-specified query has a low occurrence in a text corpus, making it difficult for traditional topic models built on word cooccurrence patterns to identify relevant topics.Experimental results demonstrate the effectiveness of our model in comparison with both classical topic models and neural topic models.
New Protocols and Ideas for Practical Quantum Position Verification

Loss of inputs can be detrimental to the security of quantum position verification (QPV) protocols, as it may allow attackers to not answer on all played rounds, but only on those they perform well on. In this work, we study loss-tolerant QPV protocols. We propose a new fully loss-tolerant protocol, based on the SWAP test, with several desirable properties. The task of the protocol, which can be implemented using only a single beam splitter and two detectors, is to estimate the overlap between two input states. By formulating possible attacks as a semi-definite program (SDP), we prove full loss tolerance against unentangled attackers restricted to local operations and classical communication (LOCC), and that the attack probability decays exponentially under parallel repetition of rounds. Furthermore, the role of loss and quantum communication attacks in QPV is investigated more generally. We construct the first known example of a QPV protocol that is provably secure against unentangled attackers restricted to LOCC, but can be perfectly attacked by local operations and a single round of simultaneous quantum communication. However, it is also shown that any protocol secure against classical communication can be transformed into a protocol secure against quantum communication. Finally, we observe that any multi-round QPV protocol can be attacked with a linear amount of entanglement if the loss is high enough.
Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter

Datasets with induced emotion labels are scarce but of utmost importance for many NLP tasks.We present a new, automated method for collecting texts along with their induced reaction labels.The method exploits the online use of reaction GIFs, which capture complex affective states.We show how to augment the data with induced emotions and induced sentiment labels.We use our method to create and publish ReactionGIF, a first-of-its-kind affective dataset of 30K tweets.We provide baselines for three new tasks, including induced sentiment prediction and multilabel classification of induced emotions.Our method and dataset open new research opportunities in emotion detection and affective computing.
Research on the Text Similarity Algorithms in Automatic Scoring of Subjective Questions

Abstract The theme revolves around the automatic scoring of subjective questions. The key technologies involved in the whole automatic scoring process are discussed in this paper, analyzes the advantages, disadvantages and application scope of different calculation methods for text similarity to understand the development trend of this field. It is helpful for further research on the automatic scoring method of subjective questions based on Word2vec improved model.
Methods for Learning Articulated Attractors over Internal Representations

Recurrent attractor networks have many virtues which have prompted their use in a wide variety of connectionist cognitive models. One of these virtues is the ability of these networks to learn articulated attractors — meaningful basins of attraction arising from the systematic interaction of explicitly trained patterns. Such attractors can improve generalization by enforcing “well formedness” constraints on representations, massaging noisy and ill formed patterns of activity into clean and useful patterns. This paper investigates methods for learning articulated attractors at the hidden layers of recurrent backpropagation networks. It has previously been shown that standard connectionist learning techniques fail to form such structured attractors over internal representations. To address this problem, this paper presents two unsupervised learning rules that give rise to componential attractor structures over hidden units. The performance of these learning methods on a simple structured memory task is analyzed.
The improvement of academic and methodological support of psychological-pedagogical disciplines in the conditions of digitalization of higher education

The article discusses the problem of reconsideration of components and improvement of academic and methodological support of university psychological and pedagogical disciplines. The object of this research is the academic and methodological support of psychological-pedagogical disciplines in the digital educational environment of higher education. The subject is the improvement of academic and methodological support of psychological-pedagogical disciplines, taking into account competencies (components of competencies) developed in the course of their study for use in digital educational environment. The author determines the specificity and forms of providing academic and methodological support in transition towards digitalization of higher education. The author summarizes the productive experience of implementing these forms of work in Tula State Lev Tolstoy Pedagogical University. The article suggests the vectors for improvement, gives theoretical substantiation to the new variants of development and provision of academic and methodological support for psychological-pedagogical disciplines demanded in the digital environment. It is established that the process and dynamics of improvement of academic and methodological support are primarily associated with overcoming psychological and qualification barriers that impede the participation of pedagogues in the digital transformation of education, and increasing their readiness to learning and using new information and communication technologies and software, which is most effective when they receive specialized training based on implementation of the principles of system-activity, competency-based, information, and individually-differentiated approaches, interdisciplinary integration, organization of joint activity of the educators of different academic departments.
News Article Retrieval in Context for Event-centric Narrative Creation

Writers such as journalists often use automatic tools to find relevant content to include in their narratives. In this paper, we focus on supporting writers in the news domain to develop event-centric narratives. Given an incomplete narrative that specifies a main event and a context, we aim to retrieve news articles that discuss relevant events that would enable the continuation of the narrative. We formally define this task and propose a retrieval dataset construction procedure that relies on existing news articles to simulate incomplete narratives and relevant articles. Experiments on two datasets derived from this procedure show that state-of-the-art lexical and semantic rankers are not sufficient for this task. We show that combining those with a ranker that ranks articles by reverse chronological order outperforms those rankers alone. We also perform an in-depth quantitative and qualitative analysis of the results that sheds light on the characteristics of this task.
Fact-Enhanced Synthetic News Generation

The advanced text generation methods have witnessed great success in text summarization, language translation, and synthetic news generation. However, these techniques can be abused to generate disinformation and fake news. To better understand the potential threats of synthetic news, we develop a new generation method FactGen to generate high-quality news content. The existing text generation methods either afford limited supplementary information or lose consistency between the input and output which makes the synthetic news less trustworthy. To address these issues, FactGen retrieves external facts to enrich the output and reconstructs the input claim from the generated content to improve the consistency among the input and the output. Experiment results on real-world datasets show that the generated news contents of FactGen are consistent and contain rich facts. We also discuss the possible defending method to identify these synthetic news pieces if FactGen is used to generate synthetic news.
Menshenina S.G. Interdisciplinarity as a Means for Students’ Soft Skills Development

Introduction. The development of the business sphere, scientific and technological progress set the pace of engineering education modernization and stimulate the search for the optimal and close to real working conditions ways of improving and enhancing professional competences and soft skills of university graduates. Interdisciplinarity has become a professional fields trend comprising enormous potential to foster the research, implement its results into practice and facilitate students’ soft skills development. Materials and Methods . The study is based on the analysis and comparison of domestic and foreign experience on the problem of the research; pedagogical observation of the educational process; study and generalization of pedagogical experience; experimental work. Results. The interdisciplinary project “Hardware and software complex of augmented reality as a means of learning a foreign language” was carried out with students of different specialties on the bases of Samara State Technical University. The rationale for this project choice is related to the relevance of the information technologies use in educational process and the need to learn a foreign language for professional purposes. These conditions make us think of creating the innovative methodological support of educational process. Considering the development of students’ soft skills, special attention was paid to group dynamics, role functions, leadership, as well as environmental and context factors. Interdisciplinary interaction terms were identified: stable working relationships which help develop communication literacy skills; finding the effective conflict solution that stimulates thinking skills; role and time distribution for actualizing management and self-management skills; a common goal achieving which is conditioned by the combination of the main soft skills groups of effective thinking skills, communication skills, management and self-management skills. Discussion and Conclusion . Practical work within the interdisciplinary project proved the necessity of students’ soft skills development for achieving success in professional sphere. The most efficient way to better this process is to realize the interdisciplinary interaction due to its potential, represented by its resources, ideas and approaches from different fields, and opportunities for mastering the skills in question.
What is Your Article Based On? Inferring Fine-grained Provenance

When evaluating an article and the claims it makes, a critical reader must be able to assess where the information presented comes from, and whether the various claims are mutually consistent and support the conclusion.This motivates the study of claim provenance, which seeks to trace and explain the origins of claims.In this paper, we introduce new techniques to model and reason about the provenance of multiple interacting claims, including how to capture fine-grained information about the context.Our solution hinges on first identifying the sentences that potentially contain important external information.We then develop a query generator with our novel rank-aware cross attention mechanism, which aims at generating metadata for the source article, based on the context and signals collected from a search engine.This establishes relevant search queries, and it allows us to obtain source article candidates for each identified sentence and propose an ILP based algorithm to infer the best sources.We experiment with a newly created evaluation dataset 1 , Politi-Prov, based on fact-checking articles from www.politifa ct.com; our experimental results show that our solution leads to a significant improvement over baselines.
FaaSNet: Scalable and Fast Provisioning of Custom Serverless Container Runtimes at Alibaba Cloud Function Compute

Serverless computing, or Function-as-a-Service (FaaS), enables a new way of building and scaling applications by allowing users to deploy fine-grained functions while providing fully-managed resource provisioning and auto-scaling. Custom FaaS container support is gaining traction as it enables better control over OSes, versioning, and tooling for modernizing FaaS applications. However, providing rapid container provisioning introduces non-trivial challenges for FaaS providers, since container provisioning is costly, and real-world FaaS workloads exhibit highly dynamic patterns. In this paper, we design FaaSNet, a highly-scalable middleware system for accelerating FaaS container provisioning. FaaSNet is driven by the workload and infrastructure requirements of the FaaS platform at one of the world's largest cloud providers, Alibaba Cloud Function Compute. FaaSNet enables scalable container provisioning via a lightweight, adaptive function tree (FT) structure. FaaSNet uses an I/O efficient, on-demand fetching mechanism to further reduce provisioning costs at scale. We implement and integrate FaaSNet in Alibaba Cloud Function Compute. Evaluation results show that FaaSNet: (1) finishes provisioning 2500 function containers on 1000 virtual machines in 8.3 seconds, (2) scales 13.4x and 16.3x faster than Alibaba Cloud's current FaaS platform and a state-of-the-art P2P container registry (Kraken), respectively, and (3) sustains a bursty workload using 75.2% less time than an optimized baseline.
Large Norms of CNN Layers Do Not Hurt Adversarial Robustness

Since the Lipschitz properties of convolutional neural networks (CNNs) are widely considered to be related to adversarial robustness, we theoretically characterize the $\ell_1$ norm and $\ell_\infty$ norm of 2D multi-channel convolutional layers and provide efficient methods to compute the exact $\ell_1$ norm and $\ell_\infty$ norm. Based on our theorem, we propose a novel regularization method termed norm decay, which can effectively reduce the norms of convolutional layers and fully-connected layers. Experiments show that norm-regularization methods, including norm decay, weight decay, and singular value clipping, can improve generalization of CNNs. However, they can slightly hurt adversarial robustness. Observing this unexpected phenomenon, we compute the norms of layers in the CNNs trained with three different adversarial training frameworks and surprisingly find that adversarially robust CNNs have comparable or even larger layer norms than their non-adversarially robust counterparts. Furthermore, we prove that under a mild assumption, adversarially robust classifiers can be achieved using neural networks, and an adversarially robust neural network can have an arbitrarily large Lipschitz constant. For this reason, enforcing small norms on CNN layers may be neither necessary nor effective in achieving adversarial robustness. The code is available at https://github.com/youweiliang/norm_robustness.
Stochastic-Shield

Quantized neural networks (NN) are the common standard to efficiently deploy deep learning models on tiny hardware platforms. However, we notice that quantized NNs are as vulnerable to adversarial attacks as the full-precision models. With the proliferation of neural networks on small devices that we carry or surround us, there is a need for efficient models without sacrificing trust in the prediction in presence of malign perturbations. Current mitigation approaches often need adversarial training or are bypassed when the strength of adversarial examples is increased.
PAC Prediction Sets Under Covariate Shift.

An important challenge facing modern machine learning is how to rigorously quantify the uncertainty of model predictions. Conveying uncertainty is especially important when there are changes to the underlying data distribution that might invalidate the predictive model. Yet, most existing uncertainty quantification algorithms break down in the presence of such shifts. We propose a novel approach that addresses this challenge by constructing \emph{probably approximately correct (PAC)} prediction sets in the presence of covariate shift. Our approach focuses on the setting where there is a covariate shift from the source distribution (where we have labeled training examples) to the target distribution (for which we want to quantify uncertainty). Our algorithm assumes given importance weights that encode how the probabilities of the training examples change under the covariate shift. In practice, importance weights typically need to be estimated; thus, we extend our algorithm to the setting where we are given confidence intervals for the importance weights rather than their true value. We demonstrate the effectiveness of our approach on various covariate shifts designed based on the DomainNet and ImageNet datasets.
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy

Providing privacy protection has been one of the primary motivations of Federated Learning (FL). Recently, there has been a line of work on incorporating the formal privacy notion of differential privacy with FL. To guarantee the client-level differential privacy in FL algorithms, the clients' transmitted model updates have to be clipped before adding privacy noise. Such clipping operation is substantially different from its counterpart of gradient clipping in the centralized differentially private SGD and has not been well-understood. In this paper, we first empirically demonstrate that the clipped FedAvg can perform surprisingly well even with substantial data heterogeneity when training neural networks, which is partly because the clients' updates become similar for several popular deep architectures. Based on this key observation, we provide the convergence analysis of a differential private (DP) FedAvg algorithm and highlight the relationship between clipping bias and the distribution of the clients' updates. To the best of our knowledge, this is the first work that rigorously investigates theoretical and empirical issues regarding the clipping operation in FL algorithms.
Natural Strategic Abilities in Voting Protocols

Security properties are often focused on the technological side of the system. One implicitly assumes that the users will behave in the right way to preserve the property at hand. In real life, this cannot be taken for granted. In particular, security mechanisms that are difficult and costly to use are often ignored by the users, and do not really defend the system against possible attacks. Here, we propose a graded notion of security based on the complexity of the user’s strategic behavior. More precisely, we suggest that the level to which a security property $$\varphi $$ is satisfied can be defined in terms of (a) the complexity of the strategy that the voter needs to execute to make $$\varphi $$ true, and (b) the resources that the user must employ on the way. The simpler and cheaper to obtain $$\varphi $$ , the higher the degree of security. We demonstrate how the idea works in a case study based on an electronic voting scenario. To this end, we model the vVote implementation of the Prêt à Voter voting protocol for coercion-resistant and voter-verifiable elections. Then, we identify “natural” strategies for the voter to obtain receipt-freeness, and measure the voter’s effort that they require.
The impact of the digital technological platforms on the institutional system of the higher education during the COVID 19 pandemic

The article is devoted to the assessment of the impact of the digital technological platforms on the higher education in the new conditions of the Covid-19 pandemic. The modern literature presents the various approaches to the analysis of the current situation in the higher education. The novelty of the authors ’ approach is that the institutional system of the higher education is analyzed from the standpoint of the theories of new institutionalism. The authors considered the institutional system of the higher education as a four-level system. The article presents assessing the impact of the digital technological platforms on the first level of the institutional system of the higher education, which is represented by deeply rooted informal institutions (values, traditions, customs, unwritten norms and rules, and more). Assessing the impact of the digital technological platforms on other levels of the institutional system of the higher education requires the additional prospective research.
Studying Choreographic Collaboration in the Wild

Dance making is often a highly idiosyncratic, collaborative endeavour between a choreographer and a group of dancers that constitutes a rich context for designers of creativity-support tools (CSTs). However, long-term, ecologically valid studies of collaboration in dance making are rare, especially when mediated by digital tools. We present a 5-month field study in the frame of a dance course, where a choreographer and six students used a CST originally designed for choreographic writing. We contrast our findings with our initial assumptions about the role of the tool to mediate a diversity of notating styles and hierarchical roles. We highlight the value of and the challenges behind this in-the-wild study in uncovering needs and roles as they emerged over time.