Reviewerly/exHarmony huggingface dataset:
‘Are You OK?’ Students’ Trust in a Chatbot Providing Support Opportunities

Abstract Chatbots show promise as a novel way to provide support to students. However, a central issue with new technologies such as chatbots is whether students trust the technology. In the present study, we use a chatbot to proactively offer academic and non-academic support to students (N = 274) in a Finnish vocational education and training (VET) organization. Students responded to the chatbot with a very high response rate (86%), and almost one-fifth (19%) of the respondents disclosed a need for support. Survey with a subset of participants (N = 49) showed satisfactory trust (total trust score 71% as measured by a human-computer trust scale) and satisfaction (average of 3.83 as measured by a five-point customer satisfaction instrument) with the chatbot. Trust was positively correlated with satisfaction as well as students’ likelihood to respond to the chatbot. Our results show that this kind of approach is applicable for recognizing students’ latent needs for support. Future studies should target the formation of trust in more detail and cultural differences in trusting chatbots.
A Modular First Formalisation of Combinatorial Design Theory

Combinatorial design theory studies set systems with certain balance and symmetry properties and has applications to computer science and elsewhere. This paper presents a modular approach to formalising designs for the first time using Isabelle and assesses the usability of a locale-centric approach to formalisations of mathematical structures. We demonstrate how locales can be used to specify numerous types of designs and their hierarchy. The resulting library, which is concise and adaptable, includes formal definitions and proofs for many key properties, operations, and theorems on the construction and existence of designs.
Capsule networks with non-iterative cluster routing

Capsule networks use routing algorithms to flow information between consecutive layers. In the existing routing procedures, capsules produce predictions (termed votes) for capsules of the next layer. In a nutshell, the next-layer capsule's input is a weighted sum over all the votes it receives. In this paper, we propose non-iterative cluster routing for capsule networks. In the proposed cluster routing, capsules produce vote clusters instead of individual votes for next-layer capsules, and each vote cluster sends its centroid to a next-layer capsule. Generally speaking, the next-layer capsule's input is a weighted sum over the centroid of each vote cluster it receives. The centroid that comes from a cluster with a smaller variance is assigned a larger weight in the weighted sum process. Compared with the state-of-the-art capsule networks, the proposed capsule networks achieve the best accuracy on the Fashion-MNIST and SVHN datasets with fewer parameters, and achieve the best accuracy on the smallNORB and CIFAR-10 datasets with a moderate number of parameters. The proposed capsule networks also produce capsules with disentangled representation and generalize well to images captured at novel viewpoints. The proposed capsule networks also preserve 2D spatial information of an input image in the capsule channels: if the capsule channels are rotated, the object reconstructed from these channels will be rotated by the same transformation. Codes are available at https://github.com/ZHAOZHIHAO/ClusterRouting.
Demiguise Attack: Crafting Invisible Semantic Adversarial Perturbations with Perceptual Similarity

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples. Adversarial examples are malicious images with visually imperceptible perturbations. While these carefully crafted perturbations restricted with tight Lp norm bounds are small, they are still easily perceivable by humans. These perturbations also have limited success rates when attacking black-box models or models with defenses like noise reduction filters. To solve these problems, we propose Demiguise Attack, crafting "unrestricted" perturbations with Perceptual Similarity. Specifically, we can create powerful and photorealistic adversarial examples by manipulating semantic information based on Perceptual Similarity. Adversarial examples we generate are friendly to the human visual system (HVS), although the perturbations are of large magnitudes. We extend widely-used attacks with our approach, enhancing adversarial effectiveness impressively while contributing to imperceptibility. Extensive experiments show that the proposed method not only outperforms various state-of-the-art attacks in terms of fooling rate, transferability, and robustness against defenses but can also improve attacks effectively. In addition, we also notice that our implementation can simulate illumination and contrast changes that occur in real-world scenarios, which will contribute to exposing the blind spots of DNNs.
Dynamics of the Russian segment of science piblications (based on Web of Science Core Collection and Scopus data)

The findings of the study of Russian publications array of 2010-2019 are presented. The dynamics for the ten-year period (2010-2019) is demonstrated. For completeness, two of the most authoritative international polythematic resources, i. e. Web of Science Core Collection (WoS CC) and Scopus, were analyzed in parallel. The dynamics of Russian publications is presented in two versions: all types of publications indexed by the systems, and selected types of publications (research articles, reviews, book chapters, and letters). The positive dynamics of the share of Russian publications within the global array was determined, as well as the increase in Russia’s rating positions in terms of the number of publications in the period 2010-2019, were determined. The publications with Russian participation are represented in all 153 science areas (the WOS CC classifier "Research Areas"), however, the barrier of 1% of the total segment of Russian publications reflected in the WoS CC was cleared just in 42 areas. It is demonstrated that in the recent years, along with the decreasing tendency for the share of publications in physics and chemistry within the Russian publication array, there has been an increase in the rate of publications in the technical, social sciences and the humanities. The Emerging Sources Citation Index (ESCI), a WoS CC member since 2015, promotes intensively the works in the humanities and social sciences to the world publishing space. Despite the decrease in the rate of physics and chemistry in the Russian publication segment, these areas remain the undisputed leaders in the share of publications in WoS CC.
Improved Isolation Forest Algorithm for Anomaly Test Data Detection

The cigarette detection data contains a large amount of true sample data and a small amount of false sample data.The false sample data is regarded as abnormal data, and anomaly detection is performed to realize the identification of real and fake cigarettes.Binary particle swarm optimization algorithm is used to improve the isolation forest construction process, and isolation trees with high precision and large differences are selected, which improves the accuracy and efficiency of the algorithm.The distance between the obtained anomaly score and the clustering center of the k-means algorithm is used as the threshold for anomaly judgment.The experimental results show that the accuracy of the BPSO-iForest algorithm is improved compared with the standard iForest algorithm.The experimental results of multiple brand samples also show that the method in this paper can accurately use the detection data for authenticity identification.
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

Spatiotemporal representations learned using 3D convolutional neural networks (CNN) are currently used in state-of-the-art approaches for action-related tasks. However, 3D-CNN are notorious for being memory and compute resource intensive as compared with more simple 2D-CNN architectures. We propose to hallucinate spatiotemporal representations from a 3D-CNN teacher with a 2D-CNN student. By requiring the 2D-CNN to predict the future and intuit upcoming activity, it is encouraged to gain a deeper understanding of actions and how they evolve. The hallucination task is treated as an auxiliary task, which can be used with any other action-related task in a multitask learning setting. Thorough experimental evaluation, it is shown that the hallucination task indeed helps improve performance on action recognition, action quality assessment, and dynamic scene recognition tasks. From a practical standpoint, being able to hallucinate spatiotemporal representations without an actual 3D-CNN can enable deployment in resource-constrained scenarios, such as with limited computing power and/or lower bandwidth. We also observed that our hallucination task has utility not only during the training phase, but also during the pre-training phase.
Hotel Recognition via Latent Image Embeddings

We approach the problem of hotel recognition with deep metric learning. We overview the existing approaches and propose a modification to Contrastive loss called Contrastive-Triplet loss. We construct a robust pipeline for benchmarking metric learning models and perform experiments on Hotels-50K and CUB200 datasets. Contrastive-Triplet loss is shown to achieve better retrieval on Hotels-50k.
Unsupervised Learning of Disentangled Speech Content and Style Representation

We present an approach for unsupervised learning of speech representation disentangling contents and styles. Our model consists of: (1) a local encoder that captures per-frame information; (2) a global encoder that captures per-utterance information; and (3) a conditional decoder that reconstructs speech given local and global latent variables. Our experiments show that (1) the local latent variables encode speech contents, as reconstructed speech can be recognized by ASR with low word error rates (WER), even with a different global encoding; (2) the global latent variables encode speaker style, as reconstructed speech shares speaker identity with the source utterance of the global encoding. Additionally, we demonstrate an useful application from our pre-trained model, where we can train a speaker recognition model from the global latent variables and achieve high accuracy by fine-tuning with as few data as one label per speaker.
A Cost Estimating Method for Agile Software Development

In every software development project, the software effort estimating procedure is an important process in software engineering and always critical. The consistency of effort and timeline estimation, along with several factors, determines whether a project succeeds or fails. Both academics and professionals worked on the estimation approaches in software engineering. But, all these approaches have many problems that need to be addressed. One of the most difficult aspects of software engineering is estimating effort in agile development. This study aims to provide an effort estimation method for agile software development projects. Because in software engineering, the agile method is widely used for the development of software applications. The development and usage of the agile method are described in depth in this study. The framework is configured with empirical data gathered by projects from the software industry. The test findings reveal that the estimation method has great estimation accuracy in respect of mean magnitude of relative error (MMRE) and Prediction of Error PRED (n). The suggested approach achieves more accuracy for effort estimation as compare to others.
Multi-task Learning in Utterance-level and Segmental-level Spoof Detection

In this paper, we provide a series of multi-tasking benchmarks for simultaneously detecting spoofing at the segmental and utterance levels in the PartialSpoof database. First, we propose the SELCNN network, which inserts squeeze-and-excitation (SE) blocks into a light convolutional neural network (LCNN) to enhance the capacity of hidden feature selection. Then, we implement multi-task learning (MTL) frameworks with SELCNN followed by bidirectional long short-term memory (Bi-LSTM) as the basic model. We discuss MTL in PartialSpoof in terms of architecture (uni-branch/multi-branch) and training strategies (from-scratch/warm-up) step-by-step. Experiments show that the multi-task model performs better than single-task models. Also, in MTL, binary-branch architecture more adequately utilizes information from two levels than a uni-branch model. For the binary-branch architecture, fine-tuning a warm-up model works better than training from scratch. Models can handle both segment-level and utterance-level predictions simultaneously overall under binary-branch multi-task architecture. Furthermore, the multi-task model trained by fine-tuning a segmental warm-up model performs relatively better at both levels except on the evaluation set for segmental detection. Segmental detection should be explored further.
Theoretical and methodological principles of natural sciences and mathematical education (STEM-education)

The article presents the results of theoretical and practical analysis of the problem of STEM education in Ukraine. The authors assume that the definition of theoretical and methodological principles of this problem will help create conditions for the effective completion of educational and upbringing tasks in educational institutions. The article reveals the main conceptual principles of the order of the Cabinet of Ministers of Ukraine 05.082020 № 960-r «On approval of the Conception of natural sciences and mathematical education development (STEM-education)». Implementation of this Conception is envisaged by 2027. This Conception is aimed at the development of STEM-education, its large-scale implementation at all levels of education, establishing partnerships with employers and research institutions and their involvement in the development of natural sciences and mathematical education.&#x0D; The article also reveals the main theoretical and methodological principles of STEM-education such as: methodological principles, resource principles, organizational conditions and human resources, which ensures the success of the implementation of innovative learning in the education system. Creating an educational STEM-environment in an educational institution is one of the main tasks of the current stage of development of STEM-education. The educational STEM-environment of an educational institution is a set of intellectual and material conditions for the implementation of research results, technologies, engineering and integrated knowledge that ensure the self-development of a free and active personality and the realization of creative potential of students.&#x0D; STEM-education is implemented under the conditions of integration of all types of education on the basis of online platforms, media products, STEM-centers or laboratories, virtual STEM-centers, using non-standard methods, such as: STEM-excursions, interactive quests, contests or competitions in areas such as Internet of Things and robotics, STEM -festivals of makers and inventors, scientific picnics, hackathons, etc.&#x0D; The authors assume that the use of the above innovative and interactive methods and forms of work, organically combined with the peculiarities of educational activities, will effectively meet requirements of the Conception of natural sciences and mathematical education development (STEM-education), will allow students to form skills of research and engineering, invention and entrepreneurship, will provide support to students in their professional self-determination.
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization

To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model.However, these methods suffer from cumbersome procedures and inefficient computations for long-text documents.To mitigate these issues, this paper proposes HET-FORMER, a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization.Specifically, we model different types of semantic nodes in raw text as a potential heterogeneous graph and directly learn heterogeneous relationships (edges) among nodes by Transformer.Extensive experiments on both single-and multi-document summarization tasks show that HETFORMER achieves stateof-the-art performance in Rouge F1 while using less memory and fewer parameters.
Reachability types: tracking aliasing and separation in higher-order functional programs

Ownership type systems, based on the idea of enforcing unique access paths, have been primarily focused on objects and top-level classes. However, existing models do not as readily reflect the finer aspects of nested lexical scopes, capturing, or escaping closures in higher-order functional programming patterns, which are increasingly adopted even in mainstream object-oriented languages. We present a new type system, λ * , which enables expressive ownership-style reasoning across higher-order functions. It tracks sharing and separation through reachability sets, and layers additional mechanisms for selectively enforcing uniqueness on top of it. Based on reachability sets, we extend the type system with an expressive flow-sensitive effect system, which enables flavors of move semantics and ownership transfer. In addition, we present several case studies and extensions, including applications to capabilities for algebraic effects, one-shot continuations, and safe parallelization.
Digitalization, the pandemic, language ecology, market of information and educational services and the libraries: Drive for survival and sustainable living. The annual plenary paper presented at the Sixth World Professional Forum “Crimea–2021”

Abstract : The author starts with two key topics, i. e. digitalization and COVID-19 pandemic that makes impact on every sphere of human life and activity worldwide. The digitalization explosive development to change information, educational and library infrastructure has been not equal to expectations due to the pandemic and turned out to be different. However, the pandemic has stimulated digitization systems, Open access and open science. The author considers stable interest to ward reading, and printed books especially, to be “adigital era paradox”. He also points to stabilizing market of printed and electronic books. The no-less important topic is language ecology, or ecology of language, which cannot be interpreted exclusively as the fight for languagepurity; the author provides several examples of borrowings (Anglicisms) in the Russian language, which is increasingly evident due to digitalization. The modern digitalization facilitates targeted transition toward “the libraries of the future”, drives mainstream trends and goals on this way. The library collections, premises and working conditions during the pandemic are characterized. The pandemic also has exacerbated the problem of copyright transformation in the digital era. The author provides examples of legislative solutions in copyright in several countries and characterizes the Russia’s trends in copyright transformation in the digital world. The author concludes that, in the conditions of expanding digitalization and sustainable development, the libraries find themselves in the avant-garde as they support and effectually implement many sustainable development goals within the nation.
Modal amplitude and phase estimation of multimode near field patterns based on artificial neural network with the help of grey-wolf-optimizer

A simple and efficient method for estimating modal amplitude and phase of multimode near field patterns (NFPs) based on artificial-neural-network (ANN) with the help of the optimization method is proposed. The inferred amplitude and phase of measured NFPs based on ANN are refined by using a grey-wolf optimizer (GWO). By using the proposed method, the image correlation between reproduced and measured NFPs is improved without re-training of ANN, which is the most time-consuming part of ANN-based numerical modal decomposition technique. Numerical examples of three and six mode cases are presented for the estimation using simple ANN. For six-mode case, the correlation is greatly improved by using the optimizer. Finally, the estimation of the measured NFPs from three-mode exchanger and six-mode mode conversion grating is implemented, and 5% improvement in the correlation value is observed for six-mode case. The proposed method offers alternative way to improve the correlation without using elaborated ANN.
Not so fast: understanding and mitigating negative impacts of compiler optimizations on code reuse gadget sets

Despite extensive testing and correctness certification of their functional semantics, a number of compiler optimizations have been shown to violate security guarantees implemented in source code. While prior work has shed light on how such optimizations may introduce semantic security weaknesses into programs, there remains a significant knowledge gap concerning the impacts of compiler optimizations on non-semantic properties with security implications. In particular, little is currently known about how code generation and optimization decisions made by the compiler affect the availability and utility of reusable code segments called gadgets required for implementing code reuse attack methods such as return-oriented programming. In this paper, we bridge this gap through a study of the impacts of compiler optimization on code reuse gadget sets. We analyze and compare 1,187 variants of 20 different benchmark programs built with two production compilers (GCC and Clang) to determine how their optimization behaviors affect the code reuse gadget sets present in program variants with respect to both quantitative and qualitative metrics. Our study exposes an important and unexpected problem; compiler optimizations introduce new gadgets at a high rate and produce code containing gadget sets that are generally more useful to an attacker than those in unoptimized code. Using differential binary analysis, we identify several undesirable behaviors at the root of this phenomenon. In turn, we propose and evaluate several strategies to mitigate these behaviors. In particular, we show that post-production binary recompilation can effectively mitigate these behaviors with negligible performance impacts, resulting in optimized code with significantly smaller and less useful gadget sets.
Dense Hierarchical Retrieval for Open-domain Question Answering

Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA), where latent representations of questions and passages are exploited for maximum inner product search in the retrieval process.However, current dense retrievers require splitting documents into short passages that usually contain local, partial and sometimes biased context, and highly depend on the splitting process.As a consequence, it may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result.In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework which can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage.Specifically, a document-level retriever first identifies relevant documents, among which relevant passages are then retrieved by a passage-level retriever.The ranking of the retrieved passages will be further calibrated by examining the document-level relevance.In addition, hierarchical title structure and two negative sampling strategies (i.e., In-Doc and In-Sec negatives) are investigated.We apply DHR to large-scale open-domain QA datasets.DHR significantly outperforms the original dense passage retriever, and helps an end-to-end QA system outperform the strong baselines on multiple open-domain QA benchmarks.