Reviewerly/exHarmony huggingface dataset:
Efficient Proxy Re-encryption Scheme for E-Voting System

With the development of information and communication technologies, especially wireless networks and cell phones, the e-voting system becomes popular as its cost-effectiveness, swiftness, scalability, and ecological sustainability.However, the current e-voting schemes are faced with the problem of privacy leakage and further cause worse vote-buying and votercoercion problems.Moreover, in large-scale voting, some previous e-voting system encryption scheme with pairing operation also brings huge overhead pressure to the voting system.Thus, it is a vital problem to design a protocol that can protect voter privacy and simultaneously has high efficiency to guarantee the effective implementation of e-voting.To address these problems, our paper proposes an efficient unidirectional proxy re-encryption scheme that provides the re-encryption of vote content and the verification of users' identity.This function can be exactly applied in the e-voting system to protect the content of vote and preserve the privacy of the voter.Our proposal is proven to be CCA secure and collusion resistant.The detailed analysis also shows that our scheme achieves higher efficiency in computation cost and ciphertext size than the schemes in related fields.
Effective Social Chatbot Strategies for Increasing User Initiative

Many existing chatbots do not effectively support mixed initiative, forcing their users to either respond passively or lead constantly. We seek to improve this experience by introducing new mechanisms to encourage user initiative in social chatbot conversations. Since user initiative in this setting is distinct from initiative in human-human or task-oriented dialogue, we first propose a new definition that accounts for the unique behaviors users take in this context. Drawing from linguistics, we propose three mechanisms to promote user initiative: back-channeling, personal disclosure, and replacing questions with statements. We show that simple automatic metrics of utterance length, number of noun phrases, and diversity of user responses correlate with human judgement of initiative. Finally, we use these metrics to suggest that these strategies do result in statistically significant increases in user initiative, where frequent, but not excessive, back-channeling is the most effective strategy.
Utilization of SVM Method and Extraction of GLCM Features in Classifying Fish Images with Formalin

Purpose: Fish is a type of animal protein that can be consumed by humans to supplement protein in the body. Due to the fact that there is an abundance of fish in Indonesia, traders often experience losses because of rotting fish. A small proportion of traders tricked the buyers by mixing fish with formaldehyde to preserve fish in order to prevent fish spoilage until it can be consumed.Â  Thus, every fish buyer must be aware of fraud by traders.Â  Methods: To be able to find out that the fish has been mixed with formalin, the solution offered is computerized by utilizing the GLCM feature extraction as information extraction on the fish image and the SVM method as a classification method.Â  Result : The results showed an average accuracy of 0.784, precision of 0.799, recall of 0.784, and f-measure of 0.781.Â  Novelty : The effect of the SVM classification method on the performance measurement of the model is not too big compared to previous studies, but it is better. Â
Circuit models for the co-simulation of superconducting quantum computing systems

Quantum computers based on superconducting qubits have emerged as a leading candidate for a scalable quantum processor architecture. The core of a quantum processor consists of quantum devices that are manipulated using classical electronic circuits, which need to be co-designed for optimal performance and operation. As the principles governing the behavior of the classical circuits and the quantum devices are different, this presents a unique challenge in terms of the simulation, design and optimization of the joint system. A methodology is presented to transform the behavior of small-scale quantum processors to equivalent circuit models that are usable with classical circuits in a generic electrical simulator, enabling the detailed analysis of the impact of many important non-idealities. The methodology has specifically been employed to derive a circuit model of a superconducting qubit interacting with the quantized electromagnetic field of a superconducting resonator. Based on this technique, a comprehensive analysis of the qubit operation is performed, including the coherent control and readout of the qubit using electrical signals. Furthermore, the effect of several non-idealities in the system such as qubit relaxation, decoherence and leakage out of the computational subspace are captured, in contrast to previous works. As the presented method enables the co-simulation of the control electronics with the quantum system, it facilitates the design and optimization of near-term superconducting quantum processors.
A two-stage approach to serials review: minimizing journal costs through title-by-title selection with package-based acquisition

Many academic libraries evaluate their journal holdings with an emphasis on deselection rather than selection. Likewise, many consider each journal package or full-text database as a whole, through indicators such as cost per download, without explicitly accounting for the quality of the journals included in each package. Better results can be achieved through a two-stage process that first identifies the ‘wanted journals’ most important to the university’s teaching and research programs, then objectively determines the most cost-effective means of acquiring those journals. Our procedure combines an emphasis on the quality of individual journals with an iterative database/subscription selection method that minimizes costs and avoids any bias toward the journals initially held by the library. Although the process involves considerable effort, it can be used to increase the library’s holdings of wanted journals without increasing total expenditure, or to maintain the number of wanted journals while reducing total expenditure. Our results also demonstrate that title-by-title selection of journals is not inconsistent with acquisition through full-text databases.
Cracking God’s roof: Manifestation and adaptation on the intuitive nature of creating electronic music with tablet computers

Electronic music is advancing not only in the way it is being used in performance but also in the technological sense, due to software developers advancing the ability of the synthesizer to enable the composer to create newer sounds. The introduction of the amino acid and protein synthesizers from MIT is one such example, along with sampling sounds from interstellar bodies through the process of sonification in order to create presets as additional source material for the composer’s palette. The creative process used in creating electronic music on a tablet computer introduces a new musical instrument to be used in live music performances. The fluidity and immediacy of how electronic sounds can be created with tablet computer synthesizers affords the composer to have a new behavioural sense of using them as a musical instrument that can be played intuitively. Exploring this new interface of musical composition is a subject this article will address as well as the psychological aspects pertaining to how an audience can relate to electronic music as an emerging art form removed from the classical music tradition. It will also discuss how the composer of electronic music can affect the listener’s ability to envision new conceptual landscapes, leading to experiencing new ideas and subjective fields of visionary understandings. The composer’s ability to use conceptual models, which influence the way sounds are made and how those sounds influence the listener’s experience, is an important focus of this article.
A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction Stream

The streams where multiple transactions are associated with the same key are prevalent in practice, e.g., a customer has multiple shopping records arriving at different time. Itemset frequency estimation on such streams is very challenging since sampling based methods, such as the popularly used reservoir sampling, cannot be used. In this article, we propose a novel k -Minimum Value (KMV) synopsis based method to estimate the frequency of itemsets over multi-transaction streams. First, we extract the KMV synopses for each item from the stream. Then, we propose a novel estimator to estimate the frequency of an itemset over the KMV synopses. Comparing to the existing estimator, our method is not only more accurate and efficient to calculate but also follows the downward-closure property. These properties enable the incorporation of our new estimator with existing frequent itemset mining (FIM) algorithm (e.g., FP-Growth) to mine frequent itemsets over multi-transaction streams. To demonstrate this, we implement a KMV synopsis based FIM algorithm by integrating our estimator into existing FIM algorithms, and we prove it is capable of guaranteeing the accuracy of FIM with a bounded size of KMV synopsis. Experimental results on massive streams show our estimator can significantly improve on the accuracy for both estimating itemset frequency and FIM compared to the existing estimators.
Hi-DST: A Hierarchical Approach for Scalable and Extensible Dialogue State Tracking

Dialogue State Tracking (DST) is a sub-task of task-based dialogue systems where the user intention is tracked through a set of (domain, slot, slot-value) triplets. Existing DST models can be difficult to extend for new datasets with larger domains/slots mainly due to either of the two reasons- i) prediction of domain-slot as a pair, and ii) dependency of model parameters on the number of slots and domains. In this work, we propose to address these issues using a Hierarchical DST (Hi-DST) model. At a given turn, the model first detects a change in domain followed by domain prediction if required. Then it decides suitable action for each slot in the predicted domains and finds their value accordingly. The model parameters of Hi-DST are independent of the number of domains/slots. Due to the hierarchical modeling, it achieves O(|M|+|N|) belief state prediction for a single turn where M and N are the set of unique domains and slots respectively. We argue that the hierarchical structure helps in the model explainability and makes it easily extensible to new datasets. Experiments on the MultiWOZ dataset show that our proposed model achieves comparable joint accuracy performance to state-of-the-art DST models.
Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection

One of the long-standing challenges in lexical semantics consists in learning representations of words which reflect their semantic properties.The remarkable success of word embeddings for this purpose suggests that highquality representations can be obtained by summarizing the sentence contexts of word mentions.In this paper, we propose a method for learning word representations that follows this basic strategy, but differs from standard word embeddings in two important ways.First, we take advantage of contextualized language models (CLMs) rather than bags of word vectors to encode contexts.Second, rather than learning a word vector directly, we use a topic model to partition the contexts in which words appear, and then learn different topic-specific vectors for each word.Finally, we use a taskspecific supervision signal to make a soft selection of the resulting vectors.We show that this simple strategy leads to high-quality word vectors, which are more predictive of semantic properties than word embeddings and existing CLM-based strategies.
A Comparative Study of Conversational Proxemics for Virtual Agents

This paper explores proxemics—interpersonal distances—in conversations with virtual agents in virtual reality. While the real-world proxemics of human-human interaction have been well studied, the virtual-world proxemics of human-agent interaction are less well understood. We review research related to proxemics in virtual reality, noting that the previous research has not addressed proxemics with actual conversation, describe an empirical methodology for addressing our research questions, and present our results. The study used a repeated-measures, within-subjects design and had 23 participants. In the study, participants approached and conversed with a virtual agent in three conditions: no crowd, small crowd, and large crowd. The participant's distance from the agent with whom they conversed was recorded at 60 frames/second by VAIF's proxemics tool. Our results suggest that humans in a virtual world tend to position themselves closer to virtual agents than they would relative to humans in the physical world. However, the presence of other virtual agents did not appear to cause participants to change their proxemics.
Large Electroweak Corrections to Heavy WIMP Dark Matter Annihilation and Resummation

In this thesis, we investigate large electroweak corrections to heavy WIMP dark matter annihilation. We calculate the one-loop potentials for arbitrary standard model representations inorder to evaluate the non-relativistic Sommerfeld effect at NLO. Furthermore, we consider theresummation of electroweak Sudakov logarithms in the semi-inclusive χ0χ0 → γ + X annihilationprocess for wino and Higgsino dark matter to the NLL’ order and combine both corrections toprovide the most precise cross-section predictions to date.
LEARNING THROUGH INNOVATIVE COLLABORATION ENHANCED BY EDUCATIONAL TECHNOLOGY (ILIKEIT2)

1 Norwegian University of Science and Technology (NTNU) (NORWAY)2 Middlesex University (UNITED KINGDOM)3 FYG Consultores (SPAIN)4 Active Citizens Partnership (GREECE)5 CESIE (ITALY)
Separations for Estimating Large Frequency Moments on Data Streams.

We study the classical problem of moment estimation of an underlying vector whose $n$ coordinates are implicitly defined through a series of updates in a data stream. We show that if the updates to the vector arrive in the random-order insertion-only model, then there exist space efficient algorithms with improved dependencies on the approximation parameter $\varepsilon$. In particular, for any real $p > 2$, we first obtain an algorithm for $F_p$ moment estimation using $\tilde{\mathcal{O}}\left(\frac{1}{\varepsilon^{4/p}}\cdot n^{1-2/p}\right)$ bits of memory. Our techniques also give algorithms for $F_p$ moment estimation with $p>2$ on arbitrary order insertion-only and turnstile streams, using $\tilde{\mathcal{O}}\left(\frac{1}{\varepsilon^{4/p}}\cdot n^{1-2/p}\right)$ bits of space and two passes, which is the first optimal multi-pass $F_p$ estimation algorithm up to $\log n$ factors. Finally, we give an improved lower bound of $\Omega\left(\frac{1}{\varepsilon^2}\cdot n^{1-2/p}\right)$ for one-pass insertion-only streams. Our results separate the complexity of this problem both between random and non-random orders, as well as one-pass and multi-pass streams.
A Study on Fairness and Trust Perceptions in Automated Decision Making

Automated decision systems are increasingly used for consequential decision making---for a variety of reasons. These systems often rely on sophisticated yet opaque models, which do not (or hardly) allow for understanding how or why a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield undesirable (e.g., unfair) outcomes because their sanity is difficult to assess and calibrate in the first place. In this work, we conduct a study to evaluate different attempts of explaining such systems with respect to their effect on people's perceptions of fairness and trustworthiness towards the underlying mechanisms. A pilot study revealed surprising qualitative insights as well as preliminary significant effects, which will have to be verified, extended and thoroughly discussed in the larger main study.
Different kinds of cognitive plausibility: why are transformers better than RNNs at predicting N400 amplitude?

Author(s): Michaelov, James A; Bardolph, Megan D; Coulson, Seana; Bergen, Benjamin | Abstract: Despite being designed for performance rather than cognitive plausibility, transformer language models have been found to be better at predicting metrics used to assess human language comprehension than language models with other architectures, such as recurrent neural networks. Based on how well they predict the N400, a neural signal associated with processing difficulty, we propose and provide evidence for one possible explanation—their predictions are affected by the preceding context in a way analogous to the effect of semantic facilitation in humans.
Realtime COVID-19 Tracker Using React

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Shop Weatherly – A Weather based Smart E-Commerce System Using CNN

In this current era of modern online shopping, people want to spend as little time as possible when it comes to buying products, therefore they prefer online shopping. People go shopping when the weather gets changed. For travelers, there is no such E-commerce platform that can recommend clothes according to any city weather. Even when people want to gift clothes to someone living in another country there is no such platform that gives recommendation of clothes according to that city's weather. They usually face problems when they want to buy weather-based products from various E-commerce platforms where they see mixed clothes of all types of weather which is very time-consuming, they become so confused most of the time that they think about whether they should buy or not. In this paper, we proposed a novel idea by using Convolutional Neural Network Algorithm of deep learning for developing an e-commerce platform that is unique in a way that it recommends clothes according to the city weather which provides hassle-free environment eventually saves customer's time thereby increasing customer satisfaction.
FANOK: Knockoffs in Linear Time

We describe a series of algorithms that efficiently implement Gaussian model-X knockoffs to control the false discovery rate on large-scale feature selection problems. Identifying the knockoff distribution requires solving a large-scale semidefinite program for which we derive several efficient methods. One handles generic covariance matrices and has a complexity scaling as $\mathcal{O}(p^3)$, where $p$ is the ambient dimension, while another assumes a rank-$k$ factor model on the covariance matrix to reduce this complexity bound to $\mathcal{O}(pk^2)$. We review an efficient procedure to estimate factor models and show that under a factor model assumption, we can sample knockoff covariates with complexity linear in the dimension. We test our methods on problems with $p$ as large as 500 000.
IMPROVING UNSUPERVISED DIALOGUE TOPIC SEGMENTATION WITH UTTERANCE-PAIR COHERENCE SCORING

Dialogue topic segmentation is critical in several dialogue modeling problems. However, popular unsupervised approaches only exploit surface features in assessing topical coherence among utterances. In this work, we address this limitation by leveraging supervisory signals from the utterance-pair coherence scoring task. First, we present a simple yet effective strategy to generate a training corpus for utterance-pair coherence scoring. Then, we train a BERT-based neural utterance-pair coherence model with the obtained training corpus. Finally, such model is used to measure the topical relevance between utterances, acting as the basis of the segmentation inference. Experiments on three public datasets in English and Chinese demonstrate that our proposal outperforms the state-of-the-art baselines.
Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification

The analysis of public debates crucially requires the classification of political demands according to hierarchical claim ontologies (e.g. for immigration, a supercategory "Controlling Migration" might have subcategories "Asylum limit" or "Border installations").A major challenge for automatic claim classification is the large number and low frequency of such subclasses.We address it by jointly predicting pairs of matching super-and subcategories.We operationalize this idea by (a) encoding soft constraints in the claim classifier and (b) imposing hard constraints via Integer Linear Programming.Our experiments with different claim classifiers on a German immigration newspaper corpus show consistent performance increases for joint prediction, in particular for infrequent categories and discuss the complementarity of the two approaches.
Rocchio Nearest Centroid and Normalized Neural Network based Lead Generation in Social Media Marketing

The employment of the internet and social media has transposed consumer behavior and the methods in which business organizations carry over their business. Social and digital marketing recommends noteworthy changes to business establishments via cost curtailment, enhanced brand understanding and surged sales. Despite enormous amount of potentialities, noteworthy disputes subsist from gloomy digital oral message in addition to trespassing and troublesome online presence of brand. Deep learning (DL) has fascinated escalated awareness owing to its notable processing power in tasks, to name a few being, speech, image, or text processing. Due to its aggressive evolution and extensive accessibility of digital social media (SM), examining these data utilizing conventional materials and methods is substantial or even complex. Also with the large growth in the volume of data, the multifariousness in data heterogeneity, are the most distinguished reasons, why and how the SM data mounted. In this paper we study the impact of tweets on distance learning to understand people’s opinions and to discover facts. However, adding redundant features minimizes the generalization capability of the model and may also minimize the overall accuracy of a classifier. We introduce Rocchio Nearest Centroid Laplacian Feature Selection model that combines Rocchio Nearest Centroid and Laplace function for selecting relevant features or tweets. Next an Arbitrary Normalized Attention-based Recurrent Neural Network Lead Generation algorithm is designed aggregating characterizations from preceding and succeeding tweets while generating lead via digital marketing tweet funnel. We validate and evaluate our method using data from distance learning dataset. Experiments and comparisons on distance learning data show that, compared to existing SMM methods, considering generalization capability and digital marketing tweet funnel results in improvements in processing time, lead generation accuracy and precision to a significant extent.
Where Are We in Discourse Relation Recognition?

Discourse parsers recognize the intentional and inferential relationships that organize extended texts. They have had a great influence on a variety of NLP tasks as well as theoretical studies in linguistics and cognitive science. However it is often difficult to achieve good results from current discourse models, largely due to the difficulty of the task, particularly recognizing implicit discourse relations. Recent developments in transformer-based models have shown great promise on these analyses, but challenges still remain. We present a position paper which provides a systematic analysis of the state of the art discourse parsers. We aim to examine the performance of current discourse parsing models via gradual domain shift: within the corpus, on in-domain texts, and on out-of-domain texts, and discuss the differences between the transformer-based models and the previous models in predicting different types of implicit relations both inter- and intra-sentential. We conclude by describing several shortcomings of the existing models and a discussion of how future work should approach this problem.