Reviewerly/exHarmony huggingface dataset:
An improved machine learning algorithm for text-voice conversion of English letters into phonemes

Text-to-voice conversion is the core technology of intelligent translation system and intelligent teaching system, which is of great significance to English teaching and expansion. However, there are certain problems with the characteristics of factors in the current text-to- voice conversion. In order to improve the efficiency of text-to- voice conversion, this study improves the traditional machine learning algorithm and proposes an improved model that combines statistical language, factor analysis, and support vector machines. Moreover, the model is constructed as a training module and a testing module. The model combines statistical methods and rule methods in a unified framework to make full use of English language features to achieve automatic conversion of letter strings and phonetic features. In addition, in order to meet the needs of English text-to- voice conversion, this study builds a framework model, this study analyzes the performance of the model, and designs a control experiment to compare the performance of the model. The research results show that the method proposed in this paper has a certain effect.
Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Most approaches in few-shot learning rely on costly annotated data related to the goal task domain during (pre-)training. Recently, unsupervised meta-learning methods have exchanged the annotation requirement for a reduction in few-shot classification performance. Simultaneously, in settings with realistic domain shift, common transfer learning has been shown to outperform supervised meta-learning. Building on these insights and on advances in self-supervised learning, we propose a transfer learning approach which constructs a metric embedding that clusters unlabeled prototypical samples and their augmentations closely together. This pre-trained embedding is a starting point for few-shot classification by summarizing class clusters and fine-tuning. We demonstrate that our self-supervised prototypical transfer learning approach ProtoTransfer outperforms state-of-the-art unsupervised meta-learning methods on few-shot tasks from the mini-ImageNet dataset. In few-shot experiments with domain shift, our approach even has comparable performance to supervised methods, but requires orders of magnitude fewer labels.
Securing your remote workforce against new phishing attacks

As the spread of Covid-19 forced organisations across the globe to introduce preventive measures, the number of people working remotely has grown dramatically. Despite the challenging circumstances, technology has enabled the continuation of some businesses that would otherwise have ground to a halt. Yet the translation of office-based work to home-based had to happen at speed – for some, literally overnight. Under such conditions, implementing new security measures not already in place is proving a challenge. The number of people working remotely has grown dramatically, and technology has enabled the continuation of some businesses that would otherwise have ground to a halt. However, the speed at which this has happened may mean that security has been left behind. As employees adapt to unfamiliar work environments away from the office, their primary focus is not necessarily robust methods of authentication. Multi-factor authentication (MFA) solutions have an important role to play here, offering authentication protocols that work well for employees while providing the right level of protection for networks, systems and data, says Nic Sarginson of Yubico.
Detection of Human Activity by Widget

Human Activity recognition exhibits its presence in diverse research areas like medical organization, Survey system, security surveillance as well as human computer interaction. This work demonstrates a robust approach of classifying six basic human centered behaviors (Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing, and Lying implementing Logistic Regression, Logistic Regression CV and Random Forest algorithm. Computing is an up-and-coming research to comprehend individual actions and try to assimilate their social context. A precise demanding and agreeable application of sensing human body motion by smart phones to collect context information. Here, activity recognition database is considered publicly available as repository.
A SUPER* Algorithm to Optimize Paper Bidding in Peer Review

A number of applications involve sequential arrival of users, and require showing each user an ordering of items. A prime example (which forms the focus of this paper) is the bidding process in conference peer review where reviewers enter the system sequentially, each reviewer needs to be shown the list of submitted papers, and the reviewer then to review some papers. The order of the papers shown has a significant impact on the bids due to primacy effects. In deciding on the ordering of papers to show, there are two competing goals: (i) obtaining sufficiently many bids for each paper, and (ii) satisfying reviewers by showing them relevant items. In this paper, we begin by developing a framework to study this problem in a principled manner. We present an algorithm called SUPER*, inspired by the A* algorithm, for this goal. Theoretically, we show a local optimality guarantee of our algorithm and prove that popular baselines are considerably suboptimal. Moreover, under a community model for the similarities, we prove that SUPER* is near-optimal whereas the popular baselines are considerably suboptimal. In experiments on real data from ICLR 2018 and synthetic data, we find that SUPER* considerably outperforms baselines deployed in existing systems, consistently reducing the number of papers with fewer than requisite bids by 50-75% or more, and is also robust to various real world complexities.
Unbounded HIBE with Tight Security

We propose the first tightly secure and unbounded hierarchical identity-based encryption (HIBE) scheme based on standard assumptions. Our main technical contribution is a novel proof strategy that allows us to tightly randomize user secret keys for identities with arbitrary hierarchy depths using low entropy hidden in a small and hierarchy-independent master public key. The notion of unbounded HIBE is proposed by Lewko and Waters (Eurocrypt 2011). In contrast to most HIBE schemes, an unbounded scheme does not require any maximum depth to be specified in the setup phase, and user secret keys or ciphertexts can be generated for identities of arbitrary depths with hierarchy-independent system parameters. While all the previous unbounded HIBE schemes have security loss that grows at least linearly in the number of user secret key queries, the security loss of our scheme is only dependent on the security parameter, even in the multi-challenge setting, where an adversary can ask for multiple challenge ciphertexts. We prove the adaptive security of our scheme based on the Matrix Decisional Diffie-Hellman assumption in prime-order pairing groups, which generalizes a family of standard Diffie-Hellman assumptions such as k-Linear.
Sentiment Analysis Model Based on Self-Attention and Character-Level Embedding

Aiming at the problem of insufficient sentiment word extraction ability in existing text sentiment analysis methods and OOV (out-of-vocabulary) problem of pre-training word vectors, a neural network model combining multi-head self-attention and character-level embedding is proposed.An encoderdecoder (Seq2Seq) model is used for sentiment analysis of text.By adding character-level embedding to the word embedding layer, the OOV problem of pre-trained word vectors is solved.At the same time, the self-attention mechanism is used to perform attention calculations within the input sequence to find intra-sequence connections.The model uses bidirectional LSTM (Long Short-Term Memory) independently encodes the context semantic information of the input sequence to obtain deeper emotional features, to more effectively identify the emotional polarity of short text.The effectiveness of our method was verified on the public Twitter dataset and the movie review dataset published by ACL.Experimental results show that the accuracy of the model on the three categories of Twitter, movie reviews and IMDB datasets reaches 66.45%, 79.48% and 90.34%, respectively, which verifies that the model has excellent performance in datasets in different fields.
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning

Efficient exploration remains a challenging problem in reinforcement learning, especially for tasks where extrinsic rewards from environments are sparse or even totally disregarded. Significant advances based on intrinsic motivation show promising results in simple environments but often get stuck in environments with multimodal and stochastic dynamics. In this work, we propose a variational dynamic model based on the conditional variational inference to model the multimodality and stochasticity. We consider the environmental state-action transition as a conditional generative process by generating the next-state prediction under the condition of the current state, action, and latent variable, which provides a better understanding of the dynamics and leads to a better performance in exploration. We derive an upper bound of the negative log likelihood of the environmental transition and use such an upper bound as the intrinsic reward for exploration, which allows the agent to learn skills by self-supervised exploration without observing extrinsic rewards. We evaluate the proposed method on several image-based simulation tasks and a real robotic manipulating task. Our method outperforms several state-of-the-art environment model-based exploration approaches.
Relation Extraction with Self-determined Graph Convolutional Network

Relation Extraction is a way of obtaining the semantic relationship between entities in text. The state-of-the-art methods use linguistic tools to build a graph for the text in which the entities appear and then a Graph Convolutional Network (GCN) is employed to encode the pre-built graphs. Although their performance is promising, the reliance on linguistic tools results in a non end-to-end process. In this work, we propose a novel model, the Self-determined Graph Convolutional Network (SGCN), which determines a weighted graph using a self-attention mechanism, rather using any linguistic tool. Then, the self-determined graph is encoded using a GCN. We test our model on the TACRED dataset and achieve the state-of-the-art result. Our experiments show that SGCN outperforms the traditional GCN, which uses dependency parsing tools to build the graph.
Comments on “Privacy-Preserving Public Auditing Protocol for Regenerating-Code-Based Cloud Storage”

Public auditing protocol is crucial for the success of cloud computing, as it can ensure the outsourced data in cloud server are not tampered by attackers. Due to its importance, public auditing protocol has received considerable attention in the past years. In 2015, Liu et al. proposed a privacy-preserving public auditing protocol for regenerating-code-based cloud storage (IEEE Transactions on Information Forensics and Security, 10(7):1513-1528, 2015) and claimed it is secure under the considered security model. However, in this article, we will show that their protocol is not as secure as they claimed, i.e., the proxy delegated by the data owner can forge an authenticator for any data block, which obviously invalidates their protocol's security. We hope that by identifying the design flaw, similar weaknesses can be avoided in future protocol design.
DroneLight: Drone Draws in the Air using Long Exposure Light Painting and ML

We propose a novel human-drone interaction paradigm where a user directly interacts with a drone to light-paint predefined patterns or letters through hand gestures. The user wears a glove which is equipped with an IMU sensor to draw letters or patterns in the midair. The developed ML algorithm detects the drawn pattern and the drone light-paints each pattern in midair in the real time. The proposed classification model correctly predicts all of the input gestures. The DroneLight system can be applied in drone shows, advertisements, distant communication through text or pattern, rescue, and etc. To our knowledge, it would be the world's first human-centric robotic system that people can use to send messages based on light-painting over distant locations (drone-based instant messaging). Another unique application of the system would be the development of vision-driven rescue system that reads light-painting by person who is in distress and triggers rescue alarm.
On New Zero-Knowledge Proofs for Fully Anonymous Lattice-Based Group Signature Scheme with Verifier-Local Revocation

The first lattice-based verifier-local revocation group signature (GS-VLR) was introduced by Langlois et al. in 2014, and subsequently, a full and corrected version was proposed by Ling et al. in 2018. However, zero-knowledge proofs in both schemes are within a structure of Bonsai Tree, and thus have bit-sizes of the group public-key and member secret-key proportional to \(\log N\), where N is the group size. On the other hand, the revocation tokens in both schemes are related to the member secret-key and only obtain a weaker security, selfless-anonymity. For the tracing algorithms in both schemes, they just run in the linear time of N. Therefore, for a large group, the zero-knowledge proofs in lattice-based GS-VLR schemes are not that secure and efficient.In this work, we firstly utilize a compact and scalable identity-encoding technique which only needs a constant number of public matrices to encode the member’s identity information and it saves a \(\mathcal {O}(\log N)\) factor in both bit-sizes for the group public-key and member secret-key. Secondly, separating from the member secret-key, we generate revocation token within some public matrix and a short Gaussian vector, and thus obtain the strongest security, full-anonymity. Moreover, the explicit-traceability, to trace the signer’s identity in a constant time, independent of N, for the tracing authority is also satisfied. In particular, a new Stern-type statistical zero-knowledge proof protocol for a fully anonymous lattice-based GS-VLR scheme enjoying the above three advantages is proposed.
Flex-ER

Extended Reality (XR) systems (which encapsulate AR, VR and MR) is an emerging field which enables the development of novel visualization and interaction techniques. To develop and to assess such techniques, researchers and designers have to face choices in terms of which development tools to adopt, and with very little information about how such tools support some of the very basic tasks for information visualization, such as selecting data items, linking and navigating. As a solution, we propose Flex-ER, a flexible web-based environment that enables users to prototype, debug and share experimental conditions and results. Flex-ER enables users to quickly switch between hardware platforms and input modalities by using a JSON specification that supports both defining interaction techniques and tasks at a low cost. We demonstrate the flexibility of the environment through three task design examples: brushing, linking and navigating. A qualitative user study suggest that Flex-ER can be helpful to prototype and explore different interaction techniques for immersive analytics.
Writing Process Approach and Its Effect on Studentsâ€™ Writing Anxiety and Performance

This study aims at investigating the effect of the process approach on student writing anxiety and performance. Teachers should find ways to cope with anxiety as it is known as the negative predictor of studentsâ€™ writing performance. Fifteen students participated in this study. They were assigned to write an argumentative essay under the topic â€˜should national exam be banned?â€. The treatment was given following the writing stages, from outlining to publishing. SLWAI questionnaire was distributed before and after the treatment to measure the effectiveness of the process approach on studentsâ€™ anxiety. The findings indicated that students writing anxiety decreases from 71.27 to 63.20, which means that the anxiety level goes down from high to moderate anxiety. The second findings informed us that there is a significant difference in students writing performance after the treatment with the level of significant .000. It can be said that the writing process approach has a significant effect on students writing anxiety and performance. The pedagogical implication is also discussed.
Understanding Self-Attention of Self-Supervised Audio Transformers

Self-supervised Audio Transformers (SAT) enable great success in many downstream speech applications like ASR, but how they work has not been widely explored yet. In this work, we present multiple strategies for the analysis of attention mechanisms in SAT. We categorize attentions into explainable categories, where we discover each category possesses its own unique functionality. We provide a visualization tool for understanding multi-head self-attention, importance ranking strategies for identifying critical attention, and attention refinement techniques to improve model performance.
Speech-Image Semantic Alignment Does Not Depend on Any Prior Classification Tasks

Semantically-aligned $(speech, image)$ datasets can be used to explore visually-grounded speech. In a majority of existing investigations, features of an image signal are extracted using neural networks on other tasks (e.g., classification on ImageNet). In still others, pre-trained networks are used to extract audio features prior to semantic embedding. Without transfer learning through pre-trained initialization or pre-trained feature extraction, previous results have tended to show low rates of recall in $speech \rightarrow image$ and $image \rightarrow speech$ queries. Choosing appropriate neural architectures for encoders in the speech and image branches and using large datasets, one can obtain competitive recall rates without any reliance on any pre-trained initialization or feature extraction: $(speech,image)$ semantic alignment and $speech \rightarrow image$ and $image \rightarrow speech$ retrieval are canonical tasks worthy of independent investigation of their own and allow one to explore other questions---e.g., the size of the audio embedder can be reduced significantly with little loss of recall rates in $speech \rightarrow image$ and $image \rightarrow speech$ queries.
Exploring the Use of an Unsupervised Autoregressive Model as a Shared Encoder for Text-Dependent Speaker Verification

In this paper, we propose a novel way of addressing text-dependent automatic speaker verification (TD-ASV) by using a shared-encoder with task-specific decoders. An autoregressive predictive coding (APC) encoder is pre-trained in an unsupervised manner using both out-of-domain (LibriSpeech, VoxCeleb) and in-domain (DeepMine) unlabeled datasets to learn generic, high-level feature representation that encapsulates speaker and phonetic content. Two task-specific decoders were trained using labeled datasets to classify speakers (SID) and phrases (PID). Speaker embeddings extracted from the SID decoder were scored using a PLDA. SID and PID systems were fused at the score level. There is a 51.9% relative improvement in minDCF for our system compared to the fully supervised x-vector baseline on the cross-lingual DeepMine dataset. However, the i-vector/HMM method outperformed the proposed APC encoder-decoder system. A fusion of the x-vector/PLDA baseline and the SID/PLDA scores prior to PID fusion further improved performance by 15% indicating complementarity of the proposed approach to the x-vector system. We show that the proposed approach can leverage from large, unlabeled, data-rich domains, and learn speech patterns independent of downstream tasks. Such a system can provide competitive performance in domain-mismatched scenarios where test data is from data-scarce domains.
Transformer with Bidirectional Decoder for Speech Recognition

Attention-based models have made tremendous progress on end-to-end automatic speech recognition(ASR) recently. However, the conventional transformer-based approaches usually generate the sequence results token by token from left to right, leaving the right-to-left contexts unexploited. In this work, we introduce a bidirectional speech transformer to utilize the different directional contexts simultaneously. Specifically, the outputs of our proposed transformer include a left-to-right target, and a right-to-left target. In inference stage, we use the introduced bidirectional beam search method, which can not only generate left-to-right candidates but also generate right-to-left candidates, and determine the best hypothesis by the score. To demonstrate our proposed speech transformer with a bidirectional decoder(STBD), we conduct extensive experiments on the AISHELL-1 dataset. The results of experiments show that STBD achieves a 3.6\% relative CER reduction(CERR) over the unidirectional speech transformer baseline. Besides, the strongest model in this paper called STBD-Big can achieve 6.64\% CER on the test set, without language model rescoring and any extra data augmentation strategies.
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games

We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with textual games. In this work, we aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We propose a stacked hierarchical attention mechanism to construct an explicit representation of the reasoning process by exploiting the structure of the knowledge graph. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents.
The effect of edge and vertex deletion on omega invariant

Recently the first and last authors defined a new graph characteristic called omega related to Euler characteristic to determine several topological and combinatorial properties of a given graph. This new characteristic is defined in terms of a given degree sequence as a graph invariant and gives a lot of information on the realizability, number of realizations, connectedness, cyclicness, number of components, chords, loops, pendant edges, faces, bridges etc. of the family of realizations. In this paper, the effect of the deletion of vertices and edges from a graph on omega invariant is studied.
Reinforcement Learning Based Personalized Neural Dialogue Generation

In this paper, we present a persona aware neural reinforcement learning response generation framework capable of optimizing long-term rewards carefully devised by system developers. The proposed model utilizes an extension of the recently introduced Hierarchical Encoder Decoder (HRED) architecture. We leverage insights from Reinforcement Learning (RL) and employ policy gradient methods to optimize rewards which are defined as simple heuristic approximations that indicate good conversation to a human mind. The proposed model is demonstrated on two benchmark datasets. Empirical results indicate that the proposed approach outperforms their counterparts that do not optimize long-term rewards, have no access to personas, standard models trained using solely maximum-likelihood estimation objective.