Reviewerly/exHarmony huggingface dataset:
Ask2Mask: Guided Data Selection for Masked Speech Modeling

Masked speech modeling (MSM) methods such as wav2vec2 or w2v-BERT learn representations over speech frames which are randomly masked within an utterance. While these methods improve performance of Automatic Speech Recognition (ASR) systems, they have one major limitation. They treat all unsupervised speech samples with equal weight, which hinders learning as not all samples have relevant information to learn meaningful representations. In this work, we address this limitation. We propose ask2mask (ATM), a novel approach to focus on specific samples during MSM pre-training. ATM employs an external ASR model or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scorer</i> to weight unsupervised input samples in two different ways: 1) A fine-grained data selection is performed by masking over the highly confident input frames as chosen by the scorer. This allows the model to learn meaningful representations. 2) ATM is further extended to focus at utterance-level by weighting the final MSM loss with the utterance-level confidence score. We conduct fine-tuning experiments on two well-benchmarked corpora: LibriSpeech (matching the pre-training data) and Commonvoice, TED-LIUM, AMI and CHiME-6 (not matching the pre-training data). The results substantiate the efficacy of ATM on significantly improving the recognition performance under mismatched conditions (up to 11.6% relative over published results and upto 4.46% relative over our internal baseline) while still yielding modest improvements under matched conditions.
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure

Multilingual pre-trained language models, such as mBERT and XLM-R, have shown impressive cross-lingual ability.Surprisingly, both of them use multilingual masked language model (MLM) without any cross-lingual supervision or aligned data.Despite the encouraging results, we still lack a clear understanding of why cross-lingual ability could emerge from multilingual MLM.In our work, we argue that crosslanguage ability comes from the commonality between languages.Specifically, we study three language properties: constituent order, composition and word co-occurrence.First, we create an artificial language by modifying property in source language.Then we study the contribution of modified property through the change of cross-language transfer results on target language.We conduct experiments on six languages and two cross-lingual NLP tasks (textual entailment, sentence retrieval).Our main conclusion is that the contribution of constituent order and word co-occurrence is limited, while the composition is more crucial to the success of cross-linguistic transfer.
Resource Scheduling Strategy for Performance Optimization Based on Heterogeneous CPU-GPU Platform

In recent years, with the development of processor architecture, heterogeneous processors including Center processing unit (CPU) and Graphics processing unit (GPU) have become the mainstream. However, due to the differences of heterogeneous core, the heterogeneous system is now facing many problems that need to be solved. In order to solve these problems, this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies. To improve the performance of the system, this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks. The combination strategy consists of two sub-strategies, the first strategy improves the execution efficiency of tasks on the GPU by changing the thread organization structure. The second focuses on the working state of the efficient core and develops more reasonable workload balancing schemes to improve resource utilization of heterogeneous systems. The multi-task scheduling strategy obtains the execution efficiency of heterogeneous cores and global task information through the processing of task samples. Based on this information, an improved ant colony algorithm is used to quickly obtain a reasonable task allocation scheme, which fully utilizes the characteristics of heterogeneous cores. The experimental results show that the combination strategy reduces task execution time by 29.13% on average. In the case of processing multiple tasks, the multi-task scheduling strategy reduces the execution time by up to 23.38% based on the combined strategy. Both strategies can make better use of the resources of heterogeneous systems and significantly reduce the execution time of tasks on heterogeneous systems.
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models

Selecting an appropriate pre-trained model (PTM) for a specific downstream task typically requires significant efforts of fine-tuning.To accelerate this process, researchers propose feature-based model selection (FMS) methods, which assess PTMs' transferability to a specific task in a fast way without fine-tuning.In this work, we argue that current FMS methods are vulnerable, as the assessment mainly relies on the static features extracted from PTMs.However, such features are derived without training PTMs on downstream tasks, and are not necessarily reliable indicators for the PTM's transferability.To validate our viewpoints, we design two methods to evaluate the robustness of FMS:(1) model disguise attack, which post-trains an inferior PTM with a contrastive objective, and (2) evaluation data selection, which selects a subset of the data points for FMS evaluation based on K-means clustering.Experimental results prove that both methods can successfully make FMS mistakenly judge the transferability of PTMs.Moreover, we find that these two methods can further be combined with the backdoor attack to misguide the FMS to select poisoned models.To the best of our knowledge, this is the first work to demonstrate the defects of current FMS algorithms and evaluate their potential security risks.By identifying previously unseen risks of FMS, our study indicates new directions for improving the robustness of FMS.
Quantum Transfer Learning Approach for Deepfake Detection

Deepfake image manipulation has achieved great attention in the previous year’s owing to brings solemn challenges from the public self-confidence. Forgery detection in face imaging has made considerable developments in detecting manipulated images. However, there is still a need for an efficient deepfake detection approach in complex background environments. This paper applies the state-of-the-art quantum transfer learning approach for classifying deepfake images from original face images. The proposed model comprises classical pre-trained ResNet-18 and quantum neural network layers that provide efficient features extraction to learn the different patterns of the deepfake face images. The proposed model is validated on a real-world deepfake dataset created using commercial software. An accuracy of 96.1 % was obtained.
Comparison between RSA and CAST-128 with Adaptive Key for Video Frames Encryption with Highest Average Entropy

Encryption of data is translating data to another shape or symbol which enables people only with an access to the secret key or a password that can read it. The data which are encrypted are generally referred to as cipher text, while data which are unencrypted are known plain text. Entropy can be used as a measure which gives the number of bits that are needed for coding the data of an image. As the values of pixel within an image are dispensed through further gray-levels, the entropy increases. The aim of this research is to compare between CAST-128 with proposed adaptive key and RSA encryption methods for video frames to determine the more accurate method with highest entropy. The first method is achieved by applying the "CAST-128" and the second is achieved by applying the "RSA ". CAST-128 utilizes a pair of sub-keys for each round as a quantum of five bits that was utilized as a key of rotation for each round and a quantum of 32 (bits) was utilized as a key of masking into a round . The proposed adaptive 128-bits key can be extracted from the main diagonal of each frame before encryption. RSA is a public-key cryptographic technique which can be known as (asymmetric) cryptography. An asymmetry of a key depends on factoring a product of two big prime values. A comparison was applied on several videos and the results showed that CAST-128 method proved the highest degree of entropy even if the frames have lots of distorted data or unclear image pixels. For example, the entropy value of a sample of a girl video is 2581.921 when using CAST-128, while it is 2271.329 when using the RSA; also the entropy value of a sample of a scooter video is 2569.814 when using the CAST-128, while it is 2282.844 when using RSA.
Team DMG at CMCL 2022 Shared Task: Transformer Adapters for the Multi- and Cross-Lingual Prediction of Human Reading Behavior

In this paper, we present the details of our approaches that attained the second place in the shared task of the ACL 2022 Cognitive Modeling and Computational Linguistics Workshop.The shared task is focused on multiand cross-lingual prediction of eye movement features in human reading behavior, which could provide valuable information regarding language processing.To this end, we train 'adapters' inserted into the layers of frozen transformer-based pretrained language models.We find that multilingual models equipped with adapters perform well in predicting eyetracking features.Our results suggest that utilizing language-and task-specific adapters is beneficial and translating test sets into similar languages that exist in the training set could help with zero-shot transferability in the prediction of human reading behavior.
A Greater GIFT: Strengthening GIFT Against Statistical Cryptanalysis

GIFT-64 is a 64-bit block cipher with a 128-bit key that is more lightweight than PRESENT. This paper provides a detailed analysis of GIFT-64 against differential and linear attacks. Our work complements automatic search methods for the best differential and linear characteristics with a careful manual analysis. This hybrid approach leads to new insights. In the differential setting, we theoretically explain the existence of differential characteristics with two active S-boxes per round and derive some novel properties of these characteristics. Furthermore, we prove that all optimal differential characteristics of GIFT-64 covering more than seven rounds must activate two S-boxes per round. We can construct all optimal characteristics by hand. In parallel to the work in the differential setting, we conduct a similar analysis in the linear setting. However, unlike the clear view in differential setting, the optimal linear characteristics of GIFT-64 must have at least one round activating only one S-box. Moreover, with the assistance of automatic searching methods, we identify 24 GIFT-64 variants achieving better resistance against differential attack while maintaining a similar security level against a linear attack. Since the new variants strengthen GIFT-64 against statistical cryptanalysis, we claim that the number of rounds could be reduced from 28 to 26 for the variants. This observation enables us to create a cipher with lower energy consumption than GIFT-64. Similarly to the case in GIFT-64, we do not claim any related-key security for the round-reduced variant as this is not relevant for most applications.
An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation

We study the interpretability issue of taskoriented dialogue systems in this paper.Previously, most neural-based task-oriented dialogue systems employ an implicit reasoning strategy that makes the model predictions uninterpretable to humans.To obtain a transparent reasoning process, we introduce neurosymbolic to perform explicit reasoning that justifies model decisions by reasoning chains.Since deriving reasoning chains requires multihop reasoning for task-oriented dialogues, existing neuro-symbolic approaches would induce error propagation due to the one-phase design.To overcome this, we propose a twophase approach that consists of a hypothesis generator and a reasoner.We first obtain multiple hypotheses, i.e., potential operations to perform the desired task, through the hypothesis generator.Each hypothesis is then verified by the reasoner, and the valid one is selected to conduct the final prediction.The whole system is trained by exploiting raw textual dialogues without using any reasoning chain annotations.Experimental studies on two public benchmark datasets demonstrate that the proposed approach not only achieves better results, but also introduces an interpretable decision process.
Towards Tackling QSAT Problems with Deep Learning and Monte Carlo Tree Search

Recent achievements of AlphaZero using self-play has shown remarkable performance on several board games. It is plausible to think that self-play, starting from zero knowledge, can gradually approximate a winning strategy for certain two-player games after an amount of training. In this paper, we present a proof-of-concept to solve small instances of Quantified Boolean Formula Satisfaction (QSAT) problems by leveraging the computational power from neural Monte Carlo Tree Search (neural MCTS). QSAT is a PSPACE-complete problem with many practical applications. We propose a way to encode Quantified Boolean Formulas (QBFs) as graphs and apply a graph neural network (GNN) to embed the QBFs into the neural MCTS. After training, an off-the-shelf QSAT solver is used to evaluate the performance of the algorithm. Our result shows that, for problems within a limited size, the algorithm learns to solve the problem correctly merely from self-play. It is impressive that neural MCTS is succeeding on small QSAT problems but research is needed to better understand the algorithm and its parameters.
Generic Construction of Boolean Functions with A Few Walsh Transform Values of Any Possible Algebraic Degree

<p style='text-indent:20px;'>Bent functions, semi-bent functions and other Boolean functions with a few Walsh spectra have important applications in coding theory, cryptography and sequence design. In this paper, motivated by the work of Tang et al.(IEEE Trans. Inf. Theory 63(10), 6149-6157, 2017), we provide several infinite families of bent, semi-bent functions and Boolean functions of <inline-formula><tex-math id="M1">\begin{document}$ n = 2m $\end{document}</tex-math></inline-formula> (<inline-formula><tex-math id="M2">\begin{document}$ m\ge 4 $\end{document}</tex-math></inline-formula> an integer) variables with five to thirteen-valued Walsh spectra by Kasami types, Gold-like types and Maiorana-MacFarland types. The conditions for these functions to be bent and semi-bent are sufficient and necessary. Furthermore, the dual functions of the new bent functions are given. The algebraic degree of the new functions range from <inline-formula><tex-math id="M3">\begin{document}$ 4 $\end{document}</tex-math></inline-formula> to <inline-formula><tex-math id="M4">\begin{document}$ m $\end{document}</tex-math></inline-formula> (or <inline-formula><tex-math id="M5">\begin{document}$ m+2 $\end{document}</tex-math></inline-formula> except for bent and semi-bent functions) and all the functions possess high nonlinearity.
Optimistic Motion Planning Using Recursive Sub- Sampling: A New Approach to Sampling-Based Motion Planning

Sampling-based motion planning in the field of robot motion planning has provided an effective approach to finding path for even high dimensional configuration space and with the motivation from the concepts of sampling based-motion planners, this paper presents a new sampling-based planning strategy called Optimistic Motion Planning using Recursive Sub-Sampling (OMPRSS), for finding a path from a source to a destination sanguinely without having to construct a roadmap or a tree.The random sample points are generated recursively and connected by straight lines.Generating sample points is limited to a range and edge connectivity is prioritized based on their distances from the line connecting through the parent samples with the intention to shorten the path.The planner is analysed and compared with some sampling strategies of probabilistic roadmap method (PRM) and the experimental results show agile planning with early convergence.
Geospatial analysis delineates lode gold prospectivity in Alaska

Data-driven geospatial analyses of geological data delineate lode gold potential for Alaska.Analyses indicate new potential lode source areas in gold placer districts and identify new areas in Alaska prospective for lode gold deposits.Adaptable geospatial analytical techniques can focus future investigations, data collection, and exploration strategies for gold and associated byproduct critical minerals, such as As, W, Sb, and Te.Photograph of Independance Mine northeast of Anchorage, Alaska, by S. Karl, U.S. Geological Survey (banner).Estimated resource potential and certainty for lode gold
Synthesizing fuzzy tree automata

Fuzzy tree automata are mathematical devices for modeling and analyzing vaguely defined tree structures. The behavior of a fuzzy tree automaton generates a fuzzy tree language by mapping a set of regular trees on a ranked alphabet to fuzzy membership values. It calculates the membership grade of trees using a set of rules that process their structural characteristics. This paper deals with constructing fuzzy tree automata models that their behavior satisfies a set of given logical propositions (called properties) on the structure of trees. Our goal is uncertainty modeling by synthesizing fuzzy tree automata whose behavior is described by fuzzy linguistic variables. In this regard, we first provide several patterns and heuristic tricks and techniques for constructing fuzzy tree automata that satisfy simple properties. Then, we develop a method for modeling complex propositional formulas based on the conversion of a logical formula into a computation tree, as well as a step-by-step combination of models.
Application of Chatbot at a Higher Education Institution in Republic of Serbia

A chatbot is an algorithm, a programming script, designed to give users the ability to communicate and interact with it online in order to perform a specific task, without any human factor involvement. In the beginning, chatbots were exclusively programs with which it was possible to correspond by exchanging instant text messages. The main focus of the paper is on the presentation of usage of chatbot ADA (Academic Digital Assistant) based on artificial intelligence. ADA chatbot was developed to provide students with an enhanced study service and the necessary information during their studies. Statistical parameters of the use of chatbot ADA and the analysis of statistics of conversations by channels show growing trend in the use of chatbots in all three surveys of communication channels (Viber, FB Messenger and Website). The conclusions of the analysis of top 10 scenarios and number of conversations are shown that students' interests were in accordance with the school calendar and obligations.
Why Videos Do Not Guide Translations in Video-guided Machine Translation? An Empirical Evaluation of Video-guided Machine Translation Dataset

Video-guided machine translation (VMT) is a type of multimodal machine translation that uses information from videos to guide translation. However, in the VMT 2020 challenge, adding videos only marginally improved the performance of VMT models compared to their text-only baselines. In this study, we systematically analyze why videos did not guide translation. Specifically, we evaluate the models in input degradation and visual sensitivity experiments and compare the results with a human evaluation using VATEX, which is the dataset used in the VMT 2020 challenge. The results indicate that short and straightforward video descriptions in VATEX are sufficient to perform the translations, which renders the videos redundant in the process. Based on our findings, we provide suggestions on the design of future VMT datasets. Code and human-evaluated data are publicly available for future research.
Explainable Assessment of Healthcare Articles with QA

The healthcare domain suffers from the spread of poor quality articles on the Internet.While manual efforts exist to check the quality of online healthcare articles, they are not sufficient to assess all those in circulation.Such quality assessment can be automated as a text classification task, however, explanations for the labels are necessary for the users to trust the model predictions.While current explainable systems tackle explanation generation as summarization, we propose a new approach based on question answering (QA) that allows us to generate explanations for multiple criteria using a single model.We show that this QA-based approach is competitive with the current state-of-the-art, and complements summarization-based models for explainable quality assessment.We also introduce a human evaluation protocol more appropriate than automatic metrics for the evaluation of explanation generation models.
Image Encryption Using RSA Algorithm

In the current covid pandemic situation, secure online transmission of data has the highest precedence over other activities. For providing computational hardness that is for making tough to break the key for finding the unique message, there are various algorithms are present. For secure data transmission, many researchers have applied different cryptography algorithms and in order to improve the level of information security, different hybrid cryptography algorithms have been proposed. In cryptography algorithm implementation, key management plays a major role. For this reason, we have applied an image encryption technique in which a random image is considered as the key. Using the random image as a key, we have encrypted another image as information using the RSA algorithm. The comparison of the proposed method is done with the traditional approach and concluded that the cryptography algorithm implemented using an image as key provides more security in terms of encryption and decryption time.
Microsoft 365 as a Tool for Teaching During the Covid-19 Pandemic: Perceptions of Portuguese Teachers of Basic and Secondary Education

Abstract In the last year, and largely due to the pandemic situation of COVID-19, teachers experimented with technology in an innovative way not only to keep students engaged, but also to make learning more active and more significant. The Microsoft 365 was one of the solutions that provided schools, teachers and students with all the necessary resources to continue teaching and learning in a remote mode. In this work, we present a study that aims to know the opinions of several teachers about the use they made of Microsoft 365 during distance education and to know the degree of satisfaction obtained with its use. The adopted method has a descriptive and exploratory nature, focusing on a quantitative paradigm. The study participants were 101 teachers of basic1 and secondary education2 from schools in Northern Portugal. A questionnaire survey was used as a data collection instrument, which was mainly disseminated by email from Outlook. Data analysis was performed using Excel (Office 2016). The results suggest that Microsoft 365 was the appropriate response to the constraints imposed by the pandemic, and that most respondents revealed a high level of satisfaction with its use, despite not having much training in the use of teaching platforms.