Reviewerly/exHarmony huggingface dataset:
Verifiable Functional Encryption Using Intel SGX

Most functional encryption schemes implicitly assume that inputs to decryption algorithms, i.e., secret keys and ciphertexts, are generated honestly. However, they may be tampered by malicious adversaries. Thus, verifiable functional encryption (VFE) was proposed by Badrinarayanan et al. in ASIACRYPT 2016 where anyone can publicly check the validity of secret keys and ciphertexts. They employed indistinguishability-based (IND-based) security due to an impossibility result of simulation-based (SIM-based) VFE even though SIM-based security is more desirable. In this paper, we propose a SIM-based VFE scheme. To bypass the impossibility result, we introduce a trusted setup assumption. Although it appears to be a strong assumption, we demonstrate that it is reasonable in a hardware-based construction, e.g., Fisch et al. in ACM CCS 2017. Our construction is based on a verifiable public-key encryption scheme (Nieto et al. in SCN 2012), a signature scheme, and a secure hardware scheme, which we refer to as VFE-HW. Finally, we discuss an implementation of VFE-HW using Intel Software Guard Extensions (Intel SGX).
Contextual-Lexicon Approach for Abusive Language Detection

Since a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and hate speech detection on social media, which embodies a lexicon of implicit and explicit offensive and swearing expressions annotated with contextual information.Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the models.Nevertheless, our method may be applied to any other language.The conducted experiments show the effectiveness of the proposed approach, outperforming the current baseline methods for the Portuguese language.
Probabilistic Ensembles of Zero- and Few-Shot Learning Models for Emotion Classification

Emotion Classification is the task of automatically associating a text with a human emotion.State-of-the-art models are usually learned using annotated corpora or rely on hand-crafted affective lexicons.We present an emotion classification model that does not require a large annotated corpus to be competitive.We experiment with pretrained language models in both a zero-shot and few-shot configuration.We build several of such models and consider them as biased, noisy annotators, whose individual performance is poor.We aggregate the predictions of these models using a Bayesian method originally developed for modelling crowdsourced annotations.Next, we show that the resulting system performs better than the strongest individual model.Finally, we show that when trained on few labelled data, our systems outperform fully-supervised models.
Glance-and-Gaze Vision Transformer

Recently, there emerges a series of vision Transformers, which show superior performance with a more compact model size than conventional convolutional neural networks, thanks to the strong ability of Transformers to model long-range dependencies. However, the advantages of vision Transformers also come with a price: Self-attention, the core part of Transformer, has a quadratic complexity to the input sequence length. This leads to a dramatic increase of computation and memory cost with the increase of sequence length, thus introducing difficulties when applying Transformers to the vision tasks that require dense predictions based on high-resolution feature maps. In this paper, we propose a new vision Transformer, named Glance-and-Gaze Transformer (GG-Transformer), to address the aforementioned issues. It is motivated by the Glance and Gaze behavior of human beings when recognizing objects in natural scenes, with the ability to efficiently model both long-range dependencies and local context. In GG-Transformer, the Glance and Gaze behavior is realized by two parallel branches: The Glance branch is achieved by performing self-attention on the adaptively-dilated partitions of the input, which leads to a linear complexity while still enjoying a global receptive field; The Gaze branch is implemented by a simple depth-wise convolutional layer, which compensates local image context to the features obtained by the Glance mechanism. We empirically demonstrate our method achieves consistently superior performance over previous state-of-the-art Transformers on various vision tasks and benchmarks. The codes and models will be made available at this https URL.
Image to Image Transfer Makes Malpositioned Teeth Orderly

Deep learning’s continuous development has spawned many applications and neural networks that tackle challenging tasks in different fields. The fusion of these applications and networks could solve more complex problems in the real world. Taking image generation as an example, combining a pure image generator by CNN and a single text processor by RNN can form a more complex and capable fusion network for text to image generation. Generally, a basic successful deep learning application requires at least three elements: a suitable application scenario, an appropriate dataset, and a proper model. Under this principle, in this paper, we introduce a new task for virtual orthodontics, a new image-to-image transfer task from malpositioned-teeth-image to neat-teeth-image. We call it orthodontics transfer. To make up for the lack of datasets, we constructed a paired dataset about orthodontics before and after surgery from real medical cases. Simultaneously, a new orthodontics transfer network with a teeth-code transfer as a bridge is proposed. The experimental results show that our proposed method is effective, which can realize the orthodontic effect on teeth photos by image-to-image transfer.
Modern Talking in Key Point Analysis: Key Point Matching using Pretrained Encoders

We contribute to the ArgMining 2021 shared task on Quantitative Summarization and Key Point Analysis with two approaches for argument key point matching.For key point matching the task is to decide if a short key point matches the content of an argument with the same topic and stance towards the topic.We approach this task in two ways: First, we develop a simple rule-based baseline matcher by computing token overlap after removing stop words, stemming, and adding synonyms/antonyms.Second, we fine-tune pretrained BERT and RoBERTa language models as a regression classifier for only a single epoch.We manually examine errors of our proposed matcher models and find that long arguments are harder to classify.Our fine-tuned RoBERTa-Base model achieves a mean average precision score of 0.913, the best score for strict labels of all participating teams.
A Trustless GQ Multi-signature Scheme with Identifiable Abort

Guillou-Quisquater (GQ) signature is an efficient RSA-based digital signature scheme amongst the most famous Fiat-Shamir follow-ons owing to its good simplicity. However, there exist two bottlenecks for GQ hindering its application in industry or academia: the RSA trapdoor \(n=pq\) in the key generation phase and its high bandwidth caused by the storage-consuming representation of RSA group elements (3072 bits per one element in 128-bit security).In this paper, we first formalize the definition and security proof of class group based GQ signature (CL-GQ), which eliminates the trapdoor in key generation phase and improves the bandwidth efficiency from the RSA-based GQ signature. Then, we construct a trustless GQ multi-signature scheme by applying non-malleable equivocable commitments and our well-designed compact non-interactive zero-knowledge proofs (NIZK). Our scheme has a well-rounded performance compared to existing multiparty GQ, Schnorr and ECDSA schemes, in the aspects of bandwidth (no range proof or multiplication-to-addition protocol required), rather few interactions (only 4 rounds in signing), provable security in dishonest majority model and identifiable abort property. Another interesting finding is that, our NIZK is highly efficient (only one round required) by using the Bezout formula, and this trick can also optimize the ZK proof of Paillier ciphertext which greatly improves the speed of Yi’s Blind ECDSA (AsiaCCS 2019).
Assessment of the comparative efficiency of <scp>software‐based</scp> Boolean, electronic, <scp>software‐based</scp> fractional value and simplified quantum principal expert systems

Abstract Software‐based expert systems typically use an iterative algorithm for rule‐fact network processing. However, other approaches to expert system implementation also exist. This paper proposes simplified quantum principle expert systems and compares the computational efficiency and comparative efficacy of four types of expert systems: software‐based Boolean expert systems, electronic (gate‐based) expert systems, software‐based fractional value (SBFV) expert systems and simplified quantum principal expert systems (SQPES). For two expert system types, software‐based Boolean and SBFV, back chaining and mixed changing techniques, respectively, are also evaluated. The performance of these systems approaches is compared quantitatively, and the efficacy of the different systems types are compared both qualitatively and quantitatively.
Load Balancing Scheme for optimization of Virtual Machine Migration using swarm in Cloud Environment

In today's world, Cloud Computing is one of fastest growing model that gives access to the users for on demand service of resources and platforms. In Cloud Computing, Load Balancing is considered as one of the vigorous problems to distribute the work to various Virtual Machines (VM) uniformly among each node on the internet. Load balancing is defined as a job allocation and de-allocation of resources among various allocated resources for the reduction of resource usages. For enhancing load balancing, different authors have proposed various algorithms. In past few years, Osmotic Hybrid Artificial Bee and Ant Colony (OH-BAC) method has suggested aimed at optimizing load balancing in self-motivated cloud environment. In the proposed algorithm, VM that need to be migrated are permitted by osmotic behavior. However the drawback of above mentioned algorithm was that it only focuses on reducing the total number of Physical Machines (PM) based on existing requirements of the resources. Due to this reason, there are nonessential VM migrations and it will also affect the Service Level Agreement (SLA) violation. So, a new algorithm, OH-BAC with another algorithm Future Utilization Prediction, also known as FUP has been proposes to reduce VM migrations and focuses on enhancing Load Balancing onto the cloud. In the proposed algorithm OH-BAC-FUP, together existing and future utilization of resources must be measured as one the host for the migration of VMs from the existing number of lively PMs. The approaching source operation is projected by two distinct forecast models like linear regression and optimal linear regression algorithm. Hence, above stated regression models must be estimating the possible usage of resources of VM and PM. Then, the estimated value can be used in the fitness function of OH-BAC and from there it chooses the best VM that need to be migrated to suitable PM.
Finnish Dialect Identification: The Effect of Audio and Text

Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice.We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recording in a dataset consisting of 23 different dialects.Our results show that the best accuracy is received by combining both of the modalities, as text only reaches to an overall accuracy of 57%, where as text and audio reach to 85%.Our code, models and data have been released openly on Github and Zenodo.
Research visualization of Indian LIS research using VOSviewer and Bibliometrix

Purpose This study aimed to visualize the trend topics in the research area of library and information science (LIS) in India during 1989–2021. Design/methodology/approach The data was extracted from the Web of Science core collection database (WoSCC) database from 1989 to 2021. For creating the network visualization maps from the data, freely available softwares, VOSviewer (VV) and Biblioshiny (a Web-interface for bibliometrix), were used. Findings Results support the prediction that in the future, Indian LIS research will focus on areas such as deep learning, machine learning, artificial intelligence and block chain technology. Among the most prolific authors Satija, M.P (Guru Nanak Dev University, Amritsar) and Prathap, G (APJ Abdul Kalam Technology University) secured top positions in LIS research. Scientometric ranked the top journal or the core journal after applying Bradford law. Originality/value The visualization of trend topics in LIS research from 1989 to 2021, 32 years of time span, is the first of its kind.
Tracing Source Language Interference in Translation with Graph-Isomorphism Measures

Previous research has used linguistic features to show that translations exhibit traces of source language interference and that phylogenetic trees between languages can be reconstructed from the results of translations into the same language.Recent research has shown that instances of translationese (source language interference) can even be detected in embedding spaces, comparing embeddings spaces of original language data with embedding spaces resulting from translations into the same language, using a simple Eigenvectorbased divergence from isomorphism measure.To date, it remains an open question whether alternative graph-isomorphism measures can produce better results.In this paper, we (i) explore Gromov-Hausdorff distance, (ii) present a novel spectral version of the Eigenvectorbased method, and (iii) evaluate all approaches against a broad linguistic typological database (URIEL).We show that language distances resulting from our spectral isomorphism approaches can reproduce genetic trees on a par with previous work without requiring any explicit linguistic information and that the results can be extended to non-Indo-European languages.Finally, we show that the methods are robust under a variety of modeling conditions.
Pre-training a BERT with Curriculum Learning by Increasing Block-Size of Input Text

Recently, pre-trained language representation models such as BERT and RoBERTa have achieved significant results in a wide range of natural language processing (NLP) tasks, however, it requires extremely high computational cost.Curriculum learning (CL) is one of the potential solutions to alleviate this problem.CL is a training strategy where training samples are given to models in a meaningful order instead of random sampling.In this work, we propose a new CL method which gradually increases the block-size of input text for training the self-attention mechanism of BERT and its variants using the maximum available batch-size.Experiments in low-resource settings show that our approach outperforms the baseline in terms of convergence speed and final performance on down-stream tasks.
Estimating Partially Observed Graph Signals by Learning Spectrally Concentrated Graph Kernels

Graph models provide flexible tools for the representation and analysis of signals defined over irregular domains such as social or sensor networks. However, in real applications data observations are often not available over the whole graph, due to practical problems such as sensor failure or connection loss. In this paper, we study the estimation of partially observed graph signals on multiple graphs. We learn a sparse representation of partially observed graph signals over spectrally concentrated graph dictionaries. Our dictionary model consists of several sub-dictionaries each of which is generated from a Gaussian kernel centered at a certain graph frequency in order to capture a particular spectral component of the graph signals at hand. The problem of jointly learning the spectral kernels and the sparse codes is solved with an alternating optimization approach. Finally, the incomplete entries of the given graph signals are estimated using the learnt dictionaries and the sparse coefficients. Experimental results on synthetic and real graph data sets suggest that the proposed method yields promising performance in comparison to reference solutions.
Exponential Moving Average

Exponential Moving Average (EMA) is another smoothing indicator. Like the Simple Moving Average, it is a low pass filter, which removes high frequency components and allows low frequency components to pass.
Revisiting Pivot-Based Paraphrase Generation: Language Is Not the Only Optional Pivot

Paraphrases refer to texts that convey the same meaning with different expression forms.Pivot-based methods, also known as the roundtrip translation, have shown promising results in generating high-quality paraphrases.However, existing pivot-based methods all rely on language as the pivot, where large-scale, highquality parallel bilingual texts are required.In this paper, we explore the feasibility of using semantic and syntactic representations as the pivot for paraphrase generation.Concretely, we transform a sentence into a variety of different semantic or syntactic representations (including AMR, UD, and latent semantic representation), and then decode the sentence back from the semantic representations.We further explore a pretraining-based approach to compress the pipeline process into an endto-end framework.We conduct experiments comparing different approaches with different kinds of pivots.Experimental results show that taking AMR as pivot can obtain paraphrases with better quality than taking language as the pivot.The end-to-end framework can reduce semantic shift when language is used as the pivot.Besides, several unsupervised pivot-based methods can generate paraphrases with similar quality as the supervised encoderdecoder model, which indicates that parallel data of paraphrases may not be necessary for paraphrase generation.
Modification of the key schedule of the 2-GOST block cipher and its implementation on FPGA

For 2-GOST block lightweight cipher we will consider the Ashur-Bar-On-Dunkelman attack, show that its time complexity is underestimated and propose a modification of this attack with lower time and memory complexity ( $$2^{2.49}$$ and $$2^{6.15}$$ times smaller respectively). A new key schedule for $${\text {2-GOST}}$$ will be proposed to counteract attacks using key schedule features such as fixed points, sliding pairs and so on. It will be shown that the new key schedule of 2-GOST leave it in the class of lightweight ciphers.
Interacting with Visualization on Mobile Devices

This chapter characterizes how interacting with data visualization using mobile devices, specifically a phone or tablet, differs from analogous experiences using a PC. It presents an overview of the topic according to three interaction modalities: touch, spatial gesture, and voice. The final section of the chapter considers opportunities for future research and innovation with respect to interacting with visualization on mobile devices, including prospects for multimodal interaction and multi-device interaction, as well as possible applications of recently proposed techniques in the field of mobile human-computer interaction.
Machine learning algorithm for feature space clustering of mixed data with missing information based on molecule similarity

Clustering Algorithms have just fascinated significant devotion in machine learning applications owing to their great competence. Nevertheless, the existing algorithms quite have approximately disputes that need to be further deciphered. For example, most existing algorithms transform one type of feature into another type, which disregards the explicit possessions of information. In addition, most of them deliberate whole features, which may lead to difficulty in calculation and effect in sub-optimal presentation. To address the above difficulties, this paper proposes a novel technique for clustering categorical and numerical features based on feature space clustering of mixed data with missing information (FSCMMI). The procedure involves three stages. Initially, FSCMMI divides the given dataset depending on missing information in instances and features types. The second stage uses the decision-tree procedure to identify the association between instances. Finally, the third stage is used for computing the closeness measure for numerical features and categorical features. Meanwhile, we propose a new training algorithm to cluster mixed datasets. Extensive experimental results on benchmark datasets show that the proposed FSCMMI outperforms several state-of-art clustering methods in terms of accuracy and efficiency.
Emotion Recognition of Foreign Language Teachers in College English Classroom Teaching

In order to improve students' learning effect, more and more universities favor foreign language teachers who are native speakers of English. Based on the analysis and summary of the research status of emotion recognition, this paper proposes that, in college English classroom teaching, foreign language teachers can reduce the communication barriers with Chinese students through emotion recognition. Based on literature review and actual situation investigation, this study identified four influencing factors on emotion recognition of foreign language teachers, namely, interactive action, facial expression, vocal emotion, and body posture. In our opinion, in the teaching process, teachers can adjust the four factors of emotion recognition to achieve better teaching effect. Further, improve students' learning efficiency. Analytic Hierarchy Process (AHP) is chosen as the research method in this study. After building the analysis model, we collected the questionnaire using the Questionnaire Star, and finally got 12 valid data. After determining the importance of different factors by pairwise comparison, we draw the following conclusions: the influence degree of emotion recognition factors of foreign language teachers is in descending order, interactive action (43%), facial expression (28%), vocal emotion (21%), and body posture (9%). Our research adds to the body of knowledge on emotion recognition among college English teachers. Furthermore, this research assists students in improving their grasp of course content based on the emotions of foreign English lecturers. Based on the findings, we recommend that foreign language teachers in college English classrooms alter their interactive behaviors, facial expressions, and vocal emotions in response to various instructional materials and emphases.
Powerful embedding networks for few-shot image classification

Few-shot image classification commits to recognizing new concepts from limited annotated samples. Our insight is to obtain a sufficiently powerful embedding network (PEN) to solve few-shot classification tasks. We propose a method to tackle the few-shot classification tasks, namely PENs for few-shot image classification. The key core of PEN is gaining a well-trained embedding network that is capable of extracting strong discriminating representations to represent an image by utilizing two strategies. One strategy is that the multi-scale feature maps are fused instead of only utilizing the final top-level feature maps. We consider that low-level features also play an important role instead of only utilizing top-level representations. Another significant strategy is knowledge distillation (KD). The characteristics of KD can help us get better performance of an embedding network to extract features. Finally, a distance function is employed to classify unlabeled samples. Comprehensive experiments are conducted on few-shot benchmarks. Our method achieves promising performances. The results demonstrate that KD and future fusion are beneficial to gain an expected embedding network for few-shot classification tasks.