Reviewerly/exHarmony huggingface dataset:
New Lattice-Based Signature Based on Fiat-Shamir Framework Without Aborts

Recently, a number of side-channel attacks were launched on lattice-based signatures based on “Fiat-Shamir with aborts”. This shows that signature based on Fiat-Shamir with aborts is vulnerable to side-channel attacks. In this paper, we construct a lattice-based signature scheme based on Fiat-Shamir framework without aborts, and instantiate it over NTRU lattices. The proposed signature is proved to be secure in the random oracle model under some newly defined problems. We also prove the hardness of these new problems and show that the search RLWE problem is as hard as these newly defined problems. The public key size, secret key size and signature size of the proposed signature scheme are 1920 bytes, 512 bytes and 4096 bytes respectively for 180-bit quantum security level. The key and signature sizes of the proposed signature are comparable to those of the currently known signatures, such as, Dilithium, Falcon, qTESLA, MLS, BCM and MITAKA.
A Linguistic Approach for Automatic Analysis, Recognition and Translation of Arabic Nominal Predicates

The machine translation of nominal predicates from Arabic into French requires a judicious and analytical description in both the source language (SL) and target language (TL). The main interest of the present research work is to define the nominal predicates and support verbs. To do so, we firstly translated from Arabic into French two types of nominal predicates, namely the category <نُصْحٌ> “advice” and the category <نظرة> “look”, whose linguistic characteristics were then studied. Next, we downloaded these linguistic data on the NooJ platform to show the efficiency of machine translation and its limits.
Zellig S. Harris’ Transfer Grammar and Its Application with NooJ

In 1954, with his article entitled Transfer Grammar (published in “The International Journal of American Linguistics”, Vol. 20, No. 4, pp. 259–270, University of Chicago Press), Zellig S. Harris was the first linguist to approach the nascent Automatic Translation (AT) from the point of view of structuralist and formal linguistics. This article, written in the pivotal period for the first AT attempts in the US, outlines a translation method that wants to: At the time, the purposes of Harris’s article were therefore extremely innovative, since they considered translation as a process in which meaning transfers could only be achieved based on morphosyntactic analyses and evaluations. Moreover, it is worth stressing that at the time the first AT experiments performed word-for-word translations, without taking into account (not even statistically) the contexts in which the words co-occurred. As is known, this method proved to be unsuccessful, as regards the quality, time and costs of the translations made automatically. In 1966, this led ALPAC [1] to end AT research in the US, and cut off the flow of funding to it.
Study of Malaysian Cloud Industry and Conjoint Analysis of Healthcare and Education Cloud Service Utiliztion

The paper initially addresses Malaysia’s readiness concerning the adoption of cloud computing services and provides insights into the country’s internet infrastructure and internet users, fixed and mobile broadband deployment, and usage of international bandwidth for improvising cloud services. In addition, the paper investigates the scenario around policy areas, including privacy and security laws, being the chief contributors for the adoption of cloud services across regions and borders. The objective behind conducting the research is to validate the current scenario in Malaysia for the cloud service adoption by healthcare and education segment and identifying the consumer service attribute preferences. By performing the conjoint analysis the research would help in identifying the determinants and prioritizing them based on user preferences with prior use of Multinomial Logit choice model (MNL) and Max Likelihood function. A statistical discussion is also presented concerning the current global market scenario and scenario in the Asia Pacific across the verticals.
Automatic Analysis of Appreciative Morphology: The Case of Paronomasia in Colombian Spanish

Paronomasia is defined as the substitution of one lexical item for another, based on partial homophony. While this phenomenon has been the subject of several studies focused on stylistic aspects [1, 2] - and, to a lesser extent, those of grammar [3] - computational linguistics approaches are practically nonexistent. Previous barriers include difficulties in automatically assigning correct labels (both semantic and morphological) due to ambiguity in these expressions. To this end, this paper analyzes a group of Colombian Spanish nouns and their corresponding paronomastic variants from the Generative Lexicon Theory [4, 5] (GLT) with a view toward computational modelling and automatic recognition by means of NooJ [6]. The methodology consists of the following steps: (i) selection of a list of Colombian Spanish nouns included in Varela’s work [7]; (ii) creation of semantic structures (SS) according to GLT; and (iii) elaboration of productive morphological and constrained syntactic grammars [8]. The resources are tested on a set of sentences, yielding promising results to further investigate the automatic analysis of paronomasia from this perspective.
Zero-Error Digitisation and Contextualisation of Piping and Instrumentation Diagrams Using Node Classification and Sub-graph Search

Thousands of huge printed sheets depicting engineering drawings keep record of complex industrial structures from Oil & Gas facilities. Currently, there is a trend of digitising these drawings, having as final end the regeneration of the original computer-aided design (CAD) file, which can be better visualised and analysed through diverse computer applications. Most efforts in literature and commercial applications have focused on converting these sheets into CAD files in an automated way. Nonetheless, this needs to be a zero-error process; as the final CAD will always be verified by an engineer for integrity and inspection. In this paper, we present a method that, on the one hand, highlights which components in the CAD are most likely to have been incorrectly identified, and on the other hand, facilitates the engineer to search some groups of components in these huge assets. These techniques are based on graph embedding, computer neural networks and sub-graph matching.
Graph Reduction Neural Networks for Structural Pattern Recognition

In industry, business, and science large amounts of data are produced and collected. In some of these data applications, the underlying entities are inherently complex, making graphs the representation formalism of choice. Actually, graphs endow us with both representational power and flexibility. On the other hand, methods for graph-based data typically have high algorithmic complexity hampering their application in domains that comprise large entities. This motivates to research graph-based algorithms that are both efficient and reliable. In the present paper, we introduce a novel pattern recognition framework that consists of two basic parts. First, we substantially reduce the size of the original graphs by means of a graph neural network. Eventually, we use the reduced graphs in conjunction with graph edit distance and a distance-based classifier. On five datasets we empirically confirm the benefit of the novel reduction scheme regarding both classification performance and computation time.
Cloud Computing Continuum Research Topics and Challenges. A Multi-source Analysis

While the emergence of COVID-19 [1] has put major cloud service providers around the world to the test, the pandemic has also provided a strong impetus for the adoption and deployment of cloud computing: the transition to a remote workforce, entertainment, e-commerce, and especially remote education have affected the cloud industry and how providers are responding to the sudden and significant increase in demand for cloud solutions and services. Obviously, while highlighting the robustness of the public cloud, the pandemic-induced situation also highlights several important research challenges that need to be addressed. This paper presents a multi-source based analysis for the identification of cloud computing research challenges as part of the road mapping methodology followed in the HUB4CLOUD project. The analysis consists of an in-depth study of several sources including analysis of the international context, analysis of academic venues, interviews with relevant stakeholders and existing funded projects. The paper also provides an overview of the main research topics identified and proposes next steps for the utilization of these finding in the development of a Cloud Computing research roadmap.
Toward Efficient Image Denoising: A Lightweight Network with Retargeting Supervision Driven Knowledge Distillation

Image denoising is a fundamental but critical task. Previous works based on deep networks have made great progress, but suffer from the problem of computational overload. This paper addresses the demands by (1) a lightweight denoising network and (2) a novel knowledge distillation algorithm. The experimental results show the usefulness of the RS-KD on the proposed lightweight network and consistent gains that can be obtained on both synthetic and real-world datasets. Especially, benefiting from the retargeting supervision, our proposed distillation framework allows for arbitrary high-performance teacher networks.
Layout and Display of Network Graphs on a Sphere

There are many advantages to displaying a network graph on a sphere instead of on a plane. For instance, 3D models can be rotated so that any node is in the center of the user’s field of view. Moreover, an opaque sphere provides a natural filtering mechanism for selecting a small subset of data to display. However, the spherical geometry presents some challenges to modeling and displaying a graph, and the literature for 3D layout algorithms is not as rich as that of 2D layout algorithms. We developed a Visualization Pipeline to parse input data and visualize it on a 3D sphere. Within this pipeline, we have developed a 3D version of the Fruchterman-Reingold Layout Algorithm, and also present a method for creating 3D arcs that connect the nodes on a sphere. We created four modified force-directed algorithms, and determined which of their objective functions produced graphs with more evenly-distributed nodes. Our implementation functions in readily available 3D visualization programs and our browser-based display functions on all common operating systems and devices.
Face Super-Resolution with Better Semantics and More Efficient Guidance

Recently, facial priors have been widely used to improve the quality of super-resolution (SR) facial images, but it is underutilized in existing methods. On the one hand, facial priors such as semantic maps may be inaccurately estimated on low-resolution (LR) images or low-scale feature maps with $$L_{1}$$ loss. On the other hand, it is inefficient to guide SR features with constant prior knowledge via concatenation at only one intermediate layer of the guidance network. In this paper, we focus on face super-resolution (FSR) based on semantic maps guidance and propose two simple and efficient designs to address the above two limitations respectively. In particular, to address the first limitation, we propose a novel one-hot supervision strategy to pursue accurate semantic maps, which focuses more on penalizing misclassified pixels by relaxing the regression constraint. In addition, a semantic progressive guidance network (SPGN) is proposed that uses semantic maps to learn modulation parameters in normalization layers to efficiently guide SR features layer by layer. Extensive experiments on two benchmark datasets show that the proposed method improves the state-of-the-art in both quantitative and qualitative results at $$\times $$ 8 scale.
A Transformer-Based Cloth-Irrelevant Patches Feature Extracting Method for Long-Term Cloth-Changing Person Re-identification

Long-term person re-identification is a more practical research task in person retrieval fields because the object retrieved may appear for a more extended period in the actual situation. To overcome the disturbances caused by different clothes of the same person or similar clothes between other people, this paper proposes a Transformer-based Cloth-irrelevant Patches (TCiP) feature extracting method, which combines local features robust to clothes changing. It contains three main modules: (1) A transformer network is used as the backbone to extract patch features for its high performance in many vision tasks. The vital feature extractability of the transformer in cloth-changing Re-ID is proved in this paper. (2) Patch selector is a novel patches sequence recombine module guided by a human parsing model. (3) Considering that there are also additional identity-related features in the clothing areas, an Inverse Cloth-Id (ICI) loss is proposed for innovatively extracting cloth-irrelevant but identity-related features. The experimental results in this paper validate the validity of our method on cloth-changing person Re-ID. Moreover, it also proved the generality of general Re-ID benchmarks. Code is available at https://github.com/oliverck/TCiP .
Rec2Real: Semantics-Guided Photo-Realistic Image Synthesis Using Rough Urban Reconstruction Models

We present a novel and effective photo-realistic image generation pipeline using the rough 3D reconstruction models from the Google Earth 3D map. Our goal is to transfer the images (rendered from the 3D models) from the reconstruction style (rec-style) to the photo-realistic style (real-style). To achieve this, we propose a bidirectional transferring approach that takes semantics as guidance. Specifically, we first design an unpaired patch-to-patch image translation method to transfer the images from real-style to rec-style, which can generate paired training data and introduce supervised information. Then, we fine-tune an auto-encoder network to transfer the images from rec-style to real-style. Our approach can generate arbitrary camera-view images with ground-truth annotations automatically, which can be used in AD perception tasks such as 2D detection and instance segmentation. Experiments show the effectiveness of our approach, which can generate diverse and photo-realistic images.
LiteAR: A Framework to Estimate Lighting for Mixed Reality Sessions for Enhanced Realism

We propose an end-to-end learning based method to estimate irradiance in real-time given a single input limited field of view image from a mobile phone camera. We further develop a technique inspired by physically based rendering to take advantage of spatially varying environment to illuminate virtual objects in augmented reality sessions to make them look more realistic. We integrate the Inertial Measurement Unit sensor to dynamically estimate illumination, making the mixed reality experience interactive. Our solution runs in real-time on mobile phones, with significantly lower computational requirements and enhanced realism in comparison to state-of-the-art methods.
Personalized User Interface Elements Recommendation System

This paper introduces a personalized user interface element recommendation system, in which the model can recommend personalized user interface elements by introducing user features and user evaluations in the offline training. Through experiments, we found that compared with common machine learning algorithms, the Field-aware Factorization Machine that introduced user feature intersections has achieved a better accuracy in the recommendation, which shows the advantages of introducing user features and feature intersections in the recommendation of interface elements.
Development of the E-Muhadatsah Kit for Non-Arabic Speakers in Malaysian Institutions of Higher Learning: A Need Analysis

Needs analysis plays an important role in the development ofa product to ensure that the product can meet the needs of consumers. The development of the E-Muhadatsah kit or Arabic conversation kit based on an android application is a self-communication platform among non-Arabic speakers in Malaysian Institutions of Higher Learning. This study aims to identify the need for developing the E-Muhadatsah kit among non-Arabic speakers at the tertiary level as well as to identify the level of interest in Arabic speaking, Arabic communication confidence, and students' willingness to communicate in Arabic. The assignment and exercise needs in improving students’ Arabic speaking or communication skills were also identified. A set of questionnaires was distributed to 214 respondents from universities in Malaysia namely UPM, UPSI, UniSZA, and UiTM. Then, the data obtained from the administration of the questionnaire were analyzed using SDSS version 25 software and interpreted based on the analysis of mean scores and standard deviations. Meanwhile, the open-ended questionnaire data were analyzed thematically. The results showed that respondents saw the development of the E-Muhadatsah kit as a great need for non-Arab students in higher education. In addition, the study also found that respondents showed a high interest in Arabic speaking, confidence in Arabic communication, and students' willingness to communicate in Arabic at a high level. Thus, this study proposes the development of a conversation-based Arabic language interactive multimedia kit to improve the communication proficiency of non-Arabic speakers, further able to help Arabic language educators to diversify multimedia-assisted teaching methods.
Mathematical Foundations

Deep learning is a branch of machine learning that uses many layers of artificial neurons stacked one on top of the other for identifying complex features within the input data and solving complex real-world problems. It can be used for both supervised and unsupervised machine-learning tasks. Deep learning is currently used in areas such as computer vision, video analytics, pattern recognition, anomaly detection, text processing, sentiment analysis, and recommender system, among other things. Also, it has widespread use in robotics, self-driving car mechanisms, and artificial intelligence systems in general.
The content of formation of present responsibility in elementary school students by means of folk art

A person's attitude to existence is a product of his experiences, life beliefs, views, and has a centuries-old history of development. A person's attitude to reality is manifested through various actions, voices, signs that represent experiences and emotional states. In addition, the feelings of a person are expressed through words and facial expressions. The attitude expressed by man towards nature and society, presentness is also reflected in woven stories. Humans expressed their relationship to the world of plants, mountains, and animals. In legends and narratives, people's attitude towards bravery and courage are expressed, and the people around them gave it an immediate assessment. They analyzed the reality in detail and expressed their conclusions. They conveyed their emotional experiences to the people around them through words.
Future university teachers’ professional sociolization

This article discusses the issues of professional socialization of new teachers, including their training at a pedagogical university, adaptation in the pedagogical community, and professional consolidation. The author investigates the methodological underpinnings, trends, and principles of young professionals’ professional socialization.
Development of civic positioning in future teachers based on personality-oriented educational technologies

This article provides extensive coverage of the issues of the development of civic pose in future teachers on the basis of personality-oriented educational technologies, which are now one of the most important issues.
Improving Adversarial Attacks with Ensemble-Based Approaches

Though Deep Neural networks (DNNs) have been applied in solving a wide variety of problems and achieved state-of-the-art performance on various vision tasks, they are vulnerable to adversarial examples which are crafted by adding human-imperceptible perturbations to legitimate inputs. However, most of the existing adversarial attacks have a low success rate under the black-box setting, where the attackers have no information about the model structure and parameters. In particular, targeted adversarial images, which are expected to predict a particular incorrect label, can hardly succeed. To address this, we propose a broad ensemble-based approach to improve the black-box attack. This method aims to find the common properties between all ensemble models. Using it in combination with Nesterov Accelerated Gradient, adversarial examples with higher transferability can be produced by a set of known models, meanwhile, keeping a higher success rate on all original models. In addition, the experiment result illustrates that, for more challenging targeted attacks, our methods exhibit higher transferability than other state-of-the-art attacks.
Collaborative Kernel Discriminant Analysis for Large Scale Multi Class Problems

The use of kernel learning methods in large-scale contexts is still today rather limited. Indeed in this case, the memory and computing footprint of the kernel matrix can be a constraining factor. Among these methods, Kernel Discriminant Analysis (KDA) is no exception to this rule. In this paper, we present a new learning strategy to solve this issue. Instead of entrusting the entire learning task to a single classifier, we propose to share it with an ensemble of classifiers with a limited storage capacity. Our contribution relies on several points: firstly, our ensemble learning algorithm is “dynamic”, i.e. a new classifier is initialized when the previous one has an overflow of its rated capacity. Secondly, a compression strategy is proposed after each learning step in order to minimize the number of classifiers generated during the learning process. based on the overall classification cost of the network allows to reduce the size of the kernel matrix build in the last layer. To our knowledge, this strategy of collaboration between classifiers is new and allows to share the computational and storage burden between several classifiers while insuring the compression of the kernel matrix. In our study, our network will be based on the spectral regression kernel discriminant analysis (SRKDA) which is an efficient multi-class classifier. Extensive experiments on several large-scale data sets show the effectiveness of the proposed algorithm.
Maintaining Structural Information by Pairwise Similarity for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Among them, the source domain and the target domain have the same label space, but the representation distributions of their input space are different. Mainstream approaches resort to domain adversarial training to align input distributions of two domains in the feature space. Although these methods have made remarkable progress, they have the risk of destroying discriminative structural information between different classes in the target domain. To alleviate this risk, we are inspired by the problem reduction method in ensemble methods and binarization techniques, and propose a novel approach Maintaining Structural Information of the target domain based on Pairwise semantic Similarity (whether two instances belong to the same class or not) (MSIPS). Specifically, We introduce Contrastive Learning to obtain feature prototypes for each category on the source domain, and then use these prototypes to predict the similarity of paired target domain samples. Finally, we restrict the target domain to maintain discriminative structural information through such weak information (i.e., pairwise similarity). Extensive experiments of various domain shift scenarios show that our method obtains competitive performance with SOTA, and qualitative visualization can demonstrate the effectiveness of our method.