Reviewerly/exHarmony huggingface dataset:
A Multi-Model And Ai-Based Collegebot Management System (Aicms) For Professional Engineering Colleges

Chatbot is a program which provides human conversation using Artificial Intelligence (AI). Chatbots are designed to work as VIRTUAL ASSISTANTS (VA). They themselves provide a platform for the promotions of the Products and Services online. All Higher Educational Institutes provide the complete information through their internet sites for students, which admits the use of social nets such as Facebook, WhatsApp, and College websites. Total-in-All, in any website, searching functionality is required to search for any information and it includes Social Media Applications like Facebook and WhatsApp regular response are utilized. Therefore, Chatbot is an effective auto-response system, and also an instant messaging platform. In this paper, AICMS an AI-Based CollegeBot management system for professional Engineering college system provide the auto-response to student queries about the college basic information, class timetables, examination schedules related to academics. Many Queries about the subjects and placements can be inputted to the system. Here the system AICMS is designed with Dialogflow which is supported by the Google API. AI and running as a messenger in the Facebook, which takes the input as the text and voice and it provides the response as text and voice. It gives a quick, accurate response to student and staff queries in an interactive fashion.
Construction of Chinese Language Teaching System Model Based on Deep Learning under the Background of Artificial Intelligence

The openness of modern network intelligent education provides a broader learning space for Chinese language learners. People pay more and more attention to network learning, and network intelligent teaching platforms are constantly emerging under this background. Intelligence is one of the most important characteristics of ITS. However, the current network teaching system is far from enough in the intellectualization of teaching content, form, and process. In order to improve the intelligence level of ITS, this paper studies the use of DL network with strong self-learning ability to build ITS. From the point of view of teaching students in accordance with their aptitude and accurately reflecting students’ learning state and characteristics, this paper analyzes the influencing factors in students’ learning process, puts forward factors such as students’ learning style and learning habits into the construction of a student model, and designs the student model. The future trend of ITS is predicted, and it is pointed out that this field with attractive development prospect is worthy of further research and design.
Fusion of Blockchain, IoT and Artificial Intelligence - A Survey

Blockchain is one of the prominent rapidly used technology in the last decade in various applications. In recent years, many researchers explored the capabilities of blockchain in smart IoT to address various security challenges. Integration of IoT and blockchain solves the security problems but scalability still remains a huge challenge. To address this, various AI techniques can be applied in the blockchain IoT framework, thus providing an efficient information system. In this survey, various works pertaining to the domains which integrate AI, IoT and Blockchain has been explored. Also, this article discusses potential industrial use cases on fusion of blockchain, AI and IoT applications and its challenges.
Vectorized linear approximations for attacks on SNOW 3G

SNOW 3G is a stream cipher designed in 2006 by ETSI/SAGE, serving in 3GPP as one of the standard algorithms for data confidentiality and integrity protection. It is also included in the 4G LTE standard. In this paper we derive vectorized linear approximations of the finite state machine in SNOW3G. In particular,we show one 24-bit approximation with a bias around 2−37 and one byte-oriented approximation with a bias around 2−40. We then use the approximations to launch attacks on SNOW 3G. The first approximation is used in a distinguishing attack resulting in an expected complexity of 2172 and the second one can be used in a standard fast correlation attack resulting in key recovery in an expected complexity of 2177. If the key length in SNOW 3G would be increased to 256 bits, the results show that there are then academic attacks on such a version faster than the exhaustive key search.
Recommendation of Online Learning Resources for Personalized Fragmented Learning Based on Mobile Devices

Fragmented learning aims to fully utilize fragmented time slices to learn and accumulate fragmented knowledge. The current mobile online learning apps fail to fully consider the preferences, demands, and adaptability of users. The content and difficulty of the recommended resources are not in match with user features. Therefore, this paper explored the issue of the recommendation of personalized online learning resources for fragmented learning based on mobile devices. Firstly, the authors developed an architecture for the adaptive recommendation model of online learning resources, modeled the learners and fragmented learning resources. Next, the recommendation model was constructed for personalized online learning resources, the flow of the recommendation engine was detailed, and the degrees of resource recommendation and matching were calculated. The proposed model was proved valid through experiments.
A Short Note On The Kernel Subgroup Of Intuitionistic Fuzzy Groups

This paper defines the concept of kernel subgroup of an intuitionistic fuzzy group. Also, it proves that this kernel is a group in the ordinary algebraic meaning as a direct application of the concept of kernel in fuzzy and anti-fuzzy groups. Also, we derive some properties of intuitionistic fuzzy groups.
Tools for Quantum Computing Based on Decision Diagrams

With quantum computers promising advantages even in the near-term NISQ era, there is a lively community that develops software and toolkits for the design of corresponding quantum circuits. Although the underlying problems are different, expertise from the design automation community, which developed sophisticated design solutions for the conventional realm in the past decades, can help here. In this respect, decision diagrams provide a promising foundation for tackling many design tasks such as simulation, synthesis, and verification of quantum circuits. However, users of the corresponding tools often do not have a proper background or an intuition about how these methods based on decision diagrams work and what their strengths and limits are. In this work, we first review the concepts of how decision diagrams can be employed, e.g., for the simulation and verification of quantum circuits. Afterwards, in an effort to make decision diagrams for quantum computing more accessible, we then present a visualization tool for quantum decision diagrams, which allows users to explore the behavior of decision diagrams in the design tasks mentioned above. Finally, we present decision diagram-based tools for simulation and verification of quantum circuits using the methods discussed above as part of the open-source Munich Quantum Toolkit (MQT)—a set of tools for quantum computing developed at the Technical University of Munich and the Johannes Kepler University Linz and released under the MIT license. More information about the corresponding tools is available at https://github.com/cda-tum/ddsim . By this, we provide an introduction of the concepts and tools for potential users who would like to work with them as well as potential developers aiming to extend them.
Pollen-Based Maps of Past Regional Vegetation Cover in Europe Over 12 Millennia—Evaluation and Potential

Realistic and accurate reconstructions of past vegetation cover are necessary to study past environmental changes. This is important since the effects of human land-use changes (e.g. agriculture, deforestation and afforestation/reforestation) on biodiversity and climate are still under debate. Over the last decade, development, validation, and application of pollen-vegetation relationship models have made it possible to estimate plant abundance from fossil pollen data at both local and regional scales. In particular, the REVEALS model has been applied to produce datasets of past regional plant cover at 1° spatial resolution at large subcontinental scales (North America, Europe, and China). However, such reconstructions are spatially discontinuous due to the discrete and irregular geographical distribution of sites (lakes and peat bogs) from which fossil pollen records have been produced. Therefore, spatial statistical models have been developed to create continuous maps of past plant cover using the REVEALS-based land cover estimates. In this paper, we present the first continuous time series of spatially complete maps of past plant cover across Europe during the Holocene (25 time windows covering the period from 11.7 k BP to present). We use a spatial-statistical model for compositional data to interpolate REVEALS-based estimates of three major land-cover types (LCTs), i.e., evergreen trees, summer-green trees and open land (grasses, herbs and low shrubs); producing spatially complete maps of the past coverage of these three LCTs. The spatial model uses four auxiliary data sets—latitude, longitude, elevation, and independent scenarios of past anthropogenic land-cover change based on per-capita land-use estimates (“standard” KK10 scenarios)—to improve model performance for areas with complex topography or few observations. We evaluate the resulting reconstructions for selected time windows using present day maps from the European Forest Institute, cross validate, and compare the results with earlier pollen-based spatially-continuous estimates for five selected time windows, i.e., 100 BP-present, 350–100 BP, 700–350 BP, 3.2–2.7 k BP, and 6.2–5.7 k BP. The evaluations suggest that the statistical model provides robust spatial reconstructions. From the maps we observe the broad change in the land-cover of Europe from dominance of naturally open land and persisting remnants of continental ice in the Early Holocene to a high fraction of forest cover in the Mid Holocene, and anthropogenic deforestation in the Late Holocene. The temporal and spatial continuity is relevant for land-use, land-cover, and climate research.
3D MODELING WITH 5K 360° VIDEOS

Abstract. Video acquisition with 360° (spherical) cameras is becoming increasingly popular for the opportunity to capture the entire scene around the user in a relatively short time. The method can also be attractive for photogrammetric applications. As the overlap between consecutive frames is undoubtedly guaranteed, 3D models can be generated with an automated processing workflow. The paper illustrates the results achieved with 5k 360° videos captured with different Insta360 cameras. As the number of frames can become large, two complementary solutions are proposed to provide approximate initial exterior orientation parameters: the integration of the trajectory captured through GNSS, and the creation of an acquisition plan with a GIS-based application. The availability of approximated EO parameters provides a visibility map between the frames and reduces the computational cost during image matching. Experimental results demonstrate that such preliminary information is necessary for large datasets. Indeed, the photogrammetric processing of the entire dataset without the proposed preliminary EO parameters resulted in unreliable or incomplete orientation results.
An approach based on genetic algorithms and neural networks for QoS-aware IoT services composition

In the Internet of Things (IoT) environment, the services provided by the connected objects are published as services through the web. This allows to machines to interact between them and, makes the IoT services composition possible. However, the vast proliferation of smart object generates services with the same functionalities but different in terms of quality of services (QoS) proprieties. This makes the satisfaction of the user requirements often complex and a NP-hard problem. Indeed, respecting the QoS constraints (user preferences in terms of QoS) is a challenge, due to the high number of candidate services for the composition. This challenge consists of selecting the most appropriate services so that the composite service must meet both the functional and the non-functional requirements of the user. To deal with this challenge, we propose an approach based on Genetic Algorithms (GA) and Neural Networks (NN) for QoS-aware IoT Services Composition in the context of large-scale environments. The combination between GA and NN allows finding the quasi-optimal IoT service composition. This latter is based on global QoS optimization. To reach this objective, the QoS intervals are decomposed into M QoS-levels to engage them into the theoretical composition. Then, the proposed first GA is used to obtain the ideal theoretical composition with an overall QoS optimization. Afterward, the proposed NN is used to eliminate the inappropriate concrete IoT services, and retain only the services having the same categories of the atomic theoretical services composing the ideal theoretical composition. This allows us to optimize the search space as well as the execution time. Finally, we apply the second GA on the concrete services of the retained categories, in order to obtain the IoT service concrete composition with an overall QoS optimization. The simulation results show that the proposed approach has the best composition time, the best Hypervolume indicator and the best compositional optimality compared to SC-FLA, Improved GA and MGA approaches. On another side, it has almost the same performances compared to TS-QCA and, it finds the near-optimal composition in a very short time compared to PSA, which is an optimal approach. Thus, the obtained results show the effectiveness of our approach.
Adversarial Detection by Latent Style Transformations

Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we investigate an attack-agnostic defense against adversarial attacks on high-resolution images by detecting suspicious inputs. The intuition behind our approach is that the essential characteristics of a normal image are generally consistent with non-essential style transformations, e.g., slightly changing the facial expression of human portraits. In contrast, adversarial examples are generally sensitive to such transformations. In our approach to detect adversarial instances, we propose an in\underline{V}ertible \underline{A}utoencoder based on the \underline{S}tyleGAN2 generator via \underline{A}dversarial training (VASA) to inverse images to disentangled latent codes that reveal hierarchical styles. We then build a set of edited copies with non-essential style transformations by performing latent shifting and reconstruction, based on the correspondences between latent codes and style transformations. The classification-based consistency of these edited copies is used to distinguish adversarial instances.
“They’re All the Same!” Stereotypical Thinking and Systematic Errors in Users’ Privacy-Related Judgments About Online Services

Given the ever-increasing volume of online services, it has become impractical for Internet users to study every company's handling of information privacy separately and in detail.This challenges a central assumption held by most information privacy research to date-that users engage in deliberate information processing when forming their privacy-related beliefs about online services.In this research, we complement previous studies that emphasize the role of mental shortcuts when individuals assess how a service will handle their personal information.We investigate how a particular mental shortcut-users' stereotypical thinking about providers' handling of user information-can cause systematic judgment errors when individuals form their beliefs about an online service.In addition, we explore the effectiveness of counter-stereotypic privacy statements in preventing such judgment errors.Drawing on data collected at two points in time from a representative sample of smartphone users, we studied systematic errors caused by stereotypical thinking in the context of a mobile news app.We found evidence for stereotype-induced errors in users' judgments regarding this provider, despite the presence of counter-stereotypic privacy statements.Our results further suggest that the tone of these statements makes a significant difference in mitigating the judgment errors caused by stereotypical thinking.Our findings contribute to emerging knowledge about the role of cognitive biases and systematic errors in the context of information privacy.
A Deep Analysis on the Role of Deep Learning Models Using Generative Adversarial Networks

A comparatively novel advance field of deep learning is the Generative Adversarial Network called GAN. These different types of networks if they start working in line with each other and work not to get the better of each other but start working keeping arm to arm connected to each other's world will be different. GAN is a category of machine learning frameworks intended for generation of images in which neural networks challenge each other in format of playing game. One network generates metaphors also called the generator and an additional network attempt to differentiate between the fake and real from the data set called as the discriminator. If suppose a training set is presented this procedure guides to manufacture a innovative information comparable to training set. Pictures created from GAN are same metaphors giving the notion of genuine to any observer having real features. GAN networks work on supervised, unsupervised and for reinforcement learning but earlier it applies or used only unsupervised learning only. This generative network produces candidate and on the other hand, the discriminative network evaluate them. Here producer is a complex neural network and differentiator is a convolution neural network. The GAN network divided into three categories to produce generative model learns and generation of data by probabilistic ideas. Next training of model can be completed in contradictory state. Lastly for training using neural networks, deep learning, and artificial intelligence methods. If generative networks used deep leaning methods then deep learning models can employ a very large amount of dataset, heavily dependent on high-end Machines, tries to solves problem from end to end machines, takes longer time to train means the results are better after getting trained on the other hand takes lesser time to test the data. Applications of GAN networks are increasing day by day as it is touching every sphere of our day today life. Some of the benefits of the deep learning are to creating artificial Intelligence function which mimics the mechanism of human brain in handing out data for decision making. Deep learning if combined with artificial intelligence can be capable of learning data which is considered unlabeled and unstructured. The chapter will relate different models of deep learning and their efficiency can be measured by studying different methods, models and simulation techniques.
CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction

This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a Closed-loop Transcriptionbetween a multi-class, multi-dimensional data distribution and a Linear discriminative representation (CTRL) in the feature space that consists of multiple independent multi-dimensional linear subspaces. In particular, we argue that the optimal encoding and decoding mappings sought can be formulated as a two-player minimax game between the encoder and decoderfor the learned representation. A natural utility function for this game is the so-called rate reduction, a simple information-theoretic measure for distances between mixtures of subspace-like Gaussians in the feature space. Our formulation draws inspiration from closed-loop error feedback from control systems and avoids expensive evaluating and minimizing of approximated distances between arbitrary distributions in either the data space or the feature space. To a large extent, this new formulation unifies the concepts and benefits of Auto-Encoding and GAN and naturally extends them to the settings of learning a both discriminative and generative representation for multi-class and multi-dimensional real-world data. Our extensive experiments on many benchmark imagery datasets demonstrate tremendous potential of this new closed-loop formulation: under fair comparison, visual quality of the learned decoder and classification performance of the encoder is competitive and arguably better than existing methods based on GAN, VAE, or a combination of both. Unlike existing generative models, the so-learned features of the multiple classes are structured instead of hidden: different classes are explicitly mapped onto corresponding independent principal subspaces in the feature space, and diverse visual attributes within each class are modeled by the independent principal components within each subspace.
ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling

Traditional text classification approaches often require a good amount of labeled data, which is difficult to obtain, especially in restricted domains or less widespread languages. This lack of labeled data has led to the rise of low-resource methods, that assume low data availability in natural language processing. Among them, zero-shot learning stands out, which consists of learning a classifier without any previously labeled data. The best results reported with this approach use language models such as Transformers, but fall into two problems: high execution time and inability to handle long texts as input. This paper proposes a new model, ZeroBERTo, which leverages an unsupervised clustering step to obtain a compressed data representation before the classification task. We show that ZeroBERTo has better performance for long inputs and shorter execution time, outperforming XLM-R by about 12% in the F1 score in the FolhaUOL dataset. Keywords: Low-Resource NLP, Unlabeled data, Zero-Shot Learning, Topic Modeling, Transformers.
End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting

Spoken Language Understanding (SLU) is a core task in most human–machine interaction systems . With the emergence of smart homes, smart phones and smart speakers, SLU has become a key technology for the industry. In a classical SLU approach, an Automatic Speech Recognition (ASR) module transcribes the speech signal into a textual representation from which a Natural Language Understanding (NLU) module extracts semantic information. Recently End-to-End SLU (E2E SLU) based on Deep Neural Networks has gained momentum since it benefits from the joint optimisation of the ASR and the NLU parts, hence limiting the cascade of error effect of the pipeline architecture. However, little is known about the actual linguistic properties used by E2E models to predict concepts and intents from speech input. In this paper, we present a study identifying the signal features and other linguistic properties used by an E2E model to perform the SLU task. The study is carried out in the application domain of a smart home that has to handle non-English (here French) voice commands. The results show that a good E2E SLU performance does not always require a perfect ASR capability. Furthermore, the results show the superior capabilities of the E2E model in handling background noise and syntactic variation compared to the pipeline model. Finally, a finer-grained analysis suggests that the E2E model uses the pitch information of the input signal to identify voice command concepts. The results and methodology outlined in this paper provide a springboard for further analyses of E2E models in speech processing.