Reviewerly/exHarmony huggingface dataset:
Formulating Quizzes Questions Using Artificial Intelligent Techniques

Formulating the question is one of the most important things for evidence-based practice, regardless of the discipline in question. Formulating questions, and even answering questions, is a powerful key to our profession, and our practice can be seen through research in information science and by significant developments in evidence-based practice. We should therefore develop a comprehensive classification of the types of frequently asked questions and prioritize initial research. In this paper, we will discuss how to create and make tests questions based on artificial intelligence techniques and algorithms and make a simple comparison between the most recent methods used to make questions, and finally, we will do an analytical study at Cadi Ayyad University Marrakech Morocco by one of the ways based on artificial intelligence (AI) algorithms.
Acoustic Scene Classifier Based on Gaussian Mixture Model in the Concept Drift Situation

The data distribution used in model training is assumed to be similar with that when the model is applied.However, in some applications, data distributions may change over time.This situation is called the concept drift, which might decrease the model performance because the model is trained and evaluated in different distributions.To solve this problem for scene audio classification, this study proposes the kernel density drift detection (KD3) algorithm to detect the concept drift and the combine-merge Gaussian mixture model (CMGMM) algorithm to adapt to the concept drift.The strength of the CMGMM algorithm is its ability to perform adaptation and continuously learn from stream data with a local replacement strategy that enables it to preserve previously learned knowledge and avoid catastrophic forgetting.KD3 plays an essential role in detecting the concept drift and supplying adaptation data to the CMGMM.Their performance is evaluated for four types of concept drift with three systematically generated scenarios.The CMGMM is evaluated with and without the concept drift detector.In summary, the combination of the CMGMM and KD3 outperforms two of four other combination methods and shows its best performance at a recurring concept drift.
Counterfactual Samples Synthesizing and Training for Robust Visual Question Answering

Today's VQA models still tend to capture superficial linguistic correlations in the training set and fail to generalize to the test set with different QA distributions. To reduce these language biases, recent VQA works introduce an auxiliary question-only model to regularize the training of targeted VQA model, and achieve dominating performance on diagnostic benchmarks for out-of-distribution testing. However, due to the complex model design, ensemble-based methods are unable to equip themselves with two indispensable characteristics of an ideal VQA model: 1) Visual-explainable: The model should rely on the right visual regions when making decisions. 2) Question-sensitive: The model should be sensitive to the linguistic variations in questions. To this end, we propose a novel model-agnostic Counterfactual Samples Synthesizing and Training (CSST) strategy. After training with CSST, VQA models are forced to focus on all critical objects and words, which significantly improves both visual-explainable and question-sensitive abilities. Specifically, CSST is composed of two parts: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Counterfactual Samples Synthesizing</i> (CSS) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Counterfactual Samples Training</i> (CST). CSS generates counterfactual samples by carefully masking critical objects in images or words in questions and assigning pseudo ground-truth answers. CST not only trains the VQA models with both complementary samples to predict respective ground-truth answers, but also urges the VQA models to further distinguish the original samples and superficially similar counterfactual ones. To facilitate the CST training, we propose two variants of supervised contrastive loss for VQA, and design an effective positive and negative sample selection mechanism based on CSS. Extensive experiments have shown the effectiveness of CSST. Particularly, by building on top of model LMH+SAR [1], [2], we achieve record-breaking performance on all out-of-distribution benchmarks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , VQA-CP v2, VQA-CP v1, and GQA-OOD).
Ring learning with errors: a crossroads between post-quantum cryptography, machine learning and number theory

The present survey is intended to serve as a comprehensive account of the main areas of the cryptography based on the Ring Learning With Errors Problem.We cover the major topics, from their mathematical foundations to the main primitives, as well as several open ends and recent progress with an emphasis in the connections with algebraic number theory.
Physical Representation Learning and Parameter Identification from Video Using Differentiable Physics

Abstract Representation learning for video is increasingly gaining attention in the field of computer vision. For instance, video prediction models enable activity and scene forecasting or vision-based planning and control. In this article, we investigate the combination of differentiable physics and spatial transformers in a deep action conditional video representation network. By this combination our model learns a physically interpretable latent representation and can identify physical parameters. We propose supervised and self-supervised learning methods for our architecture. In experiments, we consider simulated scenarios with pushing, sliding and colliding objects, for which we also analyze the observability of the physical properties. We demonstrate that our network can learn to encode images and identify physical properties like mass and friction from videos and action sequences. We evaluate the accuracy of our training methods, and demonstrate the ability of our method to predict future video frames from input images and actions.
Application of Big Data Technology in Campus Security Management under the Background of Information Age

Abstract To explore the construction of safety management in universities against the background of big data is not only an important way to ensure the personal and property safety of teachers, students and staff, and to promote the construction of safe campuses, but also an essential part to realize long-term social harmony and stability. This paper based on big data technology, this paper establishes a visualization application platform to comprehensively improve the informatization and intelligence level of the university’s safety management, and the system was tested. The test results show that the response time of the system performs well, and the actual response time is no more than 3 seconds. The system recovery trial user login response time is 0.057s, and the completed login user format is 258 per second. The system realizes the refinement, digitalization, automation and mobility of campus security management, so as to improve campus security and provide support and data basis for the information construction, talent reserve and information decision-making of smart campus.
Digital tools for improving teenager's speaking

In EFL classes, Mexican teenagers often face problems when trying to communicate orally in English. Martin and Molina (2011) argue that “…not knowing how to speak effectively limits severely our career possibilities and personal” (p. 92). The present paper reports on a study of the impact of incorporating different online resources to improve adolescents’ speaking in English through an exploration of students’ perceptions regarding their use, as part of an extracurricular English language course at a Mexican public secondary school.&#x0D; Students recorded themselves once a week using specific online tools for their homework. The main purpose was to give students the opportunity to record and hear themselves as many times as necessary until they considered their work was acceptable. Additionally, they were aware of being listened by their teacher and classmates. Results seem to suggest that students’ self-confidence when speaking in the classroom, motivation to speak and awareness of features of acceptable speech were enhanced by using these online tools to practice speaking independently.
The Automatic Question Generation System for CET

In this paper, we apply the abstractive text summarization technology to automatic generation system of reading comprehension, which is part of College English Test (CET) in China.At present, there is a growing demand of English reading examination questions, yet the manual examination question generating is time-consuming and labor-intensive.To relieve the pressure on question generating task, we put the related automatic technology into application, which aims to assist teachers in question generating, meanwhile, to provide more CET exercises for students.We combine seq2seq model and attention mechanism to generate the abstractive text summarization.The abstract generated by this method is easy to understand and in line with the question generating of long reading comprehension, the experiment showed good results of question generating.
Encoding Post-Spectral Sound

This article examines computer-based music (ca. 1982–87) created by Finnish composer Kaija Saariaho at the Institut de Recherche et Coordination Acoustique/Musique (IRCAM) in Paris. A detailed account of archival materials for an early étude in voice synthesis, Vers le blanc (1982), demonstrates the music-theoretical import of software to Saariaho’s development of a robust compositional method that resonated with the emergent aesthetics of a post-spectral milieu. Subsequent analyses of two additional works from this period— Jardin secret II (1984–86) for harpsichord and tape, and IO (1987) for large ensemble and electronics—serve to illustrate Saariaho’s extension of this method into instrumental settings. Specific techniques highlighted include the use of interpolation systems to create continuous processes of transformation, the organization of individual musical parameters into multidimensional formal networks, and the exploration of harmonic structures based on the analysis of timbral phenomena. Relating these techniques to the affordances of contemporaneous IRCAM technologies, including CHANT, FORMES, and Saariaho’s own customized program, “transkaija,” this article adopts a transductive approach to archival research that is responsive to the diverse media artifacts associated with computer-based composition.
MODERN CO-REVOLUTION: ACTIVITY CONTENT AND DIALECTICS OF INFORMATIVE AND COGNITIVE SYSTEMS

Modern co-revolution is presented as an important component of the historical transition from the era of objectification to the era of humanization, from the industrial era to the post-industrial and epoch-making humanizing revolution; the dialectical unity of the latest industrial and knowledge-information-digital revolutions in the context of global evolutionary-revolutionary processes. The past mechanical, modern electronic-digital and future post-electronic stages of the knowledge-information-digital revolution are characterized. The transformation of an electronic computer into an information and communication machine with a possible quantum, biological and/or optical element base is considered. The concepts of “human-sized cognitive system” and “machine-sized cognitive system” are substantiated. Within the dialectic of these systems, emphasis is placed on their relative isolation, primacy, the dominance of the former over the latter, and the inclusion of the latter in the former. The probable structure of machine-sized cognitive systems are determined. In particular, it is accepted that sensory-cognitive work with the surface layer of the object is performed by sensor machines, generalizing-cognitive work with the subsurface layer of the object by generalizing machines, intellectual-cognitive work with essential layers of the object by intellectual machines, cognitive-applied work with the target layer of the object by pragmatist machines, cognitive-integrative work with the object as a whole by integrator machines. Energy, communication, management work and storage functions of derived information and digital products are designed to be performed, respectively, by such machines as energizers, communicators, managers and storagers. The example of the interaction of sensory-emotional cognitive activity of man and the activity of the sensory machine, as well as human memory and storager systems shows the decisive role of man and the growing role of the machine in modern cognitive processes. Thus, despite the active mechanization of the spiritual and mental humanized essential human forces, the main actor of modern co-revolution and knowledge-information-digital revolution remains man.
Trajectory planning of the nursing robot based on the center of gravity for aluminum alloy structure

Abstract In this paper, the robot arm is manufactured to increase the structural strength and improve safety. The stability of the nursing robot in the process of carrying out the typical nursing task of holding patients was studied, and the influence of the center of gravity on the movement stability of the nursing robot was analyzed. The mathematical model of the stability of the robot is built by using the inverse kinematics solution of the robot. By studying the trajectory planning of a nursing robot under the condition of ZMP constraint, the robot can move safely and optimally along the prescribed trajectory between two working points. The simulation results show that the algorithm can significantly improve the work safety of the robot. In the experiment, four pressure sensors are used to measure the pressure of four wheels on the ground, the data are obtained and substituted into the expression of center of pressure (COP) method. The results show that the stability is in a reasonable moving area without any hidden danger, and its COP value is less than the stable qualitative boundary, which verifies the rationality and effectiveness of the optimal center of gravity stability planning algorithm.
Client perceptions of Emirati innovation strategy on service quality in UAE tourism sector

Quality tourism is indispensable to national economic development, given that it makes a major contribution to the economies of the most developed and developing countries. Therefore, this study examined the effect of Emirati sustainable innovation strategy on service quality in the UAE tourism sector. Using a cross-sectional survey approach, the researchers collected data from 100 clients of the UAE tourism agencies. The data were analysed. The overall result indicates that Emirati sustainable innovation strategy (management innovation, technical innovation, and sustainable innovation) has a significant positive effect on service quality, indicating that Emirati sustainable innovation strategy is a significant driver of service quality in the UAE tourism sector. It also shows that innovation is an indispensable tool in enhancing quality tourism. However, the findings of this study are not exhaustive, future research can dwell on this research area and replicate the current study in another context.
Analysis of the Impact of the Stylometric Characteristics of Different Levels for the Verification of Authors of the Prose

This article is dedicated to the analysis of various stylometric characteristics combinations of different levels for the quality of verification of authorship of Russian, English and French prose texts. The research was carried out for both low-level stylometric characteristics based on words and symbols and higher-level structural characteristics. All stylometric characteristics were calculated automatically with the help of the ProseRhythmDetector program. This approach gave a possibility to analyze the works of a large volume and of many writers at the same time. During the work, vectors of stylometric characteristics of the level of symbols, words and structure were compared to each text. During the experiments, the sets of parameters of these three levels were combined with each other in all possible ways. The resulting vectors of stylometric characteristics were applied to the input of various classifiers to perform verification and identify the most appropriate classifier for solving the problem. The best results were obtained with the help of the AdaBoost classifier. The average F-score for all languages turned out to be more than 92 %. Detailed assessments of the quality of verification are given and analyzed for each author. Use of high-level stylometric characteristics, in particular, frequency of using N-grams of POS tags, offers the prospect of a more detailed analysis of the style of one or another author. The results of the experiments show that when the characteristics of the structure level are combined with the characteristics of the level of words and / or symbols, the most accurate results of verification of authorship for literary texts in Russian, English and French are obtained. Additionally, the authors were able to conclude about a different degree of impact of stylometric characteristics for the quality of verification of authorship for different languages.
SecRSL: security separation logic for C11 release-acquire concurrency

We present Security Relaxed Separation Logic (SecRSL), a separation logic for proving information-flow security of C11 programs in the Release-Acquire fragment with relaxed accesses. SecRSL is the first security logic that (1) supports weak-memory reasoning about programs in a high-level language; (2) inherits separation logic’s virtues of compositional, local reasoning about (3) expressive security policies like value-dependent classification. SecRSL is also, to our knowledge, the first security logic developed over an axiomatic memory model. Thus we also present the first definitions of information-flow security for an axiomatic weak memory model, against which we prove SecRSL sound. SecRSL ensures that programs satisfy a constant-time security guarantee, while being free of undefined behaviour. We apply SecRSL to implement and verify the functional correctness and constant-time security of a range of concurrency primitives, including a spinlock module, a mixed-sensitivity mutex, and multiple synchronous channel implementations. Empirical performance evaluations of the latter demonstrate SecRSL’s power to support the development of secure and performant concurrent C programs.
Chatbot as a Tool for Knowledge Sharing in the Maintenance and Repair Processes

Abstract In the era of digitization and general access to Internet resources, it seems natural to expect fast, direct, and tailored access to the knowledge of interest. The research shows that the use of modern technologies is not reserved only for industrial solutions, but can be applied in common use. Solutions based on artificial intelligence can provide solutions to many problems of the modern world. This is particularly visible in the era of challenges related to ecology (e.g. minimization of paper consumption) and expectations of fast access to information regardless of place and time. Treating chatbots as a tool supporting the customer in current activities, quick problem-solving shows the potential and care for the image of the producers. Developing the functionality of virtual assistants used for marketing activities with the functionality of technical support can bring tangible benefits both in the economic and social spheres. It should be noted that electronic versions of documents, knowledge transferred in an interactive form can be easily updated and presented in different forms. This increases the attractiveness of transmitted knowledge, users can choose the appropriate form, and access to these resources becomes easier. Artificial intelligence-based tools in the form of chatbots (also called virtual assistants) seem to be a solution worthy of interest. This paper presents the process of developing a virtual assistant to support the maintenance and repair activities of small household appliances. A chatbot aims at gathering in one place and then providing dedicated knowledge resources to an individual user on a just-in-time basis.
Research on the Training Path of Professional Core Literacy Matching Ability of Private Universities under the Background of New Liberal Arts

Guided by the core theory of comprehensively launching the new liberal arts construction conference, aiming at cultivating applied and compound talents, based on the perspective of OBE teaching theory, integrating the characteristics of our own university, taking the characteristics of economic statistics as an example, this paper constructs the core literacy index matching with the core literacy of Chinese students' development, and studies the matching curriculum and education path.Reconstruct the design in the aspects of basic knowledge, professional quality, application of statistical tools, practical innovation and cross-cultural cooperation, form a training path framework, enable students to form the ability to match the professional core quality in the new liberal arts era, then the fuzzy comprehensive evaluation matrix is further constructed, and the index evaluation system based on the development of core literacy is established.Finally, the research results are discussed.
Human Motion Recognition Using Zero-Shot Learning

In this study, we use motion recognition to recognize unseen and unlabeled movement patterns, which are widely used and challenging in machine learning. Motion recognition tackles some of the emerging challenges in computer vision problems, such as analyzing actions in a surveillance video where there is a lack of sufficient training data. Motion recognition also plays a pivotal role in human action and behavior recognition. In this paper, we propose a novel action and motion recognition method using zero-shot learning. We overcome a limitation of machine learning by recognizing unseen and unlabeled classes in the field of human action recognition. In order to evaluate the effectiveness of the proposed solution, we use a dataset available from the UCI machine learning repository. This dataset enables us to apply zero-shot learning to human motion and action recognition. Our results verify that the proposed method outperforms state-of-the-art algorithms.
Ethics Lines and Machine Learning: A Design and Simulation of an Association Rules Algorithm for Exploiting the Data

Data mining techniques offer great opportunities for developing ethics lines, tools for communication, participation and innovation whose main aim is to ensure improvements and compliance with the values, conduct and commitments making up the code of ethics. The aim of this study is to suggest a process for exploiting the data generated by the data generated and collected from an ethics line by extracting rules of association and applying the Apriori algorithm. This makes it possible to identify anomalies and behaviour patterns requiring action to review, correct, promote or expand them, as appropriate. Finally, I offer a simulated application of the Apriori algorithm, supplying it with synthetic data to find out its potential, strengths and limitations.