Reviewerly/exHarmony huggingface dataset:
Serialized Recommendation Technology Based on Deep Neural Network

Since the construction of brain network is like organic brain organization, profound brain network has high effectiveness and high accuracy in separating data from profound elements, fit for multifacet learning, conceptual component portrayal, cross-space learning capacity, multisource, heterogeneous data content. This paper presents brain network models, multifacet perceptrons, RBM models, and convolutional brain organizations. In this paper, the experimental design is carried out according to the principle of the model. Compared with the traditional commonly used CMF, PMF, SVD, and item-based algorithms, the proposed two models have lower root mean square error and higher recall rate. The trial brought about by this paper shows that through investigation, it is trusted that because of the impact of the component of the info information, too many secret layers might prompt overfitting. Accordingly, this paper sets the quantity of stowed away layers in the DNN organization to 2.
Application of an Artificial Intelligence System Recognition Based on the Deep Neural Network Algorithm

With the development of artificial intelligence and computer technology, the deep neural network algorithm is applied to the intelligentization of various fields of production and life. However, from the current application status, the application of artificial intelligence technology has many shortcomings. Based on this, this paper starts with the deep neural network algorithm, takes face recognition as the research tool, and deeply studies how to use the deep neural network algorithm to demonstrate the application of intelligent face recognition in complex environments. A face recognition neural network algorithm is proposed, and the accuracy of the algorithm is checked by testing. The results show that the average accuracy of a single sample in the LFW dataset is 99.17%, and the efficiency of using a single sample is close to that of many smelting models, which can be applied to various intelligent recognition scenarios.
Membership Inference Defense in Distributed Federated Learning Based on Gradient Differential Privacy and Trust Domain Division Mechanisms

Distributed federated learning models are vulnerable to membership inference attacks (MIA) because they remember information about their training data. Through a comprehensive privacy analysis of distributed federated learning models, we design an attack model based on generative adversarial networks (GAN) and member inference attacks (MIA). Malicious participants (attackers) utilize the attack model to successfully reconstruct training sets of other regular participants without any negative impact on the global model. To solve this problem, we apply the differential privacy method to the training process of the model, which effectively reduces the accuracy of member inference attacks by clipping the gradient and adding noise to it. In addition, we manage the participants hierarchically through the method of trust domain division to alleviate the performance degradation of the model caused by differential privacy processing. Experimental results show that in distributed federated learning, our designed scheme can effectively defend against member inference attacks in white-box scenarios and maintain the usability of the global model, realizing an effective trade-off between privacy and usability.
An Improved Math Word Problem (MWP) Model Using Unified Pretrained Language Model (UniLM) for Pretraining

Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the general methods that support machine understanding of text content. They play a very important role in the text information processing system including recommendation and question and answer systems. There are many researches in the field of NLU such as Bag of words, N-Gram, and neural network language model. These models have achieved a good performance in NLU and NLG tasks. However, since they require lots of training data, it is difficult to obtain rich data in practical applications. Thus, pretraining becomes important. This paper proposes a semisupervised way to deal with math word problem (MWP) tasks using unsupervised pretraining and supervised tuning methods, which are based on the Unified pretrained Language Model (UniLM). The proposed model requires fewer training data than traditional models since it uses model parameters of tasks that have been learned before to initialize the model parameters of new tasks. In this way, old knowledge helps new models successfully perform new tasks from old experiences instead of from scratch. Moreover, in order to help the decoder make accurate predictions, we combine the advantages of AR and AE language models to support one-way, sequence-to-sequence, and two-way predictions. Experiments, carried out on MWP tasks with 20,000+ mathematical questions, show that the improved model outperforms the traditional models with a maximum accuracy of 79.57%. The impact of different experiment parameters is also studied in the paper and we found that a wrong arithmetic order leads to incorrect solution expression generation.
Simulation of Piano Teaching System Based on Virtual Data Space System and Neural Network

Cloud computing takes the analysis and digital processing technology based on virtual data space as the foundation and core of cloud computing technology development and research, and the significance and importance of data is self-evident. This paper designs a piano teaching system based on the virtual data space system and neural network, which can help users to master and use piano teaching information through the extensive application of this system. The functional test results show that the system fully realizes the automatic allocation of all virtual data spaces in the cloud computing environment and has the function of real-time synchronization with user terminals and applications in the virtual data space. Teachers can directly give lectures to students or give students detailed answers through live webcasting and other methods. Only by using this simple operation mode can they be able to solve the problems of piano teaching very conveniently. For the on-demand video, this paper manages the piano teaching videos by classification, that is, displays this type of video in an interface or table. The improved network recognition performance based on the loss function also achieves better improvement and improvement than the original network. The model has also achieved good performance in training and recognition on datasets in a strong noise environment.
K-Means Algorithm to Identify the Elderly Psychological Stress Analysis Algorithm

In order to study the psychological anxiety of the elderly, a psychological stress recognition algorithm based on ECG signal acquisition and heart rate variability was proposed based on the K-means algorithm. By collecting ECG signals of the elderly, HRV information can be accurately obtained, and RFC can be used to identify the psychological changes of the elderly under different degrees of stress. The research results show that the LR and RFC models have a better recognition effect on psychological stress when comparing with the KNN model. When the psychological stress is high, the test accuracy of the KNN model is 84.51%. The accuracy of stress recognition was 93.45%, and the accuracy of RFC’s recognition of psychological stress was 93.88%.
Intelligent Face Recognition System Based on Universal Design Concept

The rapid development of science and technology, i.e., integrated modules that are actuators and sensors, promotes the comprehensive popularization of intelligent products in people's life. More particularly, with the advent of the hybrid of the Internet of things and artificial intelligence, more and more activities preferably linked to the human beings have been automated and developed. Among those fields, intelligent face recognition has also become a basic technology in work and life. This technology has been widely used in various products and is well known by people. However, the intelligent face recognition system developed at present lacks universal design concept, and the designed system cannot be applied to various products. During the use of users, there are some problems, such as difficult operation and unfriendly interface. In order to improve the satisfaction of users' physical examination and the accuracy of intelligent face recognition, this study develops an intelligent face recognition system based on the universal design concept. First, the universal design concept is briefly described, and the calculation process of face detection algorithm and face detection algorithm based on the optical flow method is introduced in detail. Then, when designing a face recognition system, this algorithm is used to build a complete system framework. The main functional modules in this system are face detection module, face recognition module, and face training module. The functions of each module are described in detail. Finally, the face feature extraction results of the face detection algorithm based on the optical flow method are verified on the Yale face database and PIE face database. The results show that the algorithm has the highest detection and recognition rate. At the same time, the ORL face database is used to compare and analyze the system performance. The face image recognition rate of this algorithm is 92, which is the highest compared with other algorithms.
Object Recognition to Support Navigation Systems for Blind in Uncontrolled Environments

Efficient navigation is a challenge for visually impaired people. Several technologies combine sensors, cameras, or feedback chan-nels to increase the autonomy and mobility of visually impaired people. Still, many existing systems are expensive and complexto a blind person’s needs. This work presents a dataset for indoornavigation purposes with annotated ground-truth representingreal-world situations. We also performed a study on the efficiencyof deep-learning-based approaches on such dataset. These resultsrepresent initial efforts to develop a real-time navigation systemfor visually impaired people in uncontrolled indoor environments.We analyzed the use of video-based object recognition algorithms for the automatic detection of five groups of objects: i) fire extin-guisher; ii) emergency sign; iii) attention sign; iv) internal sign, and v) other. We produced an experimental database with 20 minutesand 6 seconds of videos recorded by a person walking throughthe corridors of the largest building on campus. In addition to thetesting database, other contributions of this work are the study onthe efficiency of five state-of-the-art deep-learning-based models(YOLO-v3, YOLO-v3 tiny, YOLO-v4, YOLO-v4 tiny, and YOLO-v4scaled), achieving results above 82% performance in uncontrolledenvironments, reaching up to 93% with YOLO-v4. It was possible toprocess between 62 and 371 Frames Per Second (FPS) concerning thespeed, being the YOLO-v4 tiny architecture, the fastest one. Codeand dataset available at: https://github.com/ICDI/navigation4blind.
Sensor-less Obstacle Collision Detection for Robot Manipulator

Accurate collision detection has been a challenging task for researchers for the past few years. Many studies include using sensors to detect a collision, but recent research has been done on sensor-less collision detection. Many researchers present different observer techniques to estimate collision, but the drawback of these techniques is that they are based on exact dynamic assumptions. In the real system, it is challenging to calculate the exact dynamic model of the system as the Degree of Freedom (DOF) in the system increases. This study proposed a Sliding Perturbation Observer (SPO) for obstacle collision detection, which is sensor-less and based on minimal dynamic (linear dynamics) information. The technique is implemented on 5 DOF robot manipulator. The external disturbance (which is collision) has been estimated by subtracting the perturbation (from SPO) from gravity (calculated graphically). A threshold has been set for the collision so that if the estimated disturbance exceeds the threshold, it can be concluded that the collision has occurred.
Lightning and Thunder: The Early Days of Interactive Information Visualization at the University of Maryland

The thrill of scientific discovery, the excitement of engineering development, and the fresh thinking of design explorations were invigorating as we participated in the birth of a new discipline: Information Visualization. This discipline, based on graphical user interfaces with pointing devices, became possible as software matured, hardware sped up, and screen resolution improved. Driven by the concepts of direct manipulation and dynamic queries, we made interactive interfaces that empowered users and opened up new possibilities for the next generation of designers. We worked with professionals who had real problems and tested real users to get their feedback. Some projects failed and some papers never got published, but many of the new ideas found their way into widely used commercial products. Our great satisfaction is that our students have spread the community spirit of the Human-Computer Interaction Laboratory as they continue to make further contributions.
Unity Snappable Meshes

<h2>Abstract</h2> The Snappable Meshes algorithm procedurally generates 3D maps for computer games by iteratively selecting and linking pre-built map pieces via designer-specified connectors. In this paper we present an implementation of this algorithm in the Unity game engine, describing its architecture and discussing core implementational solutions. A number of examples illustrate the potential of the algorithm and the capabilities of the software. We assess the application's impact on past and ongoing research, and how it can be improved to support future research questions.
Lithium Potential of the Indian Granitic Pegmatites

There is a progressively increasing demand for lithium (Li) due to its growing applications in the Li-ion batteries for electric vehicles. Accordingly, to take stock of the situation with respect to lithium potentiality of the Indian granitic pegmatites, a critical synthesis is presented here. India's exploration programme for lithium was not sustained due to fluctuating domestic demand and its availability in the world market. Data reveals that the Indian Li-pegmatites belong to the Lithium-Caesium-Tantalum (LCT) family, and occur manly in parts of Bihar-Jharkhand, Bastar-Odisha and Karnataka, with some in Rajasthan, Maharashtra, West Bengal and Assam. Major mineral phases of Li are lepidolite, amblygonite and spodumene, the last one being dominant in Karnataka. Bulk of the Li-pegmatites have been derived from the peraluminous, geochemically-evolved S-type granites. During fractionation, wherever saturation was attained, precipitation of independent minerals of Li took place. Lithium minerals are largely restricted to the replacement zones of pegmatites, and, at places, in the greisenised pockets within the pegmatites and parent granite margins. Pegmatites from Karnataka appear to have more potential relative to those from Bihar-Jharkhand and Rajasthan. Systematic exploration of Li-pegmatites may prove rewarding in establishing their Li-potential. Li-pegmatite fields may be mapped by applications of remote sensing, using various methods and sensors through spectral angle mapper (SAM) classification on 'HyMap' data. SAM classification may be used to assess the efficacy of advanced spaceborne thermal emission and reflection radiometer (ASTER), and Landsat-8 operational land imager (OLI) data, including new combinations of red, green and blue (RGB). By combined use of ASTER imagery and spectro-radiometric analysis, mapping of pegmatite veins may be carried out. As the LCT-type pegmatites are sourced mostly from previously un-melted, mica-enriched metamorphic rocks, muscovite and biotite groups of mica host bulk of the trace elements and thus fingerprint for the LCT-type pegmatites. Also, P- and Cs-enriched Li-pegmatites lend further support to the S-type sources for the LCT-type pegmatites. Accordingly, all the LCT-type pegmatites in India should form the first order target for Li-exploration. Characteristic replacement mineral assemblages like cleavelandite, topaz, greisen, schorl and lithian muscovite may also serve as guides for lithium exploration.
3D Modeling for virtual tour of a Higher Education Institution

The pandemic caused by COVID 19 has generated crowds of people, this becomes a critical point that leads to a safe contagion. And it is for this reason that the idea of a virtual tour was born so that people can get to know the physical facilities of a Higher Education Institution (HEI) through a virtual environment. Universidad Tecnológica Israel (UISRAEL), does not have a digital space where its different areas can be appreciated by means of a 3D virtual tour. In this sense, this work presents the development of a 3D model, offering the user a digital interaction of the physical space from his home on his computer. The main benefit is that the user can get to know the facilities in an almost real way, generating a sense of orientation. This work has given way to a future virtual community at UISRAEL, making it possible to diversify and face the new challenges that virtualization brings with it. It will not only be a new means of information and communication, but also a space for interaction, entertainment and learning. All this leaves the basis for an application that in the future could develop more functionalities and be used in other research projects.
Semi-automatic document processing and text-recognition for digital transformation of low-resources organizations

The digital transformation of organizations has become critical for their survival. Smaller organizations, and especially those in developing countries, are facing bigger challenges in this process due to limited resources and lack of digital skills. One of the keys of this transformation is the digitalization of information, where optical character recognition (OCR) plays an essential role. This research analyzes current OCR tools and systems for document digitalization in low-resources organizations and presents a semi-automatic document processing and text-recognition solution that addresses their needs. The study then presents an experimental design to assess the efficiency of the tool. The results support the adequacy of using these tools, compared to current manual digitalization processes.
Neural Machine Translation Approach in Automatic Translations between Portuguese Language and Portuguese Sign Language Glosses

Communication between deaf and non-deaf people is a difficult task in Portugal. Only a very small portion of the population is able to communicate with the impaired ones. Unfortunately, there aren’t a lot of systems which are available to the public to help in this communication. In this paper we present a component to translate from Portuguese Natural Language to Portuguese Sign Language, using Machine Learning approach, which is a sub-component of a bigger system designed to allow communication between deaf and non-deaf via smartphones. Our approach uses a Neural Machine Translation system to provide translations between Portuguese Natural and Portuguese Sign Language Glosses to be used to animate a 3D avatar. Early testing of different machine learning architectures showed promising results of BiLingual Evaluation Understudy (BLEU) evaluation on translations. The component uses a supervised learning model and results have shown that, under normal operating conditions an accuracy in the detection to translate from Portuguese-to-Portuguese sign language glosses, achieving above 80% on testing data using BLEU performance scores.
Readability of Graphical Contents on World Wide Web (WWW)

the growth of the Web has been exponential over the last decade. Graphical contents are usually more effective than just the written text in web readability because people hardly have time to sit down and read anything lengthy anymore. However, irrelevant or bad-quality graphical content on the web contributes to poor readability, distracting the reader from the focus of the reading. The main aim of this research is to make images on the Web more readable to people. For this, we will analyze the factors that affect the readability of graphical contents on the web, propose a new measure to automatically calculate the relevance of the graphical contents, and finally design the guidelines to improve the readability of graphical contents on the web. User studies will be performed and the findings will be used to devise guidelines that web designers can use to improve the readability of their web pages.
Machine Learning Algorithm for Text Categorization of News Articles from Senegalese Online News Websites

The growth of information in the form of news articles is a big problem in every society. This information lies in unstructured form and manually managing and effectively making use of it is tedious. In this study, we aimed to build a classifier to categorize news articles from Senegalese online news websites according to their contents: "politique", "religion", "justice", "santé", "education", "faits-divers", and "people".We developed, based on web scrapping techniques, a crawler for collecting news articles from the Senegalese online press. We used OvA method to transform our multi-labels problem into binary classification problem(s) and classify online news articles using different supervised machine learning algorithms.We use the Area Under the ROC Curve (AUC) to measure performances of selected models. Most of the themes being recognized correctly with more than 80% of micro-average AUC. Overall, the Random Forest algorithm showed the best performance with 90% of micro-average AUC.Using the collection of data through various Senegalese online news portals, a tool to automatically categorize news published articles can be developed with our final prediction model and to guide future research in this area.
Software development model in Small and Medium Enterprises: A still uncertain process

The Systematic Literature Review (SLR) objective was to search for evidence and investigate the state of the art of hybrid software development methodologies and answer the following research questions.RQ1: What hybrid software development methods have been reported in the literature in the context of Small and Medium Enterprises (SMEs)?RQ2: What challenges are faced by software development SMEs in the adoption, execution, and governance of projects carried out with hybrid methodologies?RQ3. What are the best practices reported in the literature to successfully adopt hybrid methods in SMEs?To justify the SRL, the "Information and Software Technology" database was used, the search string was tested: Systematic Literature Review AND Hybrid Software Development AND Small and Medium Enterprise. The search yielded 43 articles, of which 6 analyzed study topics similar to those proposed for this research but did not focus on the particular context of SMEs.Taking into account the limitations of any SLR, the analysis carried out allows us to understand what has been the evolution of the methodologies in the last decade and extends the frontiers of understanding to conclude that hybrid methodologies constitute a field of software engineering, which can still mature to give more scientific support to this branch of knowledge.