Reviewerly/exHarmony huggingface dataset:
A Collaborative Recommender System Enhanced with a Neural Network

Nowadays, the creation of a recommendation system is a significant research area which attracts many researchers and scientists worldwide. Recommendation systems are wildly accepted in movies, songs, search queries and also in successful commercial products. In the recommendation system, the collaborative filtering approach is advantageous and robust. It also finds the close results, which helps to recommend an accurate movie to the end-user (Bobadilla J, 2013). Neural networks give a splendid output explicitly in this case. This paper represents the methodology which represents a different method to recommend a film to the users. We have built a simple neural network using Pytorch. The analysis on the benchmarks MovieLens 1M data set has shown drastic improvement over the state of the art recommendation system algorithm. The evaluation of the model is done using the mean square error (MSE).
Multi-view Transformation in Recommender Systems

In fact, there are grumpy users who tend to give lower ratings than others even if they feel well about the purchased items. This will lead to a decrease in the accuracy of recommender systems. In addition, users can rate purchased items from different views such as scores or comments. In this study, we proposed a model to transform the rating scores of grumpy users to match with other users by using users’ review text, then we used those ratings for improving the performance of the recommender systems. We did experiments on an Amazon dataset and compared the results of the proposed model with individual models of recommender systems to show that the transformation algorithm helped to balance users’ rating scores and changed the results recommended for users.
EGOF-Net: epipolar guided optical flow network for unrectified stereo matching

It is challenging to realize stereo matching in dynamic stereo vision systems. We present an epipolar guided optical flow network (EGOF-Net) for unrectified stereo matching by estimating robust epipolar geometry with a deep cross-checking-based fundamental matrix estimation method (DCCM) and then surpassing false matches with a 4D epipolar modulator (4D-EM) module. On synthetic and real-scene datasets, our network outperforms the state-of-the-art methods by a substantial margin. Also, we test the network in an existing dynamic stereo system and successfully reconstruct the 3D point clouds. The technique can simplify the stereo vision pipeline by ticking out rectification operations. Moreover, it suggests a new opportunity for combining heuristic algorithms with neural networks. The code is available on https://github.com/psyrocloud/EGOF-Net.
Parallel hierarchical clustering of big text corpora

Clustering is a technique that facilitates unsupervised learning of patterns or groups among entities in any application domain. Typically, clustering algorithms are iterative in nature and are designed to operate on huge application datasets. This results in prohibitively excessive demand on computational and storage resources that cannot be met using a single workstation processor. Keeping in mind the advances in parallel processing systems like multicore systems, distributed cluster computers, graphic processing units and programming platforms like Open Multi-Processing (OpenMP), Hadoop, Compute Unified Device Architecture (CUDA) and Message Passing Interface (MPI), it is imperative to design algorithms for tackling the compute and storage demands by applications involving huge datasets. In the recent past, much seminal advancement has been made in the field of parallel clustering algorithms. In this article we highlight seminal research attempts and attempt to weave a timeline illustrating the developments in this happening field with an inference of the pros and cons of each of the proposed algorithms and some open research problems. This is likely to be useful for newbie researchers who would want to pursue their research in designing and analyzing parallel algorithms for clustering. We begin by emphasizing the significance of designing parallel algorithms for the hierarchical clustering of large-scale text collections. Some of the key challenges involved and the related seminal research attempts are presented in this article with a subsequent enlisting of the open research problems.
Building a communication strategy of the university of culture in the market of educational services

Researched is the theme of the need of the university of culture to create and maintain a steady interest of consumers in its educational service, which has its own specifics, since education at the university is based on the artistic inclinations and creative abilities of students. The purpose of the preparation and writing of this article was the urgent need to review the educational policy of the regional university and the formation of such a marketing program, where the educational service produced by the university is considered as a product that requires a well-thought-out approach for successful positioning in the market of educational services. In this article, the main communication channels and methods that can contribute to increasing the popularity of a regional university and the growth of its influence on the market of educational services are formulated. One of the objectives of the marketing policy of the University of Culture is to develop the concept of a production center, through which the university will be able to create the necessary conditions for communication with its public and promote the results of creative activities of students and teachers. The research methodology is based on modern communication theories, which allow us to look at the problem under study at a new level and develop a sustainable mechanism that will allow the university of culture to successfully compete with other regional universities. The production center "Constellation", created on the basis of the University of Culture, will allow you to start a new round of the communication process and successfully implement a number of marketing technologies necessary to promote the educational services of the university to the market of regional services, and the business reputation of the university will be improved by promoting the creative product of its graduates. The regional university of culture has undeniable specific advantages that can be disclosed and implemented in professional production activities.
A Comprehensive Survey of Sentiment Analysis: Word Embeddings Approach, Research Challenges and Opportunities

In this paper, we present a review of sentiment analysis along with the concept of word embeddings, natural language processing and crucial aspects that are essential for model for sentiment analysis. First, we discuss the basic steps for collecting the corpus of sentences from different sentiments or opinions such as movie reviews, Twitter data, and etc., Usually, the corpus of data is optimized with regard to the only the desirable or subjective part of the sentence and it is retained whereas the rest is discarded. The resultant dataset is split into train and test segments in a balanced ratio so that the problem of overfitting of classifiers is overcome. We also discuss various techniques such as Bag-Of-Words, TF/IDF, Word Embedding like word2vec, BERT (Latest one), etc. to convert the entire dataset into machine-readable form i.e., numerical. These vectors are fed as input to the machine learning or deep learning classifiers to predict the polarity of the subjective sentences. In our study we explain the techniques from very basic bag-of-words to the latest word embeddings BERT. In the last, we identified few research issues that are open to research community in this active field of sentiment analysis. One of the major challenges is to design a domain independent model for sentiment analysis using word embeddings. Further, an additional issue is related to use word embedding for translation in the local languages such as Dogri, Kashmiri, and many more.
The Pintz–Steiger–Szemerédi estimate for intersective quadratic polynomials in function fields

Let [Formula: see text] be the polynomial ring over the finite field [Formula: see text] of [Formula: see text] elements. For a natural number [Formula: see text] let [Formula: see text] be the set of all polynomials in [Formula: see text] of degree less than [Formula: see text] Let [Formula: see text] be a quadratic polynomial over [Formula: see text] Suppose that [Formula: see text] is intersective, that is, which satisfies [Formula: see text] for any [Formula: see text] with [Formula: see text] where [Formula: see text] denotes the difference set of [Formula: see text] Let [Formula: see text] Suppose that [Formula: see text] and that the characteristic of [Formula: see text] is not divisible by 2. It is proved that [Formula: see text] for any [Formula: see text] where [Formula: see text] is a constant depending only on [Formula: see text] and [Formula: see text]
THE ROLE OF COMMUNICATION IN ENHANCING EMPLOYEES ORGANIZATIONAL COMMITMENT

Purpose - The purpose of this paper is to determine the role of communication in enhancing employees organizational commitment. The general objective of the study involves examining the impact of communication satisfaction, communication climate, relationship with superiors, organization integration, media quality, horizontal and informal communication channels, organizational perspective, relationship with other employees, personal feedback and the flow of informationon employees organizational commitment.Design/methodology/approach Ã¢Â€Â“The study utilized quantitative research methodology toinvestigatethe reason why employeescommunicate with their superiors and subordinates, the role of communication, their satisfaction and commitment towards their organization. Data was collected using a survey of structured questionnaire. The data analysis was conducted through descriptive statistical analysis by using the Statistical Package for Social Sciences (SPSS). Demographic data such as gender, age, academic degree, experience years, and the management level were analyzed by using frequency distributions and Percentages with the results presented in charts and tables. Inferential statistics were obtained by carrying out correlation and regressions analyses, to test for the correlations between the variables and their influence on employees organizational commitment.Findings and conclusions of the study - The results of the study revealed that communication satisfaction has a significant positive correlation withorganizational commitment, communication climate was also positively correlated to commitment, organization integration and perspective was highly and positively correlated to employees commitment whilepersonal feedback, horizontal and informal communication channels, and relationship with superiors was minimally positively related to employees organizational commitment. Additionally, the regression analysis indicate thatcommunication dimensions, internal communication and organizational integration are closely associated with employees organizational commitment. Therefore, nine (9) hypotheses were developed and tested accordingly to conclude that communication has a significant and direct impact on employees organizational commitment. Furthermore, the main implication of this study is associated with the fact that, organizations could use the findings of this study to develop communication practices and channels that encourage commitment within the organization which might translate to increased innovation, productivity, opportunity, collaboration, and effective commitment. The study also shows that employees organizational commitment can be improved through job enhancement together with matching the individuals values to those of the organization rather than, finding ways and means of improving job satisfaction through different strategies that involves improving the standard of the supervision, decentralization of power and counseling.
Binary MOGWO Based On Competition and Teaching for Computationally Complex Engineering Applications

A new efficient optimization method, called binary multi-objective grey wolf optimizer based on competition and teaching (BMOGWO-CT) mechanisms, is proposed for computationally complex engineering applications. The proposed algorithm first divides the population into four parts belonging to three levels through the competition mechanism, thereby reducing the population number during the following procedure of position updating. Then, the teaching mechanism supervises different parts to update their positions according to their priorities within the whole population therefore further reducing the computational cost for solving the problems. To check the effectiveness of the method, the BMOGWO-CT is tested on ten benchmark test functions and compared to other population-based optimization methods, indicating BMOGWO-CT is more effective and efficient. Furthermore, the novel optimization method is extended to a computationally time-consuming engineering problem- multi-objective optimization of antenna topology. This example verifies the effectiveness of our proposed method in engineering applications.
Con&amp;Net: A Cross-Network Anchor Link Discovery Method Based on Embedding Representation

Cross-network anchor link discovery is an important research problem and has many applications in heterogeneous social network. Existing schemes of cross-network anchor link discovery can provide reasonable link discovery results, but the quality of these results depends on the features of the platform. Therefore, there is no theoretical guarantee to the stability. This article employs user embedding feature to model the relationship between cross-platform accounts, that is, the more similar the user embedding features are, the more similar the two accounts are. The similarity of user embedding features is determined by the distance of the user features in the latent space. Based on the user embedding features, this article proposes an embedding representation-based method Con&amp;Net(Content and Network) to solve cross-network anchor link discovery problem. Con&amp;Net combines the user’s profile features, user-generated content (UGC) features, and user’s social structure features to measure the similarity of two user accounts. Con&amp;Net first trains the user’s profile features to get profile embedding. Then it trains the network structure of the nodes to get structure embedding. It connects the two features through vector concatenating, and calculates the cosine similarity of the vector based on the embedding vector. This cosine similarity is used to measure the similarity of the user accounts. Finally, Con&amp;Net predicts the link based on similarity for account pairs across the two networks. A large number of experiments in Sina Weibo and Twitter networks show that the proposed method Con&amp;Net is better than state-of-the-art method. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve predicted by the anchor link is 11% higher than the baseline method, and Precision@30 is 25% higher than the baseline method.
Design of a Mixed Reality Game for Exploring How IoT Technologies Can Support the Decision Making Process

There is a trend in industries towards adoption of new technologies coming from Industry 4.0 to improve logistics operations. This has also led to new requirements on engineering education. This paper presents work in progress on a mixed reality game that is intended to be used for the education on engineers and engineering students in the blended lab environments. The game itself consist of a physical and a digital component, and the focus in this article is to describe the design and prototypical implementation of the physical part and how that can be operated in a remote lab environment.
FuzzyKey: Comparing Fuzzy Cryptographic Primitives on Resource-Constrained Devices

Implantable medical devices, sensors and wearables are widely deployed today. However, establishing a secure wireless communication channel to these devices is a major challenge, amongst others due to the constraints on energy consumption and the need to obtain immediate access in emergencies. To address this issue, researchers have proposed various key agreement protocols based on the measurement of physiological signals such as a person’s heart signal. At the core of such protocols are fuzzy cryptographic primitives that allow to agree on a shared secret based on several simultaneous, noisy measurements of the same signal. So far, although many fuzzy primitives have been proposed, there is no comprehensive evaluation and comparison yet of the overhead that such methods incur on resource-constrained embedded devices. In this paper, we study the feasibility of six types of fuzzy cryptographic primitives on embedded devices for 128-bit key agreement. We configure several variants for each fuzzy primitive under different parameter selections and mismatch rates of the physiological signal measurements on an MSP430 microcontroller, and then measure and compare their energy consumption and communication overhead. The most efficient constructions consume between 0.021 mJ and 0.198 mJ for the transmitter and between 0.029 mJ and 0.380 mJ for the receiver under different mismatch rates. Subsequently, we modify the best performing methods so that they run in constant time to protect against timing side-channel attacks, and observe that these changes only minimally affect resource consumption. Finally, we provide open-source implementations and energy consumption data of each fuzzy primitive as a reference for real-world designs.
AI-Based Chatbot to Solve Modern-Day Enterprise Business Problems

Communicating with employees through forums and emails has become an increasingly popular way for many multinational companies to provide human resource services in real time. Today, employee chat service agents are often replaced by conversational software agents or chatbots. These systems are designed to communicate with human users through natural language, generally based on artificial intelligence (AI). Time and cost saving opportunities have led to the widespread deployment of AI-based chatbots. Chatbots are one of the most basic and popular examples of human-computer intelligent interaction (HCI). Designed to convincingly simulate the way humans behave as dialogue partners. In the proposed system, we propose a chat robot that can dynamically respond to employee human resource queries. The proposed HR system is based on the Microsoft Cognitive Services chatbot. This Microsoft Teams-based platform provides a broad foundation of intelligence and is trained based on various data sets provided by the organization's HR.
Design-time performance modeling of compositional parallel programs

Performance models are powerful instruments for understanding the performance of parallel systems and uncovering their bottlenecks. Already during system design, performance models can help ponder alternative development options. However, creating a performance model – whether theoretically or empirically – for an entire application that does not exist yet is challenging. In this paper, we propose to generate performance models of full programs from performance models of their components using formal composition operators derived from parallel design patterns. As long as the design of the overall system follows such a pattern, its performance model can be predicted with reasonable accuracy without an actual implementation. We demonstrate our approach with design patterns of varying complexity, including pipeline, task pool, and eventually MapReduce, which is representative of a broad class of data-analytics applications.
Multi-source based approach for Visual Domain Adaptation

In current scenario, transfer learning or domain adaptation has been emerged as fruitful approach to handle problems of distribution mismatch between the training and test data. Standard machine learning techniques utilize labeled data for getting better performance. In practical scenario, due to scarcity of labeled data, the classifier trained on the source domain cannot be utilized efficiently for classifying the target domain data. So, there is a need to use the previously acquired knowledge from a related source domain to classify the information in the target domain. Previous works on single/multiple source domain adaptation have achieved substantial growth in tackling this concern. In this work, we have proposed a novel unsupervised multi-source based approach MSVDA for visual domain adaptation in which data from multiple labeled source domains are utilized to classify the information in the target domain containing unlabeled data. Our proposed approach MSVDA extends the existing Maximum Mean Discrepancy criteria across various source domains and also preserves the discriminative information of various source domains. Further, it learns an optimal classification that diminishes the empirical risk and enhances the consistency rate between the prediction function and the manifold. Various experiments on the two publicly available datasets with multiple-domain scenario settings (double-domain, triple-domain and quadruple-domain scenarios) have demonstrated the efficacy of our proposed method MSVDA over other existing methods.
From Radiogenic Source or Sink? — Simple and Complex 4He Outgassing Behaviors and Diffusion Systematics Observed During Continuous Ramped Heating of Apatite

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]From Radiogenic Source or Sink? — Simple and Complex 4He Outgassing Behaviors and Diffusion Systematics Observed During Continuous Ramped Heating of ApatiteAuthorsHongchengGuoiDPeterZeitleriDBruceIdlemaniDKalinMcDannelliDAnniaFayonPaulFitzgeraldSee all authors Hongcheng GuoiDCorresponding Author• Submitting AuthorLehigh UniversityiDhttps://orcid.org/0000-0003-4227-6331view email addressThe email was not providedcopy email addressPeter ZeitleriDLehigh University,Dartmouth CollegeiDhttps://orcid.org/0000-0002-6204-3159view email addressThe email was not providedcopy email addressBruce IdlemaniDLehigh UniversityiDhttps://orcid.org/0000-0002-1696-6841view email addressThe email was not providedcopy email addressKalin McDannelliDDartmouth CollegeiDhttps://orcid.org/0000-0002-3161-8124view email addressThe email was not providedcopy email addressAnnia FayonUniversity of Minnesotaview email addressThe email was not providedcopy email addressPaul FitzgeraldSyracuse Universityview email addressThe email was not providedcopy email address
Application of multidimensional data visualization technology and WebGIS in lightweight visualization system design

With the rapid development of 5G Internet of things technology, the traditional WebGIS visualization technology can not meet the needs of Oilfield Engineering for the integration and visualization of various sensor monitoring data. This paper uses the most advanced ecarts open source data visualization technology, combined with the basic architecture of WebGIS, to realize the multi-dimensional and comprehensive visualization of all kinds of sensor data with temporal and spatial characteristics, and improve the ability of integration and analysis of multi-source data.
Skeletonization of Arabic Characters Using a Neural Network Mapped on Maspar

In this paper, we employ a back-prop neural network for skeletonization of Arabic characters to be used in an Arabic character recognition system. The neural network is trained to learn the thinning algorithm which is a variation of the parallel algorithm proposed by Zhang and Suen. Zhang and suen’s algorithm was modified so that dots are thinned into single pixels. The neural network as well as the binary image are mapped on a massively parallel machine (Maspar) in order to speed up the skeletonization process.