Reviewerly/exHarmony huggingface dataset:
Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students

In order to study the application value of personality development theory and deep learning neural network in college students' entrepreneurship psychological education courses, the probability matrix factorization (PMF) algorithm is introduced to optimize the deep neural network algorithm model. Based on the personality development theory, a recommendation algorithm system for entrepreneurial projects under optimized deep neural network is established. Then 128 students of Northeastern University are divided into experimental group and control group. In addition to the normal courses of entrepreneurship psychology education, students in the experimental group are taught the entrepreneurship project recommendation system based on optimized deep neural network designed in this research, while students in the control group are taught entrepreneurship psychology education normally. The intervention effect before and after entrepreneurship education is evaluated by the questionnaire of college students' entrepreneurial intention and college students' entrepreneurial mental resilience scale. The results show that the system recall rate and accuracy based on the algorithm in this research have been significantly higher than that of PMF algorithm and deep belief network (DBN) algorithm, and the difference is statistically significant (P<0.05); the mean square error (MSE) of the proposed algorithm is significantly smaller than that of PMF algorithm and DBN algorithm, and the difference is statistically significant (P<0.05); the improvement of entrepreneurial toughness, entrepreneurial strength, optimism, entrepreneurial possibility, and behavioral tendency of the experimental group after test was significantly higher than that of the control group (P<0.05), which shows that compared with traditional algorithms, the performance of recommendation algorithm for entrepreneurial projects based on the theory of personality development and the optimized deep neural network is better, which can effectively improve the entrepreneurial intention and psychological resilience of college students.
Visual Reference of Ambiguous Objects for Augmented Reality-Powered Human-Robot Communication in a Shared Workspace

In shared workspaces, teammates working with a common set of objects must be able to unambiguously reference individual objects in order to effectively collaborate. When teammates are autonomous robots, human teammates must be able to communicate their intended reference object without overtly interfering with their workflow. In human-robot interaction, the problem of visual reference is defined as identifying the specific object referred to by a human (e.g., through a pointing gesture recognized by an augmented reality device), and relating this object to the associated object in the robotic teammate’s field of view, thereby identifying the intended object from a set of ambiguous objects. As human and robot teammates typically observe their shared workspace from differing perspectives, achieving visual reference of objects is a challenging yet crucial problem. In this paper, we present a novel approach to visual reference of ambiguous objects that introduces a graph matching-based approach which fuses visual and spatial information of the objects in a shared workspace through augmented reality-powered human-robot communication. Our approach represents the objects in a scene with a graph where edges encoding the spatial relationships among objects and attribute vectors describing each object’s appearance associated with each node. Then, we formulate visual object reference for human-robot communication in a shared workspace as an optimization-based graph matching problem, which identifies the correspondence of nodes in graphs built from the human and robot teammates’ observations. We conduct extensive experimental evaluation on two introduced datasets, showing that our approach is able to obtain accurate visual references of ambiguous objects and outperforms existing visual reference methods.
Dynamical blockade in a bosonic Josephson junction using optimal coupling

In this article we use time-dependent Josephson coupling to enhance unconventional photon blockade in a system of two coupled nonlinear bosonic modes which are initially loaded with weakly populated coherent states, so the evolution is restricted to the manifold of up to two field quanta. Using numerical optimal control, we find the optimal coupling which minimizes the two-photon occupation of one mode, which is actually transferred to the other, while maintains a non-zero one-photon occupation in the same mode. Moreover, we choose the continuous coupling to vanish after the transfer between the modes such that they are decoupled and one of them is left only with some one-photon population which can be observed upon its decay. We numerically find lower values of the second-order correlation function obtained at earlier times than with constant coupling, with larger one-photon populations and for longer time windows, corresponding thus to higher emission efficiency and easier detection. The presented methodology is not restricted to the system under study, but it can also be transferred to other related frameworks, to find the optimal driving fields which can improve the single-photon emission statistics from these systems.
Modular End-to-End Automatic Speech Recognition Framework for Acoustic-to-Word Model

End-to-end (E2E) systems have played a more and more important role in automatic speech recognition (ASR) and achieved great performance. However, E2E systems recognize output word sequences directly with the input acoustic feature, which can only be trained on limited acoustic data. The extra text data is widely used to improve the results of traditional artificial neural network-hidden Markov model (ANN-HMM) hybrid systems. The involving of extra text data to standard E2E ASR systems may break the E2E property during decoding. In this paper, a novel modular E2E ASR system is proposed. The modular E2E ASR system consists of two parts: an acoustic-to-phoneme (A2P) model and a phoneme-to-word (P2W) model. The A2P model is trained on acoustic data, while extra data including large scale text data can be used to train the P2W model. This additional data enables the modular E2E ASR system to model not only the acoustic part but also the language part. During the decoding phase, the two models will be integrated and act as a standard acoustic-to-word (A2W) model. In other words, the proposed modular E2E ASR system can be easily trained with extra text data and decoded in the same way as a standard E2E ASR system. Experimental results on the Switchboard corpus show that the modular E2E model achieves better word error rate (WER) than standard A2W models.
Statistical Machine Translation Pada Bahasa Lampung Dialek Api Ke Bahasa Indonesia

In this research, automatic translation of the Lampung dialect into Indonesian was carried out using the statistical machine translation (SMT) approach. Translation of the Lampung language to Indonesian can be done by using a dictionary. Another alternative is to use the Lampung parallel body corpus and its translation in Indonesian with the SMT approach. The SMT approach is carried out in several phases. Starting from the pre-processing phase which is the initial stage to prepare a parallel corpus. Then proceed with the training phase, namely the parallel corpus processing phase to obtain a language model and translation model. Then the testing phase, and ends with the evaluation phase. SMT testing uses 25 single sentences without out-of-vocabulary (OOV), 25 single sentences with OOV, 25 compound sentences without OOV and 25 compound sentences with OOV. The results of testing the translation of Lampung sentences into Indonesian shows the accuracy of the Bilingual Evaluation Undestudy (BLEU) obtained is 77.07% in 25 single sentences without out-of-vocabulary (OOV), 72.29% in 25 single sentences with OOV, 79.84% at 25 compound sentences without OOV and 80.84% at 25 compound sentences with OOV.
Logic Analysis of Natural Language Based on Predicate Linear Logic

This work discusses the formalization of sentence composition and the discovery of the semantic ambiguities of natural language.It also discusses the original connection between the logic area represented by predicate linear logic and ludics, as well as, the linguistic area represented by the Montague grammar.Montague grammar is a linguistic tool that allows analysing sentences in their extensional and intentional contexts.Predicate linear logic is a non-traditional logic of actions and resources where assumptions are consumed after the use of linear implication.Ludics uses proofs from predicate linear logic to analyse the strategies of actors in dialogues.The contribution of this work is to practically demonstrate this approach by translating a natural-language sentence into a predicate linear formula and describe it in time-spatial calculus.
Complete Information Balance in Quantum Measurement

Quantum measurement is a basic tool to manifest intrinsic quantum effects from fundamental tests to quantum information applications. While a measurement is typically performed to gain information on a quantum state, its role in quantum technology is indeed manifold. For instance, quantum measurement is a crucial process element in measurement-based quantum computation. It is also used to detect and correct errors thereby protecting quantum information in error-correcting frameworks. It is therefore important to fully characterize the roles of quantum measurement encompassing information gain, state disturbance and reversibility, together with their fundamental relations. Numerous efforts have been made to obtain the trade-off between information gain and state disturbance, which becomes a practical basis for secure information processing. However, a complete information balance is necessary to include the reversibility of quantum measurement, which constitutes an integral part of practical quantum information processing. We here establish all pairs of trade-off relations involving information gain, disturbance, and reversibility, and crucially the one among all of them together. By doing so, we show that the reversibility plays a vital role in completing the information balance. Remarkably, our result can be interpreted as an information-conservation law of quantum measurement in a nontrivial form. We completely identify the conditions for optimal measurements that satisfy the conservation for each tradeoff relation with their potential applications. Our work can provide a useful guideline for designing a quantum measurement in accordance with the aims of quantum information processors.
Tighter monogamy relations of multiqubit entanglement in terms of Rényi-<i>α</i> entanglement

We explore the existence of monogamy relations in terms of Rényi-α entanglement. By using the power of the Rényi-α entanglement, we establish a class of tight monogamy relations of multiqubit entanglement with larger lower bounds than the existing monogamy relations for α ≥ 2, the power η > 1, and , the power η > 2, respectively.
The Effectiveness of Interactive Multimedia in Learning Indonesian Language Skills in Higher Education

Computer-based media such as interactive multimedia plays an important role in learning Indonesian language skills.The objective of this research is to determine the effectiveness of interactive multimedia in learning Indonesian language skills in higher education.This experimental research used the port-test along with a control group design.The population of this research was the second semester students of the Elementary School Education Study Program at four higher education institutions.To determine the experimental class, the random sampling technique was used considerably.IKIP PGRI Wates is an experimental class through which interactive multimedia is compared to three control groups namely Slamet Riyadi University of Surakarta using video subscription media, Tunas Pembangunan Surakarta University applying media prize, and Ahmad Dahlan University of Yogyakarta using the powerpoint media.The data on Indonesian language skills tests include tests of listening, speaking, reading and writing abilities.The data were then analyzed using the one-way ANOVA.The results show that the application of interactive multimedia is effectively used in learning language language skills compared to learning using other media.In addition, interactive multimedia can also be applied to study independently outside the classroom.
THE EFFECTS OF SUMMARIZING USING INFOGRAPHICS ON EFL LEARNERS’ READING COMPREHENSION.

The purpose of this paper is to describe EFL learners’ experiences while using infographics in a reading class. The pedagogical intervention, based on the use of infographic application (Canva) as an alternative tool to help them summarize reading passages, was implemented to enhance their reading comprehension. The data were gathered from teacher’s journal, questionnaires and the results of students’ reading comprehension tests. The findings revealed that the students viewed the use of infographics could motivate and enabled them to summarize the assigned reading topics much easier. In addition, the assignments incorporating the use of infographic application encouraged them to participate actively in English reading activities in a collaborative atmosphere, allow them to develop their creativity and raise positive attitudes toward the role of technological tools in educational settings.Keywords: English reading, infographics, reading comprehension
An Enhanced Collaborative Filtering-based Approach for Recommender Systems

Recommender systems are software applications that provide product recommendations for users based on their purchase history or ratings of items.The product recommendations are likely to be of interest to the users and encompass items such as books, music CDs, movies, restaurants, documents (news articles, medical texts, and Wikipedia articles), and other services.In this paper, we propose a framework for collaborative filtering to enhance recommendation accuracy.The proposed approach summarized in two steps: (1) itembased collaborative filtering and (2) singular-valuedecomposition-based collaborative filtering.In item-based collaborative filtering, the similarity between the target item and any other item is calculated.Then, the most similar items are recommended.The Singular Value Decomposition based approach handles the problem of scalability and sparsity posed by collaborative filtering and improves the performance of item-based collaborative filtering.We have tested the proposed approach by A Large-Scale Arabic Book Reviews (LABR) dataset.We used four different datasets to compare our approach with existing work.The proposed approach evaluated using the most common metrics found in the collaborative filtering: the mean absolute error (MAE) and the root mean squared error (RMSE).The proposed approach achieved high performance and obtained minimum errors in terms of RMSE and MAE values.
A Cost-Effective OCR Implementation to Prevent Phishing on Mobile Platforms

Phishing is currently defined as a criminal mechanism employing both social engineering and technical subterfuge to gather any useful information such as user personal data or financial account credentials. Many users are sensible about this kind of attack from suspicious URL addresses or obvious warning information from browsers, but phishing still accounts for a larger proportion of all of malicious attacks. Moreover, these warning features will be eliminated if the victim is under a DNS hijacking attack. There is much research about the prevention and evaluation of phishing, in both PC platforms and mobile platforms, but there are still technical challenges to reducing the risk from phishing, especially in mobile platforms.We presented a novel method to prevent phishing attacks by using an Optical Character Recognition (OCR) technology in a previous paper. This method not only overcomes the limitation of current preventions, but also provides a high detection accuracy rate. However, whether this method can be implemented ideally in mobile devices needed to be further examined, especially in relation to the challenges of limited resources (power and bandwidth). In this paper, we apply the OCR method in a mobile platform and provide a prototype implementation scheme to determine applicability. Experiments are performed to test the technique under DNS hijacking attacks.
How Much Method-in-Use Matters? A Case Study of Agile and Waterfall Software Projects

Development methods are rarely followed to the letter, and, consequently, their effects are often in doubt. At the same time, information systems scholars know little about the extent to which a given method truly influences software design and its outcomes. In this paper, we approach this gap by adopting a routine lens and using a novel methodological approach. Theoretically, we treat methods as (organizational) ostensive routine specifications and deploy routine construct as a feasible unit of analysis to analyze the effects of a method on actual, “performed” design routines. We formulated a research framework that identifies method, situation fitness, agency, and random noise as main sources of software design routine variation. Empirically, we applied the framework to examine the extent to which waterfall and agile methods induce variation in software design routines. We trace-enacted design activities in three software projects in a large IT organization that followed an object-oriented waterfall method and three software projects that followed an agile method and then analyzed these traces using a mixed-methods approach involving gene sequencing methods, Markov models, and qualitative content analysis. Our analysis shows that, in both cases, method-induced variation using agile and waterfall methods accounts for about 40% of all activities, while the remaining 60% can be explained by a designer’s personal habits, the project’s fitness conditions, and environmental noise. Generally, the effect of method on software design activities is smaller than assumed and the impact of designer and project conditions on software processes and outcomes should thus not be understated.
EDUCATIONAL TECHNOLOGIES IN TRAINING FUTURE MANAGERS

The introduction of educational technologies, which are developing at a rapid pace, requires professionalism and readiness of managers to implement the latest information technologies. Therefore, the use of electronic resources in the educational process in higher education can contribute both to developing professional skills and gaining the experience of using online environment. Purpose of the paper is to analyse the electronic educational resources and describe the process of implementing online games in future managers' training. Method. The survey on the use of electronic resources in the learning process is conducted among 50 Master students who study full-time and part-time to gain the speciality "Management". Online courses and tests developed with the help of electronic resources are the most popular among the students, while WebQuest, quizzes and online games are used only by a small number of respondents. The survey shows that the use of electronic resources is insufficient in future managers' training. Findings. Within the course "Information Systems and Technologies in Management of Organisation" an interactive online game on online platform Goosechase was developed. This resource can give the opportunity to fulfil assignments in the course by displaying photos and video materials, which contribute to involving participants into а team game and developing future managers' key competences in the course. Implications for research and practice. The results of this study allow to conclude that training of students is successful if an online game is used which can be adapted for various specialities and courses.
Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient

The k-prototypes algorithm is a hybrid clustering algorithm that can process Categorical Data and Numerical Data. In this study, the method of initial Cluster Center selection was improved and a new Hybrid Dissimilarity Coefficient was proposed. Based on the proposed Hybrid Dissimilarity Coefficient, a weighted k-prototype clustering algorithm based on the hybrid dissimilarity coefficient was proposed (WKPCA). The proposed WKPCA algorithm not only improves the selection of initial Cluster Centers, but also puts a new method to calculate the dissimilarity between data objects and Cluster Centers. The real dataset of UCI was used to test the WKPCA algorithm. Experimental results show that WKPCA algorithm is more efficient and robust than other k-prototypes algorithms.
Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer using Deep Learning

Breast cancer is the leading cause of death among women with cancer.Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery.Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field.This means that the low accuracy of the classification methods used for cancer detection is a critical issue.This problem is accentuated when it comes to blurry mammogram images.In this paper, convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches.The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images.In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour.The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images.Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics.