Reviewerly/exHarmony huggingface dataset:
VALIDATING AN INSTRUMENT TO MEASURE OPEN INNOVATION PRACTICES IN RESEARCH COMMERCIALIZATION

The Open Innovation Practice is getting well known as an initiative to help increase the rate of commercializing research output. Implementing open innovation practices within academia is essential in facilitating collaboration, fostering knowledge sharing, and facilitating technology transfer between academic institutions and industries. This pilot study aims to investigate and assess the validity and reliability of the instrument used to measure open innovation practices. Quantitative methodology was employed in this study, and data were gathered via a survey from 109 respondents. The respondents are among the lecturers at public universities in Malaysia that are actively involved in the commercialization of research output. The descriptive analysis shows that the respondents often practice open innovation in commercializing their research output. During the Exploratory Factor Analysis (EFA), the findings indicated that the Kaiser-Meyer-Olkin (KMO) value for the open innovation practice is 0.885, showing no severe problem of multicollinearity and Barlett's Test of Sphericity demonstrates a statistically significant result. No item is eliminated in this study. Meanwhile, the reliability test for nine instrument items in open innovation practice is accepted with Cronbach's Alpha of 0.916. To conclude, the discovery from this study indicates that the instrument acquires high validity and reliability and is well prepared to be tested for future stages.
ANALYSIS OF THE QUALITY OF CLOUD STORAGE SERVICES ON NEXTCLOUD AND PYDIO

The development of information technology has greatly influenced the development of current storage media, such as cloud-based storage (cloud). Nextcloud and pydio storage are part of an open based cloud computing system. Measuring service quality can be seen from the aspects of throughput, delay, jitter, and packet loss. This research was conducted to compare the upload and download speeds between the nextcloud server and the pydio server. Of the two cloud storage servers, five trial upload and download respectively. Based on the test results showing that the upload and download processes produce final cloud storage data which has the highest average throughput in terms of uploading files of different types and capacities, the next cloud server is 1,934.6 kbps. The cloud storage that has the lowest average packet loss value is balanced, because the two cloud storages get the same packet loss value of 0%. The cloud storage that has the lowest average file upload delay is the nextcloud server at 222,585 ms. The cloud storage that gets the lowest jitter value is the pydio server of 160.56 ms. Meanwhile, for downloading cloud storage files that have the highest average throughput value, nextcloud server is 1,332 kbps. The cloud storage that has the lowest average packet loss value is balanced, because the two cloud storages get the same packet loss value of 0%. The cloud storage that has the lowest average download delay is the nextcloud server at 132,057 ms, while the cloud storage that has the lowest jitter is the nextcloud server at 132,139 ms.
Interactive mixed reality live streaming technology in manufacturing

This paper presents the first field-tested application of interactive mixed reality live streaming technology in a manufacturing setting and elaborates on its implications for practitioners and scholars. A proof-of-concept of a mixed reality live streaming device that uses 360° video calls for remote presence is developed and applied in a field test during a tool changeover at a Stanley Black & Decker factory. This study documents that interactive mixed reality live streaming for shop-floor environments has passed Technology Readiness Level 6, representing a radical new way of organizing remote shop-floor tours, inspections, audits, and training.
PUPIL BASED MOUSE CONTROL FOR DISABLED PEOPLE USING ARTIFCIAL NEURAL NETWORK

We describe a system that allows humans and computers to interact without using their hands. Our technology uses human facial traits to replace traditional mice in a novel way. It employs image processing techniques such as face detection and eye extraction. It captures an input image using a standard webcam. Controlling the mouse cursor is accomplished by moving the face up, down, left, and right, and mouse events are controlled by eye blinks, while keyboard events are controlled by handgestures. To carry out these actions, various algorithms such as the Haar Cascade method and Dlib are used. Our solution is primarily intended for impaired individuals to communicate effectively with computers.
Experimental Verification of Photon Pair Indistinguishability Enhancement via Heralded Post-Selection

The Hong-Ou-Mandel (HOM) effect is a direct consequence of the bosonic nature of photons [1], as such, it occurs maximally in the presence of truly indistinguishable photons. Measuring the magnitude of the HOM effect is required to determine the reliability with which a photon source is able to produce indistinguishable photons. Using this method, single photon sources have shown the ability to provide photons with remarkable levels of indistinguishability [2].
Emotion Detection from Text in Social Networks

Emotion detection from text in social networks is an important tool for monitoring and analyzing discussions on social networks. However, the difficulty of the problem related to emotional analysis from texts is caused by the lack of additional features such as facial expressions or tone of voice, and the use of colloquial language and various signs and symbols in the texts contributes to misinterpretation of the content of the statement. For this reason, this article proposes an approach to automatically detect emotions from real text data from social networking sites and classify them into appropriate emotion categories. The aim of the article is to develop a model for detecting and classifying emotions through the use of natural language processing techniques and machine learning algorithms. In addition, it was decided to analyze the impact of the number of types of emotions on the classification results. Our research was tested for six classifiers (CART, SVM, AdaBoost, Bagging, Random Forest and K-NN) and three word weighting measures (TF, TF-IDF and Binary). Four measures of classification quality were used to evaluate the classifiers, i.e. accuracy, precision, recall and F1-score. Two datasets from different social networking sites were used for the research - one is a collection of comments from a social networking site and the other is tweets from Twitter. The analysis of the results confirmed that the proposed solution allows detecting emotions from the actual content of tweets, and the results obtained are better for a smaller number of emotion categories.
Edu-Pro: Blockchain based Education System

Many academic institutions have begun to incorporate blockchain technology into their educational programs with the majority of them using it to assist degree management and summative evaluation for learning outcomes. This paper uses a blockchain network and smart contract to implement a new assessment system based on the ’learning is earning model.’ It is observed that students often feel the burden of studies and lack the motivation to complete assignments. In addition, the traditional method for teacher evaluation is based on students’ feedback, however it tends to be one-sided, lacking subjectivity and is hardly helpful for teachers’ improvement. Teachers also often face difficulty in correcting and interpreting theory answers of multiple students which leads to students getting biased marks. Moreover, online testing comes with its own challenges such as test result data storage, non- repudiation and result tampering prevention. To address these shortcomings, our system provides appreciation and encouragement to students as well as teachers by rewarding them with digital currency proportional to their performance. With the help of students’ unambiguous feedback, teacher evaluation will be conducted eliminating subjectivity. Further, subjective answer checking is facilitated by machine learning algorithms. This system aims to achieve increased productivity, fair evaluation, active participation and improvement in student grades.
EFFECTIVE FRAUD DETECTION IN BLOCKCHAIN USING XGBOOST WITH RANDOM FOREST

To solve the issue of fraud and abnormalities in the Bitcoin network, we offer a model. Here, we classify transactions based on integrated and fraudulent transaction patterns using machine learning methods like XGboost and Random Forest (RF). Then, future incoming transactions are predicted using the trained dataset. To identify fraudulent transactions, the authors use blockchain technology with machine learning algorithms. The proposed model performs a security analysis of the proposed smart contract to show the system's robustness and determines the precision and AUC of the models to gauge accuracy. To guard against assaults and vulnerabilities on the suggested system, the authors additionally suggest an attacker model. Overall, as financial technology advances, the proposed approach seeks to offer a more secure method for spotting fraud in the Bitcoin network. The authors assert that their suggested system's use of machine learning and blockchain technology may successfully identify and stop fraudulent transactions.
Optical Waveform Engineering of Non-Gaussian States

Toward the realization of quantum information processing (QIP), continuous-variable (CV) system is a promising candidate, where scalable quantum information processors are already demonstrated [1]. The next milestone is the demonstration of universality and fault-tolerance on these processors by injecting highly non-classical quantum states, non-Gaussian states (NGSs), with a proper temporal waveform [2]. NGSs are very fragile to optical losses, however, and engineering temporal waveform of NGSs is a quite nontrivial task that requires completely different methodology from conventional optical wave shaping.
Higher-Dimensional Hong-Ou-Mandel Effect with Linear-Optical Grover Multiports

We expand traditional two-photon Hong-Ou-Mandel (HOM) effect onto a higher-dimensional set of spatial modes and introduce a quantum network switch providing controllable redistribution of entangled states over four modes using directionally unbiased linear-optical Grover four-ports.
Experimental Demonstration of Entangled Photonic Qubits in a Continuous-Time Quantum Walk

Photonic quantum walks (PQWs), realized in lattices of evanescently coupled waveguides, are a powerful platform for demonstrating quantum [1], topological [2] and localization phenomena [3]. The PQW dynamics are captured with a tight-binding Hamiltonian, whose parameters can be engineered with the geometry of the waveguide array. It has been proposed that the two-qubit controlled-NOT (CNOT) gate can be implemented by quantum interference of two-photons in a carefully engineered PQW [4]. Whereas photonic quantum logic gates typically rely on a network of beamsplitters [5], the PQW implements the quantum logic in a single step. While multi-photon experiments in PQWs have been demonstrated [1]–[3], where photon-photon bunching governs the dynamics, the controlled preparation of entangling logic gates is yet to be experimentally explored.
Multiport Cyclic Interferometer for Genuine n-photon Indistinguishability Assessment

The generation of quantum states of many indistinguishable photons is a crucial resource for the realization of photonic computer architectures that can enable a quantum speedup and create fault-tolerant multi-party quantum networks. On the experimental side, the measurement of indistinguishability for a multi-photon state is a challenging task. In the case of a two-photon state, the photon indistinguishability is fully assessed by the visibility of the interference fringe of the Hong-Ou-Mandel (HOM) interference experiment on a balanced beam splitter. However, pairwise HOM experiments are unable to completely determine the multi-photon indistinguishability when more than two photons are involved [1]–[2].
Second-Order Duality of Light and its Nonclassical Complementarity

A single photon exhibits wave-particle duality in the Young's double-slit interferometer. The duality character-ized by an interference visibility <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$V$</tex> as the wave-like property and a which-path information <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> as the particle-like property has tradeoff relation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D^{2}+V^{2}\leq 1$</tex> known as complementarity [1]–[3]. There are a lot of studies about the complementary nature of photons from various viewpoints such as delayed-choice experiments, theoretical works related to the uncertainty principle, and so on. More recently, studies have expanded the concept of the wave-particle duality to higher-order interferometric scenarios, in which the input light includes two or more pho-tons [4], [5]. Despite having a long history, all previous studies have been related to phase correlation of optical states, which reason would be why the main focus in the first place is the investigation of the mysterious quantum nature of a photon, including the superposition principle.
LP SVM with A Novel Similarity function Outperforms Powerful LP-QP-Kernel-SVM Considering Efficient Classification

– While the core quality of SVM comes from its ability to get the global optima, its classification performance also depends on computing kernels. However, while this kernel-complexity generates the power of machine, it is also responsible for the compu- tational load to execute this kernel. Moreover, insisting on a similarity function to be a positive definite kernel demands some properties to be satisfied that seem unproductive sometimes raising a question about which similarity measures to be used for classifier. We model Vapnik’s LPSVM proposing a new similarity function replacing kernel func- tion. Following the strategy of ”Accuracy first, speed second”, we have modelled a similarity function that is mathematically well-defined depending on analysis as well as geometry and complex enough to train the machine for generating solid generalization ability. Being consistent with the theory of learning by Balcan and Blum [1], our similar ity function does not need to be a valid kernel function and demands less computational cost for executing compared to its counterpart like RBF or other kernels while provides sufficient power to the classifier using its optimal complexity. Benchmarking shows that our similarity function based LPSVM poses test error 0.86 times of the most powerful RBF based QP SVM but demands only 0.40 times of its computational cost.
The client’s consent strengthens the ‘Gamers’ hand; The deterrence theory’s perspective of the Internet romance fraudsters.

Internet romance fraud (IRF) is an international crime which has great consequences for individuals, businesses and nations. This emerging crime is based on a romantic love relationship over the internet between a victim (with love intention) and an offender with a criminal intention. Offenders recruit victims from dating sites with the sole intention of defrauding these victims by deception. This study surveyed 320 respondents from validated internet romance fraud (IRF) hotspots which includes, internet cafés and university campuses in Ghana. Structural equation modelling (SEM), with partial least square (PLS) was use to model and analyze the casual relationships of the dependent and independent variables. The evidence from this study suggests, deterrence - dimensions of punishment (certainty, severity) and shame (certainty severity) negatively influence intention to commit internet romance fraud (IRF) however, intention to commit internet romance fraud (IRF) positively influence the actual IRF crime. Whereas, punishment dimensions are negative and significant, shame dimensions are negative and insignificant, implying IRF are not shamed in their quest to defraud their victims. Internet romance fraudsters are not deterred by punishment and or shame to defraud their victims, although they know their acts are criminal in nature and punishable by law. Offenders’ criminal intention ensures that victims are ultimately defrauded under the pretense of romantic love relationship. This embolden attitude of internet romance offenders must inform law enforcement and prosecutions. Maybe the punishment meted to IRF criminals are not deterrent enough, thereby, sending the wrong signals to IRF criminals and encouraging more young persons to participate in internet romance fraud (IRF) activities.&#x0D;
A Study on the Analysis of College Students' Perceptions of China, Chinese, and Chinese Learning Amid the Rise of Anti-China Sentiment

This study analyzed the perceptions of students who studied beginner-level Chinese for one semester in college Chinese courses. The recent deterioration in relations between South Korea and China has led to a rise in anti-Chinese sentiment, and this could lead to a decline in Chinese language education. The purpose of this study is to find out whether they actually have a low opinion of China or the Chinese language, and whether they have negative thoughts about learning Chinese. This paper analyzed perceptions of China, Chinese language, and Chinese language learning, changes in perceptions through college Chinese class, and learners' perceptions and intentions to continue learning Chinese. The results of the analysis showed that learners themselves recognize the importance of Chinese language, and more importantly, their positive perception of Chinese language affects their willingness to continue learning Chinese in the future. It can be seen that we should focus on developing relevant teaching methods and educational contents in the future.
Conserving Semantic Unit Information and Simplifying Syntactic Constituents to Improve Implicit Discourse Relation Recognition

Implicit discourse relation recognition (IDRR) has long been considered a challenging problem in shallow discourse parsing. The absence of connectives makes such relations implicit and requires much more effort to understand the semantics of the text. Thus, it is important to preserve the semantic completeness before any attempt to predict the discourse relation. However, word level embedding, widely used in existing works, may lead to a loss of semantics by splitting some phrases that should be treated as complete semantic units. In this article, we proposed three methods to segment a sentence into complete semantic units: a corpus-based method to serve as the baseline, a constituent parsing tree-based method, and a dependency parsing tree-based method to provide a more flexible and automatic way to divide the sentence. The segmented sentence will then be embedded at the level of semantic units so the embeddings could be fed into the IDRR networks and play the same role as word embeddings. We implemented our methods into one of the recent IDRR models to compare the performance with the original version using word level embeddings. Results show that proper embedding level better conserves the semantic information in the sentence and helps to enhance the performance of IDRR models.
Alaska Native and Native Hawaiian Culture and Arts Development Grant Program (DOI)

Federal Grants & ContractsVolume 47, Issue 19 p. 4-4 Grants Alerts Alaska Native and Native Hawaiian Culture and Arts Development Grant Program (DOI) First published: 04 September 2023 https://doi.org/10.1002/fgc.33226Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue197 September 2023Pages 4-4 RelatedInformation
An effective source number detection method for single-channel signals based on signal reconstruction and deep learning at low SNR

Abstract Determining the number of sources under low signal-to-noise ratio (SNR) and signal interference with the same frequency and modulation presents a significant challenge. To address this challenge, we propose a novel method for detecting the number of signal sources from single-channel that leverages signal reconstruction and deep learning. The method employs subspace projection based on the Hankel matrix to reconstruct the measured single-channel signals, effectively suppressing noise. Furthermore, we incorporate the correlation of information and the integrity of feature in the signal, by fusing the in-phase component, quadrature component, and frequency spectrum feature of the reconstructed complex signal into a one-dimensional feature suitable for convolutional neural network (CNN). To address the source number detection task, we design a one-dimensional CNN based on convolutional block attention module, transforming it into a classification problem. Finally, experimental measurements demonstrate the effectiveness of our proposed method, with an detection accuracy of 94% even at an SNR of −10 dB.
PENGARUH PENGGUNAAN MEDIA DOMINO CARD PENGALAMANKU TERHADAP HASIL BELAJAR SISWA KELAS II UPT SDN 203 GRESIK

The problem in this study is that the teaching process is still teacher-centered which results in the teacher being more active than the students so that during the learning process students get bored easily which affects their learning outcomes so they use domino card learning media from my experience. The purpose of this research is to determine the effect of using domino card media my experience with the learning outcomes of class II students at UPT SD Negeri 203 Gresik which is known from the difference in the average pretest and posttest that has been done. Data collection techniques in the form of questions pretest and posttest. This research uses a type of quantitative research with Pre-Experimental Design. Because it's not a real experiment yet. Data analysis used parametric analysis test with paired t-test analysis (2 tailed). The results of this study indicate that there are differences in student learning outcomes before and after the implementation of the domino card media, my experience with an average increase of 70.65 compared to before using the domino card media, my experience was an average of 68.71. the results of the output test statistic test paired T-Test (2 tailed) which shows sig (2.tailed) 0.000 &lt;0.05. This shows that my experience domino card learning media can effectively improve student learning outcomes, because student responses are very positive with the use of my experience domino card media.