Reviewerly/exHarmony huggingface dataset:
THE IMPACT OF KNOWLEDGE SHARING ON INNOVATION DURING THE COVID 19 PANDEMIC: THE MEDIATING EFFECT OF CULTURE

This paper intends to examine the importance of knowledge management in increasing the competitive advantage of organizations during the COVID-19 pandemic. This paper also tackles the degree of acceptance of advanced technology in Lebanon overall. The surveys were circulated among 1,000 executives in diverse industries across the country. The total of 246 questionnaires was completed, with the response rate of 24.6% from the total population. The results of our study demonstrate how considering the significance of knowledge management will help organizations to be successful during the pandemic, namely, by improving strategic management decisions, taking into consideration long-run performance of the company and achieving organizational objectives. Our results also show that technology as such is most welcome in Lebanon, however, only a few organizations have been actually planning to invest in it due to the economic conditions that Lebanon is going through along with the currency freefall. Finally, the study shows the need for cooperation and social capital features among community organizations.
Research on Camera Sign-In System Based on SIFT Image Splicing Algorithm

This paper focuses on the image splicing algorithm based on SIFT to help teachers check in more quickly. Multiple images with different angles and overlapping areas monitored by the camera in classroom are selected for image splicing so as to solve the problem of limited view field of the camera lens. At the same time, spatial domain method is used to process the images. The result shows splicing degree is complete that can meet the application requirements of sign-in system with high precision and wide perspective.
Using Skip-Gram Model to Predict from which Show a Given Line is

Text classification has a wide range of usage such as extracting the sentiment out of a product review, analyzing the topic of a document and spam detection. In this research, the text classification task is to predict from which TV-show a given line is. The skip-gram model, originally used to train the Word2Vec sentence embeddings [Mikolov et al, 2013], is adapted to determine the likelihood of occurrence of a sentence in a TV-show. Based on this feature, a classifier is built to perform the task of this research. The results of the cross-validation show that it reaches an accuracy of 58% when running on the transcript data of 3 shows and 43% on 4 shows, while the accuracies of random guessing are supposed to be 33% and 25%. The difference between the neural networks and the skip-gram model becomes smaller when more shows are added to evaluate the model. Among each 5 fold cross-validation of the two models, the best results appear in the midmost iterations.
Frequency Injection Locking-Controlled Oscillations for Synchronized Operations in VO<sub>2</sub> Crossbar Devices

Compact oscillating devices can be realized by exploiting the insulator-to-metal transition of VO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> (IMT). Coupled oscillators can be locked in frequency and phase through connections realized with resistors or capacitors. Oscillatory neural networks based on VO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> devices are researched as neuromorphic computing hardware for image recognition applications [1] , [2] . Physical demonstrations of large networks of coupled oscillators are yet hindered by the device-to-device variability [3] . In this work, we present a frequency-injection scheme which allows additional tuning and control of the oscillator network comprising non-ideal VO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> devices.
Intelligent re-deployment feedback loop for hybrid applications

We propose enabling continuous performance optimisation of distributed hybrid applications in heterogeneous cloud, Edge, and HPC environments by employing an intelligent re-deployment feedback loop.
Una secuencia didáctica diseñada para la metodología de aula invertida

espanolEn Mexico a traves del programa Prepa en Linea-SEP, la SEP ofrece un nuevo servicio educativo que otorga estudios de bachillerato en la modalidad virtual. Esta modalidad educativa no escolarizada tiene la caracteristica de no necesitar asistir a un salon de clases ni cumplir con un horario rigido, ya que la plataforma de aprendizaje y las aulas virtuales estan disponibles las 24 horas, los 365 dias del ano. En este trabajo se presentan los resultados que se obtuvieron durante la implementacion de un material didactico disenado para alumnos de esta modalidad, mostrando ademas la metodologia para disenar material didactico en linea con la metodologia Flipped Classroom y la forma de la implementacion del material y analisis de resultados. EnglishIn Mexico, through Prepa en linea-SEP, the SEP offers a new educational service that provides baccalaureate studies in the virtual modality. This non-school educational modality has the characteristic of not needing to attend a classroom or comply with a rigid schedule, since the learning platform and virtual classrooms are available 24 hours a day, 365 days a year. This paper presents the results that were obtained during the implementation of a didactic material designed for students of this modality, also showing the methodology to design didactic material in line with the Flipped Classroom methodology and the form of the material implementation and analysis of results.
A Process Of Developing An Internet Of Things Based Model For Manufacture Monitoring In Automobile Industry

In this modern era, monitoring various aspects like temperature, pressure, equipment status, working environment, inventory, and productivity is complex. The deviation in the values due to inaccurate measurement will have a significant effect on productivity. To overcome this problem, the Internet of Things-based sensor system plays an essential role in effectively monitoring the manufacturing systems. In this research, a real-time monitoring system using Internet of things based sensors for big-data processing and prediction has been proposed for applications in the automobile industries. Initially, the IoT-based sensor collects important parametric data. The obtained big-data is processed using Apache Kafka, which is a type of message quence. For further real-time processing, and Apache storm engine is proposed. MongoDB is used to store the information from the sensor data collected during the manufacturing process. DBSCAN [Density-Based Spatial Clustering of Applications] and Random forest-based noise-based outlier detection are used to remove the outlier information from the sensor data. It points out the fault detection during the manufacturing process. The model's outcome proves that the Internet of Things-based sensors for processing big data seems to be a successful process during product manufacturing. From the outcome, it was found that the proposed model is having 100% more accuracy than the other existing model. The study's outcome proves that it may help the automobile management regarding unexpected loss due to fault in the initial stage of manufacturing, and it also improves the decision-making for the manufacturing process.
Fighting Fire with Fire - On the Effectiveness of Neural Backdoors in Countering Test-Time Evasion Attacks

Due to its outstanding performances, over the years deep learning - i.e. the branch of machine learning which uses models known as deep neural networks - has become increasingly prevalent in several application domains, including security- or safety-sensitive ones such as anomaly detection, authentication systems, and autonomous driving, among others. In such contexts an adversary might be motivated to look for ways to induce the misclassification of given inputs, e.g. in order to pass a malware off as benign software or to provoke an accident. Test-time evasion attacks and neural backdoors are two types of attacks against deep learning models which, albeit very different in nature, both allow reaching this same adversarial goal, the former by taking advantage of malicious inputs known as adversarial samples at inference time, the latter by manipulating the training of the target model. In this document we explore the interactions between these two attack types, with the intent of setting them against one another. More precisely, first we assess how infecting a model with a neural backdoor changes the classification of adversarial samples depending on the properties of the backdoor trigger, i.e. the secret component which activates the backdoor functionality when recognized by the infected model; then, we determine the type of trigger which maximizes the discrepancy in behaviour between adversarial and natural samples; finally, we evaluate the degree to which such discrepancy could potentially be exploited to improve the robustness of a given model against evasion attacks by leveraging an ensemble of infected versions of it.
LibGuides: Humanities and Arts Management: eBooks

Useful links, resources and research assistance for all Humanities and Arts Management programmes at IADT
Inside the digital society: lessons from little laptops

Once again, children are trying to learn from home, often on an inadequate device and with hard-pressed and under-supporting parents struggling to cope. Even for families lucky enough to get a government laptop, is this enough? What can we learn from previous efforts to drop tech into homes and expect children to keep up with their education? In this blog, David Souter discusses evidence from a much-publicised initiative in developing countries to explore the value of technologies for education and how pitfalls such as technological determinism and lack of sustainability might limit their real-life development potential.
General problems faced by research scholars and academicians in paper writing

Genç, B. &amp; Bada, E. (2010) Every researcher work gets notified by the other scholars or researchers in the worldwide if they published their work in universal platform. At starting researchers publish the work in different languages to get notified by the others because there is no common language. Every country fallows their own language. But to do like this researcher face problems like language compatibility
AN ENHANCED SPAM DETECTION MODEL IN ARABIC TWEETS

Spam is an activity that impacts the experience of users on the internet. One of the most popular social networks is twitter, where people exchange short text messages about news, politics, life experiences, etc. Twitter has led to an increase in the spread of spam which is used for advertisements, spread malicious, or just irrelevant content which introduces new security issues and waste of resources. Recently, several approaches have been identified in research for identifying spammers. Even though these approaches presented essential contributions to the field in the English language, few until now covered in the Arabic language. Several challenges are facing existing approaches for Arabic spam detection, especially, handling the morphological nature of Arabic and feature extraction. Therefore, we focus on having a robust spam detection model that could detect the advertisements in trending hashtags in Saudi Arabia. Accordingly, this thesis proposes a deep learning model based on Long Short Term Memory (LSTM) an artificial Recurrent Neural Network (RNN) architecture supported by pre-trained word embedding as a modern feature engineering to detect Arabic spam tweets. Our model achieves an F1-score of 99.28%, which outperforms other machine learning algorithms used in Arabic spam detection.
MC-LSTM: Mass-Conserving LSTM

The success of Convolutional Neural Networks (CNNs) in computer vision is mainly driven by their strong inductive bias, which is strong enough to allow CNNs to solve vision-related tasks with random weights, meaning without learning. Similarly, Long Short-Term Memory (LSTM) has a strong inductive bias towards storing information over time. However, many real-world systems are governed by conservation laws, which lead to the redistribution of particular quantities —e.g. in physical and economical systems. Our novel Mass-Conserving LSTM (MC-LSTM) adheres to these conservation laws by extending the inductive bias of LSTM to model the redistribution of those stored quantities. MC-LSTMs set a new state-of-the-art for neural arithmetic units at learning arithmetic operations, such as addition tasks, which have a strong conservation law, as the sum is constant overtime. Further, MC-LSTM is applied to traffic forecasting, modeling a pendulum, and a large benchmark dataset in hydrology, where it sets a new state-of-the-art for predicting peak flows. In the hydrology example, we show that MC-LSTM states correlate with real world processes and are therefore interpretable.
IMPROVING STUDENTS’ WRITING PROCEDURE TEXT ABILITY THROUGH DEMONSTRATION METHOD AT THE X-3 SMA NEGERI 6 PADANGSIDIMPUAN

The objective of this research was to describe about how to improve students’ writing procedure text ability through demonstration method at the X-3 students of SMA Negeri 6 Padangsidimpuan. Action research method was the research method that used by the writer to conduct this research. Subject of this research was the X-3 students of SMA Negeri 6 Padangsidimpuan, namely 34 students in the 2018/2019 Academic Year. Next, the writer limits this method in two cycles. Next, the writer took many kinds of techniques for collecting data, namely observation, field note, test, and documentation. To analysis data, the writer used qualitative analysis based on the Hubermen’s model. For this case, the writer should make reducation, description, and take conclusion of the data. The result of this research showed that the students got improvement. Whereby, the improvement could be seen from students’ result on test, exactly the improvement move from cycle 1 to cycle 2. In cycle 1 students got 71.17 and students got 76.32 in cycle 2.
LibGuides: Open Access Publishing: Memorial Library Agreements

Information and guidance about open access scholarly publishing for researchers in all disciplines.
The Importance of Internal and External Communication in Increasing Knowledge and Loyalty of Employees in Qi Malaysia

Many organizations in Malaysia today face difficulties in retaining employees since they are unable to identify the factors that contribute to both the increase of employee knowledge on the company and loyalty towards the company. This study goes on to identify how with internal and external communications, and organisation can increase its employee’s knowledge on the company in a positive way and these better-informed employees are then more inclined to be loyal to the company. In QI Malaysia, there is at least one resignation a month and employees hardly know much about the organisation. The aim is to identify if the internal and external communications carried out did in fact help increase employee knowledge about the company and boost their loyalty in a quantitative method. The employees selected for this research received a survey form to fill independently. They were encouraged to complete the questionnaire during their break time or after work. From the response of the survey, we can understand and conclude that employees of QI Malaysia favour timely, frequent and more personal communication through internal communications and external communications methods. They are happy to know that through the internal communications that they are remembered by the company top level management and that their company is portrayed as an employer of choice, leading to individuals they know wanting to work in QI Malaysia. As recommendation, it is suggested that more content about the company is sent out through both internal and external communication of choice, in this case, mass mails being internal communication channel and Facebook as the external communication channel as most employees are engaged here. With more related contents through these two platforms, it will encourage engagement that will increase employee knowledge and loyalty towards the company. (Abstract by author)
Cuerpos secuestrados, pedagogías confinadas: Narrativas del alumnado universitario en tiempos de Pandemia.

Following Montaigne’s spirit, we asked several university students how they felt and experienced confinement, online education and the situation called “new normality”. Thestructure of a story is adopted, to address the origins of higher education institutions,their meanings and their nonsenses, reading narratives of youths whose kid-napped bodies proved on “confined pedagogies”. In this knot we listen to their soliloquies in front ofscreen, perceiving what they miss and what they fear, in this “virtual pre-sent” that confuses and rebuilds pasts and futures. At the end, their stolen future is spun. The idealimage of University as a space for transition to adulthood ad as a way to acquire a fullmembership of a community are frozen by the lack of immunity. Once upon a time, therewas a world of empty streets, silent classrooms, and words encoded in algorithms. This isa tale in where we have listened to our students as a way not to feel so alone.
A survey on detection of Phishing Websites using Machine Learning

Phishing internet sites contents and internet-predicated consummately data includes varied hints.The victim's personal and sensitive records is obtained by phishing sites which lead them to surf a phishing internet site that resembles a valid internet site, that's one of the illegal assaults triumphing with inside the cyber world.The proposed a brilliant version for detecting phishing internet pages primarily predicated on Extreme Learning Machine.Types of internet pages are one of a kind in phrases in their features.Hence, we require to utilize a web page feature set to preserve any phishing assault.A Machine Learning approach is implemented to resist these attacks.The projected technique for importing phishing dataset, legitimate URLs from the database, and also data that is obtained are pre-processed.Phishing website detection is performed on four classes of URL features: domain, address, abnormal based, HTML, JavaScript features.With the aid of processed data URL features are extracted also values for URL attribute are generated.URL analysis is performed by ML techniques that calculates the threshold value as well as range value for URL attributes.The objective of this project is to implement an ELM classification for several features and some phishing sites within the database.
Quanta / Signal / Noise

This is an iOS app that provides an interactive music playback interface for the ensemble The Langham Research Centre.
Image Encryption for Secure Internet Transfer

Information security has always been an important aspect of consideration in daily life communication and with the advent of COVID-19 and communication, including sensitive data like corporate files, personal data, etc., increasingly moving online, it has become more important than ever to provide secure communication methods. One such method is using encryption and steganography. Encryption is the process of converting ordinary plaintext data into an alternative form that is unreadable by perpetrators without authorization. Furthermore, steganography is the practice of hiding a file, message, video, or image within another such file. In this paper, we introduce a web application that will enable the user to encrypt the data in a file of his choice and then hide it within a picture or GIF. This will provide the layman an application that is easy to use to communicate securely, even without extensive technical knowledge. The encryption algorithm used will be the AES-256 algorithm and the steganographic algorithm will be Syndrome-Trellis Codes (STCs) for their high embedding efficiency. The results display that the system designed offers a significant security level.
Study and Countermeasure to Improve Cadets’ Learning Enthusiasm

By means of questionnaire survey and opinion exchange, combined with personal work experience, the study enthusiasm of undergraduats in Army Logistics Academy was qualitatively analyzed and thought.Through statistical analysis of the distribution of cadets' enthusiasm, the paper explores the deep reasons that affect the cadets' enthusiasm for learning, and puts forward countermeasures and methods to improve their enthusiasm.In the end, the limitations of this study and the direction of further research are expounded.
Web sentiment analysis: Social media networking

Social media networking sites are very important in this technological era and the comments, reviews played vital role to make business decision. In general analysis of fundamental and technical analysis can predict a stock to the certain extent but social media platform will reflect further prediction of stocks. In this study three industries such as FMCG industry, Telecom Industry, IT Industry are taken for the analysis. The financial data is extracted from the money control and social media reviews are extracted from Trackur tool. Sentiment analysis is used to extract such opinion and remarks of users by classifying them as positive, negative and neutral sentiment. Then the stock price changes can be compared. Pearson correlation has found and then regression model has built and researcher can find stock price movements and influences to social media in the same.
A Descriptive Study of Interest Learns High Class Students on Face to Face and Online Learning in UPTD SDN 57 Parepare

Abstra ct . This research is a descriptive research, the aim of this research is to find out the learning interest of high class students in face to face and online learning at UPTD SDN 57 Parepare. The variable in this research is interest in learning which can be measured by several indicators, namely pleasure, interest, attention, and involvement. The method used is a survey method with data collection techniques using a questionnaire. The population in this research were 111 students of UPTD SDN 57 Parepare and the sample studied was 56 high class students. This research approach was quantitative with data analysis techniques using descriptive statistical analysis.  The result of this research indicate that the average learning interest of high class student in face to face learning is 64 in the high category, and the average learning interest of high class student in online learning is 55 in the medium category.   K eywords : Interest to learn, face to face learning, online learning.
Undefined class-label detection vs out-of-distribution detection

We introduce a new problem, that of undefined class-label (UCL) detection. For instance, if we try to classify an image of a radio as cat vs dog, there will be no well-defined class label. In contrast, in out-of-distribution (OOD) detection, we are interested in the related but different problem of identifying regions of the input space with little training data, which might result in poor classifier performance. This difference is critical: it is quite possible for there to be a region of the input space where little training data is available but where class-labels are well-defined. Likewise, there may be regions with lots of training data, but without well-defined class-labels (though in practice this would often be the result of a bug in the labelling pipeline). We note that certain methods originally intended to detect OOD inputs might actually be detecting UCL points and develop a method for training on UCL points based on a generative model of data-curation originally used to explain the cold posterior effect in Bayesian neural networks. This approach gives superior performance to past methods originally intended for OOD detection.
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