Reviewerly/exHarmony huggingface dataset:
Computational Overhead of Locality Reduction in Binary Optimization Problems

Recently, there has been considerable interest in solving optimization problems by mapping these onto a binary representation, sparked mostly by the use of quantum annealing machines. Such binary representation is reminiscent of a discrete physical two-state system, such as the Ising model. As such, physics-inspired techniques -- commonly used in fundamental physics studies -- are ideally suited to solve optimization problems in a binary format. While binary representations can be often found for paradigmatic optimization problems, these typically result in k-local higher-order unconstrained binary optimization cost functions. In this work, we discuss the effects of locality reduction needed for the majority of the currently available quantum and quantum-inspired solvers that can only accommodate 2-local (quadratic) cost functions. General locality reduction approaches require the introduction of ancillary variables which cause an overhead over the native problem. Using a parallel tempering Monte Carlo solver on Microsoft Azure Quantum, as well as k-local binary problems with planted solutions, we show that post reduction to a corresponding 2-local representation the problems become considerably harder to solve. We further quantify the increase in computational hardness introduced by the reduction algorithm by measuring the variation of number of variables, statistics of the coefficient values, and the population annealing entropic family size. Our results demonstrate the importance of avoiding locality reduction when solving optimization problems.
ENT 530 Social Media Porfolio: Bergedil Bites / Nor Alia Adila Che Musa

Bergedil Bites Facebook page controlled by me who is Alia Adila has sold Popia Bergedil serves a Popia Bergedil with ingredients such as potatoes with Ramly meat and potatoes with sausages. It is different with Tauhu Bergedil because Popia Bergedil using popiah skin that crunchier than the tofu. Bergedil Bites has 228 followers and 255 likes. There are 8 teasers on this Bergedil Bites Facebook which are separated into two categories. Firstly, is the start of the Popia Bergedil to be launched. It introduced products that will be sold later in the Facebook page. Secondly, it is about 50% off Christmas discount that will launched on 25 December 2020. Next, for hard sells, there are 16 posts about Popia Bergedil. It contains the price of Popia Bergedil as well as details such as contact number and address to make it easier for customers who want to buy it. There are also 16 posts for soft sells that contains benefit and tips for all the ingredients that used to make Popia Bergedil. Lastly, there are 22 frequency postings that which is a random posting that are not related with Popia Bergedil.
Factors influencing social media adoption among smes during COVID-19 crisis

Social media is considered a dominant platform for ensuring business success and survival, especially in the case of small and medium enterprises businesses (SMEs). It offers many beneﬁts for businesses in terms of enhancing customer relations, increasing proﬁt, reducing cost, and allowing �?exibility. During the Covid-19 pandemic crisis, social media has become essential for businesses. The focus has shifted to conducting businesses’ daily activities remotely. Despite its importance, this research aims to explore factors that affect social media implementation on the part of SMEs in Jordan. The Technological, Environmental, and Organizational (TOE) and the Technology Acceptance Model (TAM) has been employed. These theories provide useful insights and explanations with regard to the internal and external contexts of social media adoption. Data was collected from 250 SMEs in the city of Irbid, north Jordan. The results reveal that all factors signiﬁcantly affect social media adoption on the part of SMEs. Among these factors, environmental context was the most signiﬁcant predictor of social media adoption during the Covid-19 pandemic crisis; this ﬁnding could provide a good basis for SME decision-makers and practitioners regarding assessing the factors that in�?uence social media implementation on the part of SMEs.
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Face Recognition-Based Automatic Hospital Admission With SMS Alerts

When the person met with an accident is brought to the hospital, there are many official formalities before the treatment can start (e.g., admission form to be filled). In some severe cases, these formalities can delay the treatment, which could be fatal to the patient. The automated system which can fill these forms with the help of face recognition could severely cut down the delays. But, in some cases, the injury and the blood on to the face fail facial recognition. To overcome this problem, we have proposed a facial vector-based algorithm. In the current work, we have also demonstrated, sending the SMS to the concerned authorities (police) and even to the relatives of the patient automatically using GSM modules. The patient's information was received from centralized databases of different hospitals that are linked through the internet. We have tested the proof-of-concept with 12 patients and found out that the accuracy of the system was about 92%. Automated filling improved hospital admission and helped in treatment because of the medical history was auto-filled even in the case of an unconscious patient.
Comparative Analysis of Tamil and English News Text Summarization Using Text Rank Algorithm

The exponential growth of newsgroups has made it more difficult to gain accurate access to a large amount of data. To deal with the massive amounts of data, efficient and effective methods are needed. One such method is text summarization, which presents data in a condensed format. It would be beneficial for readers to be able to get a wide variety of news in a short amount of time if the news is simplified.In this article, we use the English Newsgroup datasets and the Tamil Newsgroup datasets to automate News Summaries using the text rank algorithm. The proposed work was created using a changed Text Rank algorithm based on the principle of word frequency. The suggested approach creates vectors of words as nodes and similarities between two words as the edge between them, which is the webbing between them. Term frequency assigns various weights to different terms in a sentence, while standard cosine similarity regards both of them similarly. The vector is rendered sparse and divided into clusters based on the premise that sentences inside a cluster are identical and sentences from various clusters reflect their dissimilarity. The performance assessment of the proposed summarization strategy in two types of Newsgroup datasets demonstrates its usefulness in terms of the accuracy parameter.
위기 상황 대응을 위한 항공기 급유차 소요대수 산출모형

As Korea Air Force treats jet fuel as a key support element for aviation operations, it is operating a special vehicle “Aircraft Refueler”. If the jet fuel and refueler retention rate falls to a level that cannot support the planned air operation mission, the air base is converted into a crisis stage. As refuelers are not subject to stockpiling, it is best to preserve the appropriate number of refuelers. Nevertheless, the criteria have not been systematically studied. In this study, a model to calculate the appropriate number of refuelers required to support the maximum amount of air operations in war is presented using Markov chain. In addition, the result from the model is validated by the real operation data. The model suggested in this study will help analyze operation capability of Korea Air Force in an emergency situation.
Secure Healthcare System Based On Blockchain And Public Key Cryptography

Growing healthcare is very essentials nowadays. In the present situation, healthcare organizations are facing a serious problem in sharing medical information without giving up the privacy and integrity of information. Also an important research issue is to store large volume of health information and provide safety towards tampering and misuse of records. This paper aims to address the issues of information security and authentication in healthcare. Blockchain technology has the probable of immutability, integrity and decentralized architecture to manage records of the health sector. Here, we propose a blockchain based system using public key cryptography to be tamper resistant and secure the information. Our proposed architecture provides numerous opportunities for healthcare industry such as reduced transaction costs, increased transparency for regulatory reporting, effective healthcare data management and healthcare records universality.
Development of Software for Reading Thesis Abstracts

This research aims to develop software for read abstracts from a computer science project. To use for searching the data for students , researchers and to evaluate the satisfaction of the software users. The functioning of the system starts from user uses a phone with an Android operating system scan QR Code from the student's project. Then the system will send data code from QR Code to server. The server will search data from the database through PHP language program. And search data with SQL language, and returns abstracts to the system. The system will convert the text to an audio format using Google technology Text-to-Speech. System performance assessment results from a poll of 15 users who have a total average of 4.57, the standard deviation equal to 0.52. We can show that the level performance assessment results of the software development system for reading Thesis abstracts is the best.
한국과 중국의 정보교육 교육과정 비교

Today, the fourth industrial revolution began with advances in cutting-edge technologies such as artificial intelligence, big data and the Internet of Things. Education is needed to foster new talent in the new era. Emphasizing the importance of software education in the era of the Fourth Industrial Revolution, countries around the world included software education in their school curriculum. This study investigates Chinese information technology education courses and information service education courses in the revised curriculum in 2015. Through this study, we can help in the development direction of information education in our country.
KETIDAKADILAN GENDER DALAM NOVEL PEREMPUAN BERKALUNG SORBAN KARYA ABIDAH EL KHALIEQY SERTA NILAI PENDIDIKAN KARAKTERNYA

This study aims to describe: (1) the form of gender injustice experienced by Javanese women in the novel Perempuan Berkalung Sorban (2) the value of character education contained in the novel Perempuan Berkalung Sorban. This research is a descriptive qualitative research with literary feminism approach. The data source in this study is a document in the form of the novel Perempuan Berkalung Sorban by Abidah El Khalieqy. The data collection of this research includes reading, recording, and analyzing documents. The data analysis technique used in this study is a content analysis technique which includes data reduction, data presentation, and data inference. The results of this study (1) the forms of gender injustice against female figures are subordination, stereotypes, violence and double work. (2) the value of character education that appears in the novel Perempuan Berkalung Sorban is religious, hard work, democratic, curious, fond of reading, and social care
Analisis Nilai dan Keterbacaan Kisah Rasulullah sebagai Sumber Bacaan Mata Pelajaran Bahasa Indonesia

The use of text with character content in learning is one of the efforts to strengthen children's moral kognition which is one of the keys to ordering characters. This study aimed to analyze the character values contained in the story of Rasulullah and test the readability of the story. The results of the value analysis by were validated by value education experts, language education experts in elementary schools and hadis experts. Researchers conducted a readability test using Raygor and fry graph. The results show that there are five stories that contained the main values of social care and the accompaniment values of patience, empathy and generosity. The readability test results show that the five stories are suitable for reading by children aged 1o to 12 years. From these results, the story of the Prophet is right for use in literary learning with the aim of instilling character.
Handwritten Digit Classification

In the current age of digitization, handwriting recognition plays an important role in information processing. There is a lot of information available on paper and less processing of digital files than the processing of traditional paper files. The purpose of the handwriting recognition system is to convert handwritten letters into machine-readable formats. Major applications include vehicle license-plate identification, postal paper-sorting services, historical document preservation in the check truncation system (CTS) scanning and archaeology departments, old document automation in libraries and banks, and more. All of these areas deal with large databases and therefore require high identification accuracy, low computational complexity, and consistent performance of the identification system.Over time, the number of fields that can implement deep learning is increasing. In deep learning, convolutional neural networks (CNN) are being used for visual image analysis. CNN can be used in object detection, facial recognition, robotics, video analysis, segmentation, pattern recognition, natural language processing, spam detection, topical gradation, regression analysis, speech recognition, image classification.Detection of handwritten numbers, including accuracy in these areas, has reached human perfection using deep convolutional neural networks (CNNs). Recently CNN has become one of the most attractive approaches and has been the ultimate factor in recent success and in several challenging machine learning applications. Considering all the factors stated above we have chosen CNN for our challenging tasks of image classification. We can use it to identify handwritten numbers, which is one of the higher education and business transactions. There are many applications of handwritten digit recognition for our real-life purposes. Hence we are using the Convolutional Neural Network (CNN) and MNIST dataset.
Users on Instagram, Twitter, TikTok decry censorship of Israel posts

Users on Instagram, Twitter and TikTok are highlighting instances of post removals and other forms of censorship after sharing content related to the Israel-Palestinian conflict.
Epistemic verbs produce spatial models

Author(s): Khemlani, Sangeet | Abstract: Verbs such as ‘know’ and ‘think’ help people describe mental states, and reasoners without any training in logic can make epistemic inferences about mental states. For instance, verbs such as ‘know’ are factive, i.e., they describe true propositions, and the statement Ora knows that it’s sunny licenses the inference that it’s sunny. Logicians have accordingly developed epistemic logics capable of characterizing valid and invalid epistemic inferences based on operators that serve as analogs to verbs such as ‘know’ and ‘think’. Recent work suggests that no existing logical system can capture the inferences that naive individuals tend to make. This paper describes a new theory of epistemic reasoning that operates on the assumption that reasoners represent epistemic relations as spatial models. The theory accords with recent theoretical advances, existing data, as well as two novel experiments that show how reasoners cope with nested epistemic verbs, e.g., Ami knows that Ora thinks it’s sunny.
Perception Analysis of Covid-19 Pandemic, Cybercrime and Well-Being of Online Fraud Victims in Calabar, Nigeria

The study examines the relationship between Covid-19 pandemic, increase in cybercrime and well-being of online fraud victims in Calabar, Cross River state, Nigeria. Specifically, the study (i) examines the most prevalent cybercrime experienced by individuals during Covid-19 outbreak (ii) examine the most vulnerable group of people to cybercrime (iii) examine vulnerability factors (iv) determine the consequences of cybercrime on the well-being of victims in Calabar, Cross River state, Nigeria. The study is descriptive in nature and adopts the mixed methods of data collection. Using qualitative and quantitative research approaches, a sample of 622respondents is drawn from Calabar, the capital of Cross River State, Nigeria. The study adopts multi-stage sampling technique, which consist of purposive, simple random and stratified random sampling techniques to reach the respondents. Findings from the study reveals that during Covid-19 outbreak there is an increase in cybercrime as a result of increased reliance on the internet for social interaction. It is observed that phishing and hacking are among the two prominent types of cybercrime used in attacking victims in Calabar, Cross River State, Nigeria. The study recommends that issues of cyber security should be taking seriously by government and other stakeholders in the communication industry.
Generalization Error Bounds for Deep Unfolding RNNs

Recurrent Neural Networks (RNNs) are powerful models with the ability to model sequential data. However, they are often viewed as black-boxes and lack in interpretability. Deep unfolding methods take a step towards interpretability by designing deep neural networks as learned variations of iterative optimization algorithms to solve various signal processing tasks. In this paper, we explore theoretical aspects of deep unfolding RNNs in terms of their generalization ability. Specifically, we derive generalization error bounds for a class of deep unfolding RNNs via Rademacher complexity analysis. To our knowledge, these are the first generalization bounds proposed for deep unfolding RNNs. We show theoretically that our bounds are tighter than similar ones for other recent RNNs, in terms of the number of timesteps. By training models in a classification setting, we demonstrate that deep unfolding RNNs can outperform traditional RNNs in standard sequence classification tasks. These experiments allow us to relate the empirical generalization error to the theoretical bounds. In particular, we show that over-parametrized deep unfolding models like reweighted-RNN achieve tight theoretical error bounds with minimal decrease in accuracy, when trained with explicit regularization.
Structural Comparisons of Noun and Verb Networks in the Mental Lexicon

Author(s): Qiu, Mengyang; Castro, Nichol; Johns, Brendan | Abstract: Recent studies have applied network-based approaches to analyze the organization and retrieval of specific semantic categories, with a focus on the animal category. The current study extended previous studies by using network science tools to quantitatively investigate the structural differences of noun and verb categories of various levels of specificity. Specific (animal and body movement) and general noun and verb networks were constructed from four verbal fluency tasks. Common network measures indicated that the two verb networks were more condensed and less modular than the noun networks, supporting that nouns are more well-organized in the mental lexicon than verbs. Comparing the specific and general networks within each lexical category also corroborated lexical semantic studies that nouns have a more clear hierarchical structure. The results of this paper, along with recent semantic network studies, provide converging evidence for the usefulness of network science in semantic memory research.
Modelling Characters’ Mental Depth in Stories Told by Children Aged 4-10

Author(s): Dijk, Bram, van; van Duijn, Max | Abstract: From age 3-4, children are generally capable of telling stories about a topic free of choice. Over the years their stories become more sophisticated in content and structure, reflecting various aspects of cognitive development. Here we focus on children’s ability to construe characters with increasing levels of mental depth, arguably reflecting socio-cognitive capacities including Theory of Mind. Within our sample of 51 stories told by children aged 4-10, characters range from flat “actors” performing simple actions, to “agents” having basic perceptive, emotional, and intentional capacities, to fully-blown “persons” with complex inner lives. We argue for the underexplored potential of computationally extracted story-internal factors (e.g. lexical/syntactic complexity) in explaining variance in character depth, as opposed to story-external factors (e.g. age, socioeconomic status) on which existing work has focused. We show that especially lexical richness explains variance in character depth, and this effect is larger than and not moderated by age.
A Federated Machine Learning Protocol for Fog Networks

In this paper, we present a federated learning (FL) protocol for fog networking applications. The fog networking architecture is compatible with the Internet of Things (IoT) edge computing concept of the Internet Engineering Task Force (IETF). The FL protocol is designed and specified for constrained IoT devices extended to the cloud through the edge. The proposed distributed edge intelligence solution is tested through experimental trials for specific application scenarios. The results depicts the performance of the proposed FL protocol in terms of accuracy of the intelligence and latency of the messaging. Next generation Internet will rely on such protocols, which can deploy edge intelligence more efficient to the extreme amount of newly connected IoT devices.
RANCANG BANGUN SISTEM INFORMASI EMPAT DARA KOST BERBASIS WEB DI KOTA BATAM

Four Dara Kost is a place for lodging services such as boarding houses, but this boarding house only rents rooms, this house has been established since 2010 in the city of Batam. Four Dara Kost have problems in managing their boarding houses sucg as collecting data on prospective occupants, checking rooms and explaining information about boarding room facilities. The researcher aims to help boarding house owners and assistanst on how to desing and implement web-based information systems at the four dara boarding house. Researchers will conduct this research use the waterfall method Results of this research are boarding house owners and boarding house assistants find it easier to process their renting rooms, checking rooms and explaining information on room facilities provided.
Aktifitas Pembelajaran Bahasa Inggris di Pondok Pesantren Nurul Hidayah Bantan dalam Meningkatkan Speaking Performance Santri

This study is aimed to investigate the English learning activities at Nurul Hidayah boarding school in developing students’ speaking performance with their strengths and weaknesses. This research is descriptive qualitative research with triangulation analysis. Methods for obtaining data were carried out through interviews and documentation. English learning activities at Nurul Hidayah Islamic Boarding School are divided into two; activities in the classroom and outside of the classroom. The classroom activities refer to KMI Gontor. The activities outside of the classroom are Daily Conversations, English Video Screenings, English Speech Exercises, and English Contest and Games. The advantages of the activities are that the boarding school already has a good and effective English learning system and method in training the students' English language skills. The student's awareness and interest in speaking English have grown. The language environment has developed. Meanwhile, its weaknesses are that the number of personnel of the language driving organization is still inadequate compared to the total number of whole students. Most of the teachers do not consistent in using English. Outside of classroom activities programs are so limited. Most of the students are tend to use Arabic than English. Most of the students are still facing some difficulties in memorizing vocabulary and in pronouncing English words. The correction systems toward the language mistakes are still low.