Reviewerly/exHarmony huggingface dataset:
Hotel Recommendation System Based on User Profiles and Collaborative Filtering

Nowadays, people start to use online reservation systems to plan their vacations since they have vast amount of choices available. Selecting when and where to go from this large-scale options is getting harder. In addition, sometimes consumers can miss the better options due to the wealth of information to be found on the online reservation systems. In this sense, personalized services such as recommender systems play a crucial role in decision making. Two traditional recommendation techniques are content-based and collaborative filtering. While both methods have their advantages, they also have certain disadvantages, some of which can be solved by combining both techniques to improve the quality of the recommendation. The resulting system is known as a hybrid recommender system. This paper presents a new hybrid hotel recommendation system that has been developed by combining content-based and collaborative filtering approaches that recommends customer the hotel they need and save them from time loss.
Genetic Algorithm for Optimizing Traveling Salesman Problems with Time Windows (TSP-TW)

The concept of Traveling Salesman Problem (TSP) used in the discussion of this paper is the Traveling Salesman Problem with Time Windows (TSP-TW), where the time variable considered is the time of availability of attractions for tourists to visit. The algorithm used for optimizing the solution of Traveling Salesman Problem with Time Windows (TSP-TW) is a genetic algorithm. The search for a solution for determining the best route begins with the formation of an initial population that contains a collection of individuals. Each individual has a combination of different tourist sequence. Then it is processed by genetic operators, namely crossover with Partially Mapped Crossover (PMX) method, mutation using reciprocal exchange method, and selection using ranked-based fitness method. The research method used is GRAPPLE. Based on tests conducted, the optimal generation size results obtained in solving the TSP-TW problem on the tourist route in the Province of DIY using genetic algorithms is 700, population size is 40, and the combination of crossover rate and mutation rate is 0.70 and 0.30 There is a tolerance time of 5 seconds between the process of requesting distance and travel time and the process of forming a tourist route for the genetic algorithm process.
Enhancement of SentiWordNet using contextual valence shifters

Sentence structure has a considerable impact on the sentiment polarity of a sentence. In the presence of contextual valence shifters like conjunctions, conditionals and intensifiers some parts of the sentence are more relevant to determine the sentence polarity. In this work we have used valence shifters in sentences to enhance the sentiment lexicon SentiWordNet in a given document set. They have also been used to improve the sentiment analysis at document level. In the near past, micro blogging services like Twitter have become an important data source for sentiment analysis. Tweets, being restricted to 140 characters have slangs, are grammatically incorrect, have spelling mistakes and have informal expressions. The method is aimed at noisy and unstructured data like tweets on which computationally intensive tools like dependency parsers are not very successful. Our proposed system works better on both noisy (Stanford and airlines datasets of Twitter) and structured (movie review) datasets.
On Rent—An Android Mobile Application

In today’s era of development, people are more industry-oriented and hence are shifting in different cities for their jobs as per their requirements. Such citizens are often occupied with their work life and they want everything to be done easily and quickly. They have limited time to explore and visit different places in order to search required commodities according to their needs. Additionally, traditional way to find anything or to advertise anything on lease is time-consuming and tedious task as one has to explore different applications for searching different commodities. Hence, to save the time and effort from the hectic schedules of individuals, an android application has developed namely ‘On Rent,’ which provides different services on a single platform. This platform equips the user to borrow and lend equipment like books and electrical appliances on rent. It also facilitates users to take accommodation on rent according to their needs.
Extra-Linguistic Elements in Learning Second Language

Extra-linguistic is a situation beyond the boundaries of linguistics; however, it is a free pattern that can exist in any field of study. In the field of language studies, extra-linguistic elements are taken into consideration as they provide the information in relation with motivation, emotion, attitudes, personality, and so on that are proven in helping a student in grasping certain languages. In this paper, the researcher connected a few ancillaries and extra-linguistic elements in language learning which are essential in order to indicate how other language learners accept and are willing to learn languages other than the mother tongue. Each learner or instructor needs to acknowledge that learning second language requires more than reading, writing or speaking in the language they want to learn but also the extra efforts to make the learning of the second language more meaningful. Educators must take the existence of extra-linguistic in language learning seriously because the elements in it can help students learn any language easily. Extra-linguistic elements are crucial in achieving educational success. Students, teachers, parents, school and educational ministry should be more concerned in order to ensure students success.
Automated Negotiation in E Commerce: Protocol Relevance and Improvement Techniques

We all negotiate, formally or informally, in jobs, in day today lives and outcomes of negotiations affect those processes of life. Although negotiation is an intrinsic nature of human psyche, it is very complex phenomenon to implement using computing and internet for the various purposes in E Commerce. Automation of negotiation process poses unique challenges for computer scientists and researchers, so here we study how negotiation can be modeled and analyzed mathematically, what can be different techniques and strategies or set of rules/protocols to be implemented and how they can be relevantly implemented. We are in a quest to find out how this complex process, which involves human psyche can be automated using computers and modern day technologies. Now, the quest is not only automation, looking at the research in the related field in last ten years; but it is all about finding solutions to make e-negotiation more efficient and more accurate, as well as useful in any kind of electronic trading situations. Here is an attempt to consolidate our work of last few years on automation of negotiation process; we call it as negotiation protocol on research, study as well as implementation level of negotiation automation. Overall, we are trying to give few solutions to make the automation more efficient.
Improving clinical named entity recognition in Chinese using the graphical and phonetic feature

Abstract Background Clinical Named Entity Recognition is to find the name of diseases, body parts and other related terms from the given text. Because Chinese language is quite different with English language, the machine cannot simply get the graphical and phonetic information form Chinese characters. The method for Chinese should be different from that for English. Chinese characters present abundant information with the graphical features, recent research on Chinese word embedding tries to use graphical information as subword. This paper uses both graphical and phonetic features to improve Chinese Clinical Named Entity Recognition based on the presence of phono-semantic characters. Methods This paper proposed three different embedding models and tested them on the annotated data. The data have been divided into two sections for exploring the effect of the proportion of phono-semantic characters. Results The model using primary radical and pinyin can improve Clinical Named Entity Recognition in Chinese and get the F-measure of 0.712. More phono-semantic characters does not give a better result. Conclusions The paper proves that the use of the combination of graphical and phonetic features can improve the Clinical Named Entity Recognition in Chinese.
Sentiment Analysis of Keenly Intellective Smart Phone Product Review Utilizing SVM Classification Technique

There is a massive increase in number of people who access various social networking and micro-blogging websites that gives new shapes the impression of today's generation. Many reviews for a specific product, brand, individual, and movies etc. are helpful in directing the perception of people thus the analysts are begun to create algorithms to automate the classification of distinctive reviews on the basis of their polarities in particular: Positive, Negative and Neutral. This automated classification mechanism is referred as Sentiment Analysis. The basic idea behind this paper is to apply support vector machine (SVM) classification technique to classify the sentiments of smart phone product review which analyses datasets used for classification of sentiments and texts. Data sets are also used for learning and testing purpose and implemented by SVM methods for finding the polarity of the ambiguous tweets. The obtain results show to achieve high accuracy as predicted on the basis of reviews of smart phone.
Collecting the Tourism Contextual Information data to support the tourism recommendation system

Recommendation system requires supporting data that sometimes are heterogeneous either from its sources or from its data format. In the context of tourism recommendation system, a previous research resulted in the concept of collecting the Tourism Contextual Information (TCI) data to determine the relevant information as the support of tourism recommendation system. However, what things and how the data were collected have not been explained yet. By adopting the global Extract Transform Load (ETL) process, this research aims to show how the supporting data including the database structure, the data architecture, and the data representation in the tourism recommendation system are collected. The result of the research can be used for the need of further research - particularly on the tourism recommendation system considering the change of weather or traffic condition and its effect on making decision when planning a travel or a tour.
Hate Speech Detection in Indonesian Language on Instagram Comment Section Using Maximum Entropy Classification Method

Social media nowadays is a platform for many things and has become a place to delivers opinions. Opinions in a form of hate speech are one of the problems that authorities find hard to solve, because of its number and variations. Instagram, with almost 100 million users in Indonesia, is the object of this research because it is one of the most popular social media in Indonesia where people can share their photo and video, has become one of the most popular places to express hatred towards others. Because of that, a system will be made to detect hate speech on Instagram using Maximum Entropy classification algorithm. TF-IDF is also used as the feature extraction method, with the improvement from Part of Speech (POS) Tagging as the parameter. Accuracy generated from this research is 86,67% using Maximum Entropy with POS parameter TF-IDF and 80% when using TF-IDF without POS parameter. This system is expected to determine whether a comment on Instagram is a hate speech or not, and to see how TF IDF and POS Tagging improve the performance of Maximum Entropy Classifier.
Applying data mining techniques with data of campus card system

Abstract In recent years, campus card system (CCS) has been widely used in universities. At the same time, the scale of the data stored in the database of the CCS is also increasing rapidly. Therefore, it would be better to adopt data mining techniques to reveal the status, causes and trends of students’ consumption data. In this research, the K-Means clustering algorithm is applied to the students’ consumption behavior. The Apriori algorithm is used to study the students’ consumption behavior and performance. The results not only improve the efficiency of student work but also help to build smart campus for our university.
Adaptive Background Subtraction for Monitoring System

Security is one of the most important things for human life at this time. Complex activities often cause homes to be left unattended. One of the actions taken by most people to guard their homes while traveling is to use CCTV (Closed Circuit Television) cameras. This conventional CCTV is less effective because the camera is only recording without analyzing objects. From the shortcomings, the camera is made so that it can monitor the activities of the changes in the movement of objects seen by the camera, in this case the object detected is human movement. The monitoring system using this camera can detect passing objects. This paper proposes the adaptive background subtraction method needed to adapt to frame changes. The background frame will always be updated against the previous background intensity inference. Then it will analyze the effectiveness of the method. The effectiveness of the method used is then evaluated by comparing the results of object extraction with ground truth. The best success rate in object detection from object detection method is measured by calculating recall precision and F-measure values. The experimental results show satisfactory performance from the proposed method.
Human Language Question To SQL Query Using Deep Learning

Inferring structured queries from human language is a complex task that should take into account the lack of knowledge of structural languages like SQL, XQuery, XPath, etc..., in order to effectively use the content of database systems. In this paper, we describe the pros & cons of the existing approaches in the task of text to SQL. We put the light on the ones making use of syntax parsing and the others that utilize deep learning methods to predict the appropriate queries.
Pseudo Code Length Detection in Direct Sequence Spread Spectrum Signal Using Deep Learning

We designed and simulated a deep learning model to find the pseudo code length of a direct sequence spread spectrum signal. We decompose the received complex sequence into real and imaginary parts and input them into the deep learning model. We constructed training and test data sets considering various SNRs and fully reflected in the model. Finally, we conclude with a discussion of further research that can be performed.
Adversarial deep learning for energy management in buildings

Deep learning is a powerful means to classify and thus optimize Energy management in Buildings. Deep learning is effective especially when the training dataset has a reduced volume or when the test set changes at a higher frequency than the training set. Notwithstanding these favourable properties, the classification with deep learning could be distorted by an adversary who can be interested to alter the classification of the energy consumption. Several kinds of fraud could require this attack, as those aimed at energy theft. In this paper we will provide experimental implants where a dataset is tampered with in order to lead the classifier to acquire it as valid, while it contains samples attributable to energy thefts.
An Efficient ASIC Implementation of CLEFIA Encryption/Decryption Algorithm With Novel S-Box Architectures

The demand for lightweight cryptography is inevitable considering the need for security and privacy in the resource constrained environment like active smart devices, RFID and smart edge nodes in Internet of Things (IoT). CLEFIA is one of the ISO/IEC 29191-2 standard lightweight cipher suitable for these applications. For the first time in literature, a novel Composite Field Approach (CFA) based architecture with 4-stage pipelining is derived for CLEFIA Substitution-1 box (S1 box) and a unique Algebraic Normal Form (ANF) based implementation is done for CLEFIA Substitution-0 box (S0 box). The proposed S-box architectures have achieved lowest area in-terms of Gate Equivalence (GE) compared to the traditional implementations. Thereby, a low-cost top-level CLEFIA encryption/decryption architecture is implemented by integrating the novel sub-blocks and its performance is analyzed. The proposed architecture has achieved a low area of 3282 GE, a high throughput of 592.58 Mbps and a low power consumption of 6.1 mW when synthesized using Semi-Conductor Laboratory (SCL) 180nm technology library. As a proof of concept, a working model of the proposed encryption/decryption architecture is prototyped on Basys-3 FPGA with the help of LogiCore VIO.
Advanced Motion-Constrained AV1 Encoder for 8K 360 VR Tiled Streaming

This paper presents the advanced motion-constrained AV1 encoder for a 360 8K VR streaming application. Currently, 360 8K VR streaming faces problems in limited network bandwidth, limited server-side computing power that makes difficult to serve ultra-high resolution videos to a large number of clients, and limiting power of a consumer electronic device to decode the stream. We propose a motion-constrained AV1 encoder that can solve these problems. The proposed method is composed of three different restrictions: motion vector search range, AV1 encoding options, and constrained loop filters in the boundary region of tiles. The experimental results show the average BDBR overhead of the proposed method with various tiles is 10.00% with saving 17.30% encoding time compared to the existing AV1 encoder. Also, the proposed AV1 encoder improves the BDBR by an average of 12.50% compared to a motion-constrained HEVC encoder.
Features Selection for Facial Expression Recognition

Facial expressions play a main role in determining the internal impressions of the human. Therefore, dealing with these expressions by machine learning attract the attention of many researches. However, the large number of extracted features of faces is a hard task in this process due to the numbers of irrelative and repeated features among them. This paper tries to decrease the extracted feature of facial expression images by deleting repeated features to increase the performance and decrease the computation time of the recognition task. Therefore, in this paper, a dataset of students' facial expression is used, and the binary Sine-Cosine Algorithm (SCA) is applied to select the most relative features. The experiment results showed that, the SCA reduced the features number by 87% as well as it achieved the high accuracy results and outperformed all other methods.
Boomerang: Blockchain-based Freelance Paradigm on Hyperledger

There has been a great focus on services across the Internet. Freelancing is one such service that is thought to be in the middle of a dramatic change. Here, the online freelancing platforms have created global opportunities for people to outsource their works, and seek employment slots, like a job. Online freelancing platforms have transformed how, when, and where the work is to be performed. However, these platforms also carry risks. These platforms lack credibility, security, and flexibility d ue to centralized nature. Designing n ew approach, that overcomes these issues, is very important for growth of freelancing. This paper proposes Boomerang, a decentralized freelance administration paradigm on Hyperledger Fabric. Hyperledger Fabric is a distributed operating system for permissioned blockchains. The Boomerang integrates blockchain-based distributed ledger, consensus protocol, and a fine-grained access control. The design includes demonstration of Boomerang and a detailed description of Boomerang architecture. The prototype implementation, based on the Boomerang architecture, is detailed next. The implementation of database and application logic are illustrated for better understanding. In addition, the tools chosen and assumptions made in prototype development are mentioned. This research would help the coming and existing startups to secure grow fast with requisite security.
Convolutional Neural Networks, Image Recognition and Financial Time Series Forecasting

Convolutional Neural Networks (CNN) are best known as good image classifiers. This model is recently been used for financial forecasting. The purpose of this work is to show that by converting financial information into images and feeding these financial-image representation to the CNN, it results in an improvement in classification.
Predicting Breast Cancer with Deep Neural Networks

In this study, a deep neural network (DNN) MODEL was developed which diagnoses breast cancer using information about age, BMI, glucose, insulin, homa, leptin, adiponectin, resistin and MCP-1. The data used in this model was collected by Patrício et al. [7] from 116 women of which 64 has breast cancer and 52 do not. While 70% of this data (81 cases) was used for instructing the DNN model, 30% (35 cases) was used for testing. The DNN model was created in Python programming language using Keras Deep Learning Library. After model creation, machine learning was conducted using probable optimisation algorithms, loss functions and activation functions and the best three models were saved. For performance evaluation of the models, metrics of specificity, sensitivity and accuracy were employed. The specificity values of the best three models were calculated as [0.882, 0.941] and sensitivity values were found to be [0.888, 0.944]. In other words, while the models predict healthy women at the rates of minimum 88.2% and maximum 94.1%; they predict women with breast cancer at the rates of minimum 88.8% and 94.4%. For both women with and without breast cancer these prediction rates are sufficient and much higher than those reported by Patrício et al. [7].
Deep Gated Recurrent Unit Convolution Network for Radio Signal Recognition

This paper proposes a Deep Gated Recurrent Unit (GRU) Convolution Network for feature extraction and classification of radio signals. First, a set of 1-D filters are constructed to extract the hierarchical features of raw signals. Then a GRU layer is designed to explore the low-term memory for dealing with sequence signals. This network can not only extract discriminant features of raw signals, but also deal with sequence signals. The authors evaluate the proposed method on 31 categories of signal which have different modulation and channel coding types, and the results show that the significant performance gains can be obtained.
The Effect of Data Acquisition Techniques in Profiling Analysis Based on Twitter

Twitter have a tremendous number of tweets from its user. The bigger of the data's volume and more complete the tweet's data that gathered, the more accurate the personality analysis will be. This study is sought to analyze and measure which acquisition technique that viable to be used on the personality profile analysis and to know whether the acquisition technique will always return a huge amount of data volume in a longer time period with a faster time or not. The most commonly used acquisition technique is crawling, streaming and scrapping. This study makes uses of those three acquisition methods to measure the compatibility toward the personality profile analysis. Based on the previous study, the usage of scrapping acquisition method is proved to be an arguably good method as it can acquire a more completed tweet data in a huge amount with bigger computation time and the acquisition time is faster. It caused by the big volume of the data and it need more computational time, so the acquisition speed of the tweet data is faster. Based on that result, scrapping technique is said to be a good option to analyze someone's personality profile.