Reviewerly/exHarmony huggingface dataset:
Jargon Terms Used in Food and Beverage Department at The Highland Park Resort-Hotel Bogor

This study aims to identify the forms of F&B jargon terms and reveal the meanings of jargon terms employed by Food and Beverage Department in The Highland Park Resort-Hotel Bogor. The terms which are selected were related to the documents, facilities, objects, and hotel daily activities. The study applies descriptive qualitative method which portrayed the data analysis which mostly used in spoken form collected by the observation, documents, and website elaborated into the form of sentences. Whereas, the data are analyzed uses the theory of Halligan (2004) as the reference which focuses on identifying the form and explaining the obscure meanings of the jargon terms. The findings of the study reveal 60 jargon items in F&B department at The Highland Park Resort-Hotel Bogor which divided into F&B product and service such as A’la carte, Buffet, Dressing, and BEO. The jargon terms are elaborated in the form of word, phrase, abbreviation, and acronym. Most of the jargons are in the form of phrases with 27 jargon items. This implies that phrases are the most prominent form found and used in F&B department at The Highland Park Resort-Hotel Bogor.Keywords: Sociolinguistic, Language Variety, Jargon, Hotel
Impact of Sales Force Automation System on Performance of Salesman: Pakistani Companies’ Perspective

Rapid growth in advance technologies haschanged the life of sales force. Sales Force Automation (SFA) ismarketing tool which provides the functions to sales team andmanagers to monitor sales, forecast sales and analyze employeeperformance. Acceptance of the SFA tools such as phone, pagers,wireless devices etc., in sales tasks will remain an issue for salesforce. Researcher wants to investigate the impact of SFA systemon performance of salesman from Pakistani Fast MovingConsumer Goods (FMCG) perspective. They have selected 162salespersons from Lahore based companies (who are usingautomated sales devices), and sample size and MANOVA(Multivariate Analysis of Variance) were utilized to find out therelationship between independent and dependent variables. Ithas found that SFA system has positive relationship withimprovement in performance and sales of salesforce.
A study of the levy distribution in generation of BRKGA random keys applied to global optimization

Biased Random-Key Genetic Algorithm (BRKGA) is a variation of genetic algorithms that represents the solutions of a problem as real-key vectors defined by values randomly generated in the continuous interval [0, 1) and uses a deterministic decoder to map these vectors into feasible solutions to the problem. In this work, is investigated the impact of the substitution of the uniform distribution, used in the generation of BRKGA random keys, by the use of the Levy distribution. This variation is inserted in two versions of the BRKGA present in the literature. The proposed versions were compared with the literature versions using a set of global optimization benchmark functions, present in several works of the research area. The algorithms are compared in terms of performance and quality of the solutions and the variations proposed in this work reach very promising results.
Introduction to Azure Functions

In the software industry, we are now in an era where everything we develop is oriented toward the cloud. To help developers achieve more productivity, cloud platforms such as Microsoft Azure, Amazon Web Services, Google Cloud Platform, and so on, implement a concept known as serverless computing. With serverless computing, companies and developers can concentrate on developing products rather than worrying about the maintenance and administration of the server.
LibGuides: MLA Micro-course: MLA In-text Citations

Using hands-on practice, learn how to create in-text and Works Cited citations, as well as craft paraphrases and summaries of source material.
Simulation of Episode Blending in the AMBR Model

This paper presents a series of simulation experiments related to the interaction of memory and analogy-making in the AMBR model. This interaction makes it possible to demonstrate blending between superficially dissimilar episodes as a result of the established analogical mapping between them and of superficially and structurally dissimilar episodes as a result of a double analogy with a third episode. Both simulation experiments model the blending effect of analogy-making. The conditions for the emergence of such blending are explored on the basis of a proposed specific analogy-like retrieval mechanism.
Arabic sign language recognition using vision and hand tracking features with HMM

Sign language employs signs made by hands and facial expressions to convey meaning. Sign language recognition facilitates the communication between community and hearing-impaired people. This work proposes a recognition system for Arabic sign language using four types of features, namely modified Fourier transform, local binary pattern, histogram of oriented gradients, and a combination of histogram of oriented gradients and histogram of optical flow. These features are evaluated using hidden Markov model on two databases. The best performance is achieved with modified Fourier transform and histogram of oriented gradients features with 99.11% and 99.33% accuracies, respectively. In addition, two algorithms are proposed, one for segmenting sign video streams captured by Microsoft Kinect V2 into signs and the second for hand detection in video streams. The obtained results show that our algorithms are efficient in segmenting sign video streams and detecting hands in video streams.
Lightweight Semantic Location and Activity Recognition on Android Smartphones with TensorFlow

Context-awareness is the parent concept that guides the applications we will discuss here. This concept is tightly related with ubiquitous computing, a term that encloses all those technologies that have pervasively made their way in countless aspects of our everyday life. What we want from this work is to find a way to approach the problem of Semantic Location detection and Activity Recognition, addressing two of the main factors in pollutants exposure: involved activity and microenvironment.We focus this work on exploiting the possibilities that mobile device can offer today, motivated by their pervasive presence in our daily life, their always increasing technological capabilities and their diffusion: more than seventy-five billion devices will be connected to the internet by the year 2025—this is ten times the Earth population today.We use onboard sensors on common smartphones to collect motion data for activity recognition, and other sensors and similar information to determine indoor or outdoor positioning. We exploit the following smartphone sensors and components. This approach is supported and inspired by plenty of research literature and tailored to our specific needs. We work with Android devices and aim to build a prototype for a mobile application that can collect this data from the device and run a machine learning based model to run inference locally. We introduce a less known version of neural networks called Long Short-Term Memories that are uniquely intended to work with temporal data, and that today is employed by most of the major technology companies out there for their top tier products involving machine learning.
The Design of APP Content Guiding System Based on Deep Learning Algorithm

This paper presents a deep learning algorithm based evaluation system of the relationship between user's mind and behavior. The system is achieved by categorizing the mutual interactions between user and APP product which inherently reflect the relationship between user's mind and behavior. The numerical annotations of the mutual interactions, combined with numerous feedbacks from users when interacting with APP, allow machines to learn and master the evaluation system, in order to achieve a customer-satisfactory content guidance.
A Security-Sensitive Function Mining Framework for Source Code

The security-sensitive functions can be effectively used to improve the efficiency of vulnerability mining techniques, but mining security-sensitive functions of the large-scale code base is difficult. An automatic mining framework for security-sensitive functions is proposed. Firstly, a class of high-resolution code features is used to extract suspected security-sensitive function sets, and then a class of code features is applied to measure the sensitivity of each suspected security-sensitive function. Ultimately, the final security-sensitive function set is ensured based on the measurement result. Established along the framework, a mining algorithm for a type security-sensitive function is proposed. Through the mining experiments on three well-known open source codes, the performance of this algorithm is better than the existing methods.
Deep multiple kernel least squares support vector regression using PSO

In this paper, we propose a deep multiple kernel least squares support vector regression (DMK-LSSVR) using particle swarm optimization (PSO). Unlike multilayer neural networks (MNN), each LSSVR in the DMK-LSSVR is trained to minimize the penalized objective function. Therefore, the learning of DMK-LSSVR is completely different from that of MNN that minimizes only the final objective function. In DMK-LSSVR the grid search using GCV function is used to find optimal values of hyperparameters of each LSSVR, which has a disadvantage that takes a lot of computational time. And the back propagation algorithm is used for the optimal values of weights and biases, which has a weakness to results in local minima. In DMK-LSSVR which utilizes PSO (DMK-LSSVR-PSO), we find the optimal values of hyperparameters of LSSVRs, weights and biases using PSO in one process. Using PSO, the only needed on hyperparameters are the lower and upper bound, and estimating weights and biases results in global minimizers. Numerical studies show that DMK-LSSVR-PSO has advantages over DMK-LSSVR and other machine learning models that use back propagation algorithm and grid search for regression problems.
3D scene viewpoint selection based on chaos-particle swarm optimization

The efficiency in perception of 3D scenes is a significant problem. This paper presents a novel viewpoint evaluation method combining geometric and visual perception information. Firstly, we generalize the criteria of the best viewpoint selection from the references. Then, we introduce a dynamic inertia weight and Multi-start PSO algorithm into original particle swarm algorithm to prevent the problem of local optimum. Finally, we develop a system with friendly interfaces to demonstrate the practicality of our method. The experimental results verify that this approach can achieve viewpoint selection automatically and adapt to multi-model scene.
How to Generate Unbiased Data

One motto of our times could be "data is the new gold"—however, it will shine only if it is pure and free of dirt. Biased data can be lethally polluted and thus worthless. For example, a tax authority once asked me to help them build an algorithm to direct customs inspectors to those containers in the port that were most likely to contain contraband. The project could not go ahead because the only data they had was from a very limited number of customs inspections their officers had done in the past year. The problem: Customs inspectors had chosen which containers to check, and they had complete freedom in how to conduct the checks (e.g., they may have limited themselves to opening the first box falling into their hands and accepting the shipment as containing "Louis Vuitton Croisette handbags" because the duffel bags with a "Luis Vitton" label seemed close enough, or they may have completely emptied the container and carefully checked the L’s, O’s, and T’s of the Louis Vuitton stamp of two dozen bags, knowing that variations in these letters are among the frequent tell-tale signs of fake bags).
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Xiaomi Brand Appraisal Research Based on Zhihu by Text Mining Technology

As the largest knowledge social platform on the Chinese Internet, Zhihu has gradually become an important resource for merchants to improve publicity and optimize products, and the public to understand the brand image. The topic of "Xiaomi Technology" remains hot on Zhihu. In this context, this paper takes the essences of the "Xiaomi Technology" topic on Zhihu as the research object. First we carry on the data collection and preprocessing. Then by extracting feature based on word segmentation results, we build a corpus and construct an LDA topic model for text mining. Besides, by calculating and comparing the perplexity index, we select 20 as the number of topics. According to the results, the relationship between document-topic and topic-term is analyzed to form a topic description of the text, which shows that Xiaomi products have received great attention from consumers and are often used for comparison with other brands in the same industry; Xiaomi product launches have received much attention and had a direct impact on product sales; Xiaomi is widely recognized as one of the representatives of China's future technology.
Password-Based Cryptographic Protocols in the Client-Server Setting

Author(s): Xu, Jiayu | Advisor(s): Jarecki, Stanislaw | Abstract: Passwords have become the most ubiquitous form of client-server authentication on the Internet nowadays. Password-over-TLS, the almost universal password authentication protocol in practice, suffers from two major drawbacks: (i) it requires a secure channel; and (ii)the server sees the client's password in the clear.This dissertation focuses on another approach of password authentication, which eliminates the two shortcomings above. We study cryptographic protocols in the password-only setting, that is, the only information shared between the client and the server is the short and low-entropy password. We present highly efficient realizations of two kinds of such protocols: (1) Password-Protected Secret Sharing (PPSS), in which the client stores a long secret (e.g., its private key) in a group of servers, and recovers the secret via interacting with a subset of servers using a password; and (2) asymmetric Password-Authenticated Key Exchange (aPAKE), in which the client (which enters a password) and the server (which stores a password file) establish the same secret key. All these protocols are resilient to man-in-the-middle attacks (i.e., no authenticated channel is required) as well as server compromise: the only forms of attacks are the unavoidable ones, namely online password guessing attacks and offline dictionary attacks in case of server compromise.We present thorough description of our protocols, the proofs of their security, and analyses of their computational costs. All security proofs are in the Universally Composable (UC) framework, which addresses subtle vulnerabilities of passwords (non-uniform distribution over the dictionary, reuse of the same password over different accounts, etc.) in a natural and easy-to-argue way, and thus is preferred over the traditional game-based security model.
GENDER’S DIFFERENCES IN WRITING ACHIEVEMENT OF NARRATIVE TEXT

Abstract : This study was aimed at the difference between the second semester students’ writing achievement of narrative text based on gender at Baturaja University. The method used in this study was descriptive method. All the populations of this study were 139 students from six classes and the samples were 68 students taken by purposive sampling. The essay test form was used as a the technique for collecting the data. The data of the test was analyzed by using independent t-test on SPSS 16.0 version. Based on the data analysis, the findings of this study indicated that the mean score of male students were 48.30. Further, there was no (0%) student in very good level, there were 5 students (14.71%) on good level, there were 5 students (14.71%) on enough level, there were 25 students (73.59%) on poor level, and there were 16 students (47%) on failed level. While, the mean score of female students were 55.68. Further, there was 1 student (0.03%) on very good, there were 9 students (26.47%) on good level, there were 7 students (20.59) on enough level, there were 7 students (20.59%) on poor level, and there were 10 students (29.41%) on failed level. It meant that female students had higher level than male students in writing achievement. The result of mean difference were 7.386. It was described that there was significant difference in writing achievement of narrative text based on gender.
Activity and Fall Detection in the Habitational Environment: Subsystem: Fall detection algorithm

This report describes the design and implementation of a fall detection algorithm for a fall detection system using a pressure based floor sensor. The goal of the system is to detect falls and alarm the relevant personnel when an elderly person has fallen. The fall detection algorithm has a strong connection with the interface subsystem, which uses the algorithm as a function. The interface subsystem supplies matrices containing the raw sensor values of the pressure floor. The algorithm has been divided into multiple sub-algorithms. First, pre-processing: data linearization was applied on the raw sensor values and the sensor matrix was processed such that an image formed that looked like the real world scenario. Second, image processing techniques were applied to detect contours. Contours were being tracked through time, and being grouped. The characteristics of the contours and groups were used to classify falls. Tests have been done to validate the behaviour of the algorithm, from which an average false negative ratio of 30% was achieved in a time window of 30 seconds. The created prototype proves that image processing is a viable tool for detecting falls with the use of a pressure-based floor sensor. Overall, this results in a strong alternative for fall detection that could be used to improve the time an elderly person can live at home safely without the need to move to a nursing home
AN ANALYSIS OF STUDENTS’ ERROR IN USING PERSONAL PRONOUN AS SUBJECT AND OBJECT AT SEVENTH GRADE STUDENTS OF SMP NEGERI 6 MERAUKE ACADEMIC YEAR 2018/2019

The objectives of this research are to find students' error in using personal pronouns as subject and object which made by seventh grade students of SMP Negeri 6 Merauke. The subject in this research was the students of SMP Negeri 6 Merauke grade VII A that consists of 35 students that was selected by using purposive sampling. This research was used descriptive qualitative method. The data were collected from the test. The type of test was essay test that given twice to students. Data from the test results were used to find out students' errors. First, student errors were classified using Corder theory that consists of four kinds of error; those are omission, addition, selection, and ordering. After that, the errors were analyzed by calculating the frequency of each type of error and then calculating the percentage to know what the types of error that are most often done by students. From the results of this study, has been obtained the total error of students are 429 errors. The most error are selection errors with 385 errors (89.74%), and then ordering errors with 38 errors (8.86%), and then omission errors with 3 errors (0.70%) and error addition also has the same total that is 3 errors (0.70%). Therefore, the most error that made by students of grade VII A of SMP Negeri 6 Merauke is selection error.&#x0D; Keywords: personal pronoun, students’ error, types of error.&#x0D; &#x0D; &#x0D; &#x0D;
A 2D example study of Deep FRAME model

Author(s): Zhu, Yaxuan | Advisor(s): Zhu, Song-chun | Abstract: In this work, we use a 2D to study many aspects of Deep FRAME model. We first do visualization on the training process, showing how the fitted probability distribution evolves during training, how the model captures different modes and how these results are influenced by the choice of prior distributions and activation functions. We then study the activation pattern of the learned network and the corresponding partition of the input space. Based on the input space partition and statistics matching, we then compare the multi-layer model trained with SGD with the original one-layer model. Finally, we analyze the case in which we train the model with finite-MCMC sampling, showing the difference between tting the energy function and synthesizing samples.
Fast Facial Image Analogy with Spatial Guidance

This paper proposes a novel method for fast facial image analogy with spatial guidance. Given a facial image A and another one B in a different style (color, tone, or texture), we are allowed to render the face in A with style B to output a stylized facial image A′ in a timely manner. For traditional image analogy, such a process could take an unbearable time and considerable computing resources. In this paper, for the first time, we make it possible to do image analogy at a speed much faster than the state-of-the-art method. Specifically, we first extract deep image features using a VGG-19 encoder, and implement patch-match with the guidance of facial landmarks. Then Procrustes analysis is applied to accelerate the program as a coarse-to-fine strategy. We repeat the above process at each layer of VGG-19 and decode image A′ from bottom to top. Experimental results show that our method not only spends much less time but also provides high-quality image analogy results. Moreover, our method can be naturally extended to many face-related applications including but not limited to face swapping, makeup, and style to photo.
Chapter 8. Designing the User Experience

Electronic Laboratory Notebooks (ELNs) have the potential to not only replace paper notebooks for recording our research and our day-to-day activities, but they can also provide a myriad of additional capabilities for supporting scientists that go far beyond what is possible in a paper notebook. However, we know that many scientists are sceptical of the technology and have anxieties about the complexity of the software, and perceived ease of use is of primary importance to users in selecting an ELN provider. It is essential to ensure that usability is at the forefront in the design of digital tools to encourage adoption and also to ensure that the users get the best value out of the functions that are provided. The design of interfaces for digital tools not only impacts whether users are willing to use them, but also whether users are able to make the best use of them. In this chapter, we discuss a variety of user experience design techniques, including interaction design, that are used to help make software both useful and usable for the intended audience. We will investigate how these techniques can be used to identify requirements for new tools based on user needs and present some examples of potential ELN capabilities based on the needs and behaviours of real-world researchers. This chapter also includes guidelines and best practices for designing interfaces for desktop, mobile, and Web-based software.