Reviewerly/exHarmony huggingface dataset:
Determine The Best Travel Website Service Quality Using Fuzzy TOPSIS Approach

One of the crucial things to make a travel plan is to choose the best travel website quality. This is because there are many travel website that provides diverse services with different prices and many more. Therefore, it is important to make the right decision about choosing a travel website. This study implements Fuzzy TOPSIS to determine the best travel website quality. There are many factors that influence users’ selection such as responsiveness, efficiency and privacy and the best travel websites choose were Traveloka, Skyscanner and Expedia. Decision-makers are asked to rank these factors from very high (VH) to very low (VL) for QA weight, while for alternatives assessment from very good (VG) to very poor (VP); very high and very good specifies the most influencing factors while very low and very poor indicates the least influencing factors. Next, each data ranking transforms into matrix form. Then, calculate a Normalized Decision Matrix (NDM). Furthermore, calculate the weighted (NDM) and distance of each alternative from the worse condition (A ) and the best condition (A+). Lastly, calculate the closeness of the coefficient of each alternative and rank them. The higher the value of relative closeness, the higher the ranking order has the best travel website service quality that can satisfy consumer’s preferences.
Review of Artificial Intelligence

Over many centuries, tools of increasing sophistication have been developed to serve the human race Digital computers are, in many respects, just another tool. They can perform the same sort of numerical and symbolic manipulations that an ordinary person can, but faster and more reliably. This paper represents review of artificial intelligence algorithms applying in computer application and software. Include knowledge-based systems; computational intelligence, which leads to Artificial intelligence, is the science of mimicking human mental faculties in a computer. That assists Physician to make dissection in medical diagnosis.
Artificial Intelligence and Unfair Competition – Unveiling an Underestimated Building Block of the AI Regulation Landscape

The article illustrates the underestimated role unfair competition law (UCL) can play as a building block of the regulatory landscape relating to Artificial Intelligence (AI). To this end, it examines to what extent overarching prominent principles of AI regulation such as Fairness, Transparency, Autonomy or Innovation are reflected in paradigms of UCL and on this basis evaluates how the latter can contribute to the realization of the former. In this course, prominent problems raised by AI that are commonly discussed under different legal regimes are reconsidered under a UCL perspective, showing that this perspective may both complement or even substitute traditional regulatory approaches. Finally, the article indicates how AI could inversely give impulses for the doctrinal advancement of UCL as a still ambiguous and insufficiently understood body of law.
Interrupt me Politely: Recommending Products and Services by Joining Human Conversation

We propose a novel way of conversational recommendation, where instead of asking questions to the user to acquire their preferences; the recommender tracks their conversation with other people, including customer support agents (CSA), and joins the conversation only when it is time to introduce a recommendation. Building a recommender that joins a human conversation (RJC), we propose information extraction, discourse and argumentation analyses, as well as dialogue management techniques to compute a recommendation for a product and service that is needed by the customer, as inferred from the conversation. A special case of such conversations is considered where the customer raises his problem with CSA in an attempt to resolve it, along with receiving a recommendation for a product with features addressing this problem. We evaluate performance of RJC is in a number of human-human and human-chat bot dialogues, and demonstrate that RJC is an efficient and less intrusive way to provide high relevance and persuasive recommendations.
Surface Realization Using Pretrained Language Models

In the context of Natural Language Generation, surface realization is the task of generating the linear form of a text following a given grammar. Surface realization models usually consist of a cascade of complex sub-modules, either rule-based or neural network-based, each responsible for a specific sub-task. In this work, we show that a single encoder-decoder language model can be used in an end-to-end fashion for all sub-tasks of surface realization. The model is designed based on the BART language model that receives a linear representation of unordered and non-inflected tokens in a sentence along with their corresponding Universal Dependency information and produces the linear sequence of inflected tokens along with the missing words. The model was evaluated on the shallow and deep tracks of the 2020 Surface Realization Shared Task (SR’20) using both human and automatic evaluation. The results indicate that despite its simplicity, our model achieves competitive results among all participants in the shared task.
A Framework for Global Outlier Detection from Distributed Data Streams

Distributed data streams are continuous streams of data that come from different sources acrossthe globe in real time. It is different from traditional centralized data streams and throws challenges indetecting outliers from such dynamic data streams. The existing methods used to handle such data streamsare built from different perspectives. However a framework that can perform global outlier detection fromsuch distributed data streams is still desired. In this paper, we proposed and implemented a frameworkknown as Distributed Global Outlier Detection (DGOD) framework. The generated global outliers areconsistent with the centralized paradigms for outlier detection. The framework also takes care of privacywhile performing the detection process. Optimizations are suggested to further improve the performance ofthe framework. The experimental results revealed the significance of the proposed framework in miningoutliers from distributed data streams.Keywords – Outlier, outlier detection,
Design of Lifter Flyover Segment with a Structure of Lifting Beam can Escape Pairs

The flyover development today is very much at all to do because it is one solution as degrading congestion and lifter segment flyover is one important tool in the construction of flyovers. This final project offers an example of the calculation and planning lifter segment fly over the beam structure for lifting can be separated pairs which can later be changed in accordance with the specification segment flyover to be lifted. The final project is expected to be one of the references manufacture segments lifter fly over. This design uses Pahl and Beitz method and the results of this research. Specifications lifter segment fly over with beam structure for lifting to be loose tide is Lifting Equipment; Wire rope with two types of options on each drum are strands left and right torsion. Mechanism trolley; Can move on the path along the 3,019 mm, diameter the wheel trolley 381 mm, Trolley the wheel shaft diameter: 12 cm or 120 mm with material JIS S45C. Struktur lifter ; Beam lifting using a beam with a weight per unit length of 1.97 N / mm with JIS G3101 SS540 material along the 4900 mm and a surface area of retaining cylindrical pedestal A of 5024 mm2 with material JIS G3101 SS540. The mechanism of motion lifter can move on the path along the 4000 mm. Wheel lifter diameter 533.4 mm. Hydraulic of lifters driver using a double acting hydraulic with a maximum
Privacy-preserving proof-of-location using homomorphic encryption

Location-based software services require knowledge about a user's geographic data. Sharing these data risks compromising the user's privacy, exposes the user to targeted marketing, and enables pote ...
A Study on the Influence of Watching Youtube Sound Content (ASMR) on Youth Learning and Life

Recently we have lots of Youtube contents and their influence. But Just a few Studies have announced Youtube content’s effect. The purpose of this paper is to see if ASMR content, which is popular through Youtube recently, helps teenagers stabilize their minds and improve their learning abilities. To that end, a survey of teenagers found that ASMR content is very familiar to teenagers, and that 66.7 percent of teenagers use ASMR content for sleep and learning. About the change before and after watching, half of the respondents said they felt a positive difference in learning and psychological stability. As a result, ASMR is a significant content for teenagers with a specific purpose. Therefore, policies such as 'after-school' in terms of school education are proposed as alternatives rather than unilateral measures such as banning ASMR content to teenagers.
Proof of Retrievability for Image Data in Industrial Cloud Platform

With the increasing popularity of cloud storage, a large number of consumers rent cloud storage services to store their own data. However, with the loss of direct control over data, the integrity and availability of outsourced data become a major concern of users. In order to ensure the reliability and integrity of data in cloud storage server, some scholars have proposed various data integrity auditing schemes.
The modalities of distance education: advantages and disadvantages

The modality of distance studies has been developing in the entire world with diverse results. It constitutes a form of achieving a bigger access, very massive and also allow that so much young as adults that cannot attend present classes of superior studies and other overcoming forms for its labor linking, can continue; however it is required certain condition from the technical point of view as of attitudes and aptitudes on the part of those that are welcomed especially to this study form.Keywords: Distance education, virtuality, virtual classrooms. RESUMENLa modalidad de estudios a distancia se ha venido desarrollando en todo el mundo con diversos resultados, constituye una forma de lograr una mayor masividad de acceso y tambien para permitir que tanto jovenes como adultos que por su vinculacion laboral no puedan asistir a clases presenciales puedan continuar estudios superiores y otras formas de superacion; sin embargo se requiere determinadas condiciones tanto desde el punto de vista tecnico como de actitudes y aptitudes por parte de los que se acogen a esta forma de estudio en especial en la forma virtual.Keywords: Educacion a distancia, virtualidad, aulas virtuales.
Image Mosaicing Using Homography Estimation

In this article, we propose to join multiple images using the homography estimation technique. First, the images are classified, then the pairwise homography between the images is calculated, and then a combination of images is performed with the lens. Then a new decision fusion framework based on a spanning tree and Exif data is proposed to obtain the order of the images. After obtaining the order of the images, an estimation of the projective homographies is made in pairs between the images. Then we deform the images according to the computed homography. Deformed images are later faded and laplacian mixed for seamless seams.
Cookery: A Framework for Creating Data Processing Pipeline Using Online Services

With the increasing amount of data the importance of data analysis in various scientific domains has grown. A large amount of the scientific data has shifted to cloud based storage. The cloud offers storage and computation power. The Cookery framework is a tool developed to build scientific applications using cloud services. In this paper we present the Cookery systems and how they can be used to authenticate and use standard online third party services to easily create data analytic pipelines. Cookery framework is not limited to work with standard web services; it can also integrate and work with the emerging AWS Lambda which is part of a new computing paradigm, collectively, known as serverless computing. The combination of AWS Lambda and Cookery, which makes it possible for users in many scientific domains, who do not have any program experience, to create data processing pipelines using cloud services in a short time.
The Speech Acts Recognition of ESP Students with and without Body Language

This paper mainly deals with recognition of speech acts of body language by suggesting various types of body language in order to achieve the study objectives which aim at identifying the difference between speech act recognition of the ESP students with and without body language, once by listening to the speaker then by listening and watching the speaker body language of the speaker. This study is limited to 1st year students at Computer Science department  College of     Science for Women at University of Baghdad.    Two listening tests are designed which consist of various types of speech acts in the audio that suit ESP university students’ level of English language. In the first test, the students listened to the audio voice of the speaker talked about English proverbs and the students are asked to tick the speech acts items each time they appear, while in the second test, the same students listen and watch videos of body language of the same speaker talking about the same subject and they are also asked to tick the items of speech acts that they are ticked in the first test each time they appear.     Results show that there is a significant difference between the speech acts tests by listening to the audio recording of the presenter on one hand, and watching the video recording on the other hand, and in favour of watching the video that used body language. The results validated  listening and watching the video since the computed value of the t- test (1.98) is higher than the tabulated t- value (1.69) at (29) degree of freedom and (0.05) level of significance.
EFEKTIVITAS METODE KANCING GEMERINCING BERBASIS PROBLEM BASED LEARNING TERHADAP KEMAMPUAN PEMECAHAN MASALAH PADA MATERI PROGRAM LINIER

The 2013 curriculum implemented at the Semarang High School Institute of Indonesia demands that students be able to be active in learning, but in reality students were previously guided by the teacher while studying, so that the activity of students is weak. Curiosity is also still weak, thus encouraging the low learning achievement of class X students. The purpose of this study was to determine whether the jingle based learning model based on problem based learning was effective. This research is experimental research, with a population of all classes XI. The variables in this study consisted of problem solving abilities, curiosity and activity. Methods of data collection in the form of interviews, documentation, tests, questionnaires, and observations. The results showed that students achieved mastery with an average value of 73.77 and classical completeness of 68.79%. In addition, the influence test shows the influence of curiosity and independence 61.3%. The results of the average difference test showed that the experimental class was 73.77, and the control class was 68.79. The conclusion of this study is problem based learning jingling studs learning model to influence curiosity and activity in the effective class XI Linear Program material. Keywords: Talking Chips, Problem Based Learning, Problem Solving Ability
Robust Selfie and General Video Stabilization

Author(s): Yu, Jiyang | Advisor(s): Ramamoorthi, Ravi | Abstract: Video stabilization is one of the most widely sought features in video processing. The problem of video stabilization typically consists of two steps: video motion analysis and stabilized frame synthesis. Traditional video motion analysis relies heavily on local image features and is prone to error in the presence of occlusions and motion blur. It is also challenging to avoid visual artifacts in the stabilized frame synthesis due to complexity of the scenes, e.g. parallax, dynamic objects. In this dissertation, we focus on exploring video stabilization methods that generate high quality stable results and are generalizable to real world videos. We start from selfie videos, which is a special type of video that is challenging for all video stabilization methods due to the dynamic occlusion. Our solution is to analyze motion of the foreground/background separately and jointly stabilize the motion. Specifically, we seek to use 3D human face model as a prior information of foreground motion. For the background, our approach tracks random pixels using optical flow. We exploit non-linear least squares optimization to stablize both the foreground/background motion. To make the process practical for commercial applications, we also designed an online version of the pipeline using a sliding window scheme. We also exploit deep learning techniques to replace optimization, resulting in orders of magnitude speed improvement compared to the state-of-the-art optimization based approaches. Our works also generalize to the general video stabilization context. General video occlusion is free-form, therefore a more general scheme is required to appropriately constrain the video stabilization. We proposed two different solutions based on this principle. Both of our approaches use optical flow to analyze the motion and generate a dense warp field for stabilizing input frames. Our first approach seeks to constrain the frame warp field using a global linear transformation. In the second we designed specific metrics based on optical flow to exclude dynamic occlusions and motion boundaries. Our experiments show that our approach is more generalizable and produces both visually and quantatively better results compared to previous video stabilization methods.
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension

Despite recent achievements in natural language understanding, reasoning over commonsense knowledge still represents a big challenge to AI systems. As the name suggests, common sense is related to perception and as such, humans derive it from experience rather than from literary education. Recent works in the NLP and the computer vision field have made the effort of making such knowledge explicit using written language and visual inputs, respectively. Our premise is that the latter source fits better with the characteristics of commonsense acquisition. In this work, we explore to what extent the descriptions of real-world scenes are sufficient to learn common sense about different daily situations, drawing upon visual information to answer script knowledge questions.
Tensorizing Subgraph Search in the Supernet.

Recently, a special kind of graph, i.e., supernet, which allows two nodes connected by multi-choice edges, has exhibited its power in neural architecture search (NAS) by searching for better architectures for computer vision (CV) and natural language processing (NLP) tasks. In this paper, we discover that the design of such discrete architectures also appears in many other important learning tasks, e.g., logical chain inference in knowledge graphs (KGs) and meta-path discovery in heterogeneous information networks (HINs). Thus, we are motivated to generalize the supernet search problem on a broader horizon. However, none of the existing works are effective since the supernet topology is highly task-dependent and diverse. To address this issue, we propose to tensorize the supernet, i.e., unify the subgraph search problems by a tensor formulation and encode the topology inside the supernet by a tensor network. We further propose an efficient algorithm that admits both stochastic and deterministic objectives to solve the search problem. Finally, we perform extensive experiments on diverse learning tasks, i.e., architecture design for CV, logic inference for KG, and meta-path discovery for HIN. Empirical results demonstrate that our method leads to better performance and architectures.
Machine-oriented NMT Adaptation for Zero-shot NLP tasks: Comparing the Usefulness of Close and Distant Languages.

Neural Machine Translation (NMT) models are typically trained by considering humans as end-users and maximizing human-oriented objectives. However, in some scenarios, their output is consumed by automatic NLP components rather than by humans. In these scenarios, translations’ quality is measured in terms of their “fitness for purpose” (i.e. maximizing performance of external NLP tools) rather than in terms of standard human fluency/adequacy criteria. Recently, reinforcement learning techniques exploiting the feedback from downstream NLP tools have been proposed for “machine-oriented” NMT adaptation. In this work, we tackle the problem in a multilingual setting where a single NMT model translates from multiple languages for downstream automatic processing in the target language. Knowledge sharing across close and distant languages allows to apply our machine-oriented approach in the zero-shot setting where no labeled data for the test language is seen at training time. Moreover, we incorporate multi-lingual BERT in the source side of our NMT system to benefit from the knowledge embedded in this model. Our experiments show coherent performance gains, for different language directions over both i) “generic” NMT models (trained for human consumption), and ii) fine-tuned multilingual BERT. This gain for zero-shot language directions (e.g. Spanish–English) is higher when the models are fine-tuned on a closely-related source language (Italian) than a distant one (German).
A STUDY ON STUDENTS’ MOTIVATION IN LEARNING ENGLISH AT THE SEVENTH GRADE STUDENTS OF SMPN 1 TENGGARONG SEBERANG IN ACADEMIC YEAR 2019/2020

The objective of this research is to know what the factors influencing students’ English learning motivation viewed from their intrinsic and extrinsic motivation at the seventh grade students of SMPN 1 Tenggarong Seberang in academic year 2019/2020. This research is a descriptive qualitative research. Data sources of this research were the English teacher and the seventh grade students of SMPN 1 Tenggarong Seberang in academic year 2019/2020. The researcher conducted observation and interview to collect the required data. In validating the data, the researcher used data source triangulation by comparing the result of observation and interview. This research was analyzed by using Miles and Huberman’s interactive model of data analysis including reducing the data, displaying the data and taking conclusion and verification. Based on the research finding in the result of observation and interview, it can be concluded that the students have high motivation in learning English. Therefore, it can be said the students has two types of motivation. They are intrinsic motivation and extrinsic motivation. There are many factors of intrinsic motivation such us; interest, need, attitude, aspiration and incentive. Then, there are two factors of extrinsic motivation such us; teacher and parent. The students were motivated by the enjoyment of the learning process itself or by their internal desire. They want to learn English because it gives them pleasure and develops a particular skill. Through interviewing the students, the researcher found some students’ extrinsic motivation. It showed that some students of SMP N 1 Tenggarong Seberang motivated by their external desire. They were motivated to learn English because of their goals such as they want to get a good score in final examination, they are supported by their parents or they want to study abroad. Keywords: motivation, learning motivation, English learning