Reviewerly/exHarmony huggingface dataset:
Transfer Learning Based Adaptive Automated Negotiating Agent Framework

With the availability of domain specific historical negotiation data, the practical applications of machine learning techniques can prove to be increasingly effective in the field of automated negotiation. Yet a large portion of the literature focuses on domain independent negotiation and thus passes the possibility of leveraging any domain specific insights from historical data. Moreover, during sequential negotiation, utility functions may alter due to various reasons including market demand, partner agreements, weather conditions, etc. This poses a unique set of challenges and one can easily infer that one strategy that fits all is rather impossible in such scenarios. In this work, we present a simple yet effective method of learning an end-to-end negotiation strategy from historical negotiation data. Next, we show that transfer learning based solutions are effective in designing adaptive strategies when underlying utility functions of agents change. Additionally, we also propose an online method of detecting and measuring such changes in the utility functions. Combining all three contributions we propose an adaptive automated negotiating agent framework that enables the automatic creation of transfer learning based negotiating agents capable of adapting to changes in utility functions. Finally, we present the results of an agent generated using our framework in different ANAC domains with 100 different utility functions each and show that our agent outperforms the benchmark score by domain independent agents by 6%.
Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems

To best assist human designers with different styles, Machine Learning (ML) systems need to be able to adapt to them. However, there has been relatively little prior work on how and when to best adapt an ML system to a co-designer. In this paper we present threshold designer adaptation: a novel method for adapting a creative ML model to an individual designer. We evaluate our approach with a human subject study using a co-creative rhythm game design tool. We find that designers prefer our proposed method and produce higher quality content in comparison to an existing baseline.
Possibilistic Logic Underlies Abstract Dialectical Frameworks

Abstract dialectical frameworks (in short, ADFs) are one of the most general and unifying approaches to formal argumentation. As the semantics of ADFs are based on three-valued interpretations, we ask which monotonic three-valued logic allows to capture the main semantic concepts underlying ADFs. We show that possibilistic logic is the unique logic that can faithfully encode all other semantical concepts for ADFs. Based on this result, we also characterise strong equivalence and introduce possibilistic ADFs.
Multi-Proxy Learning from an Entropy Optimization Perspective

Deep Metric Learning, a task that learns a feature embedding space where semantically similar samples are located closer than dissimilar samples, is a cornerstone of many computer vision applications. Most of the existing proxy-based approaches usually exploit the global context via learning a single proxy for each training class, which struggles in capturing the complex non-uniform data distribution with different patterns. In this work, we present an easy-to-implement framework to effectively capture the local neighbor relationships via learning multiple proxies for each class that collectively approximate the intra-class distribution. In the context of large intra-class visual diversity, we revisit the entropy learning under the multi-proxy learning framework and provide a training routine that both minimizes the entropy of intra-class probability distribution and maximizes the entropy of inter-class probability distribution. In this way, our model is able to better capture the intra-class variations and smooth the inter-class differences and thus facilitates to extract more semantic feature representations for the downstream tasks. Extensive experimental results demonstrate that the proposed approach achieves competitive performances. Codes and an appendix are provided.
Universal Video Style Transfer via Crystallization, Separation, and Blending

Universal video style transfer aims to migrate arbitrary styles to input videos. However, how to maintain the temporal consistency of videos while achieving high-quality arbitrary style transfer is still a hard nut to crack. To resolve this dilemma, in this paper, we propose the CSBNet which involves three key modules: 1) the Crystallization (Cr) Module that generates several orthogonal crystal nuclei, representing hierarchical stability-aware content and style components, from raw VGG features; 2) the Separation (Sp) Module that separates these crystal nuclei to generate the stability-enhanced content and style features; 3) the Blending (Bd) Module to cross-blend these stability-enhanced content and style features, producing more stable and higher-quality stylized videos. Moreover, we also introduce a new pair of component enhancement losses to improve network performance. Extensive qualitative and quantitative experiments are conducted to demonstrate the effectiveness and superiority of our CSBNet. Compared with the state-of-the-art models, it not only produces temporally more consistent and stable results for arbitrary videos but also achieves higher-quality stylizations for arbitrary images.
CARD: Semi-supervised Semantic Segmentation via Class-agnostic Relation based Denoising

Recent semi-supervised semantic segmentation methods focus on mining extra supervision from unlabeled data by generating pseudo labels. However, noisy labels are inevitable in this process which prevent effective self-supervision. This paper proposes that noisy labels can be corrected based on semantic connections among features. Since a segmentation classifier produces both high and low-quality predictions, we can trace back to feature encoder to investigate how a feature in a noisy group is related to those in the confident groups. Discarding the weak predictions from the classifier, rectified predictions are assigned to the wrongly predicted features through the feature relations. The key to such an idea lies in mining reliable feature connections. With this goal, we propose a class-agnostic relation network to precisely capture semantic connections among features while ignoring their semantic categories. The feature relations enable us to perform effective noisy label corrections to boost self-training performance. Extensive experiments on PASCAL VOC and Cityscapes demonstrate the state-of-the-art performances of the proposed methods under various semi-supervised settings.
Deep Video Harmonization With Color Mapping Consistency

Video harmonization aims to adjust the foreground of a composite video to make it compatible with the background. So far, video harmonization has only received limited attention and there is no public dataset for video harmonization. In this work, we construct a new video harmonization dataset HYouTube by adjusting the foreground of real videos to create synthetic composite videos. Moreover, we consider the temporal consistency in video harmonization task. Unlike previous works which establish the spatial correspondence, we design a novel framework based on the assumption of color mapping consistency, which leverages the color mapping of neighboring frames to refine the current frame. Extensive experiments on our HYouTube dataset prove the effectiveness of our proposed framework. Our dataset and code are available at https://github.com/bcmi/Video-Harmonization-Dataset-HYouTube.
Multivariate Times Series Classification Using Multichannel CNN

Multivariate time series classification is an important and demanding task in sequence data mining. We focus on the multichannel representation of the time series and its corresponding convolutional neural network (CNN) classifier. The proposed method transforms multivariate time series into multichannel analogous image and it is fed into a pretrained multichannel CNN with transfer learning. To verify the efficacy of the proposed method, we compared it with recent deep learning-based time series classification models on five datasets with small amounts of training data. The results indicate that the proposed method provides improved performance on average compared with the other methods when incorporated with transfer learning.
A case study on major cloud platforms digital forensics readiness - are we there yet

Digital forensics is a post crime activity, carried out to identify the culprit responsible for the crime. The forensic activity requires the crime evidence that are typically found in a log that stores the events. Therefore, the logs detailing user activities are a valuable and critical source of information for digital forensics in the cloud computing environment. Cloud service providers (CSPs) usually provide logging services which records the activities and events with varying level of details. In this work, we present a detailed and methodological study of the logging services provided by three major CSPs, i.e., Amazon Web Services, Microsoft Azure and Google Cloud Platform, to elicit their forensic compliance. Our work aims to measure the forensic readiness of the three cloud platforms using their prime log services. More specifically, this paper: 1) proposes a systematic approach that specifies the cloud forensic requirements; 2) uses a generic case study of crime incident to evaluate the digital forensic readiness. It shows how to extract the crime evidence from the logs and validate them against a set of forensic requirements; 3) identifies and quantifies the gaps which the CSPs failed to satisfy.
Do green banking initiatives lead to competitiveness and improved financial performance An empirical analysis of Indian banks

The purpose of this paper is to examine whether green banking initiatives taken by the Indian banks have increased their competiveness and financial performance or not. This study makes an attempt to develop a conceptual model from literature sources. In order to validate the model, the primary data was collected using structured questionnaire administered on employees of the banks. The data has been analysed applying the exploratory factor analysis (EFA) and multiple regression for competiveness. For analysing financial performance secondary data was collected from the websites of Reserve Bank of India, Indian Banking Association and annual reports of the banks for the period 2005 to 2016. The panel data regression has been used to test the impact of possible factors including green banking initiatives on financial performance measured by return on asset (ROA). Result indicates that there is no significant impact of green banking initiative on the financial performance of the bank but green banking initiatives have increased the competitiveness of the banks through improved operational efficiency, cost saving, financial efficiency and product improvement. Thus, we have observed green banking directly improves competitiveness and indirectly improves financial performance.
Intelligent Interfaces: Pedagogical Agents and Virtual Humans

Little by little, we are entering the new era, intelligent interfaces are absorbing us more and more every day, and artificial intelligence makes its presence in a stealthy way.Virtual humans that represent an evolution of autonomous virtual agents; they are computer programs and in the future capable of carrying out different activities in certain environments.They will give the illusion of being human; they will have a body, and they will be immersed in an environment.They will have a set of senses that will allow them: 1) Sensations and therefore associated expressions; 2) Communication; 3) Learning; 4) Remembering events, among others.By integrating the above, they will have a personality and autonomy, so they will be able to plan with respect to objectives; allowing them to decide and take actions with their body, in other words, they will count on awareness.The applications will be focused on environments that they will inhabit, or as interfaces that will interact with other systems.The application domains will be multiple; one of them being education.This article shows the design of OANNA like an avatar with the role of pedagogical agent.It was modeled as an affective-cognitive structure related to the teaching-learning process linked to a pedagogical agent that represents the interface of an artilect.OANNA, has the necessary animations for intervention within the teaching-learning process.
PENERAPAN BUILDING INFORMATION MODELLING (BIM) PADA PROYEK PASAR SOREANG KABUPATEN BANDUNG

The application of BIM in the Soreang Market development project, Bandung Regency using the Tekla Structure application. How is the time performance in the implementation of the Pasar Soreang project, Bandung Regency. Applying BIM to 4-dimensional (4D) modeling in the construction of the Soreang market project in Bandung Regency. Analyzing project time performance in the form of deviation of realization weight with plan weight based on s curve.Research method is basically a scientific way to get data with a specific purpose and use. Based on the statement above, it can be seen that in a study using a method of processing data and drawing conclusions related to the problem in the study.District afternoon market project. Bandung uses Tekal Structure 19 software. With 3D modeling of buildings in the form of piles, pile caps, tie beams, columns, beams, plates and roof coverings. Then the schedule is added so that it becomes a 4D modeling using the task manager in the Tekla Structure application. In the 4D modeling obtained in the form of images that are able to present the work step by step according to the schedule. The importance of surveys related to traffic conditions around the project site in order to be able to engineer traffic flow so as not to hinder the mobilization of project materials.&#x0D;
Cyber Crime Management among Students

This study investigated examined the legal correlates of cybercrime management amongst higher institution students in Nigeria with special reference to some selected tertiary institutions in Delta State, Nigeria. A correlation approach of survey research design was adopted in this study. In order to address the problem of this study, seven research questions were raised and seven research hypotheses were formulated and tested at a .05 level of significance. This study revealed that the law can provide solutions to Cyber Crime management in Nigeria. Poverty is a factor responsible for cybercrime in Nigeria. The law can promote intellectual property and ensure privacy rights. There are existing laws that adequately address challenges relating to cybercrimes. The study revealed that youths who are mostly male are the major perpetrator of cyber-crimes and the crime can be committed at any time of the day. The study found that unemployment, poverty, absence of effectual law, and corruption are the major causes of cyber-crime in the study area. Based on the findings of this study, it was therefore recommended that collective vigilance detect and report to law enforcement agencies anyone suspected to be involved in cyber-crime. The Federal Government should empower the youths in terms of job creation and regularly engage the IT organizations to develop strategies to curtail cyber-crime.
Native and Target Language Influence on Students’ Interlanguage Speech

Literature confirms that various errors made by Indonesian learners of English as a foreign language are challenging phenomenon which needs our concern. The errors found are both in speech and composition production, which portray the effectiveness of language learning. Students language that belongs neither native nor target language is called interlanguage (IL). The focus of this study is to present the empirical results of the investigation on the IL features of students' speech production. It aims to describe the types and aspects of the native language (NL) and target language (TL) influences and to explain the possible causes of IL production. The data were in the form of 144 ill-formed utterances. In gathering the data, the students were given a guided picture with the topic ‘Last Holiday’. The students' speech was video recorded. The ill-formed utterances were collected, identified, described, and explained. The result of the study shows that both of their NL and TL influenced the students' interlanguage production at the syntactical level.
Penyebab-Penyebab Kesalahan Penggunaan Kata Bahasa Mandarin: Tinjauan Terhadap Mahasiswa Jurusan Bahasa Mandarin di Indonesia

Chinese words’ usage errors in compositions of undergraduates are very attractive to be research, because the errors are truly has become the obstruction of communication in composition. The purpose of this study is to describe the cause factors of the Chinese words’ usage errors in compositions of undergraduates. Use the approach of International qualitative methodology and the data of this research is got from the students in Unesa and UWK. The data analysis contains the following procedures. First, identify the word usage errors and classify the types based on the linguistics taxonomy which consisting of phonology, semantics &amp; lexical, morphological, and syntactic. Finally, it analyzes the cause factors from both the perspective of linguistics and non-linguistics. The study finding interference between Chinese and Indonesian is the main linguistics factor causing. At the same time, the strategy of communication and language environment of undergraduates are the non-linguistic factors causing. Moreover, if viewed from the students themselves, learning duration, exposure, language governance, and media of study are also affects the words’ usage in composition.
Realization of Maxim Flouting to Create Humour in Incredibles 2

This study focuses on the analysis of humour creations based on the Griceâ€™s cooperative maxim flouting. It applies descriptive qualitative study where we examined the utterances in Incredibles 2 were flouted to create humour. The results of the study showed that (1) There were 7 kinds of humour created from 19 flouting of quality maxims. (2) There were also 7 kinds of humour created from 45 flouting of quantity maxims. (3) There were only 9 kinds of humour created from 57 flouting of manner maxims. (4) On the flouting of relation maxims, all 11 humour categories were fulfilled from 59 flouting of relation maxims. Furthermore, it is suggested that through the occurance of maxim flouting in the movie especially animation genre which is addressed to the audience gives the knowledge about conversational implicatures. With regard to the creation of humour, it is suggested that the humour is not only a deviation of the cooperative principle and its maxims. Other theory of pragmatics can also be used to analyze the humour such as, politeness principle and also the acts of speech acts.
The Use of Politeness Strategies by Indonesian Vs Malaysian Student Debaters in the 2018 World Schools Debating Championship (WSDC)

The aims of this study were to analyze the types of politeness strategies realized by student debaters and how the Indonesian Vs Malaysian student debaters used politeness strategies in the debate competition. The data of this research were taken from youTube that video series of debate competition â€œWSDC 2018 R2: Indonesia VS Malaysia.â€ The study used a descriptive qualitative method which was analyzed using the politeness strategies propossed by (Brown and Levinson, 1987). The subjects in this study were 6 student debaters (2 teams) from Malaysia and Indonesia who participated in the 2018 World Schools Debating Championship (WSDC). This study found that most of the student debaters used positive politeness strategies to deliver their arguments appropriately. There were four types of politeness strategies realized by the debaters. Sub-strategies of positive politeness were mostly used by Indonesian debaters. They used sub-strategies of positive politeness to keep the hearer's positive face, to safe opinions when delivering arguments clearly as the opposition team and make the preposition team agreed with their insight. Malaysians used off-record strategy dominantly. They used inviting implicatures to imply meanings.
PENDETEKSIAN DAN PENGENALAN JENIS MOBIL MENGGUNAKAN ALGORITMA YOU ONLY LOOK ONCE DAN CONVOLUTIONAL NEURAL NETWORK

Car Type Detection and Recogniton system is an application that is developed using You Only Look Once (YOLO) and Convolutional Neural Network (CNN) algorithm. This application purpose is to detect and recognize the car image from the data input. In this application the input image will be divided into two parts, namely the training image and test image. For the training image, the first step, the training image will be divided into two process stages, namely detecting the image of a car and searching for the unique features of a car.To detect the image of the car, the image will be processed to detect parts of the image of the car and not the car using the YOLO method. After obtaining a part of the car image, the image of the car will be saved as a detection model. The image that has been detected will be learning the car image by the CNN method. For the test images of the stages carried out as in the training image, after the image of the car is detected, an introduction will be made based on learning that has been done with the CNN method to obtain output in the form of a car that is successfully recognized and detected will be labeled by the application.
Implementing Cloud Education Based on a Government Community Hybrid Model

The advantages of cloud computing technology are created by the government community hybrid model for education services in offering optimism about information and communication technology infrastructures and resource sharing, provides widely used education services, deployment and feasibility of devices and flexibility of time and place for users, and increase participation from the public and private sector as well as collaboration with government. The purpose of this study is to find models in the learning process in accordance with the character and advantages of this technology with the main role of government to operate, as well as look for relationship between schools, government and society. The research method is qualitative by conducting a literature study and also discussing it with experts. The results of this study consist of the model of the learning process, the implementation strategy and roadmap technology.
EFFECT OF DUOLINGO APPLICATION ON STUDENTS’ ENGLISH VOCABULARY MASTERY

This paper examined the effect of Duolingo application on students English vocabulary mastery using quasi-experimental with post-test only design. The participant of this research was the eighth-grade students in one of junior high school in Jember. In this study, the experimental group got treatment by using Duolingo application; meanwhile, the control group taught as usual by using flashcard as the media. The data was collected from vocabulary test and was analyzed by using SPSS with independent sample t-test. The result showed a significant difference between students who use Duolingo application and who didnt with the value of significance (0.017) which was less than 0.05 and the mean score of the experimental group (84.69) was higher than the mean score of the control group (80.78). This demonstrates that using Duolingo Application has a significant effect on students’ vocabulary mastery.
Estimation of the Randomness of Continuous and Discrete Signals Using the Disentropy of the Autocorrelation

&lt;b&gt;The amount of randomness in a signal generated by physical or non-physical process can reveal important information about that process. In this direction, the present work proposes to use the disentropy of the autocorrelation function as a measure of randomness. Examples using noisy and chaotic signals, economic and biological data are shown.&lt;/b&gt;