Reviewerly/exHarmony huggingface dataset:
A Bi-directional Interactive System of Sign Language and Visual Speech Based on Portable Devices

At present, the natural communication between deaf and normal people is a major challenge both in theoretical research and application. In addition, in the field of Human-Machine Interaction, there is little work on bi-directional communication based on sign language and visual speech. In this paper, a portable bi-directional interactive system of sign language and visual speech was proposed to help deaf people communicate naturally under certain circumstances. In the section of modeling, Network in Network model was employed to classify sign language words, while the method using a network combined DenseNet and LSTM together was used for lip reading. By wearing the portable device, people can achieve bi-directional translation and communication of visual speech and sign language. We built our own Chinese sign language database containing more than 100 categories of words and conducted experiments in the context of the airport for experimental verification. Eventually, the average time of each round of conversation is within 15 seconds, faster than normal communication without system assistance, which verified the effectiveness and advantage of the purposed system.
Code Generation for Big Data Processing in the Web using WebAssembly

Traditional clusters for cloud computing are quite hard to configure and setup, and the number of cluster nodes is limited by the available hardware in the cluster. We hence envision the concept of a Browser Cloud: One just has to visit with his/her web browser a certain webpage in order to connect his/her computer to the Browser Cloud. In this way the setup of the Browser Cloud is much easier than those of traditional clouds. Furthermore, the Browser Cloud has a much larger number of potential nodes, as any computer running a browser may connect to and be integrated in the Browser Cloud. New challenges arise when setting up a cloud by web browsers: Data is processed within the browser, which requires to use the technologies offered by the browser for this purpose. The typically used JavaScript runtime environment may be too slow, because JavaScript is an interpreted language. Hence we investigate the possibilities for computing the work-intensive part of the query processing inside a virtual machine of the web browser. The technology WebAssemby for virtual machines is recently supported by all major browsers and promises high speedups in comparison with JavaScript. Recent approaches to efficient Big Data processing generate code for the data processing steps of queries. To run the generated code in a WebAssembly virtual machine, an online compiler is needed to generate the WebAssembly bytecode from the generated code. Hence our main contribution is an online compiler to WebAssembly bytecode especially developed to run in the web browser and for Big Data processing based on code generation of the processing steps. In our experiments, the runtimes of Big Data processing using JavaScript is compared with running WebAssembly technologies in the major web browsers.
"The Power of Knowledge, Objectified": Immaterial Labor, Cognitive Capitalism, and Academic Librarianship

This article analyzes current trends in academic librarianship from the perspective of Italian autonomist Marxism. With the rise of new technologies and the advent of a period variously called the "Fourth Industrial Revolution," "Industry 4.0," and "The Second Machine Age," academic librarianship is undergoing various changes in work-flow, technology, and service provision. The body of thought that developed out of the Italian Marxist tradition provides ways of thinking through and understanding these changes by placing them within a larger dynamic of capitalist development and the restructuring of labor processes. After looking at changes to academic librarianship from the perspective of immaterial labor and cognitive capitalism, the paper offers ways that academic librarianship can think about the possibility of resistance to these changes.
Toward Intelligent Visual Sensing and Low-cost Analysis: A Collaborative Computing Approach

In the big data era, there has been an increasing consensus that the label information, computational resources and communication bandwidth are particularly precious. State-of-the-art research is revolutionizing the vision systems of the smart city, which converts the visual signals from sensory input into feature representations and conveys the compact feature for analysis by using the computational resources of both front and back ends. To deploy a robust model, large amounts of labeled data are usually required, and thereby heavy computational and communication resources are incurred in model training as well as inference. However, the computational resources in front-end devices are usually constrained, and heavy transmission burden is imposed when leveraging multiple models amongst different ends. In this work, we propose a novel collaborative computing approach for intelligent sensing and low-cost analysis, which reduces the requirement of labeled data and communication cost, and balances the computational load in model training and inference. By incorporating the adversarial learning mechanism into collaborative model training, knowledge of different domains can be better exploited. Moreover, the learned models are deployed for inference in a collaborative manner, in which part of model is placed in front-ends for extracting intermediate feature maps, and part of the model remains in back ends for inference with received feature maps. The effectiveness of the proposed approach has been validated in the context of an emerging digital retina system for smart city intelligent applications.
Competencias Digitales: Realidad de ingreso de los estudiantes a la vida universitaria

This study aims to reveal the conditions of entry with which students start university life with respect to the acquisition of digital skills, declared as a key competence to be inserted into society by various governmental and academic institutions, given the globalization and dynamization of the society facilitated by the global internet network, which has modified all forms of communication, socialization and operation of the most basic services of everyday life.Therefore, it must not be lacking in the development of knowledge, skills and attitudes of every human being, being understood as an essential tool for all citizens.The University of Magallanes in Chile, has established since 2010 for all students entering the first year of undergraduate studies the application of a Computer Proficiency Test, which aims to validate skills and knowledge related to the Information and Communication Technologies of the students.Based basically on the proposal that the Chilean education system promotes in this order for basic and secondary education, designed by the Center for Education and Technologies -"Red Enlaces" of the Chilean Ministry of Education , in order to recognize what are the skills acquired during this process, and granting the possibility of assessing previous knowledge, and exempting oneself from taking a subject, in case of approval.The main objective of the test, in the context of this research, is to know if students really start university life with digital skills installed, if they are trained or prepared in this area, and on the other hand it has to do with knowing the areas of opportunity that are detected from the evaluation and that are primarily related to the Matrix of ICT Skills for learning and those considered from experiences of other universities and institutions in the world, which allow to generate comments and suggestions to enhance them in the university training process.
Analysis of Customer Behavior in Mobile Food Ordering Application Using UTAUT Model (Case Study: GoFood Application)

One of the mobile application technologies that popular and enjoying extraordinary success in Indonesia is GoJek application. One of many services in gojek app is called go-food. Go-food provides food online service delivery that has three main users. There are merchants, gojek drivers, and customers. Many online food ordering applications have developed, and various issues have occurred, but there has not been much research on the topic. This research is using quantitative methods. Tools to get data is a questionnaire of 60 respondents. The results are validation data and structural model calculation use Smart PLS tools. From the measurement results, there are two variables that have a positive and significant effect (with a t-statistic score> 1.65) on the user's intention to use GoFood, namely the Social Influence and Perceived Control variables. There also performance expectancy, effort expectancy, and facilitating condition variables that have a negative effect (with a t-statistic score <1.65) on the user's intention to use the GoFood application.
Designing Client-Centred Knowledge Services

Abstract In this article Sophie Thompson describes what design thinking is and gives practical exercises to see how design thinking can be used to give a fresh perspective on delivering knowledge services. The article is based on a parallel session delivered as a workshop at the BIALL Annual Conference 2019.
The Keys to Decidable HyperLTL Satisfiability: Small Models or Very Simple Formulas

HyperLTL, the extension of Linear Temporal Logic by trace quantifiers, is a uniform framework for expressing information flow policies by relating multiple traces of a security-critical system. HyperLTL has been successfully applied to express fundamental security policies like noninterference and observational determinism, but has also found applications beyond security, e.g., distributed protocols and coding theory. However, HyperLTL satisfiability is undecidable as soon as there are existential quantifiers in the scope of a universal one. To overcome this severe limitation to applicability, we investigate here restricted variants of the satisfiability problem to pinpoint the decidability border.First, we restrict the space of admissible models and show decidability when restricting the search space to models of bounded size or to finitely representable ones. Second, we consider formulas with restricted nesting of temporal operators and show that nesting depth one yields decidability for a slightly larger class of quantifier prefixes. We provide tight complexity bounds in almost all cases.
An optimized Multi-Layer Ensemble Framework for Sentiment Analysis

Public opinion plays an important role in decision making tasks of various fields. Sentiment Analysis is a key task in summarizing sentiment opinions as it classifies opinion documents according to its sentiment group of positive and negative. Machine learning based classification is efficient and versatile. The ensemble concept is used to improve classification accuracy by combining the decision of multiple classifiers. In this work, a framework for sentiment analysis is designed to extend the concept of ensemble upon all subtasks of machine learning classification in order to achieve better analysis. There are 3 subtasks in machine learning based sentiment analysis which are feature extraction, feature selection and classification. The ensemble concept is applied to all 3 tasks by combining different methods to perform the tasks and combine their results. optimization is performed by using Genetic Algorithm to find the combination of methods that could perform better. The proposed framework is tested on 4 different domain datasets and the sentiment analysis accuracy is shown to be very high. Future works includes testing the framework on different domains of classification and different optimization algorithm.
Relational ⋆⋆\star-Liftings for Differential Privacy.

Recent developments in formal verification have identified approximate liftings (also known as approximate couplings) as a clean, compositional abstraction for proving differential privacy. This construction can be defined in two styles. Earlier definitions require the existence of one or more witness distributions, while a recent definition by Sato uses universal quantification over all sets of samples. These notions have each have their own strengths: the universal version is more general than the existential ones, while existential liftings are known to satisfy more precise composition principles. We propose a novel, existential version of approximate lifting, called $\star$-lifting, and show that it is equivalent to Sato's construction for discrete probability measures. Our work unifies all known notions of approximate lifting, yielding cleaner properties, more general constructions, and more precise composition theorems for both styles of lifting, enabling richer proofs of differential privacy. We also clarify the relation between existing definitions of approximate lifting, and consider more general approximate liftings based on $f$-divergences.
Detecting Chemical Reactions in Patents

Extracting chemical reactions from patents is a crucial task for chemists working on chemical exploration. In this paper we introduce the novel task of detecting the textual spans that describe or refer to chemical reactions within patents. We formulate this task as a paragraph-level sequence tagging problem, where the system is required to return a sequence of paragraphs which contain a description of a reaction. To address this new task, we construct an annotated dataset from an existing proprietary database of chemical reactions manually extracted from patents. We introduce several baseline methods for the task and evaluate them over our dataset. Through error analysis, we discuss what makes the task complex and challenging, and suggest possible directions for future research.
From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442)

We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performance.
Knowledge acquisition and corpus for argumentation-based chatbots

Many of the conversations we have every day involve exchanges of arguments and counteraguments. In the context of artificialintelligence and argumentation theory, such phenomena fall into the areaof dialogical argumentation. Conversational agents, also known as chatbots, are versatile tools that have the potential of being used in dialogicalargumentation. We can assume that a chatbot would take a particularstance in the dialogue, opposing the stance of the user. In order to succeed, the chatbot also needs to be aware of various arguments and theinterplay between them. Such knowledge can be represented by a directedgraph, where nodes stand for arguments and arcs symbolise conflicts between them. The chatbot must be aware of both sides of the discussion,i.e. the arguments that it can play as well as ones that the user mighthave, to be able to formulate convincing responses. The availability oflarge argument graphs for research, however, is very limited. This meansthat researchers do not have corpora available which hinders the development of new chatbots and limits the e↵ectiveness of existing ones. Inthis paper, we propose a method to acquire a large number of argumentsin a graph structure using crowd sourcing. We evaluate this method in astudy with participants and present a corpus which can be used for further research in computational argumentation and chatbot technologiesfor argumentation.
Determining Project Contingency Reserve Using a Fuzzy Arithmetic-Based Risk Analysis Method

Traditional techniques for estimating contingency reserve fail to capture subjective uncertainties and expert knowledge, and they rely on historical data.This paper proposes a fuzzy risk analysis model (FRAM) that uses fuzzy arithmetic to analyze risk and opportunity events and determine construction project contingency reserve.The FRAM allows experts to use natural language to assess the probability and impact of risk and opportunity events by employing linguistic scales represented by fuzzy numbers, thus addressing the data reliance problem of probabilistic methods.It enables experts to customize linguistic scales and fuzzy numbers for different project types and stages.The FRAM also deals with the challenges associated with deterministic approaches by addressing measurement imprecision and the subjective uncertainty of experts' opinions.Moreover, the FRAM allows analysts to estimate contingency at different levels of confidence.This paper also illustrates Fuzzy Risk Analyzer © (FRA © ), software that implements the fuzzy arithmetic procedure of the FRAM.
Legal approaches to management of the risk of cloud computing insolvencies

The failure of a cloud computing service provider has been acknowledged as presenting significant potential risk, given growing usage of cloud services by businesses, including financial institutions, public authorities and individuals. The disruption that a loss of access to data, or the means to process it, could present risk of a systemic nature. However, approaches to cloud computing insolvencies are yet to receive significant attention in academic literature. This article represents an initial attempt to identify the potential impact of an insolvency in this area as well as possible contractual approaches to risk containment, noting their limitations. It then identifies possible data recovery difficulties and the tensions that this would lead to in insolvencies. Drawing upon examples of insolvencies in other sectors, it suggests preventative and reactive approaches to cases of wide public impact, as well as a need for a supranational approach given the potential for a ‘too big to fail’ scenario.
Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification

Jatropha Curcas is a very useful plant that can be used as a bio fuel for diesel engines replacing the coal. In Indonesia, there are few plantation that plant Jatropha Curcas. But there is so limited farmers that understand in detail about the disease of Jatropha Curcas and it may cause a big loss during harvesting when the disease occured with no further action. An expert system can help the farmers to identify the lant diseases of Jatropha Curcas. The objective of this research is to compare several identification and classification methods, such as Decision Tree, K-Nearest Neighbor and its modification. The comparison is based on the accuracy. Modified K-Nearest Neighbor method given the best accuracy result that is 67.74%.
An Approach Toward a Feedback Mechanism for Consensus Reaching Processes Using Gamification to Increase the Experts' Experience

Sometimes, the consensus reaching process in group decision making problems is a challenging task for the people who are in charge of the final choice (usually called experts).Firstly, the consensus is defined as a convergent and iterative process.This implies that it is necessary to keep the experts' attention during the whole process, even if it is longer than expected.Secondly, some of the experts tend to be rigid and they do not change their minds to help in the negotiation process easily.In such a way, we propose a new feedback mechanism that uses some gamification rules, designed as a reward distribution system, in order to transform that task into a game.This change can improve the consensus reaching process in both situations: keeping the experts' attention on the process and motivating those experts that should adjust their preferences.
Blind Image Watermarking in Canonical and Cepstrum Domains Based on 4-Connected t-o’clock Scrambling

Copyright protection of multimedia content is confronted with great challenges such as easy access to the Internet. Digital watermarking is widely applicable technique for copyright protection of multimedia contents. In this paper, a blind symmetric watermarking method in canonical and cepstrum domains based on four-connected t-o’clock scrambling is proposed. Initially, the watermark image is scrambled using the four-connected t-o’clock method to enhance the security. Then, the rotation operation is applied to the host image to extract the region where the watermark bits are embedded. After that, discrete linear canonical transform (DLCT) is applied to the extracted region to obtain the DLCT region. Cepstrum transform (CT) is performed on DLCT region to attain CT region. The CT region is then divided into non-overlapping blocks. The watermark bits are inserted into each block using max-heap and min-heap tree property. Experimental results illustrate that the proposed method shows high robustness against numerous attacks. Moreover, it produces high quality watermarked images and provides high security. Furthermore, it has superior performance to recent methods in terms of imperceptibility, robustness, and security.
Augmented Vector Field Navigation Cost Mapping using Inertial Sensors

In outdoor field robotics, considering the environmental characteristics is key to improving the efficiency of autonomous navigation. In this context, identifying rough terrain can significantly increase the reliability of operations. This paper addresses the problem of mapping the navigation cost associated with uneven outdoor terrains. We propose an augmented vector field representation obtained only with the use of inertial sensors. The map is determined considering characteristics such as roughness and slope. Experiments were carried out with different robots in real-world environments presenting different terrain characteristics to analyze the quality and efficiency of the mapping process. Results show that the obtained navigation cost maps provide a reliable indication of the ground characteristics of outdoor environments and can be used in the path planning stage.
The Effect of Knowledge Management System on Software Development Process with Scrum

Abstraet-Scrum as one of a popular agile frameworks is quite flexible to changes. However, this behavior is accompanied by several shortcomings, especially the documentation section since its dependence on direct communication. Therefore, the use of knowledge management in Scrum seems to be important. Knowledge management in Scrum was influenced by several challenges such as the various locations of the team members, the changes of software developers, the methods for large-scale projects, and the needs for high-quality software. These problems need to be resolved to avoid the amount of knowledge loss. Therefore researchers want to find out more about how knowledge management system influences the development of scrum-based software and its effect on developers' performance. This study uses a quasi-experimental approach with the application in the real world to startup companies. A quasi-experimental is research method used to compare the effect before and after treatment in a group by comparing the performance conditions before and after the implementation of the knowledge management system. The data collected from experiment are scrum artefact, knowledge artefact, and developers' performance (using IWQP). The first experiment for the group lasted for two sprints using a scrum support system. Knowledge management system based scrum used for the second experiment. The findings obtained were an increase in adaptation performance of 8%, contextual performance of 12%, and improvement of knowledge circulation from the development team. The Knowledge Management System is proven to be able to improve and handle the knowledge circulation in Scrum and give impact to software developers.