Reviewerly/exHarmony huggingface dataset:
Critical Making With and For Communities

Critical making offers the chance to combine practices of making with critical thinking, to explore topics utilizing technologies and materials and thereby creating tangible artefacts which have a societal impact. Many of the growing number of makerspaces globally - though by far not the whole so-called "maker movement" - foster participatory design for and with their communities to solve local problems, explicitly or implicitly basing their practices on critical design. This workshop will bring together researchers and practitioners at the 9th International Conference on Communities & Technologies to discuss emerging and evolving issues of critical making, participatory design, and maker communities. It will offer an introduction to critical making based on a short history of the maker movement, current societal impacts achieved through practices of making, as well as criticism directed towards maker culture. Together with the participants, we will look at participatory practices of critical designing and making for and with communities, and the role which makerspaces play as facilitators and drivers of these practices. Participants will share examples, best practices, tools and methods, and explore how we can better work together towards collaboratively developing theories, methods and practices, and achieving more societal impact of critical making in makerspaces around the world.
Character-Level Hybrid Convolutional and Recurrent Neural Network for Fast Text Categorization

Text categorization, or text classification, is one of key tasks for representing the semantic information of documents. Traditional deep leaning models for text categorization are generally time-consuming with large-sized datasets due to slow convergence rate. In this paper, we propose a character-level model for short text classification with a combination of convolutional neural network (CNN), gated recurrent unit (GRU) and highway network (HN), which can capture both the global and the local textual semantics while having a tractable computational complexity. In addition, error minimization extreme learning machine (EM-ELM) is incorporated into the proposed model to improve the classification accuracy further. Extensive experiments show that our approach achieves the state-of-the-art performance when the hybrid model based on EM-ELM is trained using large-sized datasets.
Automating GUI Response Time Measurements in Mobile and Web Applications

The human perception of the performance of an operation while interacting with an user interface (UI) is subjective, i.e., it may depend on how the visual elements are arranged and how they respond to actions. The research for techniques for measurement of UI response time is a nontrivial task, since most of the solutions are concentrated in specific technologies. For evaluation of web pages, tools of general purpose has been used, focusing only on the download of resources and recommendations for the code structure. This paper proposes a methodology to make feasible the measurement of scenarios specified by the product designers for both web and mobile native applications with minor modifications in the automated test code. The methodology relies on using popular automation test tools originally designed for functional testing. The results found may help product owners and stakeholders to make cost-benefit analysis and make decisions about possible enhancements taking into account how the end user interacts with the application.
Graph Grammar Models in Syntactic Pattern Recognition

The families of graph grammars used in syntactic pattern recognition are characterized in the paper. The reasons for the intractability of the problem of graph language parsing are presented. The methodological principles for the constructing of efficient syntax analysis schemes for graph-based syntactic pattern recognition are discussed.
Olympic Sports Events Classification Using Convolutional Neural Networks

The analysis of different sports data to get valuable insight has become immensely important nowadays. Profuse application of Artificial Intelligence in different sectors has become a very popular trend as well. However, the application of AI in sports analytics is still a new research domain left for exploration. With a view to applying AI in sports analytics, we have deployed Inception V3 and MobileNet which are Google’s most popular Convolutional Neural Networks to successfully recognize five different sports events from a huge image dataset of these events. Both of the models have achieved a very high performance in terms of accuracy, precision, recall, and f-measure while applied to the target dataset for successful classification.
Experiment with Lexicon Based Techniques on Domain-Specific Malay Document Sentiment Analysis

The nature of Sentiment Analysis (SA) mostly is generated by human beings. They expressed their emotion in writing or expressing their feeling via social media or blog. The Advancement of Internet and the increasing number of users in social media are the factors on why the sentiment analysis gaining its popularity in Malay languages. This research aims to implement the Sentiment Analysis on Malay language documents and propose a lexicon-based technique for Malay based sentiment analysis on specific domain such as Song, Politic and Product to find the best SA classifier on the Domain-Specific Malay Document Sentiment Analysis. Analysis of the evaluation result is based on the comparison of expert evaluation, Lexicon-based evaluation’s result and Naive Bayes SA classification’s result, which is Naive Bayes represent Machine Learning approach in this study. The result shows Lexicon-based Classification has outperformed Naive Bayes SA classification in overall 3 topics which are Song, Politic and Product in average of 70% compared to 50% average for Naive Bayes. For the future works, the researcher would like to improve in the particular area such as Sentiment Analysis based on the Malay dialect, increase the data in the dictionary and applying phrase level for better and optimum results.
Tree stream mining algorithm with Chernoff-bound and standard deviation approach for big data stream

We propose a Chernoff-bound approach and examine standard deviation value to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. It is a data stream mining algorithm that can observe and form a model tree from a large dataset. The standard FIMT-DD algorithm uses the Hoeffding bound for its splitting criterion. The use of the simplified Chernoff bound is proposed for splitting a tree more accurately than the Hoeffding bound. We verify our proposed Chernoff bound and standard deviation value examination by evaluating several large real-world datasets that consists of 100,000,000 instances. The result shows that our proposed method has an improvement in term of accuracy compared to the Hoeffding bound in the FIMT-DD algorithm. Error value measurements demonstrate that the Chernoff bound approach contributes to smaller error value compared to the standard method. In term of overall simulation time, the Chernoff bound approach increases the duration of simulation time and utilizes more memory compared to Hoeffding bound. However, Chernoff bound is much faster to get a smaller error compared to the standard algorithm.
A Flexible Instant Payment System Based on Blockchain

Improving the throughput of blockchain systems such as Bitcoin and Ethereum has been an important research problem. Off-chain payments are one of the most promising technologies to tackle this challenge. Once a payment channel, however, is established there exists a strict one-one correspondence between a payee and prepayments, which reduces the flexibility of off-chain payments. In this paper, we propose a flexible instant payment system (FIPS) based on blockchain to improve the flexibility of off-chain payments. In the FIPS system, there exists a depositor who locks enough amounts of tokens on the chain, and supervises payers to make off-chain payments. Therefore, payers can pay to multiple payees off-chain without double-spending. Even the depositor colludes with the payer, and performs double-spending attacks, payees will not suffer any losses as they can withdraw their tokens from the locked tokens of the depositor. Besides, payers can allocate flexibly the prepayments off-chain, and all transactions are settled off-chain. We present a formal generic construction for the FIPS system, prove its security strictly, analyze its related properties, and compare with related schemes in detail. Analyses show that our scheme is flexible and practical.
Ensuring the Consistency Between User Requirements and Graphical User Interfaces: A Behavior-Based Automated Approach

Ensuring the consistency between Graphical User Interfaces (GUIs) and user requirements is a critical aspect of the design process since it is through the GUIs that users perceive the system and experience the available features in order to achieve their goals. This paper presents an approach based on Behavior-Driven Development (BDD) which employs an ontology in order to provide automated assessment for web GUIs. The approach has been evaluated by exploiting user requirements described by a group of experts in the flight tickets e-commerce domain. Such requirements gave rise to a set of User Stories that have been used to automatically assess the GUIs of an existing web system for booking business trips. The results have shown our approach was able to identify different types of inconsistencies in the set of GUIs analyzed, allowing to build an effective correspondence between user requirements and their representation on the GUI.
Conversational Agents and Negative Lessons from Behaviourism

This chapter addresses theGnjatović, Milan question of whether it is enough to extract from data the knowledge needed to implement socially believable conversational agents. Contrary to the popular views, the answer is negative. In this respect, the chapter points to some shortcomings of fully data-driven approaches to dialogue management, including the lack of external criteria for the selection of dialogue corpora, and the misconception of dialogue structure and dialogue context. To point to these shortcomings is not to undervalue data-driven approaches, but to emphasize the message that big data provide only a partial account of human-machine dialogue, and thus must not remain wedded to small linguistic theory, as it is currently the case.
Asynchronous Distributed Edge-Variant Graph Filters

As the size of the sensor network grows, synchronization starts to become the main bottleneck for distributed computing. As a result, efforts in several areas have been focused on the convergence analysis of asynchronous computational methods. In this work, we aim to cross-pollinate distributed graph filters with results in parallel computing to provide guarantees for asynchronous graph filtering. To alleviate the possible reduction of convergence speed due to asynchronous updates, we also show how a slight modification to the graph filter recursion, through operator splitting, can be performed to obtain faster convergence. Finally, through numerical experiments the performance of the discussed methods is illustrated.
A Machine Learning-Based Framework for Throughput Estimation of Time-Varying Applications in Multi-Core Servers

Accurate workload prediction and throughput estimation are keys in efficient proactive power and performance management of multi-core platforms. Although hardware performance counters available on modern platforms contain important information about the application behavior, employing them efficiently is not straightforward when dealing with time-varying applications even if they have iterative structures. In this work, we propose a machine learning-based framework for workload prediction and throughput estimation using hardware events. Our framework enables throughput estimation over various available system configurations, namely, number of parallel threads and operating frequency. In particular, we first employ workload clustering and classification techniques along with Markov chains to predict the next workload for each available system configuration. Then, the predicted workload is used to estimate the next expected throughput through a machine learning-based regression model. The comparison with state of the art demonstrates that our framework is able to improve Quality of Service (QoS) by 3.4x, while consuming 15% less power thanks to the more accurate throughput estimation.
Introducing Action Planning to the Anticipatory Classifier System ACS2

This paper introduces and tests Action Planning mechanism in the Anticipatory Classifier System ACS2. Action Planning implies goal-directed learning and bidirectional search to strengthen reliable classifiers. It is shown that it can speed up the process of gaining knowledge about the learned environment. Experiments were performed over three environments (Hand-Eye, Maze and Taxi) extended with custom goal-generator functions.
An Optimized Data Distribution Model for ElasticChain to Support Blockchain Scalable Storage

The cost of trust in the real economy is obviously reduced by the technology of blockchain, however, storage scalability of blockchain is quite poor. Therefore, the ElasticChain model is took use of to solve this problem. The ElasticChain model increases the storage capacity of blockchain, while security of data is the premise which we must guarantee. In ElasticChain, the duplicate ratio regulation algorithm makes two things happen. The first one is to give a safe distribution of duplicates and then the other thing is the duplicates are stored in highly reliable nodes. However, in ElasticChain, the reliability test method of storage nodes is too simple. Therefore, in order to solve this problem, it is necessary for us to put forward an optimized data distribution method for ElasticChain. The optimized model ought be able to use the Extreme Learning Machine (ELM) to classify the nodes because of their different reliability. Moreover, the optimized model can also distribute the blockchain data in reliable nodes, so as to increase data security. Further, we brought forward a new strategy to extract the node reliability. The fact is that the node reliability includes the features below: the activeness, security, credibility and stability. There are four features in total. In this substance, the efficiency and exactness of the optimized data distribution model are certificated by the results of experiments on synthetic data.
A Cloud Bidding Framework for Deadline Constrained Jobs

Reliable completion of the computing jobs through Amazon spot instances (SIs) with proper bargaining is challenging. Therefore, an SI bidding system is developed for deadline constrained jobs considering both the conditions of the market and the condition of the user. The system tries to bargain with the provider by bidding low when the task is not urgent. After that, the system increases the price or the price distribution gradually when the progress is lower than required. To calculate the bid distribution, we compute the probability density of the price after five minutes. Then, we apply our developed equations to compute bid-prices from the probability density function. Equations are easily interpretable to both humans and machines. We also consider long-term probability distributions of the prices for the reliable completion of the job. Tasks with several days deadline are prescribed to bid considering the daily price-curve. According to the evaluation of Amazon SI price, the proposed system effectively saves 79%-87% for jobs with several hours deadline and saves 82%-100% for jobs with several days deadline compared to the on-demand instances. Moreover, our algorithm helps all bidders by keeping the price low.
Trading off between control and autonomy: a narrative review around de-design

In this work, we provide an overview of contemporary perspectives of design that may challenge the traditional design of IT and socio-technical systems. Our starting metaphor is that of ‘wicked problems’, where the singularity, incompleteness and intrinsic uncertainty of real world settings foregrounds how the worldview that designers offer to practitioners may be optimal in theory but useless in practice. To go beyond traditional notions of design and designer, we intercepted insights coming from minoritarian voices in both theoretic and practice-based design fields. ‘De-design’ is a term we coined to encompass this wide spectrum of approaches that make more resilient and sustainable information artifact, de-emphasize design as a theoretical construct, and reconsider practice as the leading principle of digital innovation. This paper is a narrative review of voices in an extensive array of fields: from Information Systems to Human-Computer Interaction, from End-User Development to Critical Design, from Software Design to Design Studies. Our contribution retraces the motivational roots of de-design and tries to characterise de-design by filling relational gaps between disparate approaches and by bringing them back to IT and socio-technical design, to make digital artifacts sustainable in all of the new environmental, organisational and cultural spaces near to come.
Real-Time Low-Cost Active and Assisted Living for the Elderly

The aging of population in recent years and the increase in life expectancy is raising challenges for finding new ways to guarantee healthy and controlled activities for the elderly. Most of them prefer living in their houses than in a community center, even if they live alone or isolated from their family; at home, their normal routine activities and comfort makes them feel well. In this paper, an Active and Assisted Living (AAL) solution to detect irregular situations in everyday life of the elderly living alone is presented. By using low-cost sensors in an Internet of Things (IoT) architecture we aim to gather data in specific areas of an elderly’s house in order to give the system enough input to detect abnormal behavior. These sensors are non-intrusive to the elderly, do not disturb them, and do not force them to wear a device at all times. These sensors can also send information to edge computing devices that analyze the data in real time using machine learning algorithms and alert family or caretakers when an unusual situation arises. The proposed solution provides a system that monitors the main activities performed by the elderly and creates patterns based on that activity to achieve its results and is scalable in terms of sensors and data input.
Analyzing the Impact of Knowledge and Search in Monte Carlo Tree Search in Go

Domain-specific knowledge plays a significant role in the success of many Monte Carlo Tree Search (MCTS) programs. The details of how knowledge affects MCTS are still not well understood. In this paper, we focus on identifying the effects of different types of knowledge on the behaviour of the Monte Carlo Tree Search algorithm, using the game of Go as a case study. We measure the performance of each type of knowledge, and of deeper search by using two main metrics: The move prediction rate on games played by professional players, and the playing strength of an implementation in the open source program Fuego. We compare the result of these two evaluation methods in detail, in order to understand how effective they are in fully understanding a program’s behaviour. A feature-based approach refines our analysis tools, and addresses some of the shortcomings of these two evaluation methods. This approach allows us to interpret different components of knowledge and deeper search in different phases of a game, and helps us to obtain a deeper understanding of the role of knowledge and its relation with search in the MCTS algorithm.
Distance-Penalized Active Learning via Markov Decision Processes

We consider the problem of active learning in the context of spatial sampling, where the measurements are obtained by a mobile sampling unit. The goal is to localize the change point of a one-dimensional threshold classifier while minimizing the total sampling time, a function of both the cost of sampling and the distance traveled. In this paper, we present a general framework for active learning by modeling the search problem as a Markov decision process. Using this framework, we present time-optimal algorithms for the spatial sampling problem when there is a uniform prior on the change point, a known non-uniform prior on the change point, and a need to return to the origin for intermittent battery recharging. We demonstrate through simulations that our proposed algorithms significantly outperform existing methods while maintaining a low computational cost.
On the impact of domain-specific knowledge in evolutionary music composition

In this paper we investigate the effect of embedding different levels of musical knowledge in the virtual machine (VM) architectures and phenotype representations of an algorithmic music composition system. We examine two separate instruction sets for a linear genetic programming framework that differ in their knowledge of musical structure: one a Turing-complete register machine, unaware of the nature of its output; the other a domain-specific language tailored to operations typically employed in the composition process. Our phenotype, the output of the VM, is rendered as a musical model comprising a sequence of notes represented by duration and pitch. We compare three different pitch schemes with differing embedded knowledge of tonal concepts, such as key and mode.
Imputation Using a Correlation-Enhanced Auto-Associative Neural Network with Dynamic Processing of Missing Values

The missing value is a common phenomenon in real-world datasets, which makes the analysis of incomplete data become an active research area. In this paper, a correlation-enhanced auto-associative neural network (CE-AANN) is proposed for imputations of missing values. We design correlation-enhanced hidden neurons and combine them with traditional hidden neurons organically, thereby constructing CE-AANN. Compared with the traditional auto-associative neural network (AANN), the improved architecture can mine cross-correlations among attributes more effectively. The introduction of correlation-enhanced hidden neurons keeps the network from learning a meaningless identity mapping. Moreover, a training scheme named MVPT is used for network training. Missing values are regarded as variables of the loss function and adjusted dynamically based on optimization algorithms. The dynamic processing mechanism takes account of the incompleteness of data during training, which makes the imputation accuracy increase as the training goes further. Experiments validate the effectiveness of the proposed method.
Man vs Machine – A Study into Language Identification of Stack Overflow Code Snippets

Software engineers produce large amounts of publicly accessible data that enables researchers to mine knowledge, fostering a better understanding of the field. Knowledge extraction often relies on meta data. This meta data can either be harvested from user-provided tags, or inferred by algorithms from the respective data. The question arises to which extent either type of meta data can be trusted and relied upon. We study this problem in the context of language identification of code snippets posted on Stack Overflow. We analyse the consistency between user-provided tags and the classification obtained with GitHub linguist, an industry-strength automated language recognition tool. We find that the results obtained by both approaches are often not consistent. This indicates that both have to be used with great care. Our results also suggest that developers may not follow the evolutionary path of programming languages beyond one step when seeking or providing answers to software engineering challenges encountered.