Reviewerly/exHarmony huggingface dataset:
Tools for Public Participation in Science

The Open Science Hardware (OSH) movement has been gaining momentum to make scientific instruments widely accessible through open-source blueprints or DIY kits which can be built for a fraction of the cost of their commercial counterparts. Even though the designs of OSH are freely and openly available over the internet, in most cases, building or assembling science equipment from online designs require knowledge and skills in areas such as digital fabrication and basic electronics. Due to this, some open science practitioners face a multitude of challenges when replicating or customizing those designs to be used in their science experiments.To this end, through ethnographic field work and participatory design, my doctoral research explores such practical challenges faced by open science practitioners and tries to create new mediums and workflows to support efficient and inclusive dissemination of OSH.
Light-Weight Programming Language for Blockchain (demo)

This demo abstract introduces a new light-weight programming language koa which is suitable for blockchain system design and implementation. In this abstract, the basic features of koa are introduced including working system (with playground), architecture, and virtual machine operations. Rum-time execution of software implemented by koa will be presented during the session.
Hybrid ASP-based Approach to Pattern Mining

Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. To further demonstrate the generic nature of our hybrid framework we apply it to a problem of approximately tiling a database. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains for itemset, sequence and graph mining, as well as approximate tiling. Under consideration in Theory and Practice of Logic Programming (TPLP).
Feedback in Scrum: Data-Informed Retrospectives

Improving the way that teams work together by reflecting and improving the executed process is at the heart of agile processes. The idea of iterative process improvement takes various forms in different agile development methodologies, e.g. Scrum Retrospectives. However, these methods do not prescribe how improvement steps should be conducted in detail. In this research we investigate how agile software teams can use their development data, such as commits or tickets, created during regular development activities, to drive and track process improvement steps. Our previous research focused on data-informed process improvement in the context of student teams, where controlled circumstances and deep domain knowledge allowed creation and usage of specific process measures. Encouraged by positive results in this area, we investigate the process improvement approaches employed in industry teams. Researching how the vital mechanism of process improvement is implemented and how development data is already being used in practice in modern software development leads to a more complete picture of agile process improvement. It is the first step in enabling a data-informed feedback and improvement process, tailored to a team's context and based on the development data of individual teams.
Strong Explanations for Nonmonotonic Reasoning

The ability to generate explanations for inferences drawn from a knowledge base is of utmost importance for intelligent systems. A central notion in this context are minimal subsets of the knowledge base entailing a certain formula. Such subsets are often referred to as justifications, and their identification is called axiom pinpointing.As observed by Franz Baader, this concept of explanations is useful for monotonic logics in which additional information can never invalidate former conclusions. However, for nonmonotonic logics the concept simply makes no sense. In this paper, we introduce a different notion, called strong explanation. Strong explanations coincide with the standard notion for monotonic logics, but also handle the nonmonotonic case adequately.
Data-driven classification of the certainty of scholarly assertions

The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty; however, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three distinct categories of certainty along a spectrum from high to low. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation accuracy of 89.2% relative to an author-annotated corpus, and 82.2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide an exemplar machine-accessible representation—a Nanopublication—where certainty category is embedded as metadata in a formal, ontology-based manner within text-mined scholarly assertions.
Insights from replication on the factors affecting task engagement in mobile-based learning activities

Despite the large amount of research that has taken place with mobile devices for language learning over the years, there are still limited conclusions that can be reached as to what contributes to sustained task engagement. A common problem that has long been seen in the CALL literature is that many tasks and activities that are reported are small scale studies taking place in a single environment with a limited number of subjects, often as a result of teachers investigating the outcomes of their own teaching (see Warschauer, 1997; Hubbard, 2005). Given the nature of the environments in which many teachers find themselves, it is in some way inevitable that studies tend to be of a smaller scale and often undertaken within a single class. It is possible to gain deeper insights into the tasks used through replication studies (Porte, 2012). This paper discusses the potential insights gained from replicating approximately the same basic language learning tasks in varied contexts. Research on vocabulary and listening tasks was carried out in Japan over an 8-year period from 2010 through 2017 for Japanese learners of English. In addition, the same tasks were adapted for Australian learners of Japanese in 2012 and Taiwanese learners of English in 2013, with a total of 420 participants. Data were collected through post-treatment surveys, semi-structured interviews and server logs recording the times spent on the tasks, the scores achieved, and the devices used to engage in the tasks. The same methods of data collection were used in each of the studies, using primarily the same tasks apart from adaptations made for developments in technology and for the different language learning contexts. Carrying out the same tasks in subsequent years with increasingly newer technologies and with teachers and learners from different cultural backgrounds provided insights into the effect of the context, the technology, and role of both teachers and learners in successfully implementing the tasks.
A multi-task U-net for segmentation with lazy labels

The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation. In this paper, we propose a deep convolutional neural network for multi-class segmentation that circumvents this problem by being trainable on coarse data labels combined with only a very small number of images with pixel-wise annotations. We call this new labelling strategy 'lazy' labels. Image segmentation is then stratified into three connected tasks: rough detection of class instances, separation of wrongly connected objects without a clear boundary, and pixel-wise segmentation to find the accurate boundaries of each object. These problems are integrated into a multitask learning framework and the model is trained end-to-end in a semi-supervised fashion. The method is applied on a dataset of food microscopy images. We show that the model gives accurate segmentation results even if exact boundary labels are missing for a majority of the annotated data. This allows more flexibility and efficiency for training deep neural networks that are data hungry in a practical setting where manual annotation is expensive, by collecting more lazy (rough) annotations than precisely segmented images.
Cross-lingual Incongruences in the Annotation of Coreference

In the present paper, we deal with incongruences in English-German multilingual coreference annotation and present automated methods to discover them. More specifically, we automatically detect full coreference chains in parallel texts and analyse discrepancies in their annotations. In doing so, we wish to find out whether the discrepancies rather derive from language typological constraints, from the translation or the actual annotation process. The results of our study contribute to the referential analysis of similarities and differences across languages and support evaluation of cross-lingual coreference annotation. They are also useful for cross-lingual coreference resolution systems and contrastive linguistic studies.
Analyzing the use of existing systems for the

In this paper we describe the UniOvi-WESO classification systems proposed for the 2019 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. We explore the use of two systems trained with ReachOut data from the 2016 CLPsych task, and compare them to a baseline system trained with the data provided for this task. All the classifiers were trained with features extracted just from the text of each post, without using any other metadata. We found out that the baseline system performs slightly better than the pretrained systems, mainly due to the differences in labeling between the two tasks. However, they still work reasonably well and can detect if a user is at risk of suicide or not.
Radio Frequency Identification based Goal Line Technology for Quick Decision Making in a Football Match

With the growing popularity of football globally, the accuracy of each and every events and technology involved in the game is a concern. Critical situations arise when the referee cannot discriminate a goal or no goal by fine margins due to human visual limitations. Nowadays Video-Assistant Referee (VAR) and other technologies perform accurate decision-making can be implemented in a live match. But the process consumes a lot of time which reduces the fast pace of the game and can cause unnecessary distractions. This study aims to design an automatic goal-line detection system with the help of radio frequency identification (RFID) - Arduino interfacing. RFID incorporates the use of radio waves to extract the information stored in a tag attached to an object. The proposed system uses RFID tags which are fitted on the inside surface of a football. The information embedded in the tags is read by RFID readers, placed behind the goalpost. This technology does not require any additional programming and camera analysis in decision-making, thus making the system faster to help the referees in quick decision-making and maintaining the pace of the game.
Chinese Spelling Check via Bidirectional LSTM-CRF

Chinese spelling check is an automatic mechanism to detect Chinese spelling errors in the text. State-of-the-art Chinese Spelling Check systems using bidirectional LSTM, which does not consider the constraint between output labels. In this paper, we introduce a novel neural network architecture that combines the bidirectional LSTM and CRF. This model takes char sequence of a sentence as input, and BLSTM learns the order information, feeds the probability vector to the CRF layer, then CRF output predict best tag sequences. Our system is effective and efficient, requiring no feature engineering or data preprocessing, thus making it applicable to many tasks. We evaluate our system on two datasets — the Sogou News dataset and the Literary Novels dataset. Compared with BLSTM models, we achieve a 5% and 3% relative f1-score improvement in Sogou News and Literary Novels, respectively.
Blockchain Centered Homomorphic Encryption: A Secure Solution for E-Balloting

There is a big question arising in today's world of increased technology. If we can bank online and shop online, why we can't vote online? Proficient would confess that affirming the transparency in e-voting, security, and privacy requisites cannot feasibly guarantee through any practically available technology in the imminent future. Even though many security measures have been proposed, it is not extensively implanted, in most of the countries because of the increased security threats. Unlike e-commerce, security of e-voting systems are much more complex. An attack on an e-voting system may cause a legitimate vote to be rejected or a wrong candidate to win. In this research work, a secure online voting system is implemented with ElGamal Elliptic Curve Cryptosystem for vote encryption. The additive homomorphic feature of the Elliptic Curve Cryptosystem is used for computing the results without decrypting each ballot individually. The blockchain is used as a second level of security in the authority side for preserving ballots which utilize SHA-512 for hashing.
Real Time Twitter Sentiment Analysis for Product Reviews Using Naive Bayes Classifier

Opinions of customers in ecommerce play a crucial role in day to day usage. Whenever we have to take a decision So the opinions of other individuals plays an important role. There are various social sites and review sites where an user can post their review or opinions towards any products or any issues, So various business and corporate organization wants to know these opinions of user for taking an decision. In ecommerce market, Their is need to analyze the social data or products review automatically, So there is need to create a model which classify the huge amount of product reviews automatically. In this paper we are fetching real time reviews from the social site twitter and apply various text mining techniques to preprocess the data and than apply an machine learning approach through which we can use Naïve Bayes classification algorithm to classify the text into various emotions and polarities.
Comparison of Different Assembly Assistance Systems Under Ergonomic and Economic Aspects

More and more complex products are being fitted in small batches in manual assembly. Because of this, more information needs to be collected from employees and implemented in appropriate actions. At the same time, the informational design of assembly systems often shows deficits in operational practice. Manual assembly processes can be made more economical, reliable, and human-oriented with the help of informational assistance systems. Testing was carried out in the Laboratory for Industrial Engineering at the Ostwestfalen-Lippe University of Applied Sciences and Arts to verify this potential. Initial results on the use of augmented reality (AR) glasses in comparison to providing information in a paper-based format are presented.
Right Scaling for Right Pricing: A Case Study on Total Cost of Ownership Measurement for Cloud Migration

Cloud computing promises traditional enterprises and independent software vendors a myriad of advantages over on-premise installations including cost, operational and organizational efficiencies. The decision to migrate software configured for on-premise delivery to the cloud requires careful technical consideration and planning. In this chapter, we discuss the impact of right-scaling on the cost modelling for migration decision making and price setting of software for commercial resale. An integrated process is presented for measuring total cost of ownership, taking in to account IaaS/PaaS resource consumption based on forecast SaaS usage levels. The process is illustrated with a real world case study.
On the usefulness of comparable and parallel corpora for contrastive linguistics. Testing the semantic stability hypothesis

The last two decades, empirical evidence has shown that different types of translational effects are likely to occur in translated texts, thereby potentially reducing the representativeness of translations for contrastive-linguistic research. Yet, a question that has rarely been asked is whether translational effects can also be found on the semantic level. Consequently, the main objective of this paper is to investigate whether the (sub-)meanings associated with near-synonyms change during translation, or - put differently - whether the hypothesis that there is semantic stability between translated and non-translated language can be confirmed. We first applied Johansson’s procedure to tease apart contrastive relations and translation effects in a case study on the frequency distribution of Dutch and English verbs of inchoativity and found that in translations the most frequent expressions were overused when compared to non-translated data. Secondly, we tested the semantic stability hypothesis by comparing the meaning structure of the field of inchoativity in a parallel corpus of English-to-Dutch translations to that of the same field in a comparable corpus of authentic Dutch texts. The lexemes were selected via the semantic mirroring procedure and were submitted to the behavioral profile method, which allows us to operationalize semantic (dis)similarity by annotating the linguistic context of each lexeme for a variety of ID-tags. Via correspondence regression we explored the structure of the semantic field. The results of our analyses show that the positions of the lexemes do not remain stable among the different corpus components, running counter to the semantic stability hypothesis. Consequently, the assumption of semantic stability between source and target text, which is one of the motivations to use parallel corpora in contrastive linguistics, seems not completely tenable.
Beating the probabilistic lower bound on perfect hashing

For an integer $q\ge 2$, a perfect $q$-hash code $C$ is a block code over $[q]:=\{1,\ldots,q\}$ of length $n$ in which every subset $\{\mathbf{c}_1,\mathbf{c}_2,\dots,\mathbf{c}_q\}$ of $q$ elements is separated, i.e., there exists $i\in[n]$ such that $\{\mathrm{proj}_i(\mathbf{c}_1),\dots,\mathrm{proj}_i(\mathbf{c}_q)\}=[q]$, where $\mathrm{proj}_i(\mathbf{c}_j)$ denotes the $i$th position of $\mathbf{c}_j$. Finding the maximum size $M(n,q)$ of perfect $q$-hash codes of length $n$, for given $q$ and $n$, is a fundamental problem in combinatorics, information theory, and computer science. In this paper, we are interested in asymptotical behavior of this problem. More precisely speaking, we will focus on the quantity $R_q:=\limsup_{n\rightarrow\infty}\frac{\log_2 M(n,q)}n$. A well-known probabilistic argument shows an existence lower bound on $R_q$, namely $R_q\ge\frac1{q-1}\log_2\left(\frac1{1-q!/q^q}\right)$ \cite{FK,K86}. This is still the best-known lower bound till now except for the case $q=3$ for which K\{o}rner and Matron \cite{KM} found that the concatenation technique could lead to perfect $3$-hash codes beating this the probabilistic lower bound. The improvement on the lower bound on $R_3$ was discovered in 1988 and there has been no any progress on lower bound on $R_q$ for more than 30 years despite of some work on upper bounds on $R_q$. In this paper we show that this probabilistic lower bound can be improved for $q=4,8$ and all odd integers between $3$ and $25$, and \emph{all sufficiently large} $q$ with $q \pmod 4\neq 2$.
Application of the Finite State Machine Algorithm on 2D Platformer Rabbit Games vs Zombies

This research develops Android games by applying the FSM Algorithm (Finite State Machine) for the movement of NPC (Non-Player Character) to produce dynamic movements. The popular and legendary game with the appearance of side scroller is Super Mario Bross and Shovel Knight, the game is the most preferred game of its time to the present, but the game does not apply AI (Artificial Intelligence) so the game seems less challenging. The game is a concept game that is currently rarely used. Based on this, a side scroller-based survival game was created by implementing AI on NPCs on the Android platform. The AI used is the FSM Algorithm which functions to regulate enemy movements. The method used in developing multimedia products of this game uses MDLC (Multimedia Development Life Cycle). Based on the tests that have been carried out, the alpha test results are functionally appropriate and the results of beta testing using UserAcceptence Test (UAT) obtained a value of 86.40% which is declared feasible to use and can be developed. The advantages of this game are the presence of shock elements in the form of landslides, bursts of water, rock rain, and attacks by eagles
An Unpaired Sketch-to-Photo Translation Model.

Humans can envision a realistic photo given a free-hand sketch that is not only spatially imprecise and geometrically distorted but also without colors and visual details. We study unsupervised sketch-to-photo synthesis for the first time, learning from unpaired sketch-photo data where the target photo for a sketch is unknown during training. Existing works only deal with style change or spatial deformation alone, synthesizing photos from edge-aligned line drawings or transforming shapes within the same modality, e.g., color images. Our key insight is to decompose unsupervised sketch-to-photo synthesis into a two-stage translation task: First shape translation from sketches to grayscale photos and then content enrichment from grayscale to color photos. We also incorporate a self-supervised denoising objective and an attention module to handle abstraction and style variations that are inherent and specific to sketches. Our synthesis is sketch-faithful and photo-realistic to enable sketch-based image retrieval in practice. An exciting corollary product is a universal and promising sketch generator that captures human visual perception beyond the edge map of a photo.
A Bayesian binary algorithm for root mean squared-based acoustic signal segmentation

Changepoint analysis (also known as segmentation analysis) aims at analyzing an ordered, one-dimensional vector, in order to find locations where some characteristic of the data changes. Many models and algorithms have been studied under this theme, including models for changes in mean and / or variance, changes in linear regression parameters, etc. In this work, we are interested in an algorithm for the segmentation of long duration acoustic signals; the segmentation is based on the change of the RMS power of the signal. We investigate a Bayesian model with two possible parameterizations, and propose a binary algorithm in two versions, using non-informative or informative priors. We apply our algorithm to the segmentation of annotated acoustic signals from the Alcatrazes marine preservation park in Brazil.