Reviewerly/exHarmony huggingface dataset:
Same Same, but Different: A Descriptive Intra-IaaS Differentiation

Users of cloud computing are overwhelmed with choice, even within the services offered by one provider. As such, many users select cloud services based on description alone. In this quantitative study, we investigate the services of 2 of major IaaS providers. We use 2 representative applications to obtain longitudinal observations over 7 days of the week and over different times of the day, totalling over 14,000 executions. We give evidence of significant variations of performance offered within IaaS services, calling for data-driven brokers that are able to offer automated and adaptive decision making processes with means for incorporating expressive user constraints.
An Ontology Embedding Approach Based on Multiple Neural Networks

In this paper, we present a low-dimensional vector representation method for the concepts and instances of an ontology. The main idea is to transform the ontological entities into digestible data for machine learning and deep learning algorithms that only use digital inputs. The generated vectors will represent the semantics contained in the source ontology. We use the semantic relationships connecting the concepts as a landmark to train expert neural networks using the noise contrastive estimation technique to project them into a vector space specific to this relationship with weightings dependent on their frequency. The resulting vectors are then combined and fed into an autoencoder to generate a denser representation. The generated representation vectors can be used to find the semantically similar ontology entities, allowing creating a semantic network automatically. Thus, semantically similar ontology entities will have relatively close corresponding vector representations in the projection space.
A Framework to Evaluate the Performance of Video Watermarking Techniques

This paper focuses on the current challenging problems of video watermarking techniques, especially the robustness of spatial domain. This paper starts with background of the problem, and then illustrates a framework to evaluate the performance of video watermarking techniques.
SubISO: A Scalable and Novel Approach for Subgraph Isomorphism Search in Large Graph

Querying large graphs to retrieve information in permissible time is an emerging research problem, and it has roots in various application domains, mainly to analyse large networks. For a given query graph, the aim of subgraph isomorphism finding in a data graph is to identify all its subgraphs that are isomorphic to the query graph, and it has become the central problem for querying large graphs. Though different research groups have proposed many techniques using graph compression, postponing cartesian products, and candidate region exploration in the recent past, most of them show exponential behaviour for some query graphs on a large data graph. In this paper, we propose a subgraph isomorphism finding method, SubISO, which uses an objective function based on the eccentricity and some isomorphic invariants of the vertices of the query graph to minimize the number and size of the candidate regions in the data graph. Since subgraph isomorphism finding has a large number of solutions, especially in a large graph with repeated node labels, we propose to limit the maximum number of recursive calls of the generic subgraph search function to complete the execution of SubISO and return at most k-relevant matches. The SubISO finds at most k-relevant solutions in reasonable elapsed time over the queries for which existing state-of-the-art methods show exponential behaviour.
Online Rare Category Detection for Edge Computing

Identifying rare categories is an important data management problem in many application fields including video surveillance, ecological environment monitoring and precision medicine. Previous solutions in literature require all data instances to be first delivered to the server. Then, the rare categories identification algorithms are executed on the pool of data to find informative instances for human annotators to label. This incurs large bandwidth consumption and high latency. To deal with the problems, we propose a light-weight rare categories identification framework. At the sensor side, the designed online algorithm filters less informative data instances from the data stream and only sends the informative ones to human annotators. After labeling, the server only sends labels of the corresponding data instances in response. The sensor-side algorithm is extended to enable cooperation between embedded devices for the cases that data is collected in a distributed manner. Experiments are conducted to show our framework dramatically outperforms the baseline. The network traffic is reduced by 75% on average.
Empirical Investigation of UML Models Matching through Different Weight Calibration

UML model matching and retrieval is widely known as optimization problem. This is because of the inconsistencies between software properties. Matching is a fundamental operation for UML model reuse, as such accurate matching between models' elements results in better reuse of such models. UML models consist of number of properties such as functional properties, structural properties, and behavioral properties. Such properties are source of numerous errors during software matching, because each property represents software system from different views. In this paper we empirically investigate the use of different weight values when computing the similarity of software system from multiple views. The paper investigates the improvement of similarity values through the calibration of aggregated metrics. The result reported shows the superiority of structural properties if assign higher metric value compared to other properties.
Sentiment analysis of student responses related to information system services using Multinomial Naïve Bayes (Case study: Telkom University)

Current technological developments affect the types of information services provided, especially information system services that always related to the internet network. To improve existing services, user responses are needed regarding services provided, but if the response is very large, it will be difficult to know whether the services provided are good or not. By taking the right approach, the sentiment's response can be analysed quickly and automatically. Sentiment analysis is done by classifying responses into positive and negative classes, the classification method is Multinomial Naïve Bayes (MNB). Before the data is known for the sentiments, the data is manually labeled, then the data goes through the preprocessing stage, cross validation, feature extraction and then sentiment classification with the MNB classifier. Classification process using MNB method on positive and negative responses, obtaining the highest negative response results in iGracias which is a detailed response of the information system sub-service by 44.27%. The MNB method using lemma with the conditions for preprocessing has a good average accuracy of 83.24% for information system sub-services and 79.24% for internet sub-services.
Embedding Functions Into Reversible Circuits

In order to compute a non-invertible function on a reversible circuit, one needs to "embed" the function into a larger function which has some garbage bits, corresponding to additional lines. The problem of determining the minimal number of garbage bits that are needed to embed a given function has attracted extensive research, largely motivated by quantum computing, where the number of lines equals the number of qubits. However, all approaches that are known have either no theoretical quality guarantees (bounds on approximation factors) or require exponential runtime. We present an efficient probabilistic approximation algorithm with theoretical bounds.
Anniversary article: Then and now: 25 years of progress in natural language engineering

Abstract The paper reviews the state of the art of natural language engineering (NLE) around 1995, when this journal first appeared, and makes a critical comparison with the current state of the art in 2018, as we prepare the 25th Volume. Specifically the then state of the art in parsing, information extraction, chatbots, and dialogue systems, speech processing and machine translation are briefly reviewed. The emergence in the 1980s and 1990s of machine learning (ML) and statistical methods (SM) is noted. Important trends and areas of progress in the subsequent years are identified. In particular, the move to the use of n-grams or skip grams and/or chunking with part of speech tagging and away from whole sentence parsing is noted, as is the increasing dominance of SM and ML. Some outstanding issues which merit further research are briefly pointed out, including metaphor processing and the ethical implications of NLE.
RESEARCH TASKS AS A METHOD OF DEVELOPMENT OF PROGRAMMING SKILLS

The article reveals the relevance of the formation of research competence in high school and the first year of university in the study of informatics. It is proposed to achieve the goal while performing research tasks on programming. The structure of the study is described, a number of examples invariant to the choice of programming language is given. Performing tasks allows the student to formulate a generalized scheme for solving problems of a certain type. The use of research tasks in teaching practice contributes to the formation of the student’s holistic perception of programming language structures and corresponding data structures. As mastering the basic themes of the discipline, the student masters various mechanisms for solving problems. The proposed research tasks can expand the method of teaching informatics.
What Is an Institutional Repository to Do? Implementing Open Access Harvesting Workflows

In 2016, Florida State University adopted an institutional Open Access policy, and the library staff were tasked with implementing an outreach plan to contact authors and collect publication post-prints. In 2018, I presented at Open Repositories in Bozeman to share our workflow, methods, and results with the repository community. This workflow utilizes both restricted and open source methods of obtaining and creating research metadata and reaching out to authors to make their work more easily accessible and citable. Currently, post-print deposits added using this workflow are still in the double digits for each year since 2016. Like many institutions before us, participation rates of article deposit in the institutional repository are low and it may be too early in the implementation of this workflow to expect a real change in faculty participation.
AliCo: A New Efficient Representation for SAM Files

As genome sequencing continues to become more cost-effective and affordable, more raw and aligned genomic files are expected to be generated in future years. In addition, due to the increase in the throughput of sequencing machines, the size of these files is significantly growing. In particular, aligned files (e.g., SAM/BAM) are used for further processing of the data, and hence efficient representation of these files is a pressing need. In this work we present AliCo, a new compression method tailored to the aligned data represented in the SAM format. We demonstrate through simulations on existing datasets that AliCo outperforms in compression ratio, on average, the state-of-the-art compressors for SAM files, achieving more than 85% reduction in size when operating in its lossless mode. AliCo also supports a variety of modes for lossy compression of the quality scores, including for the first time the recently proposed lossy compressor CALQ, which uses information from the aligned reads to adjust the level of quantization for each location of the genome (achieving more than 10× compression gains in high-coverage datasets). AliCo also supports optional compression of the reference sequence used for compression, hence guaranteeing exact reconstruction of the compressed data. Finally, AliCo allows to stream the data as it is being compressed, as well as to decompress the data as it is being received, potentially providing significant time savings. AliCo can be accessed at: https://github.com/iochoa/alico.
Using Computer Vision Techniques for Parking Space Detection in Aerial Imagery

We propose a pattern recognition algorithm that uses computer vision to analyze and map the location of parking spaces from aerial images of parking lots. The analysis method developed made use of line detection coupled with selective filtering based on the prevalence of line length and angle. The goal of this algorithm was to provide a means of automated detection of regions of interest in parking lot images for further use in collection of parking data. Aerial images that used for development and testing were collected via a quad-copter. The quad-copter was equipped with a camera mounted via a gimbal that maintained a camera angle parallel to the parking lot surface. Video was collected from an altitude of 400 ft and individual frames were selected for content. The images were then split into a development set and a testing set. For analysis, images were converted to gray-scale followed by the application of a binary filter. Line features in the binary images were then detected using a Hough transform. Resulting features were then analyzed iteratively to find recurring line patterns of similar length and angle. After filtering for noise, line end-points and intersections were grouped to estimate individual parking space locations. We performed analysis of the proposed methodology over real spaces and demonstrate good performance results.
Pushing the Boundaries of Participatory Design

Participatory Design (PD) is a design approach which aims to support users to contribute as partners throughout the entire design process of a product or service intended for their use. PD researchers are interested in employing and/or developing methods and techniques that maximise users' contributions. By accommodating specific populations, PD proved to offer unique benefits when designing technology for "fringe" groups. However, a lack of understanding of the appropriateness of existing approaches across groups and contexts presents a challenge for the PD community. This workshop will encourage discussion around this challenge. The participants will have the opportunity to exchange and reflect on their experiences with using PD with "fringe" groups. Moreover, we aim to identify, synthesise and collate PD best practices across contexts and participant groups.
Semi-random deep neural networks for near real-time target classification

In recent years deep neural networks have shown great advances in image processing tasks. For modern datasets, these networks require long training times due to backpropagation, high amount of computational resources for weight updates, and memory intensive weight storage. Exploiting randomness during the training of deep neural networks can mitigate these concerns by reducing the computational costs without sacrificing network performance. However, a fully randomized network has limitations for real-time target classification as it leads to poor performance. Therefore we are motivated in using semi-random deep neural networks to exploit random fixed weights. In this paper, we demonstrate that semi-random deep neural networks can achieve near real-time training with comparable accuracies to conventional deep neural networks models. We find that these networks are enhanced by the usage of skip connections and train rapidly at the cost of dense memory usage. With greater memory resources available, these networks can train on larger datasets at a fraction of the training time costs. These semi-random deep neural network architectures open up an avenue for further research in utilizing random fixed weights in neural networks.
BookCeption

BookCeption is a web-based book recommendation system that allows readers to browse and buy books across multiple platforms at the most affordable price. It fetches data from E-commerce sites like Barnes and Nobles, Amazon, eBay along with Facebook pages and retail bookstore websites. It is a unique book recommender that uses Machine Learning techniques to recommend books as well as offers from other platforms to the registered users. This paper introduces the architecture of the proposed framework, which integrates the book recommendation system with a platform for buying books. For the book recommender discussed here, four different recommendation techniques were used- SVD, Co-clustering, NMF and Deep Learning to determine the best one for the framework. In terms of computational training time, Co-clustering works best with a time of 5.21 minutes and RMSE value of 0.868. However, on the basis of RMSE value, Deep Learning Embedding Model works best with RMSE value of 0.7599 in 8.06 minutes when the number of epochs and the batch size are 20 and 200 respectively.
Credit card fraud detection by implementing machine learning techniques

Application of machine learning algorithms in the fraud detection area for credit card operations has now become an important constituent of research in the domain of digital transactions. The evolution of various machine learning techniques like classification and clustering has shown the requirement for application of related algorithms in detecting frauds of credit card transactions. Real-time detection of fraudulent activities in the financial transactions can help to save huge financial losses as well as the goodwill of the financial institutions to its customers. In this study, the application of various classification techniques has been proposed by using machine learning algorithms for detecting the accuracy of the fraud detection. Some commonly studied classification methods are implemented by considering a large volume of data. Different algorithms that have been evaluated empirically are naïve Bayes classifier, extreme learning machine, K-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM). A machine learning classification model has been proposed by hybridizing SVM, KNN, and MLP classification models where it is observed that the predicted value of accuracy has improved significantly.
Towards automated reasoning in Herbrand structures

Abstract Herbrand structures have the advantage, computationally speaking, of being guided by the definability of all elements in them. A salient feature of the logics induced by them is that they internally exhibit the induction scheme, thus providing a congenial, computationally oriented framework for formal inductive reasoning. Nonetheless, their enhanced expressivity renders any effective proof system for them incomplete. Furthermore, the fact that they are not compact poses yet another proof-theoretic challenge. This paper offers several layers for coping with the inherent incompleteness and non-compactness of these logics. First, two types of infinitary proof system are introduced—one of infinite width and one of infinite height—which manipulate infinite sequents and are sound and complete for the intended semantics. The restriction of these systems to finite sequents induces a completeness result for finite entailments. Then, in search of effectiveness, two finite approximations of these systems are presented and explored. Interestingly, the approximation of the infinite-width system via an explicit induction scheme turns out to be weaker than the effective cyclic fragment of the infinite-height system.
Interface adaptivity by widget promotion/demotion

Promotion and demotion are a typical adaptive navigation technique making a page or a link easier to select by emphasizing it or de-emphasizing it depending on its popularity. This technique, which was successfully applied to adaptive web sites, is now generalized to mainstream graphical user interfaces by introducing bimotion user interfaces, which constantly and dynamically perform adaptivity by promoting the most predicted widgets and demoting the least predicted ones either in context or in a separated prediction window. Promoted widgets that are less frequently used become demoted, demoted widgets that are more frequently used become promoted.
Urban noises

This workshop is focused on the use of augmented reality to animate and activate graffitis of the city of Palermo. The main goal is to teach and share knowledge in digital art and augmented reality, using an easy learning process to make digital interfaces accessible to different public, ages and goals. The workshop brings the main discussion of the city as a screen of intangible interventions, using the mobile devices of the participants to overlap their visual creations on the city walls. To make it possible, we will map and photograph some of the main graffiti in the city of Palermo, working with this files to start up the interventions. The workshop outcome consists in a set of animated interactive graffiti, exploiting a free mobile app for passers-by interaction, which allows for a city tour of augmented interactive murals.
Coupled POD-Bayesian estimation of the parameters of mathematical model of the packed-bed drying of cherry stones

This paper deals with the Bayesian estimation of flow parameters in a porous material based on the measurements of local temperature at selected points inside the packed-bed. The unknown probability distribution function of estimated parameters was sampled using a Markov Chain Monte Carlo (MCMC) method. The MCMC method is efficient in sampling unknown distributions, but it is extremely time-consuming. The Proper Orthogonal Decomposition was used to construct off-line, low order approximation of the porous media flow problem. This model was further incorporated into the Metropolis-Hastings algorithm to retrieve the posterior distribution of the unknown parameters. This approach allowed us to decrease the computational time necessary to generate an ergodic Markov Chain. The efficiency of the proposed approach was tested on the simulated data generated with commercial CFD tools, and finally, it was applied to the real data acquired on the dedicated experimental stand.