Reviewerly/exHarmony huggingface dataset:
Dynamical Anatomy of NARMA10 Benchmark Task

The emulation task of a nonlinear autoregressive moving average model, i.e., the NARMA10 task, has been widely used as a benchmark task for recurrent neural networks, especially in reservoir computing. However, the type and quantity of computational capabilities required to emulate the NARMA10 model remain unclear, and, to date, the NARMA10 task has been utilized blindly. Therefore, in this study, we have investigated the properties of the NARMA10 model from a dynamical system perspective. We revealed its bifurcation structure and basin of attraction, as well as the system's Lyapunov spectra. Furthermore, we have analyzed the computational capabilities required to emulate the NARMA10 model by decomposing it into multiple combinations of orthogonal nonlinear polynomials using Legendre polynomials, and we directly evaluated its information processing capacity together with its dependences on some system parameters. The result demonstrates that the NARMA10 model contains an unstable region in the phase space that makes the system diverge according to the selection of the input range and initial conditions. Furthermore, the information processing capacity of the model varies according to the input range. These properties prevent safe application of this model and fair comparisons among experiments, which are unfavorable for a benchmark task. As a result, we propose a benchmark model that can clearly evaluate equivalent computational capacity using NARMA10. Compared to the original NARMA10 model, the proposed model is highly stable and robust against the input range settings.
Collaborative Filtering Algorithm based on Linear Regression Filling

In the collaborative filtering recommendation algorithm, sparse user rating data may result in inaccurate similarity calculation between users. To solve this problem, this paper proposes a method of filling unrated data on the user-item rating matrix by using linear regression model. Firstly, this method selects the average of user historical ratings and the average of item historical ratings as the features, selects the user’s actual rating as the label, and trains the linear regression model of rating prediction for each user. Then, use the model to predict and fill the user's unrated data. Finally, use the traditional collaborative filtering algorithm for rating prediction on the filled user-item rating matrix. Experimental results show that the improved collaborative filtering recommendation algorithm can alleviate the data sparsity, find more reliable user neighbors, and improve the accuracy of rating prediction.
Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony

Prognostic is an engineering technique used to predict the future health state or behavior of an equipment or system. In this work, a data-driven hybrid approach for prognostic is presented. The approach based on Echo State Network (ESN) and Artificial Bee Colony (ABC) algorithm is used to predict machine’s Remaining Useful Life (RUL). ESN is a new paradigm that establishes a large space dynamic reservoir to replace the hidden layer of Recurrent Neural Network (RNN). Through the application of ESN is possible to overcome the shortcomings of complicated computing and difficulties in determining the network topology of traditional RNN. This approach describes the ABC algorithm as a tool to set the ESN with optimal parameters. Historical data collected from sensors are used to train and test the proposed hybrid approach in order to estimate the RUL. To evaluate the proposed approach, a case study was carried out using turbofan engine signals show that the proposed method can achieve a good collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). The experimental results using the engine data from NASA Ames Prognostics Data Repository RUL estimation precision. The performance of this model was compared using prognostic metrics with the approaches that use the same dataset. Therefore, the ESN-ABC approach is very promising in the field of prognostics of the RUL.
A Multi-Patch Network for Non-Rigid Object Tracking

Non-rigid object tracking is an important yet challenging task in computer vision. In this paper, a multi-patch neural network (MPNet) model is presented to address the problem of non-rigid object tracking. The model learns a multiple patch based framework, which mainly consists of two branches of neural networks. One branch is to track the global target and the other branch partitions the target into multiple patches which are tracked separately. The global tracking and the multiple patch tracking are combined to compute the final tracking results. Compared with the existing methods, our model exploits the trajectories of various parts of a non-rigid object and therefore can accurately track the non-rigid object. Experiments on visual tracking datasets prove the strength of the proposed method.
Research on Cost-Driven Services Composition in an Uncertain Environment

In recent years, increasing numbers of researchers have concentrated on service workflow to support cross-domain software development. However, the uncertain characteristics of the Internet impose high risks on service workflow reliability. The risk of failure caused by unavailable services may increase costs when using service workflow-based applications. Thus, it is necessary to consider the non-functional factors, such as service cost and reliability. In this paper, we propose a cost-driven services composition approach for enterprise workflows that employs formal verification to recommend appropriate services for abstract workflows. The services composition is measured quantitatively to ensure that the configuration to service the workflow solution has the best performance, high reliability and low cost. First, this solution introduces a service search approach based on an inverted index, and the service recommendation method is based on an improved Pearson formula. Next, the solution returns a minimum set of candidate services for constructing a workflow instance. Second, the service and workflow models are defined to formalize the behaviour of service composition; this is considered to be a verification model. Third, transformation rules are provided to change BPEL4WS into a verification model, and PCTL (Probabilistic Computation Tree Logic) formulae are used to specify the reliability and cost-related properties. The quantitative verification method checks each possible plan for service composition using probabilistic model checking. Finally, the results of a series of experiments show that our approach is effective in generating an optimal service workflow.
I Wanna Talk Like You: Speaker Adaptation to Dialogue Style in L2 Practice Conversation

We present a novel method to analyse speaker alignment in second language practice dialogue. Our method represents utterances as Dialogue Acts and applies Epistemic Network Analysis to their use. ENA makes convergence between speakers visible, and enables us to confirm hypotheses that both initial similarity and final convergence increase with student ability; and that Dialogue Act use changes with ability, and over the course of an interaction. Our results can inform personalised automatic tutoring tools as well as formative assessment and feedback.
Non-representativeness in corpora: perils, pitfalls and challenges

This article presents and discusses some problems of representativeness that the author has encountered in over twenty years of corpus-based research. It argues that the inclusion in a general corpus of certain text types, such as grammar treatises or works of historical fiction, can lessen the representativeness of the data, especially if the corpus is designed to reflect the linguistic production, as opposed to the linguistic reception, of a speech community. It is argued that less emphasis should be placed on reception in the compilation of general corpora. Also addressed are problems relating to the comparison of texts in different languages, as well as two solutions that have been proposed to counter these problems. The arguments are illustrated with examples from both contemporary and historical corpora.
Joining conceptual systems - three remarks on TJS

The Theory of Joining Systems, abbreviated TJS, is a general theory of representing for example legal and other normative systems as formal structures. It uses algebraic tools and a fundamental ide ...
Model Bridging: To Interpretable Simulation Model From Neural Network.

The interpretability of machine learning, particularly for deep neural networks, is strongly required when performing decision-making in a real-world application. There are several studies that show that interpretability is obtained by replacing a non-explainable neural network with an explainable simplified surrogate model. Meanwhile, another approach to understanding the target system is simulation modeled by human knowledge with interpretable simulation parameters. Recently developed simulation learning based on applications of kernel mean embedding is a method used to estimate simulation parameters as posterior distributions. However, there was no relation between the machine learning model and the simulation model. Furthermore, the computational cost of simulation learning is very expensive because of the complexity of the simulation model. To address these difficulties, we propose a ``model bridging'' framework to bridge machine learning models with simulation models by a series of kernel mean embeddings. The proposed framework enables us to obtain predictions and interpretable simulation parameters simultaneously without the computationally expensive calculations associated with simulations. In this study, we investigate a Bayesian neural network model with a few hidden layers serving as an un-explainable machine learning model. We apply the proposed framework to production simulation, which is important in the manufacturing industry.
Convex Optimization-based Design of a Speed Planner for Autonomous Heavy Duty Vehicles

The objective of this master thesis is to design a speed planner for a heavy duty vehicle (HDV) that focuses on achieving a time efficient and comfortable driving, given different constraints such ...
Unrestricted generation of pure two-qubit states and entanglement diagnosis by single-qubit tomography

We present an experimental proof-of-principle for the generation and detection of pure two-qubit states that have been encoded in degrees of freedom that are common to both classical-light beams and single photons. Our protocol requires performing polarization tomography on a single qubit from a qubit pair. The degree of entanglement in the qubit pair is measured by concurrence, which can be directly extracted from intensity measurements&#x2014;or photon counting&#x2014;entering single-qubit polarization tomography.
A Study of User Intent in Immersive Smart Spaces

Smart spaces are typically augmented with devices capable of sensing various inputs and reacting to them. Data from these devices can be used to support system adaptation, reducing user intervention; however, mapping sensor data to user intent is difficult without a large amount of human-labeled data. We leverage the capabilities of head-mounted immersive technologies to actively capture users' visual attention in a unobtrusive manner. Our contributions are three-fold: (1) we developed a novel prototype that enables studies of user intent in an immersive environment, (2) we conducted a proof-of-concept experiment to capture internal and external state data from smart devices together with head orientation information from participants to approximate their gaze, and (3) we report on both quantitative and qualitative evaluations of the data logs and pre-/post-study survey data using machine learning and statistical analysis techniques. Our results motivate the use of direct user input (e.g. gaze inferred by head orientation) in smart home environments to infer user intent allowing us to train better activity recognition algorithms. In addition, this initial study paves a new way to conduct repeatable experimentation of smart space technologies at a lower cost.
Bibliometric-Enhanced Information Retrieval: 11th International BIR Workshop

The Bibliometric-enhanced Information Retrieval (BIR) workshop series at ECIR tackles issues related to academic search, at the intersection of Information Retrieval, Natural Language Processing and Bibliometrics. BIR is a hot topic investigated by both academia and the industry. In this overview paper, we summarize the 11th iteration of the workshop and present the workshop topics for 2021.
UTILIZATION OF SOLAR ENERGY TECHNOLOGY TO MEET WATER LOGISTIC SUPPORT IN THE MARITIME BORDER: STUDY AT NAVY POST, LABUAN BAJO, EAST NUSA TENGGARA

&lt;p&gt;The availability of clean water is a large part of the main problem in the area of West Manggarai Regency, East Nusa Tenggara. Eventhough there is correlation between factors of adequate water, health and economic development. One area that represents this condition is in the village of Labuan Bajo. The enchantment of the tourist island of Flores found in West Manggarai Regency, where Labuan Bajo is the gateway to entering the tourist charm is a place that tourists really want to visit. As a government effort to tighten border supervision and safeguards the Labuan Bajo maritime, the government formed the Indonesian Navy post. Therefore, the availability of clean water is needed in the area, besides to meet the needs of the Labuan Bajo community, improve facilities and infrastructure to bring in many tourists, also for fulfilling the logistical support of the Navy's headquarters. To meet water needs, there are 2 options that are expected to solve the problem, namely (1) mapping of underground water in the village of Labuan Bajo and pumping it using electric power from the voltaic photo results, and (2) when there is no item (1) then pumping water from the reservoir is available at a distance about 3 km from the village of Labuan Bajo. Considering the contour of the area, the needed to make new reservoirs and water pumping systems from the reservoir available, so that an adequate level of water is obtained for drain water to the village. Mapping of underground water is carried out with using the geoelectric method. If option 1 is successful, then the system that is built can be used as a model of pumping underground water, mainly in the area of West Manggarai Regency that needed it. Then the water obtained will be processed through the Reverse Osmosis process to produce ready to drink water.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; clean water supply, photovoltaic, renewable energy, rural area, water pump&lt;/p&gt;
PreBAC: a novel Access Control scheme based Proxy Re-Encryption for cloud computing

Cloud computing is widely used in information spreading and processing, which has provided a easy and quick way for users to access data and retrieve service.Generally, in order to prevent the leakage of the information, the data in cloud is transferred in the encrypted form.As one of the traditional security technologies, access control is an important part for cloud security .However , the current access control schemes are not suitable for cloud , thus, it is a vital problem to design an access control scheme which should take account of complex factors to satisfy the various requirements for cipher text protection.We present a novel access control scheme based on proxy re-encryption(PRE) technology (PreBAC) for cipher text.It will suitable for the protection of data confidently and information privacy.At first, We will give the motivations and related works, and then specify system model for our scheme.Secondly, the algorithms are given and security of our scheme is proved.Finally, the comparisons between other schemes are made to show the advantages of PreBAC.
Blockchain as organizational technology

University of Zurich, Department of Informatics IFI Colloquium, Zurich, Switzerland, 28th February 2019
A result correctness verification mechanism for cloud computing based on MapReduce

MapReduce is widely applied as a parallel programming model to process massive amounts of data in cloud computing environment. However, in open systems, the workers of MapReduce framework are provided with various administration domains that may be unreliable or malicious. The existing schemes of MapReduce processing model based on multiply duplicate tasks can effectively detect the lazy and non-collusive workers. However, they can not cope with the vulnerability that malicious workers collude to return incorrect results and thereby undermine the final computation results of users' outsourced tasks. In this paper, we present an effective result correctness verification mechanism for MapReduce in public cloud computing environment. By using task duplication and weighted correctness attestation graph, our mechanism can effectively detect both non-collusive and collusive malicious workers in public cloud environment. In order to further improve the detection speed, we introduce a workers' selection method based on trust values and consistency relationship. Finally, we conduct the analysis and experimental evaluation, and the results indicate that our mechanism can guarantee higher detection rate with proper additional computation overhead.
Social Media and Clickstream Analysis in Turkish News with Apache Spark

Text analysis in the processing of deriving information from social media channels has become more crucial in recent years. These studies can be applied to social media data to answer wide variety of questions about consumers, brands, products or any other campaign strategies for the content producers. In this context, understand the sentiment or specific emotions expressed during the social media communication channels, mostly on Twitter, identify to intent about the corresponding content and any other stages of the consumer interest. In this study, one of the biggest Turkish news provider, Hürriyet, and its top-rated authors’ tweets are analyzed by using advanced natural language processing techniques. Due to the limitation of supported libraries for the Turkish language, preprocessing steps for unstructured datasets are studied with self-developed classes using machine learning techniques. Combining Twitter circulation together with the company’s clickstream data, we motivate to find the unique patterns for user’s attention on specific categories on the news content. By measuring the full value of digital media attention data through Twitter and click datasets, we will provide the ideal scenario for using controlled marketing experiments at the digital media sector in Turkey.
Objective Function Optimization for Electrical Machine by using Multi-Objective Genetic Programming and Display Method of Its Results

Generally, electrical machines are designed using CAE (computer aided engineering) software. In addition, it is combined the design with optimization method for improving various objective functions such as magnetic path, structure and loss. However, there are various problems when multi-objective optimization is applied: modeling of design parameters is complicated, vast amounts of time are necessary, and the result is highly complex display owing to multi-objective. Therefore, it is difficult to select valid value from the complex result. In this paper, the authors have described the method that applies multi-objective function optimization for electrical machine by multi-objective genetic programming (MOGP). Moreover, its superiority and usability have been considered. Likewise, they have described the method that displays the result using dimension reduction.
A probe into the Application ability of College English Teachers' Educational Information Technology in the Information Age

With the arrival of the age of large information data, the education also gradually tends to educate the information, which has forced the demand of the teachers' educational information technology application ability of the major universities and universities to be higher and higher. Therefore, in order to meet the needs of the development of higher education in the information age and to meet the needs of the college English teaching innovation, this paper will explore the application ability of the educational information technology of the college English teachers from various angles
Adversarial Representation Learning on Large-Scale Bipartite Graphs.

Graph representation on large-scale bipartite graphs is central for a variety of applications, ranging from social network analysis to recommendation system development. Existing methods exhibit two key drawbacks: 1. unable to characterize the inconsistency of the node features within the bipartite-specific structure; 2. unfriendly to support large-scale bipartite graphs. To this end, we propose ABCGraph, a scalable model for unsupervised learning on large-scale bipartite graphs. At its heart, ABCGraph utilizes the proposed Bipartite Graph Convolutional Network (BGCN) as the encoder and adversarial learning as the training loss to learn representations from nodes in two different domains and bipartite structures, in an unsupervised manner. Moreover, we devise a cascaded architecture to capture the multi-hop relationship in bipartite structure and improves the scalability as well. Extensive experiments on multiple datasets of varying scales verify the effectiveness of ABCGraph compared to state-of-the-arts. For the experiment on a real-world large-scale bipartite graph system, fast training speed and low memory cost demonstrate the scalability of ABCGraph model.
Extraction from Medical Records.

Despite using electronic medical records, free narrative text is still widely used for medical records. Such text cannot be analyzed by statistical tools and be proceed by decision support systems. To make data from texts available for such tasks a supervised machine learning algorithms might be successfully applied. In this work, we develop and compare a prototype of a medical data extraction system based on different artificial neuron networks architectures to process free medical texts in Russian language. The best F-score (0.9763) achieved on a combination of CNN prediction model and large pre-trained word2vec model. The very close result (0.9741) has shown by the MLP model with the same embedding.
Simulation on the Zero Inflated Negative Binomial (ZINB) to Model Overdispersed, Poisson Distributed Data

Abstract Poisson regression analysis shows the relationship between predictor variables and response variables that follow the Poisson distribution which has equal dispersion and average values (λ), a situation called equidispersion. However, the variance can also be greater than the average value, called overdispersion. This can be caused by excess opportunities for the emergence of zero values in the response variable or zero excess. The parameter of the overdispersed data analysis can be underestimated so that the results become biased. This bias issue can be, hopefully, overcome by the Zero Inflated Negative Binomial (ZINB) regression analysis. In the 2016 Maternal Mortality Rate data in Bojonegoro District, overdipersion was overcome by ZINB regression even though there was no significant predictor variable found affecting the response variable. ZINB regression analysis can also be applied to generated data (simulation). We had the data with average λ = (0.2, 0.4, 0.6, 0.8, 1.0, 5.0) proportion of zeros p = (0.4, 0.6, 0.8), and the number of observations n = (200,500, 800), with each setting was repeated 100 times. From the simulation study it was found that all overdispersion events were always accompanied by zero excess events but not vice versa. The greater the value of λ then the greater the dispersion coefficient. The ZINB regression is proven to be able to overcome overdispersion in various conditions of different values of λ, p , n which can be seen from the value τ (dispersion coefficient) after ZINB regression is less than 1 in all conditions.