Reviewerly/exHarmony huggingface dataset:
Collecting Labels for Rare Anomalies via Direct Human Feedback—An Industrial Application Study

Many systems rely on the expertise from human operators, who have acquired their knowledge through practical experience over the course of many years. For the detection of anomalies in industrial settings, sensor units have been introduced to predict and classify such anomalous events, but these critically rely on annotated data for training. Lengthy data collection campaigns are needed, which tend to be combined with domain expert annotations of the data afterwards, resulting in costly and slow process. This work presents an alternative by studying live annotation of rare anomalous events in sensor streams in a real-world manufacturing setting by experienced human operators that can also observe the machinery itself. A prototype for visualization and in situ annotation of sensor signals is developed with embedded unsupervised anomaly detection algorithms to propose signals for annotation and which allows the operators to give feedback on the detection and classify anomalous events. This prototype allowed assembling a corpus of several weeks of sensor data measured in a real manufacturing surrounding and was annotated by domain experts as an evaluation basis for this study. The evaluation of live annotations reveals high user motivation after getting accustomed to the labeling prototype. After this initial period, clear anomalies with characteristic signal patterns are detected reliably in visualized envelope signals. More subtle signal deviations were less likely to be confirmed an anomaly due to either an insufficient visibility in envelope signals or the absence of characteristic signal patterns.
The Use of the Bayesian Approach in the Formation of the Student’s Competence in the ICT Direction

The theory of Bayesian networks has recently become very popular in solving various applied problems in various areas of science and production. To effectively use the Bayesian approach, a high-quality software product is needed that implements the mathematical ideas of Bayesian networks in practice. Such software products currently developed quite a large number. This paper presents the most common ones. The paper gives a small introduction about the competence of students and explores the possibility of using Bayesian networks in the formation of the competence of an IT trainer. Developed an integrated algorithm for the formation of the competence of training in the direction of IT.
ChakraSuit

ChakraSuit is an experimental wearable which aids in finding the best location for meditation, enabling one to learn about the interaction between the natural environment, sound, and the body. The prototype continuously listens to ambient sounds, and translates the identified audio frequencies to vibration on several points along the spine which correspond to chakra points. The project consists of a jumpsuit, pocket, and harness with integrated electronics running on a Raspberry Pi W Zero.
Reconstruction of multispectral images from spectrally coded light fields of flat scenes

Abstract We present a novel method to reconstruct multispectral images of flat objects from spectrally coded light fields as taken by an unfocused light field camera with a spectrally coded microlens array. In this sense, the spectrally coded light field camera is used as a multispectral snapshot imager, acquiring a multispectral datacube in a single exposure. The multispectral image, corresponding to the light field’s central view, is reconstructed by shifting the spectrally coded subapertures onto the central view according to their respective disparity. We assume that the disparity of the scene is approximately constant and non-zero. Since the spectral mask is identical for all subapertures, the missing spectral data of the central view will be filled up from the shifted spectrally coded subapertures. We investigate the reconstruction quality for different spectral masks and camera parameter sets optimized for real life applications such as in-line production monitoring for which the constant disparity constraint naturally holds. For synthesized reference scenes, using 16 color channels, we achieve a reconstruction <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>PSNR</m:mi> </m:math> \mathrm{PSNR} of up to 51 dB.
Using LastPass to facilitate the gathering of usage statistics for e-resources: a case study

The efficient curation of usage statistics for electronic collections has remained problematic for librarians. Many libraries employ multiple tools and manual processes in order to consolidate and examine usage data. In 2018, the Electronic Resources unit at The University of Alabama Libraries began using LastPass to store vendor contact information, URLs, and login credentials necessary for collecting vendor-supplied usage data in order to complete two annual projects. The implementation of LastPass into the workflow allowed for a central location to house vendor login and contact information in a cloud server environment and saved time as the staff worked to meet short deadlines.
The Higher-Order Prover Leo-III (Extended Abstract)

Leo-III is an automated theorem prover for extensional type theory with Henkin semantics. It also automates various non-classical logics, e.g., almost every normal higher-order modal logic is supported. In this extended abstract, the features of Leo-III are surveyed. This is an abstract of the homonymous paper accepted at the 9th International Joint Conference on Automated Reasoning (IJCAR 2018), see doi: 10.1007/978-3-319-94205-6_8 .
Trust Investigation in Communities Using Feature Learning

Online Q&A communities play a key role in informal learning. This paper reports on an investigation in Q&A communities aiming at bringing a new perspective to the problem of detecting trustworthy users through feature learning methods. These users write useful posts and contribute to the community growth, thus to knowledge dissemination. We propose two feature learning methods and demonstrate that they outperform the state of the as well as are competitive with works that addressed the same problem via hand-engineering process.
Interface Tesseracto UI and the Hologram

Building Tesseracto UI-type holographic interfaces is one step significant interaction in interfaces of computational devices with interaction in 3D. This follows the idea that the best user interface is no interface device, in the space of the hypercube and the fourth dimension. The contact device detects haptic interfaces, at the same time the touch in a free space as contact is made from fine ultrasonic sensors corresponding to the hologram images. The prototype was developed using the vertical and horizontal ultrasonic devices and a display hologram. The device is still in the testing phase, but the connection with the computer screens is already possible, in a prototype environment.
Research on Rating Error and Quality Metrics for Collaborative Filtering Recommendation Methods

Abstract The collaborative filtering recommendation system has been widely used in E-commerce as a relatively successful recommendation system. At present, the focus of collaborative filtering recommendation research is mainly on how to improve the accuracy of recommendation by improving the recommendation algorithm. However, in real world, the user’s rating behaviour is not perfectly rational. It is no odd that there is deviation of rating to any a given item for a user in real evaluation. In this case, what it means for the improvement of collaborative filtering recommendation methods, and how they performed when we use the commonly used quality metrics to evaluate the collaborative filtering recommendation methods? In view of these problems, this paper presupposes that the user’s rating behaviour is a bounded rational behaviour, and based on the normality hypothesis of rating error, introduces a simulated rating experiment innovatively to analyse the effect that rating prediction can achieve in sense of the commonly used quality metrics. This study is of significance for the research and application of collaborative filtering recommendation technology.
A Novel Single-Input Rule Module Connected Fuzzy Logic System and Its Applications to Medical Diagnosis

The fuzzy logic system is an expert system based on fuzzy logic and fuzzy reasoning. It consists of a series of fuzzy rules, which can better represent expert knowledge and effectively treat uncertainty and inaccuracy existing in the field of medical diagnosis. To overcome the rules explosion problem, this paper proposes a novel single-input rule module connected fuzzy logic system, in which the rules adopt single-input antecedents and multi-output consequents with the form of Fuzzy Weighted Linear Function (SIRM-FWLF for short). The updating formulas using the steepest descent algorithm are also deduced to optimize the system parameters. At last, numerical simulations are conducted in the MATLAB environment to compare our SIRM-FWLF model with Mamdani, TSK fuzzy logic systems and fuzzy functional SIRM inference model for medical diagnosis of breast cancer. The simulation results show that the proposed SIRM-FWLF FLS can provide higher diagnostic accuracy and recall rates than other fuzzy logic systems or fuzzy inference models.
Development of base ontology for a digital library of the Bulgarian museums' collections

This paper gives a further look into the process of ontology engineering for the needs of the Bulgarian museums' digital collections. The representation of the data model and a skeleton for a digital library offers a universal solution that can be used for the digitalization of movable cultural heritage ensuring its compatibility with the existing legislation in the domain. The main purpose of the base ontology is to unify and extend the usability of accumulated knowledge stored in the museum collection as well as information retrieval and query processing. The development of its structure has been proceeded following the bottom-up model due to the requirements of the front and backend users forming an important step for the standardization of I.T. solutions in the work of Bulgarian museums.
Double-layer Support Vector Machine for Robust and Efficient Classification

Abstract In this paper, double-layer support vector machine (DLSVM) for binary classification is proposed that can expeditiously learn a classification model under the premise of ensuring accuracy. In the first layer, least squares support vector machine (LSSVM) is used to evaluate all samples in the data set. Support vector machine (SVM) utilize a sparse data set to train the binary classifier in the second layer. In order to obtain excellent sparse data set for SVM, the normalized norm sequence and the normalized Lagrange multiplier Shannon entropy sequence are introduced. DLSVM provides low computation cost because the two-layer structure reduces lots of iterative operations. Experimental results show that the proposed method not only has similar classification accuracy compared to SVM but also has higher efficiency.
A compact representation of character skeleton using skeletal line based shape descriptor

Skeletonization is a quite significant technology for the shape representation in the field of image processing and pattern recognition. In order to explore its application onto the Chinese calligraphy character representation and reconstruction, a skeletal line based shape descriptor has been presented by the authors recently. Its performances evaluated by measurement of skeleton deviation (MSD), number of distorted forks (NDF), number of spurious strokes (NSS) as well as measurement of reconstructability (MR) showed that the skeleton-biased phenomenon can be greatly reduced and the pattern reconstructability near to 100% can be achieved. However, due to the use of dense skeletal line (SL) placement scheme, a lot of memory space is needed for storing the extended and dense SL information; and the computation cost is also rather expensive. Therefore, a compact strategy is presented in this paper to overcome these issues. Instead of storing all the SL information, only the sampled SL with a certain interval will be stored in the skeleton table. By performing the curve-fitting strategy derived from Vandermonde matrix onto the sampled SL information in the skeleton table, both the required skeleton and pattern contour can be readily restored, and the original pattern can thus be reconstructed. The sampling interval (SI) from 1 to 6 are used in our experiments (with 15 Chinese calligraphy characters) and the original method is regarded as the ground truth. Our experimental results show that the memory space can be approximately reduced from 54% (SI = 1) to 92% (SI = 6). The pattern reconstructability can still be maintained from 95% (SI = 1) to 92% (SI = 6). Moreover, the mean execution time of pattern reconstruction can be greatly reduced from 7.814 sec (the original method) to 0.078 sec (the improved method). The results confirm the feasibility of the proposed approach.
Feature Selection Using GSA-BBO Hybridization Algorithm with Chaos

Feature selection (FS) is a crucial data preprocessing process in classification problems. Feature selection is one of major challenges and aimed to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maximizing the classification performance and minimizing the number of features. Most existing feature selection algorithms treat the task as a single objective problem. In this paper, a novel metaheuristic optimizer, namely chaotic GSA-BBO hybridization algorithm (CGSA-BBO), is proposed to alleviate optimize feature selection the problem. In order to improve the feature selection techniques, this paper applies CGSA-BBO to optimize the feature selection process on 14 standard benchmark datasets, obtained from UCI repository. Four chaotic maps are employed during the optimization process. The experimental results demonstrate the effectiveness of chaotic GSA-BBO with Intermittency map. The proposed technique is superior to the basic GSABBO and other investigated algorithms. In addition, CGSA-BBO is able to obtain an optimal feature selection, which maximizes the classification performance and minimizes a number of selected features.
Password-based Selective Linkable Convertible Ring Blind Signature

Abstract Ring signature has become an important way of privacy protection because of its anonymity and the spontaneity of ring members. It has been widely used in the field of information security. Password-based alternative linkable convertible ring blind signature scheme combines the advantages of strong anonymity of ring signature and blindness of blind signature to signature message, and adds the optional linkable and convertible characteristics, which makes it more secure than other schemes, and can also transform ring signature into ordinary signature in special occasions. At the same time, under the random oracle model, the scheme satisfies the unforgeability under the adaptive selection message attack. Performance analysis shows that the scheme has high operating efficiency.
Development of teachers competencies in their preparation for integrated realization of science education

Science education, according to generally accepted views, should focus mainly on the practical and theoretical knowledge of the science, the development of skills and abilities applied in the science and, last but not least, the laboratory scientific research. Two basic approaches to achieving these goals are realized. The first one is based on separate training within individual science disciplines - physics, chemistry and biology. The second one deals with the idea of integrated education in the natural context. Every approach undoubtedly has its advantages and disadvantages. In both approaches, however, the weakest aspect is that the undergraduate teacher training in the Czech Republic aims to two subjects and does not, therefore, include all science disciplines. At the Faculty of Science UHK, we are preparing a new course for teachers-training students integrating all science disciplines. It should present the general principles of integrated teaching and include specific examples of science topics interdisciplinary so that the students obtain a more comprehensive view of possible strategies of science education and apply them effectively in their pedagogical practice. In this paper, the objectives and content of the course are described, some suitable topics for the elaboration are outlined.
Development and simulation of the pulsed fuzzy controller with low control costs

Abstract The article deals with the development and simulation of the control system of water level in the tank as a typical automation object. The level automatic control system (ACS) based on traditional pulse controllers is presented. The system is proposed as well that performs fuzzy logical inference to generate pulsed control action and at the same time implements low-cost control. Comparative analysis of these control systems simulated with MATLAB / Simulink / Fuzzy Logic Toolbox was made. It is shown that the developed fuzzy ACS is more effective than a traditional ACS.
RecSys '19 joint workshop on interfaces and human decision making for recommender systems

As an interactive intelligent system, recommender systems are developed to give recommendations that match users' preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users' perspectives. The field has reached a point where it is ready to look beyond algorithms, into users' interactions, decision making processes, and overall experience. This workshop will focus on the "human side" of recommender systems research. The workshop goal is to improve users' overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems.
Nonlinear system identification model of the spread of TB disease using the genetic algorithm and multilayer perceptron

Abstract System identification is the process of building a mathematical model of a dynamic system based on the observed input-output data. Artificial neural networks have been shown to complete the identification of nonlinear systems with various learning algorithms. Identification of nonlinear dynamic systems in the model of TB disease spread is needed to predict the spread of TB disease. The model for the spread of TB disease has three compartments and six parameters. The process of identifying models for the spread of TB disease by estimating parameters using the genetic algorithm and validation of the model using multilayer perceptron. The choice of multilayer perceptron architecture determines the minimum error in the nonlinear system identification process for the model of TB disease spread. From the results of the trial, it was shown that the multilayer perceptron and genetic algorithm were able to identify the spread model of TB disease with MMRE of 0.00347
Use of ARIMA Method To Predict The Number of Train Passenger In Malang City

Today, trains have become one of the most popular public transportation for medium and long distance travel. During the holidays, the number of train passengers is difficult to predict. The increase in the number of train passengers causes Indonesian Railway Company (IRC) needs to take action by adding train departures. The purpose of this study is to predict the number of train passengers with the Autoregressive Integrated Moving Average (ARIMA) method. The stages used in the study include: (1) datasets preparation, (2) preprocessing data, (3) experimental and method testing, and (4) accuracy testing. To test the accuracy of the algorithm used Mean Absolute Error (MAE). The MAE result is 1.37409 for the number of train passenger from Malang and 2.48417 for the number of train passenger to Malang. So, the ARIMA method used in predicting passenger was accurate.
Driving content recommendations by building a knowledge base using weak supervision and transfer learning

With 2.2 million subscribers and two hundred million content views, Chegg is a centralized hub where students come to get help with writing, science, math, and other educational needs. In order to impact a student's learning capabilities we present personalized content to students. Student needs are unique based on their learning style, studying environment and many other factors. Most students will engage with a subset of the products and contents available at Chegg. In order to recommend personalized content to students we have developed a generalized Machine Learning Pipeline that is able to handle training data generation and model building for a wide range of problems. We generate a knowledge base with a hierarchy of concepts and associate student-generated content, such as chat-room data, equations, chemical formulae, reviews, etc with concepts in the knowledge base. Collecting training data to generate different parts of the knowledge base is a key bottleneck in developing NLP models. Employing subject matter experts to provide annotations is prohibitively expensive. Instead, we use weak supervision and active learning techniques, with tools such as snorkel[2], an open source project from Stanford, to make training data generation dramatically easier. With these methods, training data is generated by using broad stroke filters and high precision rules. The rules are modeled probabilistically to incorporate dependencies. Features are generated using transfer learning[1] from language models for classification tasks. We explored several language models and the best performance was from sentence embeddings with skip-thought vectors predicting the previous and the next sentence. The generated structured information is then used to improve product features, and enhance recommendations made to students. In this presentation I will talk about efficient methods of tagging content with categories that come from a knowledge base. Using this information we provide relevant content recommendations to students coming to Chegg for online tutoring, studying flashcards and practicing problems.
User-centered Evaluation of Ideas by Bet-Cost-Matrix Analysis

Digital product development usually lacks tangible clues in which feature ideas to invest to improve usercenteredness. The Bet-Cost-Matrix method aims to face this issue and empower cross-functional teams to unveil assumptions or hidden risks by combining bets on user value and cost estimates. Bringing different viewpoints of team members together fosters a shared understanding of available resources as well as possible challenges. This can even lead to valuable insights to better align with users' needs. The Bet-Cost-Matrix method is suited to assess user stories, epics, features, or entire product ideas. It animates discussions, facilitates prioritization, and can be used to guide UX practices. The method is assisted by a web app to simplify the gathering of individual votes and the visualization of results.