Reviewerly/exHarmony huggingface dataset:
Optimized Automotive Fault-Diagnosis based on Knowledge Extraction from Web Resources

The maintenance and repair of modern vehicles is a challenge for garages, as different causes of faults lead to similar symptoms in the highly complex vehicles these days. Existing processes for fault-diagnosis based on manufacturer service manuals and human experiences are often inadequate and result in high effort and wrong decisions. In addition to these service manuals which provide basic models for e.g., diagnostic terms, primary physical quantities, causal relationships, and plausibilities, nowadays, internet forums offer a comprehensive source of experiences for solutions to these challenges. This paper, therefore, presents methods for the extraction of knowledge from unstructured and informal contributions in internet forums with the goal to synthesize diagnostic graphs from the established knowledge base, which are part of a maintenance software to supports garages in the maintenance of vehicles by suggesting more efficient and target-oriented diagnostic and maintenance actions in real-time.
Deep neural network approximations for Monte Carlo algorithms

Recently, it has been proposed in the literature to employ deep neural networks (DNNs) together with stochastic gradient descent methods to approximate solutions of PDEs. There are also a few results in the literature which prove that DNNs can approximate solutions of certain PDEs without the curse of dimensionality in the sense that the number of real parameters used to describe the DNN grows at most polynomially both in the PDE dimension and the reciprocal of the prescribed approximation accuracy. One key argument in most of these results is, first, to use a Monte Carlo approximation scheme which can approximate the solution of the PDE under consideration at a fixed space-time point without the curse of dimensionality and, thereafter, to prove that DNNs are flexible enough to mimic the behaviour of the used approximation scheme. Having this in mind, one could aim for a general abstract result which shows under suitable assumptions that if a certain function can be approximated by any kind of (Monte Carlo) approximation scheme without the curse of dimensionality, then this function can also be approximated with DNNs without the curse of dimensionality. It is a key contribution of this article to make a first step towards this direction. In particular, the main result of this paper, essentially, shows that if a function can be approximated by means of some suitable discrete approximation scheme without the curse of dimensionality and if there exist DNNs which satisfy certain regularity properties and which approximate this discrete approximation scheme without the curse of dimensionality, then the function itself can also be approximated with DNNs without the curse of dimensionality. As an application of this result we establish that solutions of suitable Kolmogorov PDEs can be approximated with DNNs without the curse of dimensionality.
Utilizing Hierarchies in Tree-Based Online Structured Output Prediction

Methods for online prediction of structured values are becoming more and more popular. However, hierarchical prediction, which has recently been shown to produce good results in terms of predictive performance in the batch learning setting, has not yet been applied in the online learning setting. We address the recently introduced task of hierarchical multi-target regression. To this end, we propose a hierarchical extension of iSOUP-Tree, which can address online multi-target regression. The extension weighs the split evaluation heuristic according to the location of the targets in the hierarchy. We design the experimental setup to ascertain whether the additional information contained in the hierarchy can be utilized to improve the predictive performance in the leaf targets. The proposed method shows promising results, producing potential improvements that should be investigated further.
Semi-supervised Approach for Hindi Stemming

Stemming is one of the most fundamental requirement of any Natural Language Processing tasks such as Information Retrieval.In simple words, it is the process of finding stem of a given word.This paper presents an algorithm to find the stem of a word in Hindi.The proposed algorithm uses word2vec, which is a semisupervised learning algorithm, for finding the 10 most similar words from a corpus.Then a mathematical function is proposed to achieve the above mentioned task of finding stem.Significant amount of attention need to be given to Indo-Aryan languages like Hindi, Bengali, Marathi etc. in the domain of Natural Language Processing because of their highly inflectional properties.Moreover,it is very difficult to build a rule based stemmer for such highly conflated languages.The proposed algorithm does not need any annotated corpus and does not use any hardcoded rules for finding the stem.The results are verified by selecting a set of 1000 Hindi words randomly taken from a corpus and comparing the results given by the proposed algorithm and the actual results created manually.
Introducing orbis: An extendable evaluation pipeline for named entity linking performance drill‐down analyses

ABSTRACT Most current evaluation tools are focused solely on benchmarking and comparative evaluations thus only provide aggregated statistics such as precision, recall and F1‐measure to assess overall system performance. They do not offer comprehensive analyses up to the level of individual annotations. This paper introduces Orbis, an extendable evaluation pipeline framework developed to allow visual drill‐down analyses of individual entities, computed by annotation services, in the context of the text they appear in, in reference to the entities specified in the gold standard.
Users' visual attention flow on the search result page of digital cultural heritage collection

ABSTRACT Cultural heritage breathes a new life with digital technologies and the internet. Digitization of cultural heritage artifacts do represent new materials and new contexts for research and practice. It is important to know how the public access and browse online collections of digital cultural heritage. This paper presents an eye‐tracking experiment on Digital Dunhuang, which is the most famous digital cultural heritage project in China, and identifies users' visual attention in search engine results pages (SERP). The results show how visual attention allocates in each area of interest (AOI), and the AOIs are browsed back and forth until the information needs are satisfied.
Power flow variation based on extreme learning machine algorithm in power system

&lt;p&gt;The main focus of this paper is a study that empowers us to understand how the temperature variation affects the transmission line resistance and as a result the power flow analysis with a specific end goal to assess losses in the electrical network. The paper is composed of two sections; the first part is a power flow study under normal conditions utilizing the neural network approach while the second investigated extreme learning machine algorithm efficiency and exactitude. Extreme learning machine algorithm has been used to settle several complications in power system: load forecasting, fault diagnosis, economic dispatch, security, transient stability; Thus, we proposed to study this technique to figure out this sort of complex issue.&lt;/p&gt;The study was conducted for IEEE 30 bus test system. The simulation results are exposed and analyzed in detail at the end of this paper.
Studentsâ€™ Perception on Their Motivation in Learning English (The Analysis Studentsâ€™ Motivation Based on Maslowâ€™s Concept)

The present study aims at analyzing the studentsâ€™ perception toward their motivational need in learning English. The need to learn English is important to be identified so that the education stakeholders catering the studentsâ€™ learning program can possibly figure out the relatively suitable service given to the students. The present study used mixed method. Two instruments were administered to elicit the studentsâ€™ perception on their motivational need as well as finding the determinants /factors which might have affected their motivation. The research findings indicated that the students mostly believed in the importance of learning English. However, at the same time they also indicated that their learning English was a mere instrumental and not for the sake of establishing relationship or living in an English speaking communities. They also considered that while English is important to help them realize their dream, they considered that other skills are also important for actualizing their dream.
Dataset Morphing to Analyze the Performance of Collaborative Filtering

Machine Learning algorithms are often too complex to be studied from a purely analytical point of view. Alternatively, with a reasonably large number of datasets one can empirically observe the behavior of a given algorithm in different conditions and hypothesize some general characteristics. This knowledge about algorithms can be used to choose the most appropriate one given a new dataset. This very hard problem can be approached using metalearning. Unfortunately, the number of datasets available may not be sufficient to obtain reliable meta-knowledge. Additionally, datasets may change with time, by growing, shrinking and editing, due to natural actions like people buying in a e-commerce site. In this paper we propose dataset morphing as the basis of a novel methodology that can help overcome these drawbacks and can be used to better understand ML algorithms. It consists of manipulating real datasets through the iterative application of gradual transformations (morphing) and by observing the changes in the behavior of learning algorithms while relating these changes with changes in the meta features of the morphed datasets. Although dataset morphing can be envisaged in a much wider framework, we focus on one very specific instance: the study of collaborative filtering algorithms on binary data. Results show that the proposed approach is feasible and that it can be used to identify useful metafeatures to predict the best collaborative filtering algorithm for a given dataset.
KnowBots: Discovering Relevant Patterns in Chatbot Dialogues

Chatbots have been used in business contexts as a new way of communicating with customers. They use natural language to interact with the customers, whether while offering products and services, or in the support of a specific task. In this context, an important and challenging task is to assess the effectiveness of the machine-to-human interaction, according to business’ goals. Although several analytic tools have been proposed to analyze the user interactions with chatbot systems, to the best of our knowledge they do not consider user-defined criteria, focusing on metrics of engagement and retention of the system as a whole. For this reason, we propose the KnowBots tool, which can be used to discover relevant patterns in the dialogues of chatbots, by considering specific business goals. Given the non-trivial structure of dialogues and the possibly large number of conversational records, we combined sequential pattern mining and subgroup discovery techniques to identify patterns of usage. Moreover, a friendly user-interface was developed to present the results and to allow their detailed analysis. Thus, it may serve as an alternative decision support tool for business or any entity that makes use of this type of interactions with their clients.
WEATHER THREAT ASSESSMENT BASED ON DYNAMIC BAYESIAN NETWORK

This paper aims to ascertain the variation in weather threat over time.To this end, the author established an assessment model of weather threat based on the Dynamic Bayesian Networks (DBN).Then, the Hidden Markov Model (HMM) was introduced to assess the level of weather threat.To validate the proposed method, a simulation was carried out on an Unmanned Aerial Vehicle (UAV), considering all five key weather factors affecting UAV safety.The results show that the DBN-based model is much more effective than the ordinary Bayesian Network (BN) in handling fuzzy information and complex weather conditions.The research findings shed new light on the accurate rout planning of an UAV.
Stabilization Algorithm Based on Improved Motion Model for Jittery Video in Minimally Invasive Surgery

The particularity and complexity of the minimally invasive surgical environment may cause the transmitted surgical video to jitter. For jittery video in minimally invasive surgery (MIS), this paper proposed a video stabilization method based on feature matching and improved affine motion model. Firstly, the feature points are extracted and matched using Speeded Up Robust Features (SURF) algorithm. Secondly, for the cumulative error problem, an improved motion model is established on the basis of sequential reference frame which extends adjacent two frames to adjacent multiple frames. Lastly, adjacent frames are transformed and motion compensation is implemented. The experimental results of jittery video in MIS show that the proposed algorithm has high matching precision and small cumulative error.
Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data

This paper explores the use of reinforcement learning (RL) models for autonomous racing. In contrast to passenger cars, where safety is the top priority, a racing car aims to minimize the lap-time. We frame the problem as a reinforcement learning task with a multidimensional input consisting of the vehicle telemetry, and a continuous action space. To find out which RL methods better solve the problem and whether the obtained models generalize to driving on unknown tracks, we put 10 variants of deep deterministic policy gradient (DDPG) to race in two experiments: i)~studying how RL methods learn to drive a racing car and ii)~studying how the learning scenario influences the capability of the models to generalize. Our studies show that models trained with RL are not only able to drive faster than the baseline open source handcrafted bots but also generalize to unknown tracks.
Risk assessment framework: Application of Bayesian Belief Networks in an ammunition delaboration project

Models that represent real problems have been relying so far on historical data to draw upon conclusions.One negative aspect of these models was that they could not predict future states based on real data instantly collected or new sources of risk that suddenly appeared.To overcome this problem, this work presents the process of building a realistic predictive model using Bayesian Belief Networks (BBNs) and the AgenaRisk software.BBNs are a direct representation of real problems where their graphical structure represents real causal connections and not just a flow of information.Software tools providing algorithms for dealing with conditional probabilities have been developed.The Bayesian Theorem, a theoretical background for conditional probability, was also explained in the paper.Another benefit of using BBNs is that the reasoning process can operate by propagating information in any direction (top-down or bottom-up) which makes it a powerful tool in risk assessment and a decision-making process.The paper also provides the core principles and the power of BBNs and their application in the project planning phase for ammunition delaboration (resolving problems of surplus and obsolete ammunition in stockpiles), where risk assessment is one of the required processes which helps in making a final decision for project approval or ПРЕГЛЕДНИ РАДОВИ
Developing an Offline Mobile Application with Health Condition Care and First Aid Instruction for Appropriateness of Medical Treatment

Lower income families which comprise a majority of the population have limited access to proper healthcare given scarcity of public doctors and high costs of private healthcare. However, first aid knowledge among the public is not common and incorrect treatments can be counterproductive. With these, the researchers developed a mobile application program to provide instructions for health concerns based around the needs of Our Lady of Banneux, a community made by Answering the Cry of the Poor (ANCOP) and Couples for Christ (CFC), using the community's preferred Filipino language. The community is also a partner community of the researchers' school, La Salle Green Hills (LSGH). Before the program was introduced to the respondents, a survey on the community's socio-demographic profile and a medical knowledge test on common health concerns were given. Latter was done to establish the respondents' baseline first aid knowledge. Participants were then divided into control and test groups with their medical knowledge compared. Using a t-test, the findings suggest that the level of medical knowledge between the groups is statistically the same. To see whether any of the respondents' socio-demographic variables has an association with their medical knowledge, a chi-square test of independence was made and it was revealed that, in both groups, there is not enough evidence to conclude that these have an association. The program was then introduced to the test group and a medical appropriateness test was given to both groups. Based on a t-test done on the scores of the groups, it was found that the program led to significantly higher scores for the test group, indicating that it could help in increasing the appropriateness of first aid interventions.
Automatic Twining Direction Recognition of Lockwire on Engine

Lockwires are often used as mechanical locking components for bolts on an engine. Inspecting its twining direction relative to the bolt is vital for ensuring an engine runs normally. However, the dominant manual inspection method is inefficient and limited by the professional skills of the inspector. In this paper, we propose a method that can be used to automatically determine the twining direction of a lockwire. First, a coarse-to-fine scheme for localizing the bolt is designed. Then, rough segmentation regions of the lockwire are acquired by a novel elongated shape descriptor. The entire area containing the lockwire can be obtained by connecting images from segmentation regions. An innovative skeleton tree and texture-based selection strategy are used to extract the lockwire centerline. An efficient vector fitting approach is proposed for computing the extension direction of the centerline. As a result, the twining direction can be identified via the extension direction and the bolt's position. The experimental results show that our method is very effective in determining the twining direction of a lockwire, with an accuracy reaching 95%. In addition, the results also show that our method can deal with low-quality images of lockwires at different angles.
GLORIA: A Genetic Algorithms Approach to Tetris

Tetris is a popular videogame developed by Alexey Pajinov, where the player will never win. This special feature makes it a popular reason to be used in Artificial Intelligence and Computational Intelligence techniques research to study the improvement in a game’s performance. In this work, a genetic algorithms based approach will be presented, applying it to a game engine with some of the modern Tetris’ gameplay features, where the objective is to observe the game agent performance in various test scenarios. The obtained results show the feasibility of using AI as a player in a game of Tetris.
LITERARY LEARNING FOR TEENAGER INMATES IN INSTITUTE FOR CHILDREN SPECIAL REHABILITATION

Children who are afflicted with problems of the law must remain entitled to obtain services, guidance, education, protection, and fulfillment of their rights. This is directed to help children to prepare for their future life which is better and more dignified. The study is aimed at describing the nurture of children offenders, the running, and the benefits of literacy learning for inmates who receive rehabilitation treatment in the Institute for Children Special Rehabilitation (ICSR) of Class II B of Yogyakarta Province, Indonesia. The study is descriptive qualitative research involving 23 inmates and one instructor. Data are obtained from observations, interviews, and documents. Data analyses are done in three phases; namely selection, presentation, and conclusion. The findings are as follows. First, the rehabilitation phases in the ICSR are orientation, personality nurture, advanced nurture, and integration. Orientation and nurture phases are conducted in the ICSR, while integration is done outside the institute. Second, the literary learning activities are run informally taking the inmate conditions into consideration. Instructional materials consist of poetry writing, poetry musicalization, and basic exercises in drama staging. Third, literary learning gives benefits in character education, the betterment of social adaptation, and media for catharsis.
Soft public key cipher

Encryption in general is the process of keeping up the secrecy of data (both fixed and versatile) utilizing programs that can Convert and make an interpretation of that data into codes so that whenever got to by unapproved people doesn't They can comprehend anything since what appears to them is a blend of images, numbers and unlimited characters, the file is encrypted And decryption by password, which must be known to both parties (sender and receiver) This is called With symmetric encryption, Decryption means decryption.The quality and viability of encryption rely upon two key factors: the algorithm, and the key length evaluated by Bit, the higher the bit, the greater the security and difficulty of decrypting.The concept of soft set was studied and applied on the public key cryptography like simple Knapsack and Trapdoor Knapsack ciphers.Significant results were presented that was the measure of the evaluate encryption using the SNR and PSNR.
Pengaruh Dukungan Teman Sebaya Terhadap Motivasi Belajar Siswa Kelas III Sekolah Dasar

This study examines the description of learning motivation of third grade elementary school students and the influence of peer support on the learning motivation of third grade elementary school students. This study uses a quantitative approach with the subject of research as many as 100 third grade students from SDN 1, 2 and 3 Mojoroto Kota Kediri. Data collection techniques in this study used questionnaires and documentation. Testing the hypothesis using the Krusal-Wallis Test. The results of this study found that: 1) The learning motivation of class III students in SDN 1, 2 and 3 Mojoroto Kediri City is included in the medium category because most students, namely 40% or 40 students, have the motivation to learn the medium category, 2) Friend support peer influence has a significant effect on student learning motivation (probability (sig) &lt;0.05). Peers are other people outside the family who often meet so that intimacy arises. Their age is relatively parallel and has the same idea so it is a group environment that is suitable for socializing. In the process of education and learning, children will have high motivation and achievement if they feel valued and respected by their peers.