nils-herrmann/clean_scirepeval huggingface dataset:
Applications of data mining in robotics

From a high-level viewpoint, robotics is all about data. In other words, each robot agent for performing its designated tasks should deal with data in some form - from collecting data from the environment to generating internal data. For this reason and due to broad range of possible applications, managing and dealing with such data is an important issue in the field. On the other side, data - in its different types - is known as an invaluable treasure of knowledge and insight within any information system. In this regard, extracting such hidden knowledge through data mining has been in the center of focus for more than two decades. In fact, data mining could be considered as a super-influential phenomenon on all scientific fields and robotics is not an exception. Over the recent years, many studies have been conducted on different aspects of leveraging data mining techniques in the robotic domain to improve performance of robots in various ways. However, despite the current disparate and unorganized studies in different directions, there are some potential areas that have not received enough attention. In this paper, we provide a high-level overview of the field and try to organize the current studies as a reference point and road map for future works. Moreover, challenges and future possible directions will be studied.
A recommender agent based on learning styles for better virtual collaborative learning experiences

Almost unlimited access to educational information plethora came with a drawback: finding meaningful material is not a straightforward task anymore. Based on a survey related to how students find additional bibliographical resources for university courses, we concluded there is a strong need for recommended learning materials, for specialized online search and for personalized learning tools. As a result, we developed an educational collaborative filtering recommender agent, with an integrated learning style finder. The agent produces two types of recommendations: suggestions and shortcuts for learning materials and learning tools, helping the learner to better navigate through educational resources. Shortcuts are created taking into account only the user’s profile, while suggestions are created using the choices made by the learners with similar learning styles. The learning style finder assigns to each user a profile model, taking into account an index of learning styles, as well as patterns discovered in the virtual behavior of the user. The current study presents the agent itself, as well as its integration to a virtual collaborative learning environment and its success and limitations, based on users’ feedback.
Application of artificial neural networks and related techniques to intrusion detection

The increasing complexity of today's information technology (IT) together with our dependency upon it, has led to a situation in which a security breach not only has effects for individuals but can also affect the availability of critical services (power supply, communication) or result in significant financial loss. Criminals and terrorists want to exploit system vulnerabilities to capitalise on modern society's interwovenness with IT. To counter this, organisations try to secure their IT assets to enforce security policies, to be compliant with legal and regulatory requirements and ultimately to deter unauthorised intruders from gaining access to them. At the core, the goal of intrusion detection systems is the identification of suspicious traffic flowing within, leaving or entering an organisation. To identify such traffic, intrusion detection systems may focus on data within a single host or on integrated information from various network segments. Identified traffic can then be reported to responsible authorities to take an appropriate course of action. This report is concerned with the state-of-the-art in intrusion detection systems. Systems leveraging information gathered from a single host, i.e. host-based intrusion detection systems, are presented as well as approaches observing and analysing information flowing across networks, i.e. network-based intrusion detection systems. Specific focus is placed on systems that make use of artificial neural networks and variations thereof to separate suspicious and potentially malicious traffic from ordinary traffic.
Best practices for missing data management in counseling psychology.

This article urges counseling psychology researchers to recognize and report how missing data are handled, because consumers of research cannot accurately interpret findings without knowing the amount and pattern of missing data or the strategies that were used to handle those data. Patterns of missing data are reviewed, and some of the common strategies for dealing with them are described. The authors provide an illustration in which data were simulated and evaluate 3 methods of handling missing data: mean substitution, multiple imputation, and full information maximum likelihood. Results suggest that mean substitution is a poor method for handling missing data, whereas both multiple imputation and full information maximum likelihood are recommended alternatives to this approach. The authors suggest that researchers fully consider and report the amount and pattern of missing data and the strategy for handling those data in counseling psychology research and that editors advise researchers of this expectation.
A Novel Improved Grey Wolf Optimization Algorithm for Numerical Optimization and PID Controller Design

The grey wolf optimization (GWO) algorithm, one of the recently proposed bio-inspired algorithms, simulates the leadership hierarchy and hunting mechanism of grey wolves in nature. The GWO has a good performance in some optimization tasks, but its search capacity decreases with the increasing search scope and dimension. This paper proposes an improved GWO (IGWO) algorithm, in which Levy flight strategy and a sine cosine operator with adaptive step are incorporated to significantly improve the performance of the algorithm. The Levy flight strategy is used to strengthen the efficiency of global search. The adaptive sine cosine operator is introduced to improve the local search ability. Experimental results based on twenty unconstrained benchmark problems show the superiority of the proposed IGWO. Furthermore, the IGWO is utilized in PID controller design. The comparison results show that the IGWO algorithm is better than, or at least comparable to, other well-established swarm intelligence algorithms.
Controllable Text Generation

Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible natural language sentences, whose attributes are dynamically controlled by learning disentangled latent representations with designated semantics. We propose a new neural generative model which combines variational auto-encoders and holistic attribute discriminators for effective imposition of semantic structures. With differentiable approximation to discrete text samples, explicit constraints on independent attribute controls, and efficient collaborative learning of generator and discriminators, our model learns highly interpretable representations from even only word annotations, and produces realistic sentences with desired attributes. Quantitative evaluation validates the accuracy of sentence and attribute generation.
EMG signal feature extraction based on wavelet transform

In this paper, a multi-channel electromyogram acquisition system using programmable system on chip (PSOC) microcontroller was used to obtain the surface of EMG signal. Two pairs of single-channel surface electrodes were used to measure and record the EMG signal on forearm muscles. Then, different levels of Daubechies Wavelet family were performed to analyze the EMG signal. Finally, features in terms of root mean square, logarithm of root mean square, centroid of frequency, and standard deviation were used to extract the EMG signal. The experimental results show that root mean square feature extraction method exhibits better performance for extracting the EMG signal compared to the other features. In the future, our method can be utilized to control a mechanical arm in real-time processing.
Semantic relations in information science

This chapter examines the nature of semantic relations and their main applications in information science. The nature and types of semantic relations are discussed from the perspectives of linguistics and psychology. An overview of the semantic relations used in knowledge structures such as thesauri and ontologies are provided, as well as the main techniques used in the automatic extraction of semantic relations from text. The chapter then reviews the use of semantic relations in information extraction, information retrieval, question-answering and automatic text summarization applications.
DC/DC Converter Design for Supercapacitor and Battery Power Management in Hybrid Vehicle Applications—Polynomial Control Strategy

This paper presents supercapacitor (SCAP) and battery modeling with an original energy management strategy in a hybrid storage technology. The studied dc power supply is composed of SCAPs and batteries. SCAPs are dimensioned for peak power requirement, and batteries provide the power in steady state. A bidirectional dc/dc converter is used between SCAPs and the dc bus. Batteries are directly connected to the dc bus. The originality of this study is focused on SCAP behavior modeling and energy management strategy. The proposed strategy is based on a polynomial (RST) controller. For reasons of cost and existing components (not optimized) such as batteries and semiconductors, the experimental test benches are designed in reduced scale. The characterized packs of SCAPs include two modules of ten cells in series for each one and present a maximum voltage of 27 V. The proposed strategy is implemented on a PIC18F4431 microcontroller for two dc/dc converter topology controls. Experimental and simulation results obtained from the polynomial control strategy are presented, analyzed, and compared with that of classical proportional-integral control.
A proposed semantic machine translation system for translating Arabic text to Arabic sign language

Arabic Sign Language (ArSL) is the native language for the Arabic deaf community. ArSL allows deaf people to communicate among themselves and with non-deaf people around them to express their needs, thoughts and feelings. Opposite to spoken languages, Sign Language (SL) depends on hands and facial expression to express person thoughts instead of sounds. In recent years, interest in automatically translating text to sign language for different languages has increased. However, a small set of these works are specialized in ArSL. Basically, these works translate word by word without taking care of the semantics of the translated sentence or the translation rules of Arabic text to Arabic sign language. In this paper we will present a proposed system for translating Arabic text to Arabic sign language in the jurisprudence of prayer domain. The proposed system will translate Arabic text to ArSL by applying ArSL translation rules as well as using domain ontology.
Parametric Shape-from-Shading by Radial Basis Functions

In this paper, we present a new method of shape from shading by using radial basis functions to parameterize the object depth. The radial basis functions are deformed by adjusting their centers, widths, and weights such that the intensity errors are minimized. The initial centers and widths are arranged hierarchically to speed up convergence and to stabilize the solution. Although the smoothness constraint is used, it can be eventually dropped out without causing instabilities in the solution. An important feature of our parametric shape-from-shading method is that it offers a unified framework for integration of multiple sensory information. We show that knowledge about surface depth and/or surface normals anywhere in the image can be easily incorporated into the shape from shading process. It is further demonstrated that even qualitative knowledge can be used in shape from shading to improve 3D reconstruction. Experimental comparisons of our method with several existing ones are made by using both synthetic and real images. Results show that our solution is more accurate than the others.
Joint Shape and Texture Based X-Ray Cargo Image Classification

Security & Inspection X-Ray Systems is widely used by custom to accomplish some security missions by inspecting import-export cargo. Due to the specificity of cargo X-Ray image, such as overlap, viewpoint dependence, and variants of cargo categories, it couldn't be understood easily like natural ones by human. Even for experienced screeners, it's very difficult to judge cargo category and contraband. In this paper, cargo X-Ray image is described by joint shape and texture feature, which could reflect both cargo stacking mode and interior details. Classification performance is compared with the benchmark method by top hit 1, 3, 5 ratio, and it's demonstrated that good performance is achieved here. In addition, we also discuss X-Ray image property and explore some reasons why cargo classification under X-Ray is very difficult.
Developing an Intelligent Chat-bot Tool to Assist High School Students for Learning General Knowledge Subjects

UPDATED—11 December 2017. Artificial Intelligent Chatbots are used in industries such as Banking systems, Customer services, Education. There are many intelligent tutoring systems currently in practice, but few of them are known to assist high school students for learning their general knowledge subjects. This project paper proposes an artificial intelligent chat-bot tool developed to assist high school students for learning their general knowledge subjects. The proposed intelligent chat-bot tool is a web based intelligent tutoring system which can be accessed by large number of students globally, the users are not required to pay licensing fees. The intelligent chat-bot tool will be available 24 hours a day at schools or public libraries or any other location which has internet access. The intelligent chatbot tool will make use of Natural Language Processing techniques to answer the queries by high school students. The intelligent chat-bot tool will be trained on a knowledge base comprising of general knowledge questions and answers. The intelligent chat-bot tool will have ability to participate in small talks with its learners.
Tears or Fears? Comparing Gender Stereotypes about Movie Preferences to Actual Preferences

This study investigated the accuracy of gender-specific stereotypes about movie-genre preferences for 17 genres. In Study 1, female and male participants rated the extent to which 17 movie genres are preferred by women or men. In Study 2, another sample of female and male participants rated their own preference for each genre. There were three notable results. First, Study 1 revealed the existence of gender stereotypes for the majority of genres (i.e., for 15 of 17 genres). Second, Study 2 revealed the existence of actual gender differences in preferences for the majority of genres (i.e., for 11 of 17 genres). Third, in order to assess the accuracy of gender stereotypes on movie preferences, we compared the results of both studies and found that the majority of gender stereotypes were accurate in direction, but inaccurate in size. In particular, the stereotypes overestimated actual gender differences for the majority of movie genres (i.e., 10 of 17). Practical and theoretical implications of these findings are discussed.
The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study

Many randomized trials involve measuring a continuous outcome - such as pain, body weight or blood pressure - at baseline and after treatment. In this paper, I compare four possibilities for how such trials can be analyzed: post-treatment; change between baseline and post-treatment; percentage change between baseline and post-treatment and analysis of covariance (ANCOVA) with baseline score as a covariate. The statistical power of each method was determined for a hypothetical randomized trial under a range of correlations between baseline and post-treatment scores.



ANCOVA has the highest statistical power. Change from baseline has acceptable power when correlation between baseline and post-treatment scores is high;when correlation is low, analyzing only post-treatment scores has reasonable power. Percentage change from baseline has the lowest statistical power and was highly sensitive to changes in variance. Theoretical considerations suggest that percentage change from baseline will also fail to protect from bias in the case of baseline imbalance and will lead to an excess of trials with non-normally distributed outcome data.



Percentage change from baseline should not be used in statistical analysis. Trialists wishing to report this statistic should use another method, such as ANCOVA, and convert the results to a percentage change by using mean baseline scores.
How brains beware: neural mechanisms of emotional attention

Emotional processes not only serve to record the value of sensory events, but also to elicit adaptive responses and modify perception. Recent research using functional brain imaging in human subjects has begun to reveal neural substrates by which sensory processing and attention can be modulated by the affective significance of stimuli. The amygdala plays a crucial role in providing both direct and indirect top-down signals on sensory pathways, which can influence the representation of emotional events, especially when related to threat. These modulatory effects implement specialized mechanisms of 'emotional attention' that might supplement but also compete with other sources of top-down control on perception. This work should help to elucidate the neural processes and temporal dynamics governing the integration of cognitive and affective influences in attention and behaviour.
Exercise with blood flow restriction: an updated evidence-based approach for enhanced muscular development.

A growing body of evidence supports the use of moderate blood flow restriction (BFR) combined with low-load resistance exercise to enhance hypertrophic and strength responses in skeletal muscle. Research also suggests that BFR during low-workload aerobic exercise can result in small but significant morphological and strength gains, and BFR alone may attenuate atrophy during periods of unloading. While BFR appears to be beneficial for both clinical and athletic cohorts, there is currently no common consensus amongst scientists and practitioners regarding the best practice for implementing BFR methods. If BFR is not employed appropriately, there is a risk of injury to the participant. It is also important to understand how variations in the cuff application can affect the physiological responses and subsequent adaptation to BFR training. The optimal way to manipulate acute exercise variables, such as exercise type, load, volume, inter-set rest periods and training frequency, must also be considered prior to designing a BFR training programme. The purpose of this review is to provide an evidence-based approach to implementing BFR exercise. These guidelines could be useful for practitioners using BFR training in either clinical or athletic settings, or for researchers in the design of future studies investigating BFR exercise.
JHU-ISI Gesture and Skill Assessment Working Set ( JIGSAWS ) : A Surgical Activity Dataset for Human Motion Modeling

Dexterous surgical activity is of interest to many researchers in human motion modeling. In this paper, we describe a dataset of surgical activities and release it for public use. The dataset was captured using the da Vinci Surgical System and consists of kinematic and video from eight surgeons with different levels of skill performing five repetitions of three elementary surgical tasks on a bench-top model. The tasks, which include suturing, knot-tying and needle-passing, are standard components of most surgical skills training curricula. In addition to kinematic and video data captured from the da Vinci Surgical System, we are also releasing manual annotations of surgical gestures (atomic activity segments), surgical skill using global rating scores, a standardized cross-validation experimental setup, and a C++/Matlab toolkits for analyzing surgical gestures using hidden Markov models and using linear dynamical systems. We refer to the dataset as the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) to indicate the collaboration between Johns Hopkins University (JHU) and Intuitive Surgical Inc. (ISI), Sunnyvale, CA, on collecting these data.
Depth-Based Hand Pose Estimation: Methods, Data, and Challenges

Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and have released software and evaluation code. We summarize important conclusions here: (1) Coarse pose estimation appears viable for scenes with isolated hands. However, high precision pose estimation [required for immersive virtual reality and cluttered scenes (where hands may be interacting with nearby objects and surfaces) remain a challenge. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.
Unsupervised learning with contrastive latent variable models

In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for instance a population of interest vs. control or signal vs. signal free recordings. However, there are few methods that work on sets of data as opposed to data points or sequences. Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset. The data in these sets do not need to be paired or grouped beyond set membership. By using a probabilistic model where some structure is shared amongst the two datasets and some is unique to the target dataset, we are able to recover interesting structure in the latent space of the target dataset. The method also has the advantages of a probabilistic model, namely that it allows for the incorporation of prior information, handles missing data, and can be generalized to different distributional assumptions. We describe several possible variations of the model and demonstrate the application of the technique to de-noising, feature selection, and subgroup discovery settings.