nils-herrmann/clean_scirepeval huggingface dataset:
End-effector Position Analysis of SCORBOT-ER Vplus Robot

This paper features the kinematic analysis of a SCORBOT-ER Vplus robot arm which is used for doing successful robotic manipulation task in its workspace. The SCORBOT-ER Vplus is a 5-dof vertical articulated robot and all the joints are revolute . The kinematics problem is defined as the transformation from the Cartesian space to the joint space and vice versa. The Denavit-Harbenterg (D-H) model of representation is used to model robot links and joints in this study along with 4x4 homogeneous matrix. SCORBOT-ER Vplus is a dependable and safe robotic system designed for laboratory and training applications. This versatile system allows students to gain theoretical and practical experience in robotics, automation and control systems. The MATLAB 8.0 is used to solve this mathematical model for a set of joint parameter.
A multi-resolution saliency framework to drive foveation

The Human Visual System (HVS) exhibits multi-resolution characteristics, where the fovea is at the highest resolution while the resolution tapers off towards the periphery. Given enough activity at the periphery, the HVS is then capable to foveate to the next region of interest (ROI), to attend to it at full resolution. Saliency models in the past have focused on identifying features that can be used in a bottom-up manner to generate conspicuity maps, which are then combined together to provide regions of fixated interest. However, these models neglect to take into consideration the foveal relation of an object of interest. The model proposed in this work aims to compute saliency as a function of distance from a given fixation point, using a multi-resolution framework. Apart from computational benefits, significant motivation can be found from this work in areas such as visual search, robotics, communications etc.
Review of image-based rendering techniques

In this paper, we survey the techniques for image-based rendering. Unlike traditional 3D computer graphics in which 3D geometry of the scene is known, image-based rendering techniques render novel views directly from input images. Previous image-based rendering techniques can be classified into three categories according to how much geometric information is used: rendering without geometry, rendering with implicit geometry (i.e., correspondence), and rendering with explicit geometry (either with approximate or accurate geometry). We discuss the characteristics of these categories and their representative methods. The continuum between images and geometry used in image-based rendering techniques suggests that image-based rendering with traditional 3D graphics can be united in a joint image and geometry space.
Making computing interesting to school students: teachers' perspectives

It is widely agreed that there is a need to excite more school students about computing. Considering teachers' views about student engagement is important to securing their support for any solution. We therefore present the results of a qualitative, questionnaire-based study on teachers' perceptions of the best ways to make the subject interesting. From 115 responses by UK computing teachers emerged a range of themes about the issues they felt were most important. We found that whilst their views reflected a range of approaches that are widely promoted in the literature and in national initiatives, there were also disconnects between teachers' views and wider discourses. Based on the results, we give specific recommendations for areas where more should be done to support teachers in making computing interesting to school students. Academics should do more to engage with teachers, especially if they wish to introduce deep computing principles in schools. Teachers expressed an interest in computing clubs in schools, but a strong support network for them is still needed. This may be an opportunity for businesses and universities to help support teachers.
Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks

In this study, differential evolution algorithm (DE) is proposed to train a wavelet neural network (WNN). The resulting network is named as differential evolution trained wavelet neural network (DEWNN). The efficacy of DEWNN is tested on bankruptcy prediction datasets viz. US banks, Turkish banks and Spanish banks. Further, its efficacy is also tested on benchmark datasets such as Iris, Wine and Wisconsin Breast Cancer. Moreover, Garson’s algorithm for feature selection in multi layer perceptron is adapted in the case of DEWNN. The performance of DEWNN is compared with that of threshold accepting trained wavelet neural network (TAWNN) [Vinay Kumar, K., Ravi, V., Mahil Carr, & Raj Kiran, N. (2008). Software cost estimation using wavelet neural networks. Journal of Systems and Software] and the original wavelet neural network (WNN) in the case of all data sets without feature selection and also in the case of four data sets where feature selection was performed. The whole experimentation is conducted using 10-fold cross validation method. Results show that soft computing hybrids viz., DEWNN and TAWNN outperformed the original WNN in terms of accuracy and sensitivity across all problems. Furthermore, DEWNN outscored TAWNN in terms of accuracy and sensitivity across all problems except Turkish banks dataset.
Incremental Parsing with Minimal Features Using Bi-Directional LSTM

Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger number of) atomic features created from human linguistic intuition, and potentially omitting important global context. To further reduce feature engineering to the bare minimum, we use bi-directional LSTM sentence representations to model a parser state with only three sentence positions, which automatically identifies important aspects of the entire sentence. This model achieves state-of-the-art results among greedy dependency parsers for English. We also introduce a novel transition system for constituency parsing which does not require binarization, and together with the above architecture, achieves state-of-the-art results among greedy parsers for both English and Chinese.
Classifying Syntactic Regularities for Hundreds of Languages

This paper presents a comparison of classification methods for linguistic typology for the purpose of expanding an extensive, but sparse language resource: the World Atlas of Language Structures (WALS) (Dryer and Haspelmath, 2013). We experimented with a variety of regression and nearest-neighbor methods for use in classification over a set of 325 languages and six syntactic rules drawn from WALS. To classify each rule, we consider the typological features of the other five rules; linguistic features extracted from a word-aligned Bible in each language; and genealogical features (genus and family) of each language. In general, we find that propagating the majority label among all languages of the same genus achieves the best accuracy in label prediction. Following this, a logistic regression model that combines typological and linguistic features offers the next best performance. Interestingly, this model actually outperforms the majority labels among all languages of the same family.
The Open Provenance Model core specification (v1.1)

The Open Provenance Model is a model of provenance that is designed to meet the following requirements: (1) To allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) To allow developers to build and share tools that operate on such a provenance model. (3) To define provenance in a precise, technology-agnostic manner. (4) To support a digital representation of provenance for any “thing”, whether produced by computer systems or not. (5) To allow multiple levels of description to coexist. (6) To define a core set of rules that identify the valid inferences that can be made on provenance representation. This document contains the specification of the Open Provenance Model (v1.1) resulting from a community effort to achieve inter-operability in the Provenance Challenge series.
Artificial intelligence - a modern approach: the intelligent agent book

The first edition of Artificial Intelligence: A Modern Approach has become a classic in the AI literature. It has been adopted by over 600 universities in 60 countries, and has been praised as the definitive synthesis of the field. In the second edition, every chapter has been extensively rewritten. Significant new material has been introduced to cover areas such as constraint satisfaction, fast propositional inference, planning graphs, internet agents, exact probabilistic inference, Markov Chain Monte Carlo techniques, Kalman filters, ensemble learning methods, statistical learning, probabilistic natural language models, probabilistic robotics, and ethical aspects of AI. The book is supported by a suite of online resources including source code, figures, lecture slides, a directory of over 800 links to "AI on the Web," and an online discussion group. All of this is available at: aima.cs.berkeley.edu.
Three-Phase Dual-Buck Inverter With Unified Pulsewidth Modulation

This paper presents a new type of three-phase voltage source inverter (VSI), called three-phase dual-buck inverter. The proposed inverter does not need dead time, and thus avoids the shoot-through problems of traditional VSIs, and leads to greatly enhanced system reliability. Though it is still a hard-switching inverter, the topology allows the use of power MOSFETs as the active devices instead of IGBTs typically employed by traditional hard-switching VSIs. As a result, the inverter has the benefit of lower switching loss, and it can be designed at higher switching frequency to reduce current ripple and the size of passive components. A unified pulsewidth modulation (PWM) is introduced to reduce computational burden in real-time implementation. Different PWM methods were applied to a three-phase dual-buck inverter, including sinusoidal PWM (SPWM), space vector PWM (SVPWM) and discontinuous space vector PWM (DSVPWM). A 2.5 kW prototype of a three-phase dual-buck inverter and its control system has been designed and tested under different dc bus voltage and modulation index conditions to verify the feasibility of the circuit, the effectiveness of the controller, and to compare the features of different PWMs. Efficiency measurement of different PWMs has been conducted, and the inverter sees peak efficiency of 98.8% with DSVPWM.
SLM Microscopy: Scanless Two-Photon Imaging and Photostimulation with Spatial Light Modulators

Laser microscopy has generally poor temporal resolution, caused by the serial scanning of each pixel. This is a significant problem for imaging or optically manipulating neural circuits, since neuronal activity is fast. To help surmount this limitation, we have developed a "scanless" microscope that does not contain mechanically moving parts. This microscope uses a diffractive spatial light modulator (SLM) to shape an incoming two-photon laser beam into any arbitrary light pattern. This allows the simultaneous imaging or photostimulation of different regions of a sample with three-dimensional precision. To demonstrate the usefulness of this microscope, we perform two-photon uncaging of glutamate to activate dendritic spines and cortical neurons in brain slices. We also use it to carry out fast (60 Hz) two-photon calcium imaging of action potentials in neuronal populations. Thus, SLM microscopy appears to be a powerful tool for imaging and optically manipulating neurons and neuronal circuits. Moreover, the use of SLMs expands the flexibility of laser microscopy, as it can substitute traditional simple fixed lenses with any calculated lens function.
An ECG patch combining a customized ultra-low-power ECG SoC with Bluetooth low energy for long term ambulatory monitoring

This paper presents the development of an ECG patch aiming at long term patient monitoring. The use of the recently standardized Bluetooth Low Energy (BLE) technology, together with a customized ultra-low-power ECG System on Chip (ECG SoC). including Digital Signal Processing (DSP) capabilities, enables the design of ultra low power systems able to continuously monitor patients, performing on board signal processing such as filtering, data compression, beat detection and motion artifact removal along with all the advantages provided by a standard radio technology such as Bluetooth. Early tests show how combining the ECG SoC and BLE leads to a total current consumption of only 500μA at 3.7V, while computing beat detection and transmitting heart rate remotely via BLE. This allows up to one month lifetime with a 400mAh Li-Po battery.
Motion planning with worker's trajectory prediction for assembly task partner robot

We have developed a novel assembly task partner robot to support workers in their task. This system, PaDY (in-time Parts/tools Delivery to You robot), delivers parts and tools to a worker by recognizing the worker's behavior in the car production line; thus, improving the efficiency of the work by reducing the worker's physical workload for picking parts and tools. For this purpose, it is necessary to plan the trajectory of the robot before the worker moves to the next location for another assembling task. First a prediction method for the worker's trajectory using a Markov model for a discretized work space into cells is proposed, then motion planning method is proposed using the predicted worker's trajectory and a mixture Gaussian distribution for each area corresponding to each procedure of the work process in the automobile coordinate system. Experimental results illustrate the validity of the proposed motion planning method.
Relational reinforcement learning with guided demonstrations

Model-based reinforcement learning is a powerful paradigm for learning tasks in robotics. However, in-depth exploration is usually required and the actions have to be known in advance. Thus, we propose a novel algorithm that integrates the option of requesting teacher demonstrations to learn new domains with fewer action executions and no previous knowledge. Demonstrations allow new actions to be learned and they greatly reduce the amount of exploration required, but they are only requested when they are expected to yield a significant improvement because the teacher’s time is considered to be more valuable than the robot’s time. Moreover, selecting the appropriate action to demonstrate is not an easy task, and thus some guidance is provided to the teacher. The rule-based model is analyzed to determine the parts of the state that may be incomplete, and to provide the teacher with a set of possible problems for which a demonstration is needed. Rule analysis is also used to find better alternative models and to complete subgoals before requesting help, thereby minimizing the number of requested demonstrations. These improvements were demonstrated in a set of experiments, which included domains from the international planning competition and a robotic task. Adding teacher demonstrations and rule analysis reduced the amount of exploration required by up to 60% in some domains, and improved the success ratio by 35% in other domains.
Language independent gender classification on Twitter

Online Social Networks (OSNs) generate a huge volume of user-originated texts. Gender classification can serve multiple purposes. For example, commercial organizations can use gender classification for advertising. Law enforcement may use gender classification as part of legal investigations. Others may use gender information for social reasons. Here we explore language independent gender classification. Our approach predicts gender using five color-based features extracted from Twitter profiles (e.g., the background color in a user's profile page). Most other methods for gender prediction are typically language dependent. Those methods use high-dimensional spaces consisting of unique words extracted from such text fields as postings, user names, and profile descriptions. Our approach is independent of the user's language, efficient, and scalable, while attaining a good level of accuracy. We prove the validity of our approach by examining different classifiers over a large dataset of Twitter profiles.
The Quest to Replace Passwords: A Framework for Comparative Evaluation of Web Authentication Schemes

We evaluate two decades of proposals to replace text passwords for general-purpose user authentication on the web using a broad set of twenty-five usability, deployability and security benefits that an ideal scheme might provide. The scope of proposals we survey is also extensive, including password management software, federated login protocols, graphical password schemes, cognitive authentication schemes, one-time passwords, hardware tokens, phone-aided schemes and biometrics. Our comprehensive approach leads to key insights about the difficulty of replacing passwords. Not only does no known scheme come close to providing all desired benefits: none even retains the full set of benefits that legacy passwords already provide. In particular, there is a wide range from schemes offering minor security benefits beyond legacy passwords, to those offering significant security benefits in return for being more costly to deploy or more difficult to use. We conclude that many academic proposals have failed to gain traction because researchers rarely consider a sufficiently wide range of real-world constraints. Beyond our analysis of current schemes, our framework provides an evaluation methodology and benchmark for future web authentication proposals.
Fitness Gamification : Concepts , Characteristics , and Applications

This paper reviews the findings of several studies and surveys that address the increasing popularity and usage of so-called fitness “gamification.” Fitness gamification is used as an overarching and information term for the use of video game elements in non-gaming systems to improve user experience and user engagement. In this usage, game components such as a scoreboard, competition amongst friends, and awards and achievements are employed to motivate users to achieve personal health goals. The rise in smartphone usage has also increased the number of mobile fitness applications that utilize gamification principles. The most popular and successful fitness applications are the ones that feature an assemblage of workout tracking, social sharing, and achievement systems. This paper provides an overview of gamification, a description of gamification characteristics, and specific examples of how fitness gamification applications function and is used.
Polaris: a system for query, analysis, and visualization of multidimensional databases

During the last decade, multidimensional databases have become common in the business and scientific worlds. Analysis places significant demands on the interfaces to these databases. It must be possible for analysts to easily and incrementally change both the data and their views of it as they cycle between hypothesis and experimentation.
 In this paper, we address these demands by presenting the Polaris formalism, a visual query language for precisely describing a wide range of table-based graphical presentations of data. This language compiles into both the queries and drawing commands necessary to generate the visualization, enabling us to design systems that closely integrate analysis and visualization. Using the Polaris formalism, we have built an interactive interface for exploring multidimensional databases that analysts can use to rapidly and incrementally build an expressive range of views of their data as they engage in a cycle of visual analysis.
Super twisting control algorithm for the attitude tracking of a four rotors UAV

This paper deals with the design and implementation of a nonlinear control algorithm for the attitude tracking of a four-rotor helicopter known as quadrotor. This algorithm is based on the second order sliding mode technique known as Super-Twisting Algorithm (STA) which is able to ensure robustness with respect to bounded external disturbances. In order to show the effectiveness of the proposed controller, experimental tests were carried out on a real quadrotor. The obtained results show the good performance of the proposed controller in terms of stabilization, tracking and robustness with respect to external disturbances.
De-anonymizing private data by matching statistics

Recent research has illustrated privacy breaches that can be effected on an anonymized dataset by an attacker who has access to auxiliary information about the users. Most of these attack strategies rely on the uniqueness of specific aspects of the users' data - e.g., observing a mobile user at just a few points on the time-location space are sufficient to uniquely identify him/her from an anonymized set of users. In this work, we consider de-anonymization attacks on anonymized summary statistics in the form of histograms. Such summary statistics are useful for many applications that do not need knowledge about exact user behavior. We consider an attacker who has access to an anonymized set of histograms of K users' data and an independent set of data belonging to the same users. Modeling the users' data as i.i.d., we study the composite hypothesis testing problem of identifying the correct matching between the anonymized histograms from the first set and the user data from the second. We propose a Generalized Likelihood Ratio Test as a solution to this problem and show that the solution can be identified using a minimum weight matching algorithm on an K × K complete bipartite weighted graph. We show that a variant of this solution is asymptotically optimal as the data lengths are increased.We apply the algorithm on mobility traces of over 1000 users on EPFL campus collected during two weeks and show that up to 70% of the users can be correctly matched. These results show that anonymized summary statistics of mobility traces themselves contain a significant amount of information that can be used to uniquely identify users by an attacker who has access to auxiliary information about the statistics.
HOW TO EVOLVE AUTONOMOUS ROBOTS : DIFFERENT APPROACHES IN EVOLUTIONARY ROBOTICS

number of parts or modules within the system; rather, it scales with the number of possible interactions between parts and modules. A methodology for evolving the control systems of autonomous robots has not yet been well established. In this paper we will show different examples of applications of evolutionary robotics to real robots by describing three different approaches to develop neural controllers for mobile robots. In all the experiments described real robots are involved and are indeed the ultimate means of evaluating the success and the results of the procedures employed. Each approach will be compared with the others and the relative advantages and drawbacks will be discussed. Last, but not least, we will try to tackle a few important issues related to the design of the hardware and of the evolutionary conditions in which the control system of the autonomous agent should evolve. (b) autonomous robots interact with an external environment and, therefore, the way in which they behave in the environment determines the stimuli they will receive in input (Parisi, Cecconi, and Nolfi, 1990). Each motor action has two different effects: (1) it determines how well the system performs with respect to the given task; (2) it determines the next input stimuli which will be perceived by the system (this last point strongly affects the success or the failure of a sequence of actions). Determining the correct motor action that the system should perform in order to experience good input stimuli, is thus extremely difficult because any motor action may have long term consequences. Also, the choice of a given motor action is often the result of the previous sequence of actions. A final source of uncertainty in the design of the system is the fact that often the interaction between the system and the environment is not perfectly known in advance.