nils-herrmann/clean_scirepeval huggingface dataset:
Classifying topics and detecting topic shifts in political manifestos

General political topics, like social security and foreign affairs, recur in electoral manifestos across countries. The Comparative Manifesto Project collects and manually codes manifestos of political parties from all around the world, detecting political topics at sentence level. Since manual coding is time-consuming and allows for annotation inconsistencies, in this work we present an automated approach to topical coding of political manifestos. We first train three independent sentence-level classifiers – one for detecting the topic and two for detecting topic shifts – and then globally optimize their predictions using a Markov Logic network. Experimental results show that the proposed global model achieves high classification performance and significantly outperforms the local sentence-level topic classifier.
Enhancing the Process of Requirements Prioritization in Agile Software Development - A Proposed Model

Agile software development has been used widely because it best fits projects with dynamic environments. It is an iterative and continuous process that delivers product functionalities in small increments. That enables the stakeholders to better understand their needs hence and reach their satisfaction by building the right product. Requirements prioritization (RP) is an essential phase in requirements engineering. It helps in figuring out the real requirements that are necessary to be implemented. This is important due to the limitations and constraints of any project such as budget and release date. The goal is to select the requirements that definitely maximizing customer satisfaction simultaneously meeting the restrictions such as budget and deadline. This paper proposes a model that covers the most effective RP factors based on a comparison study for existing models. The study was conducted after reviewing the literature of three models. Our model combines the most beneficial factors to enhance the RP process. Beside the model, RP technique is proposed so it can be used within the proposed model.
P2Cast: peer-to-peer patching scheme for VoD service

Providing video on demand (VoD) service over the Internet in a scalable way is a challenging problem. In this paper, we propose P2Cast - an architecture that uses a peer-to-peer approach to cooperatively stream video using patching techniques, while only relying on unicast connections among peers. We address the following two key technical issues in P2Cast: (1) constructing an application overlay appropriate for streaming; and (2) providing continuous stream playback (without glitches) in the face of disruption from an early departing client. Our simulation experiments show that P2Cast can serve many more clients than traditional client-server unicast service, and that it generally out-performs multicast-based patching if clients can cache more than of a stream's initial portion. We handle disruptions by delaying the start of playback and applying the shifted forwarding technique. A threshold on the length of time during which arriving clients are served in a single session in P2Cast serves as a knob to adjust the balance between the scalability and the clients' viewing quality in P2Cast.
Multifield visualization using local statistical complexity

Modern unsteady (multi-)field visualizations require an effective reduction of the data to be displayed. From a huge amount of information the most informative parts have to be extracted. Instead of the fuzzy application dependent notion of feature, a new approach based on information theoretic concepts is introduced in this paper to detect important regions. This is accomplished by extending the concept of local statistical complexity from finite state cellular automata to discretized (multi-)fields. Thus, informative parts of the data can be highlighted in an application-independent, purely mathematical sense. The new measure can be applied to unsteady multifields on regular grids in any application domain. The ability to detect and visualize important parts is demonstrated using diffusion, flow, and weather simulations.
Semi-supervised online visual crop and weed classification in precision farming exploiting plant arrangement

Precision farming robots offer a great potential for reducing the amount of agro-chemicals that is required in the fields through a targeted, per-plant intervention. To achieve this, robots must be able to reliably distinguish crops from weeds on different fields and across growth stages. In this paper, we tackle the problem of separating crops from weeds reliably while requiring only a minimal amount of training data through a semi-supervised approach. We exploit the fact that most crops are planted in rows with a similar spacing along the row, which in turn can be used to initialize a vision-based classifier requiring only minimal user efforts to adapt it to a new field. We implemented our approach using C++ and ROS and thoroughly tested it on real farm robots operating in different countries. The experiments presented in this paper show that with around 1 min of labeling time, we can achieve classification results with an accuracy of more than 95% in real sugar beet fields in Germany and Switzerland.
ARKref: a rule-based coreference resolution system

ARKref is a tool for noun phrase coreference that is based on the system described by Haghighi and Klein (2009) (which was never publicly released). It was originally written in 2009. At the time of writing, the last released version was in March 2011. This document describes that version, which is open-source and publicly available at http://www.ark.cs.cmu.edu/ARKref.1 ARKref is a deterministic, rule-based system that uses syntactic information from a constituent parser, and semantic information from an entity recognition component, to constrain the set of possible mention candidates (i.e., noun phrases) that could be antecedents for a given mention. It encodes syntactic constraints such as the fact that the noun phrases in predicative nominative constructions corefer (e.g., John was the teacher.), as well as semantic constraints such as the fact that he cannot corefer with a noun labeled as a location. After filtering candidates with these constraints, it selects as the antecedent the candidate noun phrase with the shortest (cross-sentence) tree distance from the target. Antecedent decisions are aggregated with a transitive closure to create the final entity graph. ARKref belongs to a family of rule-based coreference systems that use rich syntactic and semantic information to make antecedent selection decisions. Besides Haghighi and Klein, current work in this vein includes Lee et al. (2013), which was one of the best performing systems in a recent CoNLL shared task. The following example provides an illustration of ARKref’s output, in which brackets denote the extent of noun phrases and indices denote the entity to which each noun phrase refers. This example emphasizes the syntactic selection criteria:
Learning intermediate object affordances: Towards the development of a tool concept

Inspired by the extraordinary ability of young infants to learn how to grasp and manipulate objects, many works in robotics have proposed developmental approaches to allow robots to learn the effects of their own motor actions on objects, i.e., the objects affordances. While holding an object, infants also promote its contact with other objects, resulting in object-object interactions that may afford effects not possible otherwise. Depending on the characteristics of both the held object (intermediate) and the acted object (primary), systematic outcomes may occur, leading to the emergence of a primitive concept of tool. In this paper we describe experiments with a humanoid robot exploring object-object interactions in a playground scenario and learning a probabilistic causal model of the effects of actions as functions of the characteristics of both objects. The model directly links the objects' 2D shape visual cues to the effects of actions. Because no object recognition skills are required, generalization to novel objects is possible by exploiting the correlations between the shape descriptors. We show experiments where an affordance model is learned in a simulated environment, and is then used on the real robotic platform, showing generalization abilities in effect prediction. We argue that, despite the fact that during exploration no concept of tool is given to the system, this very concept may emerge from the knowledge that intermediate objects lead to significant effects when acting on other objects.
Risk aware portfolio construction using deep deterministic policy gradients

Allocation of liquid capital to the financial instruments in a portfolio is typically done using a two-step process. In the first step, predictive techniques are used to determine the future risk and rewards for the instrument. In the subsequent step, a quadratic optimization problem is solved to obtain the allocation that maximizes a relevant measure of the portfolio performance computed using a combination of the risks and the rewards. Deep Reinforcement Learning (DRL) eliminates the need for a two step process to find the allocation across the instruments that will optimize a measure of portfolio performance obtained from the market. DRL based portfolio construction autonomously adjusts to a change in the environment unlike traditional machine learning algorithms used in prediction. The existing DRL methods suffer from the challenges of stability, and do not lend themselves well to the portfolio construction problem that has a continuous action space. Proposed in 2015, Deep Deterministic Policy Gradients (DDPG) is a type of actorcritic DRL algorithm that provides support for continuous action space which is encountered in portfolio construction. This paper evaluates the use of DDPG to solve the problem of risk aware portfolio construction. Simulations are done on a portfolio of twenty stocks and the use of both Rate of Return and Sortino ratio as a measure of portfolio performance are evaluated. Results are presented that demonstrate the effectiveness of DDPG for risk aware portfolio construction. The simulation results presented in this paper show that having a risk-aware measure of portfolio performance such as Sortino ratio give a portfolio with superior return and lower variance.
Functional Search-based Testing from State Machines

The application of metaheuristic search techniques in test data generation has been extensively investigated in recent years. Most studies, however, have concentrated on the application of such techniques in structural testing. The use of search-based techniques in functional testing is less frequent, the main cause being the implicit nature of the specification. This paper investigates the use of search-based techniques for functional testing, having the specification in form of a state machine. Its purpose is to generate input data for chosen paths in a state machine, so that the parameter values provided to the methods satisfy the corresponding guards and trigger the desired transitions. A general form of a fitness function for an individual path is presented and this approach is empirically evaluated using three search techniques: simulated annealing, genetic algorithms and particle swarm optimization.
Healthcare via cell phones: a systematic review.

Regular care and informational support are helpful in improving disease-related health outcomes. Communication technologies can help in providing such care and support. The purpose of this study was to evaluate the empirical evidence related to the role of cell phones and text messaging interventions in improving health outcomes and processes of care. Scientific literature was searched to identify controlled studies evaluating cell phone voice and text message interventions to provide care and disease management support. Searches identified 25 studies that evaluated cell phone voice and text messaging interventions, with 20 randomized controlled trials and 5 controlled studies. Nineteen studies assessed outcomes of care and six assessed processes of care. Selected studies included 38,060 participants with 10,374 adults and 27,686 children. They covered 12 clinical areas and took place in 13 countries. Frequency of message delivery ranged from 5 times per day for diabetes and smoking cessation support to once a week for advice on how to overcome barriers and maintain regular physical activity. Significant improvements were noted in compliance with medicine taking, asthma symptoms, HbA1C, stress levels, smoking quit rates, and self-efficacy. Process improvements were reported in lower failed appointments, quicker diagnosis and treatment, and improved teaching and training. Cost per text message was provided by two studies. The findings that enhancing standard care with reminders, disease monitoring and management, and education through cell phone voice and short message service can help improve health outcomes and care processes have implications for both patients and providers.
Feature selection using linear classifier weights: interaction with classification models

This paper explores feature scoring and selection based on weights from linear classification models. It investigates how these methods combine with various learning models. Our comparative analysis includes three learning algorithms: Naïve Bayes, Perceptron, and Support Vector Machines (SVM) in combination with three feature weighting methods: Odds Ratio, Information Gain, and weights from linear models, the linear SVM and Perceptron. Experiments show that feature selection using weights from linear SVMs yields better classification performance than other feature weighting methods when combined with the three explored learning algorithms. The results support the conjecture that it is the sophistication of the feature weighting method rather than its apparent compatibility with the learning algorithm that improves classification performance.
PI type dynamic decoupling control scheme for PMSM high speed operation

Motor current decoupling control is of great importance for high dynamic performance of permanent magnet synchronous motor, especially when motor is at high speed range. Yet convention decoupling schemes do not perform well due to parameter sensitivity. In this paper, a PI type dynamic decoupling control scheme is proposed. Additional PI controllers are used to provide signals for motor d- and q- axes cross-coupling voltage compensation. Then the proposed decoupling control structure is revised into a simple structure in order to minimize the influence of d- and q- axes reference currents. Simulation and experimental results are presented to verify the proposed control scheme.
The Dark Side of Humor: DSM-5 Pathological Personality Traits and Humor Styles

Basic personality traits (e.g., extraversion) have been found to be associated with the humor styles that individuals employ. In the present study, we were interested in determining whether pathological personality traits were also associated with humor styles. We examined the associations between the pathological personality traits captured by the Personality Inventory for the DSM-5 (PID-5) and humor styles in a sample of college students (N = 594). Negative affectivity and detachment were negatively associated with the affiliative and self-enhancing humor styles. Antagonism was positively associated with the aggressive humor style but negatively associated with the affiliative humor style. Disinhibition was positively associated with the aggressive humor style, whereas disinhibition and psychoticism were both positively associated with the self-defeating humor style. Discussion focuses on the implications of these findings and how they can expand our understanding of the connections between the darker aspects of personality and humor.
A Comparison of External Clustering Evaluation Indices in the Context of Imbalanced Data Sets

For highly imbalanced data sets, almost all the instances are labeled as one class, whereas far fewer examples are labeled as the other classes. In this paper, we present an empirical comparison of seven different clustering evaluation indices when used to assess partitions generated from highly imbalanced data sets. Some of the metrics are based on matching of sets (F-measure), information theory (normalized mutual information and adjusted mutual information), and pair of objects counting (Rand and adjusted Rand indices). We also investigate the BCubed metric, which takes into account the concepts of recall, precision, as well as counting pairs. Furthermore, in order to avoid the class size imbalance effect, we propose a modification to the Rand index, referred to as the normalized class size Rand (NCR) index. In terms of results, apart from NCR, our experiments indicate that all the other analyzed indices are not able to deal properly with the problem of class size imbalance.
Design and optimization on ESD self-protection schemes for 700V LDMOS in high voltage power IC

This paper presents an ESD self-protection scheme for a 700V high-voltage laterally diffused metal-oxide-semiconductor (LDMOS) field effect transistor. The safe operating area (SOA) and breakdown failure mechanism of 700V LDMOS are analyzed using simulations and experimental results. The scalability of thermal failure current with LDMOS width is also demonstrated.
Resilient packet delivery through software defined redundancy: An experimental study

Mission-critical applications that use computer networks require guaranteed communication with bounds on the time needed to deliver packets, even if hardware along a path fails or congestion occurs. Current networking technologies follow a reactive approach in which retransmission mechanisms and routing update protocols respond once packet loss occurs. We present a proactive approach that pre-builds and monitors redundant paths to ensure timely delivery with no recovery latency, even in the case of a single-link failure. The paper outlines the approach, describes a new mechanism we call a redundancy controller, and explains the use of SDN to establish and change paths. The paper also reports on a prototype system and gives experimental measurements.
Systematic Review of Object Oriented Metric Tools

Tools that extract metrics from object oriented code are widely used as part of static code analysis which acts as a feedback mechanism for the managers, developers and other stake holders to improve the software quality. The software industry and academic research have confirmed the necessity of such tools and the impact they have on ensuring quality software. There is a transition of tools from measuring traditional software metrics to object oriented metrics as the focus has shifted to object oriented design and development. This paper presents a systematic review of both commercial and open source object oriented metric tools, highlighting the features supported and extensibility. The results are useful to arrive at the most suitable tool depending on the requirements of the stake holder. The results also identify a potential for an object oriented tool that can address the need for a tool that can work effectively across many object oriented languages and also be flexible for extending it to different languages and metrics.
ADVANCED POLARIMETRIC CONCEPTS – PART 1 ( Polarimetric Target Description , Speckle filtering and Decomposition Theorems )

There is currently a great deal of interest in the use of polarimetry for radar remote sensing. In this context, different and important objectives are to classify Earth terrain components within a fully polarimetric SAR image and then extract physical information from the observed scattering of microwaves by surface and volume structures. The most important observable measured by such radar systems is the 3x3-coherency matrix [T]. This matrix accounts for local variations in the scattering matrix and is the lowest order operator suitable to extract polarimetric parameters for distributed scatterers in the presence of additive (system) and/or multiplicative (speckle) noise. In the first part of this paper, the most important Target Polarimetry descriptors: Sinclair Matrix, target vectors, coherency matrix and the covariance matrix as well are presented, their interconnections and equivalences will be shown together with the respective transformations.
Path Planning with Real Time Obstacle Avoidance

One of the most important areas of research on mobile robots is that of their moving from one point in a space to the other and that too keeping aloof from the different objects in their path i. e. real time obstacle detection. This is the basic problem of path planning through a continuous domain. For this a large number of algorithms have been proposed of which only a few are really good as far as local and global path planning are concerned as to some extent a trade of has to be made between the efficiency of the algorithm and its accuracy. In this project an integrated approach for both local as well as global path planning of a robot has been proposed. The primary algorithm that has been used for path planning is the artificial Potential field approach  and a* search algorithm has been used for finding the most optimum path for the robot. Obstacle detection for collision avoidance (a high level planning problem) can be effectively done by doing complex robot operations in real time and distributing the whole problem between different control levels. This project proposes the artificial potential field algorithm not only for static but also for moving obstacles using real time potential field values by creating sub-goals which eventually lead to the main goal of the most optimal complete path found by the A* search algorithm. Apart from these scan line and convex hull techniques have been used to improve the robustness of the algorithm. To some extent the shape and size of a robot has also been taken into consideration. The effectiveness of these algorithms has been
An review of fully digital audio class D amplifiers topologies

Class D Amplifiers are widely used in portable systems such as mobile phones to achieve high efficiency. This paper presents topologies of full digital class D amplifiers in order to remove the analog DAC in the amplification path. This approach increases the playback time of embedded system. In first session, open-loop digital class D and digital modulation are discussed. The characterization of an open-loop class D prototype, using a CMOS 130 nm ASIC and a FPGA, confirms the advantage of such systems to reach a long battery life. Then, solutions with closed-loop topologies are proposed to increase linearity and power supply rejection of digital class D. Simulation results of closed-loop topologies, showing an increased in sound quality, are presented as well.
LinkNBed: Multi-Graph Representation Learning with Entity Linkage

Knowledge graphs have emerged as an important model for studying complex multirelational data. This has given rise to the construction of numerous large scale but incomplete knowledge graphs encoding information extracted from various resources. An effective and scalable approach to jointly learn over multiple graphs and eventually construct a unified graph is a crucial next step for the success of knowledge-based inference for many downstream applications. To this end, we propose LinkNBed, a deep relational learning framework that learns entity and relationship representations across multiple graphs. We identify entity linkage across graphs as a vital component to achieve our goal. We design a novel objective that leverage entity linkage and build an efficient multi-task training procedure. Experiments on link prediction and entity linkage demonstrate substantial improvements over the state-ofthe-art relational learning approaches.