nils-herrmann/clean_scirepeval huggingface dataset:
Drug knowledge bases and their applications in biomedical informatics research.

Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases.
A Comparison Among ARIMA, BP-NN, and MOGA-NN for Software Clone Evolution Prediction

Software evolution continues throughout the life cycle of the software. During the evolution of software system, it has been observed that the developers have a tendency to copy the modules completely or partially and modify them. This practice gives rise to identical or very similar code fragments called software clones. This paper examines the evolution of clone components by using advanced time series analysis. In the first phase, software clone components are extracted from the source repository of the software application by using the abstract syntax tree approach. Then, the evolution of software clone components is analyzed. In this paper, three models, Autoregressive Integrated Moving Average, back propagation neural network, and multi-objective genetic algorithm-based neural network, have been compared for the prediction of the evolution of software clone components. Evaluation is performed on the large open-source software application, ArgoUML. The ability to predict the clones helps the software developer to reduce the effort during software maintenance activities.
LQR, double-PID and pole placement stabilization and tracking control of single link inverted pendulum

This paper presents the dynamic behaviour of a nonlinear single link inverted pendulum-on-cart system based on Lagrange Equation. The nonlinear model linearization was presented based on Taylor series approximation. LQR, double-PID and simple pole placement control techniques were proposed for upright stabilization and tracking controls of the system. Simulations results for the various control techniques subjected to a unity magnitude pulse input torque with and without disturbance were compared. The performances of the proposed controllers were investigated based on response time specifications and level of disturbance rejection. Thus, the performance of LQR is more reliable and satisfactory. Finally, future work suggestions were made.
Modeling Users? Information Needs in a Document Recommender for Meetings

People are surrounded by an unprecedented wealth of information. Access to it depends on the availability of suitable search engines, but even when these are available, people often do not initiate a search, because their current activity does not allow them, or they are not aware of the existence of this information. Just-in-time retrieval brings a radical change to the process of query-based retrieval, by proactively retrieving documents relevant to users’ current activities, in an easily accessible and non-intrusive manner. This thesis presents a novel set of methods intended to improve the relevance of a just-in-time retrieval system, specifically a document recommender system designed for conversations, in terms of precision and diversity of results. Additionally, we designed an evaluation protocol to compare the proposed methods in the thesis with other ones using crowdsourcing. In contrast to previous systems, which model users’ information needs by extracting keywords from clean and well-structured texts, this system models them from the conversation transcripts, which contain noise from automatic speech recognition (ASR) and have a free structure, often switching between several topics. To deal with these issues, we first propose a novel keyword extraction method which preserves both the relevance and the diversity of topics of the conversation, to properly capture possible users’ needs with minimum ASR noise. Implicit queries are then built from these keywords. However, the presence of multiple unrelated topics in one query introduces significant noise into the retrieval results. To reduce this effect, we separate users’ needs by topically clustering keyword sets into several subsets or implicit queries. We introduce a merging method which combines the results of multiple queries which are prepared from users’ conversation to generate a concise, diverse and relevant list of documents. This method ensures that the system does not distract its users from their current conversation by frequently recommending them a large number of documents. Moreover, we address the problem of explicit queries that may be asked by users during a conversation. We introduce a query refinement method which leverages the conversation context to answer the users’ information needs without asking for additional clarifications and therefore, again, avoiding to distract users during their conversation. Finally, we implemented the end-to-end document recommender system by integrating the
Automatic Verb Classification Based on Statistical Distributions of Argument Structure

Automatic acquisition of lexical knowledge is critical to a wide range of natural language processing tasks. Especially important is knowledge about verbs, which are the primary source of relational information in a sentence-the predicate-argument structure that relates an action or state to its participants (i.e., who did what to whom). In this work, we report on supervised learning experiments to automatically classify three major types of English verbs, based on their argument structure-specifically, the thematic roles they assign to participants. We use linguistically-motivated statistical indicators extracted from large annotated corpora to train the classifier, achieving 69.8 accuracy for a task whose baseline is 34, and whose expert-based upper bound we calculate at 86.5. A detailed analysis of the performance of the algorithm and of its errors confirms that the proposed features capture properties related to the argument structure of the verbs. Our results validate our hypotheses that knowledge about thematic relations is crucial for verb classification, and that it can be gleaned from a corpus by automatic means. We thus demonstrate an effective combination of deeper linguistic knowledge with the robustness and scalability of statistical techniques.
System justification, the denial of global warming, and the possibility of "system-sanctioned change".

Despite extensive evidence of climate change and environmental destruction, polls continue to reveal widespread denial and resistance to helping the environment. It is posited here that these responses are linked to the motivational tendency to defend and justify the societal status quo in the face of the threat posed by environmental problems. The present research finds that system justification tendencies are associated with greater denial of environmental realities and less commitment to pro-environmental action. Moreover, the effects of political conservatism, national identification, and gender on denial of environmental problems are explained by variability in system justification tendencies. However, this research finds that it is possible to eliminate the negative effect of system justification on environmentalism by encouraging people to regard pro-environmental change as patriotic and consistent with protecting the status quo (i.e., as a case of "system-sanctioned change"). Theoretical and practical implications of these findings are discussed.
On Graph Query Optimization in Large Networks

The dramatic proliferation of sophisticated networks has resulted in a growing need for supporting effective querying and mining methods over such large-scale graph-structured data. At the core of many advanced network operations lies a common and critical graph query primitive: how to search graph structures efficiently within a large network? Unfortunately, the graph query is hard due to the NP-complete nature of subgraph isomorphism. It becomes even challenging when the network examined is large and diverse. In this paper, we present a high performance graph indexing mechanism, SPath, to address the graph query problem on large networks. SPath leverages decomposed shortest paths around vertex neighborhood as basic indexing units, which prove to be both effective in graph search space pruning and highly scalable in index construction and deployment. Via SPath, a graph query is processed and optimized beyond the traditional vertex-at-a-time fashion to a more efficient path-at-a-time way: the query is first decomposed to a set of shortest paths, among which a subset of candidates with good selectivity is picked by a query plan optimizer; Candidate paths are further joined together to help recover the query graph to finalize the graph query processing. We evaluate SPath with the state-of-the-art GraphQL on both real and synthetic data sets. Our experimental studies demonstrate the effectiveness and scalability of SPath, which proves to be a more practical and efficient indexing method in addressing graph queries on large networks.
Fully-integrated core chip for X-band phased array T/R modules

This paper discusses the final result of development effort leading to a fully-integrated phased-array core chip. The MMIC is especially useful for active electronically-steered arrays, and integrates a full functionality including low-noise amplifier (LNA), a medium-power amplifier (MPA) matched to common high-power amplifiers (HPA) and all control functions. The design and measurement results will be discussed. The integration of an LNA and MPA makes this MMIC an ideal candidate for a two chip T/R module solution. The MMIC is realised in the 0.25/spl mu/m pHEMT and process of UMS.
PsychoPy—Psychophysics software in Python

The vast majority of studies into visual processing are conducted using computer display technology. The current paper describes a new free suite of software tools designed to make this task easier, using the latest advances in hardware and software. PsychoPy is a platform-independent experimental control system written in the Python interpreted language using entirely free libraries. PsychoPy scripts are designed to be extremely easy to read and write, while retaining complete power for the user to customize the stimuli and environment. Tools are provided within the package to allow everything from stimulus presentation and response collection (from a wide range of devices) to simple data analysis such as psychometric function fitting. Most importantly, PsychoPy is highly extensible and the whole system can evolve via user contributions. If a user wants to add support for a particular stimulus, analysis or hardware device they can look at the code for existing examples, modify them and submit the modifications back into the package so that the whole community benefits.
Variable impedance actuators: A review

Variable Impedance Actuators (VIA) have received increasing attention in recent years as many novel applications involving interactions with an unknown and dynamic environment including humans require actuators with dynamics that are not well-achieved by classical stiff actuators. This paper presents an overview of the different VIAs developed and proposes a classification based on the principles through which the variable stiffness and damping are achieved. The main classes are active impedance by control, inherent compliance and damping actuators, inertial actuators, and combinations of them, which are then further divided into subclasses. This classification allows for designers of new devices to orientate and take inspiration and users of VIA’s to be guided in the design and implementation process for their targeted application.
A Tool for Analyzing and Fine Tuning the Real-Time Properties of an Embedded System

This paper describes a computer-aided software engineering (CASE) tool that helps designers analyze and fine-tune the timing properties of their embedded real-time software. Existing CASE tools focus on the software specification and design of embedded systems. However, they provide little, if any, support after the software has been implemented. Even if the developer used a CASE tool to design the system, their system most likely does not meet the specifications on the first try. This paper includes guidelines for implementing analyzable code, profiling a real-time system, filtering and extracting measured data, analyzing the data, and interactively predicting the effect of changes to the real-time system. The tool is a necessary first step towards automating the debugging and fine tuning of an embedded system’s temporal properties.
5G security architecture and light weight security authentication

The concept of future 5G (the 5th generation of mobile networks) widely spreads in industry, but the related standards are still unclear. The definition of 5G is to provide adequate RF coverage, more bits/Hz and to interconnect all wireless heterogeneous networks to provide seamless, consistent telecom experience to the user. In this paper we have surveyed 5G network architecture and proposed a multi-tier security architecture and a lightweight authentication measure for 5G. The proposed scheme aims at achieving fast authentication and minimizing the packet transmission overhead without compromising the security requirements.
Assessing psychopathic attributes in a noninstitutionalized population.

The present study examined antisocial dispositions in 487 university students. Primary and secondary psychopathy scales were developed to assess a protopsychopathic interpersonal philosophy. An antisocial action scale also was developed for purposes of validation. The primary, secondary, and antisocial action scales were correlated with each other and with boredom susceptibility and disinhibition but not with experience seeking and thrill and adventure seeking. Secondary psychopathy was associated with trait anxiety. Multiple regression analysis revealed that the strongest predictors of antisocial action were disinhibition, primary psychopathy, secondary psychopathy, and sex, whereas thrill and adventure seeking was a negative predictor. This argues against a singular behavioral inhibition system mediating both antisocial and risk-taking behavior. These findings are also consistent with the view that psychopathy is a continuous dimension.
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey

ÐHandwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the on-line case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered. Index TermsÐHandwriting recognition, on-line, off-line, written language, signature verification, cursive script, handwriting learning tools, writer authentification.
Bacterium-inspired robots for environmental monitoring

Locating gradient sources and tracking them over time has important applications to environmental monitoring and studies of the ecosystem. We present an approach, inspired by bacterial chemotaxis, for robots to navigate to sources using gradient measurements and a simple actuation strategy (biasing a random walk). Extensive simulations show the efficacy of the approach in varied conditions including multiple sources, dissipative sources, and noisy sensors and actuators. We also show how such an approach could be used for boundary finding. We validate our approach by testing it on a small robot (the robomote) in a phototaxis experiment. A comparison of our approach with gradient descent shows that while gradient descent is faster, our approach is better suited for boundary coverage, and performs better in the presence of multiple and dissipative sources.
Taxonomical and functional microbial community selection in soybean rhizosphere

This study addressed the selection of the rhizospheric microbial community from the bulk soil reservoir under agricultural management of soybean in Amazon forest soils. We used a shotgun metagenomics approach to investigate the taxonomic and functional diversities of microbial communities in the bulk soil and in the rhizosphere of soybean plants and tested the validity of neutral and niche theories to explain the rhizosphere community assembly processes. Our results showed a clear selection at both taxonomic and functional levels operating in the assembly of the soybean rhizosphere community. The taxonomic analysis revealed that the rhizosphere community is a subset of the bulk soil community. Species abundance in rhizosphere fits the log-normal distribution model, which is an indicator of the occurrence of niche-based processes. In addition, the data indicate that the rhizosphere community is selected based on functional cores related to the metabolisms of nitrogen, iron, phosphorus and potassium, which are related to benefits to the plant, such as growth promotion and nutrition. The network analysis including bacterial groups and functions was less complex in rhizosphere, suggesting the specialization of some specific metabolic pathways. We conclude that the assembly of the microbial community in the rhizosphere is based on niche-based processes as a result of the selection power of the plant and other environmental factors.
Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges

The Internet is continuously changing and evolving. The main communication form of present Internet is human-human. The Internet of Things (IoT) can be considered as the future evaluation of the Internet that realizes machine-to-machine (M2M) learning. Thus, IoT provides connectivity for everyone and everything. The IoT embeds some intelligence in Internet-connected objects to communicate, exchange information, take decisions, invoke actions and provide amazing services. This paper addresses the existing development trends, the generic architecture of IoT, its distinguishing features and possible future applications. This paper also forecast the key challenges associated with the development of IoT. The IoT is getting increasing popularity for academia, industry as well as government that has the potential to bring significant personal, professional and economic benefits.
A Gaussian Mixture MRF for Model-Based Iterative Reconstruction With Applications to Low-Dose X-Ray CT

Markov random fields (MRFs) have been widely used as prior models in various inverse problems such as tomographic reconstruction. While MRFs provide a simple and often effective way to model the spatial dependencies in images, they suffer from the fact that parameter estimation is difficult. In practice, this means that MRFs typically have very simple structure that cannot completely capture the subtle characteristics of complex images. In this paper, we present a novel Gaussian mixture Markov random field model (GM-MRF) that can be used as a very expressive prior model for inverse problems such as denoising and reconstruction. The GM-MRF forms a global image model by merging together individual Gaussian-mixture models (GMMs) for image patches. In addition, we present a novel analytical framework for computing MAP estimates using the GM-MRF prior model through the construction of surrogate functions that result in a sequence of quadratic optimizations. We also introduce a simple but effective method to adjust the GM-MRF so as to control the sharpness in low- and high-contrast regions of the reconstruction separately. We demonstrate the value of the model with experiments including image denoising and low-dose CT reconstruction.
From GPS traces to a routable road map

This paper presents a method for automatically converting raw GPS traces from everyday vehicles into a routable road network. The method begins by smoothing raw GPS traces using a novel aggregation technique. This technique pulls together traces that belong on the same road in response to simulated potential energy wells created around each trace. After the traces are moved in response to the potential fields, they tend to coalesce into smooth paths. To help adjust the parameters of the constituent potential fields, we present a theoretical analysis of the behavior of our algorithm on a few different road configurations. With the resulting smooth traces, we apply a custom clustering algorithm to create a graph of nodes and edges representing the road network. We show how this network can be used to plan reasonable driving routes, much like consumer-oriented mapping Web sites. We demonstrate our algorithms using real GPS data collected on public roads, and we evaluate the effectiveness of our approach on public roads, and we evaluate the effectiveness of our approach by comparing the route planning results suggested by our generated graph to a commercial route planner.
Pointing at 3D targets in a stereo head-tracked virtual environment

We present three experiments that systematically examine pointing tasks in fish tank VR using the ISO 9241-9 standard. All experiments used a tracked stylus for a both direct touch and ray-based technique. Mouse-based techniques were also studied. Our goal was to investigate means of comparing 2D and 3D pointing techniques. The first experiment used a 2D task constrained to the display surface, allowing direct validation against other 2D studies. The second experiment used targets stereoscopically presented above and parallel to the display, i.e., the same task, but without tactile feedback afforded by the screen. The third experiment used targets varying in all three dimensions. Results of these studies suggest that the conventional 2D formulation of Fitts' law works well for planar pointing tasks even without tactile feedback, and with stereo display. Fully 3D motions using the ray and mouse based techniques are less well modeled.