nils-herrmann/clean_scirepeval huggingface dataset:
The relationship between mobile phone location sensor data and depressive symptom severity

Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify depressive symptom severity.



We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate depressive symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks.



The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r's ranging from -0.43 to -0.46, p-values <  .05). We also found that these relationships were stronger when GPS features were calculated from weekend, compared to weekday, data. Although the correlation between baseline PHQ-9 scores with 2-week GPS features diminished as we moved further from baseline, correlations with the end-of-study scores remained significant regardless of the time point used to calculate the features.



Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of depressive symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict depressive symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression.
Time series forecasting using Artificial Neural Networks vs. evolving models

Time series forecasting plays an important role in many fields such as economics, finance, business intelligence, natural sciences, and the social sciences. This forecasting task can be achieved by using different techniques such as statistical methods or Artificial Neural Networks (ANN). In this paper, we present two different approaches to time series forecasting: evolving Takagi-Sugeno (eTS) fuzzy model and ANN. These two different methods will be compared taking into account the different characteristic of each approach.
Modeling and control strategy for the transition of a convertible tail-sitter UAV

This paper addresses the problem of the transition between rotary-wing and fixed-wing flight of a tail-sitter unmanned aerial vehicle (UAV). A nonlinear control design is presented to regulate the vertical-flight dynamics of the vehicle. We present the dynamic and aerodynamic equations that model the behavior of the vehicle before (vertical flight), during and after (forward flight) the transition. A low-cost embedded system, including an homemade inertial measurement unit (IMU), is used to perform autonomous attitude-stabilized flight in vertical mode.
Design of a Smart Unmanned Ground Vehicle for Hazardous Environments

A smart Unmanned Ground Vehicle (UGV) is designed and developed for some application specific missions to operate predominantly in hazardous environments. In our work, we have developed a small and lightweight vehicle to operate in general crosscountry terrains in or without daylight. The UGV can send visual feedbacks to the operator at a remote location. Onboard infrared sensors can detect the obstacles around the UGV and sends signals to the operator.
A survey of spectrum sensing algorithms for cognitive radio applications

The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given.
The Impact of Corporate Sustainability on Organizational Processes and Performance

We investigate the effect of corporate sustainability on organizational processes and performance. Using a matched sample of 180 US companies, we find that corporations that voluntarily adopted sustainability policies by 1993 – termed as High Sustainability companies – exhibit by 2009, distinct organizational processes compared to a matched sample of firms that adopted almost none of these policies – termed as Low Sustainability companies. We find that the boards of directors of these companies are more likely to be formally responsible for sustainability and top executive compensation incentives are more likely to be a function of sustainability metrics. Moreover, High Sustainability companies are more likely to have established processes for stakeholder engagement, to be more long-term oriented, and to exhibit higher measurement and disclosure of nonfinancial information. Finally, we provide evidence that High Sustainability companies significantly outperform their counterparts over the long-term, both in terms of stock market as well as accounting performance.  Robert G. Eccles is a Professor of Management Practice at Harvard Business School. Ioannis Ioannou is an Assistant Professor of Strategy and Entrepreneurship at London Business School. George Serafeim is an Assistant Professor of Business Administration at Harvard Business School, contact email: gserafeim@hbs.edu. Robert Eccles and George Serafeim gratefully acknowledge financial support from the Division of Faculty Research and Development of the Harvard Business School. We would like to thank Christopher Greenwald for supplying us with the ASSET4 data. Moreover, we would like to thank Cecile Churet and Iordanis Chatziprodromou from Sustainable Asset Management for giving us access to their proprietary data. We are grateful to Chris Allen, Jeff Cronin, Christine Rivera, and James Zeitler for research assistance. We thank Ben Esty, David Larcker (discussant), Joshua Margolis, Costas Markides, Jeremy Stein (discussant), Catherine Thomas, and seminar participants at Boston College, the NBER conference on the ―Causes and Consequences of Corporate Culture‖, Cardiff University, Saint Andrews University, International Finance Corporation, Columbia University, INSEAD and the Business and Environment Initiative at Harvard Business School for helpful comments. We are solely responsible for any errors in this manuscript.
Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks

We propose a method for embedding twodimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.
Weed Detection Using Fractal-Based Low Cost Commodity Hardware Raspberry Pi

Conventional weed control system is usually used by spraying herbicides uniformly throughout the land. Excessive use of herbicides on an ongoing basis can produce chemical waste that is harmful to plants and soil. The application of precision agriculture farming in the detection process in order to control weeds using Computer Vision On Farm becomes interesting, but it still has some problems due to computer size and power consumption. Raspberry Pi is one of the minicomputer with low price and low power consumption. Having computing like a desktop computer with the open source Linux operating system can be used for image processing and weed fractal dimension processing using OpenCV library and C programming. This research results the best fractal computation time when performing the image with dimension size of 128 x 128 pixels. It is about 7 milliseconds. Furthermore, the average speed ratio between personal computer and Raspberry Pi is 0.04 times faster. The use of Raspberry Pi is cost and power consumption efficient compared to personal computer.
Conceptions of narcissism and the DSM-5 pathological personality traits.

The Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) features two conceptions of Narcissistic Personality Disorder (NPD), one based on the retained DSM-IV's categorical diagnosis and the other based on a model that blends impairments in personality functioning with a specific trait profile intended to recapture DSM-IV NPD. Nevertheless, the broader literature contains a richer array of potential conceptualizations of narcissism, including distinguishable perspectives from psychiatric nosology, clinical observation and theory, and social/personality psychology. This raises questions about the most advantageous pattern of traits to use to reflect various conceptions of narcissistic pathology via the Personality Inventory for the DSM-5 (PID-5). In this study, we examine the associations of the Personality Disorder Questionnaire-Narcissistic Personality Disorder scale, Narcissistic Personality Inventory-16, and the Pathological Narcissism Inventory and the PID-5 dimensions and facets in a large sample (N = 1,653) of undergraduate student participants. Results point to strong associations with PID-5 Antagonism scales across narcissism measures, consistent with the DSM-5's proposed representation of NPD. However, additional notable associations emerged with PID-5 Negative Affectivity and Psychoticism scales when considering more clinically relevant narcissism measures.
A survey on comparison of electric motor types and drives used for electric vehicles

In this study, Switched Reluctance Motor (SRM), Induction Motor (IM), Brushless DC Motor, and Permanent Magnet Motor (PM), and their drives have been compared with the efficiency, cost, weight, cooling, maximum speed, reliability, fault tolerance, power ratings, and vehicle acceleration time. Hence, a comprehensive literature research on motor types and their drives used in EV has been made. According to these researches, some conclusions have been obtained. It has been seen that PM BLDC motors and their drives are the most efficient and have high power density, brushless DC motors and their drives have low cost, IM is appropriate for controllability and cost, the weight of SRM is low, its reliability is high, it operates fault-tolerance and according to the acceleration time, its performance is better than IM and BLDC. Hence, SRM is the most appropriate motor for EV.
AcuBot: a robot for radiological interventions

We report the development of a robot for radiological percutaneous interventions using uniplanar fluoroscopy, biplanar fluoroscopy, or computed tomography (CT) for needle biopsy, radio frequency ablation, cryotherapy, and other needle procedures. AcuBot is a compact six-degree-of-freedom robot for manipulating a needle or other slender surgical instrument in the confined space of the imager without inducing image artifacts. Its distinctive characteristic is its decoupled motion capability correlated to the positioning, orientation, and instrument insertion steps of the percutaneous intervention. This approach allows each step of the intervention to be performed using a separate mechanism of the robot. One major advantage of this kinematic approach is patient safety. The first feasibility experiment performed with the robot, a cadaver study of perispinal blocks under biplanar fluoroscopy, is presented. The main expected application of this system is to CT-based procedures. AcuBot has received Food and Drug Administration clearance (IDE G010331/S1), and a clinical trial of using the robot for perispinal nerve and facet blocks is presently underway at Georgetown University, Washington, DC.
Exploring Query Optimization Techniques in Relational Databases

In the modern era, digital data is considered as the more valuable asset of an organization, and the organizations assign more significance to it than the software and hardware assets. Database systems are computer-based record keeping systems, which have been developed to store data for efficient retrieval and processing. One particular approach is the relational databases in which all the information is stored in rows and columns in a series of interconnected tables, and a snippet of structured code called query is used to interact with these database tables. Database management involves indexing of data by tagging information based on some common factors and corresponding criteria. Performance of SQL query against a production database eventually becomes an issue sooner or later. The time-intensive queries not only degrade performance of servers and applications by consuming substantial system resources, but can also result in table locking and data corruption. Therefore, query optimization becomes necessary to prevent performance degradation. Query optimization primarily means selection, followed by sequencing in specific order, of the different SQL clauses to formulate an efficient query from the multiple query plans by drawing a comparison of the query plans based on the cost of the resources involved and the response time. The objective of query optimization is to provide minimum response time and maximum throughput (i.e., the efficient use of resources). This paper reviews different query optimization techniques and approaches discussed in the contemporary literature for both centralized and distributed databases. The paper also highlights merits of these techniques by critically analyzing them.
Development of Self Balance Transporter : A Segway

This paper deals with constructing the vehicle which is fully functional to transport the one person only and movement of it is achieve by naturally. Forward and backwards motion is achieved by leaning forwards and backwards. Segway uses handlebar for turning purpose. In Segway technique uses principle of inverted pendulum in which it keeps an angle of Zero degrees with vertical at all times. The rider motion enables this technique to accelerate, brake or steer the vehicle, for this it uses Gyroscopic Sensor and it is Eco-friendly and non polluting.
A Preliminary Study of Hand Hygiene Compliance Characteristics with Machine Learning Methods

Increasing hospital re-admission rates due to Hospital Acquired Infections (HAIs) are a concern at many healthcare facilities. To prevent the spread of HAIs, caregivers are expected to comply with recommended hand hygiene guidelines, which require reliable and timely hand hygiene compliance measurement systems. The current standard practice of monitoring compliance involves the direct observation of caregivers’ hand cleaning as they enter or exit a patient room by a trained observer, which can be time-consuming, resource-intensive, and subject to bias. To alleviate the heavy manual effort and reduce errors, this paper studies the characteristics of compliance that could be used to assist the direct observation approach by deciding when and where to station manual auditors and to improve compliance by providing just-in-time alerts or potentially recommending training materials to non-compliant staff. The paper analyzes location and handwashing station activation data from a 30-bed intensive care unit (ICU) and uses machine learning to assess if location, time-based factors, or other data can be used to predict handwashing non-compliance events. The results show that a care provider’s entry compliance is highly indicative of the same provider’s exit compliance and that compliance of the most recent patient room visit can also predict entry compliance of a staff member’s current patient room visit
True Inline Cross-Coupled Coaxial Cavity Filters

In this paper, a novel true inline configuration for cross-coupled coaxial cavity filters is presented, which is characterized by a simple and compact structure, improved performance, and good tunability. Instead of using folded structures, dedicated coupling probes, or extra cavities, as required by conventional techniques, cross coupling is realized by changing the orientation of selected resonators. Sequential coupling between adjacent resonators and cross coupling between nonadjacent resonators are effectively controlled by introducing small metal plates at different locations. A six-pole bandpass filter with two transmission zeros was built and tested. The measurement and simulation results agree very well, demonstrating feasibility of the inline filter configuration. It enables compact design with improved resonator Q compared to conventional combline filters. Furthermore, cross coupling can be readily adjusted using tuning elements.
Web Browsing, Mobile Computing and Academic Performance

Students in two different courses at a major research university (one a Communication course, the other a Computer Science course) were given laptop computers with wireless network access during the course of a semester. Students’ Web browsing on these laptops (including: URLs, dates, and times) was recorded 24 hours/day, 7 days/week in a log file by a proxy server during most of a semester (about 15 weeks). For each student, browsing behavior was quantified and then correlated with academic performance. The emergence of statistically significant results suggests that quantitative characteristics of browsing behavior—even prior to examining browsing content—can be useful predictors of meaningful behavioral outcomes. Variables such as Number of browsing sessions and Length of browsing sessions were found to correlate with students’ final grades; the valence and magnitude of these correlations were found to interact with Course (i.e., whether student was enrolled in the Communication or Computer Science course), Browsing Context (i.e., setting in which browsing took place: during class, on the wireless network between classes , or at home) and Gender. The implications of these findings in relation to previous studies of laptop use in education settings are discussed.
Backpropagation Through Time: What It Does and How to Do It

Backpropagation is now the most widely used tool in the field of artificial neural networks. At the core of backpropagation is a method for calculating derivatives exactly and efficiently in any large system made up of elementary subsystems or calculations which are represented by known, differentiable functions; thus, backpropagation has many applications which do not involve neural networks as such. This paper first reviews basic backpropagation, a simple method which is now being widely used in areas like pattern recognition and fault diagnosis. Next, it presents the basic equations for backpropagation through time, and discusses applications to areas like pattern recognition involving dynamic systems, systems identification, and control. Finally, i t describes further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations or true recurrent networks, and other practical issues which arise with this method. Pseudocode is provided to clarify the algorithms. The chain rule for ordered derivatives-the theorem which underlies backpropagation-is briefly discussed.
Efficient Exploratory Learning of Inverse Kinematics on a Bionic Elephant Trunk

We present an approach to learn the inverse kinematics of the “bionic handling assistant”-an elephant trunk robot. This task comprises substantial challenges including high dimensionality, restrictive and unknown actuation ranges, and nonstationary system behavior. We use a recent exploration scheme, online goal babbling, which deals with these challenges by bootstrapping and adapting the inverse kinematics on the fly. We show the success of the method in extensive real-world experiments on the nonstationary robot, including a novel combination of learning and traditional feedback control. Simulations further investigate the impact of nonstationary actuation ranges, drifting sensors, and morphological changes. The experiments provide the first substantial quantitative real-world evidence for the success of goal-directed bootstrapping schemes, moreover with the challenge of nonstationary system behavior. We thereby provide the first functioning control concept for this challenging robot platform.
Cloud robotics: A software architecture: For heterogeneous large-scale autonomous robots

The paper proposes a software architecture for cloud robotics which intends three subsystems in the cloud environment: Middleware Subsystem,  Tasks Subsystem, and Control Subsystem. The architecture invokes cloud technologies such as cloud computing, cloud storage, and other networking platforms arranged on the assistances of congregated infrastructure and shared services for robotics, for instance Robot Operating System (ROS). Since the architecture is looking for reliable, scalable, and distributed system for the heterogeneous large-scale autonomous robots, Infrastructure as a Service (IaaS) is chosen among the cloud services. Three major tasks can be handled by the proposed software architecture Computing, Storage, and Networking. Hadoop-MapReduce provides the appropriate framework in the cloud environment to process and handle these tasks.
End-to-End Packet Delay and Loss Behavior in the Internet

We use the measured round trip delays of small UDP probe packets sent at regular time intervals to analyze the end-to-end packet delay and loss behavior in the Internet. By varying the interval between probe packets, it is possible to study the structure of the Internet load over different time scales. In this paper, the time scales of interest range from a few milliseconds to a few minutes. Our observations agree with results obtained by others using simulation and experimental approaches. For example, our estimates of Internet workload are consistent with the hypothesis of a mix of bulk traffic with larger packet size, and interactive traffic with smaller packet size. We observe compression (or clustering) of the probe packets, rapid fluctuations of queueing delays over small intervals, etc. Our results also show interesting and less expected behavior. For example, we find that the losses of probe packets are essentially random unless the probe traffic uses a large fraction of the available bandwidth. We discuss the implications of these results on the design of control mechanisms for the Internet.
Fossils, genes and the evolution of animal limbs

The morphological and functional evolution of appendages has played a crucial role in the adaptive radiation of tetrapods, arthropods and winged insects. The origin and diversification of fins, wings and other structures, long a focus of palaeontology, can now be approached through developmental genetics. Modifications of appendage number and architecture in each phylum are correlated with regulatory changes in specific patterning genes. Although their respective evolutionary histories are unique, vertebrate, insect and other animal appendages are organized by a similar genetic regulatory system that may have been established in a common ancestor.