nils-herrmann/clean_scirepeval huggingface dataset:
Memristor SPICE model and crossbar simulation based on devices with nanosecond switching time

This paper presents a memristor SPICE model that is able to reproduce current-voltage relationships of previously published memristor devices. This SPICE model shows a stronger correlation to various published device data when compared to existing SPICE models. Furthermore, switching characteristics of published memristor devices with switching times in the nanosecond scale were modeled. Therefore, this model can be used to accurately simulate neural systems based on these high-speed memristors. This paper also demonstrates how this model can be used to accurately calculate switching energy of these high-speed devices, leading to more accurate power calculations in memristor based neural systems. Memristor crossbar circuits provide a potential method for developing very high density neural classifiers. This model was able to simulate crossbar circuits containing up to 256 memristors. It is significantly less likely to cause convergence errors when operating in the nanosecond switching regime with a large number of devices when compared with existing SPICE models.
Real-Time Optical Flow Calculations on FPGA and GPU Architectures: A Comparison Study

FPGA devices have often found use as higher-performance alternatives to programmable processors for implementing a variety of computations. Applications successfully implemented on FPGAs have typically contained high levels of parallelism and have often used simple statically-scheduled control and modest arithmetic. Recently introduced computing devices such as coarse grain reconfigurable arrays, multi-core processors, and graphical processing units (GPUs) promise to significantly change the computational landscape for the implementation of high-speed real-time computing tasks. One reason for this is that these architectures take advantage of many of the same application characteristics that fit well on FPGAs. One real-time computing task, optical flow, is difficult to apply in robotic vision applications in practice because of its high computational and data rate requirements, and so is a good candidate for implementation on FPGAs and other custom computing architectures. In this paper, a tensor-based optical flow algorithm is implemented on both an FPGA and a GPU and the two implementations discussed. The two implementations had similar performance, but with the FPGA implementation requiring 12× more development time. Other comparison data for these two technologies is then given for three additional applications taken from a MIMO digital communication system design, providing additional examples of the relative capabilities of these two technologies.
Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller

Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions. This novel approach introduces a second error detection to infer whether wrong automatic correction also elicits a second ErrP. Thus, two single-trial responses, instead of one, contribute to the final selection, improving the reliability of error detection. Moreover, to increase error detection, the evoked potential detected as target by the P300 classifier is combined with the evoked error potential at a feature-level. Discriminable error and positive potentials (response to correct feedback) were clearly identified. The proposed approach was tested on nine healthy participants and one tetraplegic participant. The online average accuracy for the first and second ErrPs were 88.4% and 84.8%, respectively. With automatic correction, we achieved an improvement around 5% achieving 89.9% in spelling accuracy for an effective 2.92 symbols/min. The proposed approach revealed that double ErrP detection can improve the reliability and speed of BCI systems.
IoT based weather station

A weather station can be described as an instrument or device, which provides us with the information of the weather in our neighbouring environment. For example it can provide us with details about the surrounding temperature, barometric pressure, humidity, etc. Hence, this device basically senses the temperature, pressure, humidity, light intensity, rain value. There are various types of sensors present in the prototype, using which all the aforementioned parameters can be measured. It can be used to monitor the temperature or humidity of a particular room/place. With the help of temperature and humidity we can calculate other data parameters, such as the dew point. In addition to the above mentioned functionalities, we can monitor the light intensity of the place as well. We have also enabled to monitor the atmospheric pressure of the room. We can also monitor the rain value. The brain of the prototype is the ESP8266 based Wi-fi module Nodemcu (12E). Four sensors are connected to the NodeMCU namely temperature and humidity sensor(DHT11), pressure sensor(BMP180), raindrop module, and light dependent resistor(LDR). Whenever these values exceed a chosen threshold limit for each an SMS, an E-mail and a Tweet post is published alerting the owner of the appliance to take necessary measures.
Automatic Feature Engineering for Answer Selection and Extraction

This paper proposes a framework for automatically engineering features for two important tasks of question answering: answer sentence selection and answer extraction. We represent question and answer sentence pairs with linguistic structures enriched by semantic information, where the latter is produced by automatic classifiers, e.g., question classifier and Named Entity Recognizer. Tree kernels applied to such structures enable a simple way to generate highly discriminative structural features that combine syntactic and semantic information encoded in the input trees. We conduct experiments on a public benchmark from TREC to compare with previous systems for answer sentence selection and answer extraction. The results show that our models greatly improve on the state of the art, e.g., up to 22% on F1 (relative improvement) for answer extraction, while using no additional resources and no manual feature engineering.
Development of wearable and flexible ultrasonic sensor for skeletal muscle monitoring

A wearable and flexible ultrasonic sensor was developed for monitoring of skeletal muscle contraction. The sensor was constructed using a PVDF piezoelectric polymer film without a matching layer or backing material. Due to its lightness (less than 1 gram), thinness (200 μm) and flexibility, this sensor was wearable and enabled non-invasive and continuous muscle monitoring without restricting muscle movement, which is not feasible using a conventional handheld ultrasonic probe. The developed sensor was used to monitor muscle contractions in the index finger by ultrasonic pulse-echo measurements and in the forearm by through-transmission measurements. It was successfully demonstrated that the tissue thickness variations were measured in accordance with the muscle contraction performed.
The Impact of Game Patterns on Player Experience and Social Interaction in Co-Located Multiplayer Games

Outstanding multiplayer games engage players by providing rich social interactions. Yet, it is still not clear how to purposefully design these interactions. Our paper tackles this issue by establishing a research model for social player interaction highlighting the impact of the game design, the player group, and the gaming setting. Based on that model, we investigated the influence of three particular game design patterns - player interdependence, time pressure, and shared control - on the interaction of players. For that purpose, we developed a co-located game to systematically test variations of those patterns. We analyzed the resulting player experience and social interaction by applying questionnaires and videotaping game play sessions. Results indicate that high player interdependence implies more communication and less frustration, whereas shared control results in less perceived competence and autonomy. Moreover, individual player characteristics also impact the social interaction.
DLR-Hand II Next Generation of a Dextrous Robot Hand

| this paper outlines the 2nd generation of multisensory hand design at DLR. The results of the use of DLR's Hand I were analyzed and enabled in addition to the big e orts made in grasping technology to design the next generation of dextrous robot hands. An open skeleton structure for better maintenance with semi shell housings and the new automatically recon gurable palm have been equipped with more powerful actuators to reach 30N on the ngertip. Newly designed sensors as the 6 DOF ngertip force torque sensor and integrated electronics together with the new communication architecture which enables a reduction of the cabling to the hand to only 12 lines outline the electronics concept. The Cartesian impedance control of all the ngers completes the new hand with its 13 DOF to what it is: the next step to autonomous and humanoid grasping
Multi-step linear Dyna-style planning

In this paper we introduce a multi-step linear Dyna-style pl anning algorithm. The key element of the multi-step linear Dyna is a multi-step lin ear model that enables multi-step projection of a sampled feature and multistep planning based on the simulated multi-step transition experience. We propos e two multi-step linear models. The first iterates the one-step linear model, but is g enerally computationally complex. The second interpolates between the onestep model and the infinite-step model (which turns out to be the LSTD solution) , and can be learned efficiently online. Policy evaluation on Boyan Chain shows t hat multi-step linear Dyna learns a policy faster than single-step linear Dyna, an d generally learns faster as the number of projection steps increases. Results on Moun tain-car show that multi-step linear Dyna leads to much better online performa nce than single-step linear Dyna and model-free algorithms; however, the perfor mance of multi-step linear Dyna does not always improve as the number of projecti n steps increases. Our results also suggest that previous attempts on extendin g LSTD for online control were unsuccessful because LSTD looks infinite steps into the future, and suffers from the model errors in non-stationary (control) e nvironments.
Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making

Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs. Concretely, consider two players with different beliefs and utility functions who may cooperate to build a machine that takes actions on their behalf. A representation is needed for how much the machine’s policy will prioritize each player’s interests over time. Assuming the players have reached common knowledge of their situation, this paper derives a recursion that any Pareto optimal policy must satisfy. Two qualitative observations can be made from the recursion: the machine must (1) use each player’s own beliefs in evaluating how well an action will serve that player’s utility function, and (2) shift the relative priority it assigns to each player’s expected utilities over time, by a factor proportional to how well that player’s beliefs predict the machine’s inputs. Observation (2) represents a substantial divergence from näıve linear utility aggregation (as in Harsanyi’s utilitarian theorem, and existing MORL algorithms), which is shown here to be inadequate for Pareto optimal sequential decision-making on behalf of players with different beliefs.
An investigation into the transference and survivability of human DNA following simulated manual strangulation with consideration of the problem of third party contamination

Amplification was performed on human DNA material transferred during a model of manual strangulation. A total of 29 separate experiments were performed using a single male offender-female victim combination to observe whether DNA was transferred both from the offender’s fingers to the victim’s neck and vice versa and to consider the period of time after the event during which the material could potentially be recovered and amplified. DNA was amplified from either the victim’s neck or the offender’s fingers for at least 10 days after the contact although it is discussed whether this is potentially due to primary contact or a secondary/tertiary transfer event. The study highlights the problem of contamination of the offender’s hands and victim’s neck with third party DNA, the presence of which could have a significant outcome for both the investigating authority and the third party.
SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation

Gaussian mixture models with universal backgrounds (UBMs) have become the standard method for speaker recognition. Typically, a speaker model is constructed by MAP adaptation of the means of the UBM. A GMM supervector is constructed by stacking the means of the adapted mixture components. A recent discovery is that latent factor analysis of this GMM supervector is an effective method for variability compensation. We consider this GMM supervector in the context of support vector machines. We construct a support vector machine kernel using the GMM supervector. We show similarities based on this kernel between the method of SVM nuisance attribute projection (NAP) and the recent results in latent factor analysis. Experiments on a NIST SRE 2005 corpus demonstrate the effectiveness of the new technique
Development and evaluation of a questionnaire to assess knowledge, attitudes, and behaviors towards hearing loss prevention.

To develop and evaluate a questionnaire assessing knowledge, attitudes, and behaviors (KAB) as they pertain to hearing conservation, using the constructs of the health belief model (HBM).



The KAB was completed by 235 participants. Relationships between knowledge and attitudes about hearing and hearing conservation, participation in noisy activities, and use of hearing protection were examined.



117 males and 118 females aged between 18 and 80 years (mean = 42.3, SD = 4.1) recruited from the Portland VA Medical Center, local universities, and a community college.



Knowledge scores ranged from 15.6% to 93.8%. Factor analyses revealed six attitude factors, interpreted as measuring perceived susceptibility, perceived severity, perceived benefits, perceived barriers, perceived self-efficacy, and cues to action. Over 95% of participants routinely participated in at least one noisy activity but few used hearing protection while doing so. The attitude scores of individuals who used hearing protection differed significantly from the scores of those who did not.



Significant relationships between use of hearing protection and scores on the KAB provide validation that the HBM is a valuable framework for understanding hearing health behaviors, and evidence that the KAB is a valid tool for assessing these attitudes and behaviors.
Automated Rhythm Transcription

We present a technique that, given a sequence of musical note onset times, performs simultaneous iden-tiication of the notated rhythm and the variable tempo associated with the times. Our formulation is probabilistic: We develop a stochastic model for the interconnected evolution of a rhythm process, a tempo process, and an observable process. This model allows the globally optimal identiication of the most likely rhythm and tempo sequence, given the observed onset times. We demonstrate applications to a sequence of times derived from a sampled audio le and to MIDI data.
Locally linear reconstruction based missing value imputation for supervised learning

Most learning algorithms generally assume that data is complete so each attribute of all instances is filled with a valid value. However, missing values are very common in real datasets for various reasons. In this paper, we propose a new single imputation method based on locally linear reconstruction (LLR) that improves the prediction performance of supervised learning (classification & regression) with various missing ratios. Next, we compare the proposed missing value imputation method (LLR) with six well-known single imputation methods for five different learning algorithms based on 13 classification and nine regression datasets. The experimental results showed that (1) all imputation methods helped to improve the prediction accuracy, although some were very simple; (2) the proposed LLR imputation method enhanced the modeling performance more than all other imputation methods, irrespective of the learning algorithms and the missing ratios; and (3) LLR was outstanding when the missing ratio was relatively high and its prediction accuracy was similar to that of the complete dataset. & 2013 Elsevier B.V. All rights reserved.
An Integrated Flywheel Energy Storage System with a Homopolar Inductor Motor / Generator and High-Frequency Drive

The design, construction, and test of an integrated flywheel energy storage system with a homopolar inductor motor/generator and high-frequency drive is presented in this paper. The work is presented as an integrated design of flywheel system, motor, drive, and controller. The motor design features low rotor losses, a slotless stator, construction from robust and low cost materials, and a rotor that also serves as the energy storage rotor for the flywheel system. A high-frequency six-step drive scheme is used in place of pulsewidth modulation because of the high electrical frequencies. A speed-sensorless controller that works without state estimation is also described. A prototype of the flywheel system has been demonstrated at a power level of 9.4 kW, with an average system efficiency of 83% over a 30 000–60 000-r/min speed range.
STRATEGIC DECISION PROCESSES IN HIGH VELOCITY ENVIRONMENTS : FOUR CASES IN THE MICROCOMPUTER INDUSTRY *

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Health risks in areas close to urban solid waste landfill sites.

To evaluate the association between living close to solid waste landfill sites and occurrences of cancer and congenital malformations among populations in their vicinity.



Deaths among people living in the municipality of São Paulo, Southeastern Brazil, between 1998 and 2002 were selected and geocoded, according to selected causes. Over the period evaluated, there were 351 deaths due to liver cancer, 160 due to bladder cancer and 224 due to leukemia, among adults, 25 due to childhood leukemia and 299 due to congenital malformation, in areas close to landfill sites. Buffer zones of radius 2 km around the 15 sites delimited the areas exposed. Standardized mortality ratios for each outcome were analyzed in Bayesian spatial models.



In a general manner, the highest values for the standardized mortality ratios were found in more central areas of the municipality, while the landfill sites were located in more peripheral areas. The standardized mortality ratios did not indicate any excess risk for people living in areas close to solid waste landfill sites in the municipality of São Paulo. For landfill sites in operation, there was a greater risk of bladder and liver cancer, and death due to congenital malformation, but without statistical significance.



No increase in the risk of cancer or congenital malformations was found in areas in the vicinity of urban waste dumps in the municipality of São Paulo. The weak associations and the imprecision of the estimates obtained did not allow any causal relationship to be established.
Critical infrastructure interdependency modeling: Using graph models to assess the vulnerability of smart power grid and SCADA networks

We discuss a framework for quantitative vulnerability assessment of critical infrastructure systems. We focus on the smart electric power delivery systems, i.e., electricity transmission and distribution smart grids, along with SCADA and EMS systems. We introduce concepts and results from graph and social network theories, and apply them to the study of the WSCC-Smart Power Grid Network-SCADA-EMS (WSSE) System. We also calculate values of the topological characteristics of the networks and compare their error and attack tolerances, i.e., their performance when vertices are removed, randomly or in a malicious way. Also, we study the possible topology generation models such as the random graph, small-world, and scale-free models. The WSEE system was found to follow the scale-free graph model of social network theory.
Context Aware Trace Clustering: Towards Improving Process Mining Results

Process Mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate spaghetti-like process models that are hard to comprehend. An approach to overcome this is to cluster process instances (a process instance is manifested as a trace and an event log corresponds to a multi-set of traces) such that each of the resulting clusters correspond to a coherent set of process instances that can be adequately represented by a process model. In this paper, we propose a context aware approach to trace clustering based on generic edit distance. It is well known that the generic edit distance framework is highly sensitive to the costs of edit operations. We define an automated approach to derive the costs of edit operations. The method proposed in this paper outperforms contemporary approaches to trace clustering in process mining. We evaluate the goodness of the formed clusters using established fitness and comprehensibility metrics defined in the context of process mining. The proposed approach is able to generate clusters such that the process models mined from the clustered traces show a high degree of fitness and comprehensibility when compared to contem-
An Intervention for Sensory Difficulties in Children with Autism: A Randomized Trial

This study evaluated a manualized intervention for sensory difficulties for children with autism, ages 4-8 years, using a randomized trial design. Diagnosis of autism was confirmed using gold standard measures. Results show that the children in the treatment group (n = 17) who received 30 sessions of the occupational therapy intervention scored significantly higher (p = 0.003, d = 1.2) on Goal Attainment Scales (primary outcome), and also scored significantly better on measures of caregiver assistance in self-care (p = 0.008 d = 0.9) and socialization (p = 0.04, d = 0.7) than the Usual Care control group (n = 15). The study shows high rigor in its measurement of treatment fidelity and use of a manualized protocol, and provides support for the use of this intervention for children with autism. Findings are discussed in terms of their implications for practice and future research.